EP4595324A1 - Restriction de mesure pour prédiction d'informations d'état de canal (csi) - Google Patents

Restriction de mesure pour prédiction d'informations d'état de canal (csi)

Info

Publication number
EP4595324A1
EP4595324A1 EP23783571.5A EP23783571A EP4595324A1 EP 4595324 A1 EP4595324 A1 EP 4595324A1 EP 23783571 A EP23783571 A EP 23783571A EP 4595324 A1 EP4595324 A1 EP 4595324A1
Authority
EP
European Patent Office
Prior art keywords
csi
wireless device
predicted
network node
window
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP23783571.5A
Other languages
German (de)
English (en)
Inventor
Mattias Frenne
Siva Muruganathan
Xinlin ZHANG
Yufei Blankenship
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Telefonaktiebolaget LM Ericsson AB
Original Assignee
Telefonaktiebolaget LM Ericsson AB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Telefonaktiebolaget LM Ericsson AB filed Critical Telefonaktiebolaget LM Ericsson AB
Publication of EP4595324A1 publication Critical patent/EP4595324A1/fr
Pending legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048—Allocation of pilot signals, i.e. of signals known to the receiver
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621—Feedback content
    • H04B7/0626—Channel coefficients, e.g. channel state information [CSI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0224—Channel estimation using sounding signals
    • H04L25/0228—Channel estimation using sounding signals with direct estimation from sounding signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0254—Channel estimation channel estimation algorithms using neural network algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/003—Arrangements for allocating sub-channels of the transmission path
    • H04L5/0053—Allocation of signalling, i.e. of overhead other than pilot signals
    • H04L5/0057—Physical resource allocation for CQI
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0091—Signalling for the administration of the divided path, e.g. signalling of configuration information
    • H04L5/0094—Indication of how sub-channels of the path are allocated
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00—Arrangements affording multiple use of the transmission path
    • H04L5/0001—Arrangements for dividing the transmission path
    • H04L5/0014—Three-dimensional division
    • H04L5/0023—Time-frequency-space

Definitions

  • the present disclosure relates to wireless communications, and in particular, to channel state information (CSI) prediction.
  • CSI channel state information
  • the Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems.
  • 4G Fourth Generation
  • 5G Fifth Generation
  • NR New Radio
  • Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WDs), as well as communication between network nodes and between wireless devices.
  • 6G wireless communication systems are also under development.
  • the use of CSI is an aspect of NR and LTE specifications.
  • the concept is that the wireless device measures the downlink channel from the network node to the wireless device receiver antennas.
  • the wireless device computes a representation of the channel, or a preferred MIMO precoder that the network, such as via, e.g., a network node, may use to enhance the transmission of data or control information to the wireless device. For example, beamforming can be achieved if the network node knows how to adjust the phase of the individual antenna elements when transmitting to a certain wireless device.
  • the wireless device can thus be configured to report CSI (using CSI-ReportConfig parameter in radio resource control (RRC) signaling from network node to the wireless device) to the network node based on CSI reference signal (CSLRS), which also is configured from the network node to the wireless device. Measurements can also be configured for a synchronization signal block (SSB), the sync reference signals.
  • RRC radio resource control
  • CSLRS CSI reference signal
  • SSB synchronization signal block
  • a wireless device can in NR be configured to measure on a CSLRS resource with a configured number of CSLRS ports. If the resource is configured to be present in the downlink (DL) transmission periodically, e.g., every 10 ms, then the wireless device may be able to use a subsequent measurement to improve the quality of the CSI. For example, the wireless device may make the estimate of the number of recommended layers (known as rank) more robust to noise and interference.
  • rank the number of recommended layers
  • the wireless device In LTE and NR, there is a possibility to configure the wireless device to only use one CSI-RS measurement (e.g., the most recent one, no later than the CSI reference resource as specified in 3GPP standards such as, for example, 3GPP TS 38.214 V17.3.0 clause 5.2.2.5), when computing the CSI. That is, when a RRC parameter timeRestrictionForChannelMeasurements is configured, the wireless device is disallowed from averaging the measurements over time.
  • a reason for configuring such restriction is that it enables CSI-RS resource reuse among multiple wireless devices where these wireless devices are configured to measure on the same CSI-RS.
  • the CSI- RS resource is beamformed/multiple-input multiple-output (MIMO) pre-coded in different directions for each of the CSI-RS occasions, i.e., different wireless devices. Therefore, measurement restriction was introduced to ensure that a CSI report received from the wireless device is a result of a measurement on only the latest CSI-RS occasion.
  • MIMO multiple-input multiple-output
  • timeRestrictionForChannelMeasurements in the CSI-ReportConfig, and there is a similar parameter for interference measurement (timeRestrictionForlnterferenceMeasurements).
  • Example use cases include using autoencoders for CSI compression to reduce the feedback overhead and improve channel prediction accuracy, using deep neural networks for classifying line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to enhance the positioning accuracy, using reinforcement learning for beam selection at the network side and/or the wireless device side to reduce the signaling overhead and beam alignment latency, and using deep reinforcement learning to learn an optimal precoding policy for complex MIMO precoding problems.
  • autoencoders for CSI compression to reduce the feedback overhead and improve channel prediction accuracy
  • deep neural networks for classifying line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to enhance the positioning accuracy
  • reinforcement learning for beam selection at the network side and/or the wireless device side to reduce the signaling overhead and beam alignment latency
  • deep reinforcement learning to learn an optimal precoding policy for complex MIMO precoding problems.
  • an AI/ML model is operating at one end of the communication chain (e.g., at the wireless device side), but this node gets assistance from the node(s) at the other end of the communication chain (e.g., a network node) for its AI/ML model life cycle management (e.g., for training/retraining the AI/ML model, model update).
  • CSI-RS resources cannot be reused for transmitting CSI-RS across different spatial directions (i.e., precoding CSI-RS with different precoder weights) as it requires measurement restriction.
  • Some embodiments advantageously provide methods, systems, and apparatuses for CSI prediction.
  • the reference sources used to obtain CSI prediction may be defined differently from spatial CSI prediction.
  • a new time domain restriction for channel measurements (SetConfigured) is introduced that introduces a window for CSI-RS measurements.
  • a CSI prediction or beam temporal prediction (e.g., as indicated by the RRC parameter ReportQuantity) are indicated.
  • timeRestrictionForChannelMeasurements in the CSI-ReportConfig is set to "SetConfigured"
  • the wireless device derives the channel measurements for computing CSI or reference signal received power (RSRP) reported in uplink (UL) slot n based on only the X most recent, no later than the CSI reference resource, occasion of RS or SSB associated with the CSI resource setting.
  • RSRP reference signal received power
  • X > 1 is the window length. It is, e.g., a 3GPP specified value or enabled by configuration parameter by higher layer signaling, such as medium access control (MAC) control element (CE) or RRC, or indicated dynamically in the downlink control information (DCI).
  • MAC medium access control
  • CE control element
  • RRC downlink control information
  • Specifying a window length for the prediction allows for a tradeoff and simultaneous use of network flexibility in re-using a CSI-RS resource for multiple wireless devices with different precoding weights (resource reuse benefit) and CSI prediction performance benefit.
  • a network node configured to indicate, to the wireless device, a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period.
  • Network node is configured to receive, from the wireless device, a predicted channel state information, CSI, report, the predicted-CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window.
  • Network node is configured to communicate with the wireless device based on the predicted-CSI report.
  • the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the predicted-CSI report is further based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSL IM/synchronization signal block, SSB, wireless device trajectory.
  • ML machine learning
  • the indication of the measurement restriction window is an implicit indication to the wireless device.
  • the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.
  • the measurement restriction window overlaps with a plurality of downlink, DL, slots.
  • the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.
  • the predicted-CSI report is further based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • the predicted-CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.
  • the network node is further configured to receive channel quality measurements during the measurement restriction window.
  • the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.
  • a method performed in a network node includes indicating, to the wireless device, a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period.
  • the method includes receiving, from the wireless device, a predicted channel state information, CSI, report, the predicted- CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window.
  • the method includes communicating with the wireless device based on the predicted-CSI report.
  • the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the predicted-CSI report is further based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSL IM/synchronization signal block, SSB, wireless device trajectory.
  • ML machine learning
  • the indication of the measurement restriction window is an implicit indication to the wireless device.
  • the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.
  • the measurement restriction window overlaps with a plurality of downlink, DL, slots.
  • the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.
  • the predicted-CSI report is further based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • the predicted-CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CS RS.
  • the method includes receiving channel quality measurements during the measurement restriction window.
  • the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.
  • a wireless device configured to receive, from the network node, an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSLRS, distributed over a time period.
  • Wireless device is configured to measure, during the measurement restriction window, the CSLRS s.
  • Wireless device is configured to determine a predicted channel state information, CSI, report using the measurement of the plurality of CSLRS.
  • Wireless device is configured to transmit based on the predicted-CSI report.
  • the measurement restriction window is defined by at least one of: a plurality of CSLRS resources that are based on a CSLRS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the predicted-CSI report is based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI- IM/synchronization signal block, SSB, wireless device trajectory.
  • the measurement restriction window is implicitly indicated to the wireless device.
  • the time period has a duration between a last symbol of a last CSLRS occasion and a beginning symbol of a beginning CSLRS occasion.
  • the measurement restriction window includes a plurality of downlink, DL, slots.
  • the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.
  • the predicted-CSI report is based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • the predicted-CSI report is based on a machine learning, ML, model
  • the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.
  • the processing circuitry is further configured to perform channel quality measurements during the measurement restriction window.
  • the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.
  • a method performed in a wireless device includes receiving, from the network node, an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSLRS, distributed over a time period.
  • the method includes measuring, during the measurement restriction window, the plurality of CSLRSs.
  • the method includes determining a predicted channel state information, CSI, report using the measurement of the plurality of CSI-RS.
  • the method includes transmitting based on the predicted-CSI report.
  • the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the predicted-CSI report is based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI- IM/synchronization signal block, SSB, wireless device trajectory.
  • the measurement restriction window is implicitly indicated to the wireless device.
  • the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.
  • the measurement restriction window includes a plurality of downlink, DL, slots.
  • the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.
  • the predicted-CSI report is based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • the predicted-CSI report is based on a machine learning, ML, model
  • the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.
  • the method includes performing channel quality measurements during the measurement restriction window.
  • the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.
  • FIG. 1 is a schematic diagram of an example network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure
  • FIG. 2 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure
  • FIG. 3 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure
  • FIG. 4 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure
  • FIG. 5 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure
  • FIG. 6 is a flowchart illustrating example methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure
  • FIG. 7 is a flowchart of an example process in a network node according to some embodiments of the present disclosure.
  • FIG. 8 is a flowchart of an example process in a wireless device according to some embodiments of the present disclosure.
  • FIG. 9 is a flowchart of another example process in a network node according to some embodiments of the present disclosure.
  • FIG. 10 is a flowchart of another example process in a wireless device according to some embodiments of the present disclosure.
  • FIG. 11 is a diagram of an example of implicitly indicating the measurement window according to some embodiments of the present disclosure.
  • FIG. 12. is a diagram of an example of a time of measurement window according to some embodiments of the present disclosure.
  • relational terms such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements.
  • the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein.
  • the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.
  • the joining term, “in communication with” and the like may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • electrical or data communication may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example.
  • Coupled may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.
  • network node can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multistandard radio (MSR) radio node such as MSR BS, multi -cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (
  • BS base station
  • wireless device or a user equipment (UE) are used interchangeably.
  • the WD herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD).
  • the WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.
  • D2D device to device
  • M2M machine to machine communication
  • M2M machine to machine communication
  • Tablet mobile terminals
  • smart phone laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles
  • CPE Customer Premises Equipment
  • LME Customer Premises Equipment
  • NB-IOT Narrowband loT
  • radio network node can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).
  • RNC evolved Node B
  • MCE Multi-cell/multicast Coordination Entity
  • IAB node IAB node
  • relay node access point
  • radio access point radio access point
  • RRU Remote Radio Unit
  • RRH Remote Radio Head
  • WCDMA Wide Band Code Division Multiple Access
  • WiMax Worldwide Interoperability for Microwave Access
  • UMB Ultra Mobile Broadband
  • GSM Global System for Mobile Communications
  • the general description elements in the form of “one of A and B” corresponds to A or B. In some embodiments, at least one of A and B corresponds to A, B or AB, or to one or more of A and B. In some embodiments, at least one of A, B and C corresponds to one or more of A, B and C, and/or A, B, C or a combination thereof.
  • functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes.
  • the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.
  • Some embodiments provide CSI prediction.
  • FIG. 1 a schematic diagram of a communication system 10, according to an embodiment, such as a 3 GPP -type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14.
  • the access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18).
  • Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20.
  • a first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a.
  • a second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.
  • a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16.
  • a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR.
  • WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.
  • the communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud- implemented server, a distributed server or as processing resources in a server farm.
  • the host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider.
  • the connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30.
  • the intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network.
  • the intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).
  • the communication system of FIG. 1 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24.
  • the connectivity may be described as an over-the-top (OTT) connection.
  • the host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries.
  • the OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications.
  • a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.
  • a network node 16 is configured to include a configuration unit 32 which is configured to perform one or more network node 16 functions described herein, including functions related to CSI prediction.
  • a wireless device 22 is configured to include an implementation unit 34 which is configured to perform one or more wireless device 22 functions described herein, including functions related to CSI prediction.
  • Example implementations, in accordance with an embodiment, of the WD 22, network node 16 and host computer 24 discussed in the preceding paragraphs will now be described with reference to FIG. 2.
  • a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10.
  • the host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities.
  • the processing circuitry 42 may include a processor 44 and memory 46.
  • the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • processors and/or processor cores and/or FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 46 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24.
  • Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein.
  • the host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24.
  • the instructions may be software associated with the host computer 24.
  • the software 48 may be executable by the processing circuitry 42.
  • the software 48 includes a host application 50.
  • the host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the host application 50 may provide user data which is transmitted using the OTT connection 52.
  • the “user data” may be data and information described herein as implementing the described functionality.
  • the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider.
  • the processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and/or the wireless device 22.
  • the processing circuitry 42 of the host computer 24 may include a control unit 54 configured to enable the service provider to observe/monitor/control/transmit to/receive from the network node 16 and/or the wireless device 22.
  • the communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22.
  • the hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16.
  • the radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the communication interface 60 may be configured to facilitate a connection 66 to the host computer 24.
  • the connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.
  • the hardware 58 of the network node 16 further includes processing circuitry 68.
  • the processing circuitry 68 may include a processor 70 and a memory 72.
  • the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • FPGAs Field Programmable Gate Array
  • ASICs Application Specific Integrated Circuitry
  • the processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • volatile and/or nonvolatile memory e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection.
  • the software 74 may be executable by the processing circuitry 68.
  • the processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16.
  • Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein.
  • the memory 72 is configured to store data, programmatic software code and/or other information described herein.
  • the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16.
  • processing circuitry 68 of the network node 16 may include configuration unit 32 configured to perform one or more network node 16 functions described herein, including functions related to CSI prediction.
  • the communication system 10 further includes the WD 22 already referred to.
  • the WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located.
  • the radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.
  • the hardware 80 of the WD 22 further includes processing circuitry 84.
  • the processing circuitry 84 may include a processor 86 and memory 88.
  • the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions.
  • the processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • memory 88 may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).
  • the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22.
  • the software 90 may be executable by the processing circuitry 84.
  • the software 90 may include a client application 92.
  • the client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24.
  • an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24.
  • the client application 92 may receive request data from the host application 50 and provide user data in response to the request data.
  • the OTT connection 52 may transfer both the request data and the user data.
  • the client application 92 may interact with the user to generate the user data that it provides.
  • the processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22.
  • the processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein.
  • the WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein.
  • the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.
  • the processing circuitry 84 of the wireless device 22 may include an implementation unit 34 configured to perform one or more wireless device 22 functions described herein, including functions related to CSI prediction.
  • the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 2 and independently, the surrounding network topology may be that of FIG. 1.
  • the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices.
  • Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network).
  • the wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure.
  • One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.
  • a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve.
  • the measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both.
  • sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 48, 90 may compute or estimate the monitored quantities.
  • the reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art.
  • measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like.
  • the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.
  • the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22.
  • the cellular network also includes the network node 16 with a radio interface 62.
  • the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the WD 22, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the WD 22.
  • the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16.
  • the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/supporting/ending a transmission to the network node 16, and/or preparing/terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.
  • FIGS. 1 and 2 show various “units” such as configuration unit 32, and implementation unit 34 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.
  • FIG. 3 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIGS. 1 and 2, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 2.
  • the host computer 24 provides user data (Block SI 00).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block SI 02).
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 04).
  • the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block SI 06).
  • the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block SI 08).
  • FIG. 4 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the host computer 24 provides user data (Block SI 10).
  • the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50.
  • the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12).
  • the transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure.
  • the WD 22 receives the user data carried in the transmission (Block SI 14).
  • FIG. 5 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the WD 22 receives input data provided by the host computer 24 (Block SI 16).
  • the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18).
  • the WD 22 provides user data (Block S120).
  • the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122).
  • client application 92 may further consider user input received from the user.
  • the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124).
  • the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).
  • FIG. 6 is a flowchart illustrating an example method implemented in a communication system, such as, for example, the communication system of FIG. 1, in accordance with one embodiment.
  • the communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2.
  • the network node 16 receives user data from the WD 22 (Block S128).
  • the network node 16 initiates transmission of the received user data to the host computer 24 (Block SI 30).
  • the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block SI 32).
  • the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the machine learning model is configured to use a plurality of measurements based on CSI-IM/SSB wireless device trajectory.
  • FIG. 7 is a flowchart of an example process in a network node 16 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the configuration unit 32), processor 70, radio interface 62 and/or communication interface 60.
  • Network node 16 is configured to transmit a plurality of CSI-RS to the wireless device 22, the CSI-RS resources being distributed over a time period (Block SI 34).
  • Network node 16 is configured to communicate with the wireless device 22 according to predicted CSI, the predicted CSI being based on the plurality of CSI-RS and being predicted using a ML model (Block S136).
  • the time period is defined by at least one of a window in time that includes a plurality of CSI-RS resources based on the CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the machine learning model is configured to use a plurality of measurements based on CSI-IM/SSB wireless device trajectory.
  • FIG. 8 is a flowchart of an example process in a wireless device 22 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the implementation unit 34), processor 86, radio interface 82 and/or communication interface 60.
  • Wireless device 22 is configured to predict CSI based on a plurality of CSI-RS resources, the CSI-RS resources being distributed over a time period and the prediction using a ML model (Block S138).
  • Wireless device 22 is configured to transmit using the predicted CSI (Block s 140).
  • FIG. 9 is a flowchart of another example process in a network node 16 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the configuration unit 32), processor 70, radio interface 62 and/or communication interface 60.
  • Network node 16 is configured to indicate, to the wireless device 22, a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period (Block S142).
  • Network node 16 is configured to receive, from the wireless device 22, a predicted channel state information, CSI, report, the predicted-CSI report being based on the measuring of the plurality of CSI-RSs during the measurement restriction window (Block S144).
  • Network node 16 is configured to communicate with the wireless device 22 based on the predicted-CSI report (Block S146).
  • the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the predicted-CSI report is further based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.
  • ML machine learning
  • the indication of the measurement restriction window is an implicit indication to the wireless device 22.
  • the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.
  • the measurement restriction window overlaps with a plurality of downlink, DL, slots.
  • the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.
  • the predicted-CSI report is further based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • ML machine learning
  • the predicted-CSI report is predicted based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CSI-RS.
  • the network node 16 is further configured to receive channel quality measurements during the measurement restriction window.
  • the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.
  • FIG. 10 is a flowchart of another example process in a wireless device 22 according to some embodiments of the present disclosure.
  • One or more blocks described herein may be performed by one or more elements of wireless device 22 such as by one or more of processing circuitry 84 (including the implementation unit 34), processor 86, radio interface 82 and/or communication interface 60.
  • Wireless device 22 is configured to receive, from the network node 16, an indication of a measurement restriction window for measuring a plurality of channel state information reference signals, CSI-RS, distributed over a time period (Block S148).
  • Wireless device 22 is configured to measure, during the measurement restriction window, the CSI-RSs (Block SI 50).
  • Wireless device 22 is configured to determine a predicted channel state information, CSI, report using the measurement of the plurality of CSI-RS (Block SI 52).
  • Wireless device 22 is configured to transmit based on the predicted-CSI report (Block SI 54).
  • the measurement restriction window is defined by at least one of: a plurality of CSI-RS resources that are based on a CSI-RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • the predicted-CSI report is based on a machine learning, ML, model configured to predict based on one or more of a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.
  • ML machine learning
  • the measurement restriction window is implicitly indicated to the wireless device 22.
  • the time period has a duration between a last symbol of a last CSI-RS occasion and a beginning symbol of a beginning CSI-RS occasion.
  • the measurement restriction window includes a plurality of downlink, DL, slots.
  • the measurement restriction window has a duration indicated by at least one of downlink control information, DCI, medium access control element, MAC CE, and radio resource control, RRC.
  • the predicted-CSI report is based on a machine learning, ML, model configured to predict the CSI using a plurality of slots, the plurality of slots being selected from: any measurements having a last symbol received before an indicated CSI reference slot, based on there not being a configured restriction for temporal CSI or beam prediction; or the most recent measurements having a last symbol received before an indicated CSI reference slot, based on there being a configured restriction for temporal CSI or beam prediction.
  • the predicted-CSI report is based on a machine learning, ML, model, and the measurement restriction window is defined to allow for a minimum computation delay between an end of the measurement restriction window and a start of an uplink transmission, the minimum computation delay being a minimum time for the ML model to predict the CS RS.
  • the processing circuitry is further configured to perform channel quality measurements during the measurement restriction window.
  • the channel quality measurements include at least one of layer 1 reference signal received power, Ll-RSRP, and layer 1 signal to interference noise ratio, Ll-SINR.
  • Some embodiments provide for CSI prediction.
  • One or more wireless device 22 functions described below may be performed by one or more of processing circuitry 84, processor 86, implementation unit 34, etc.
  • One or more network node 16 functions described below may be performed by one or more of processing circuitry 68, processor 70, configuration unit 32, etc.
  • the reference sources used to obtain CSI prediction are defined as a set of CSLRS resources distributed over time.
  • the input to the AI/ML model may include the set of measurements (e.g., CRI, Ll-RSRP, Ll-SINR) based on CSI-RS/CSI interference management (CSLIM)/SSB wireless device 22 trajectory.
  • the output of the AI/ML model is the temporal CSI prediction (e.g., predicted channel -quality indicator (CQI), predicted precoding-matrix indicator (PMI), predicted beam(s)).
  • CQI channel -quality indicator
  • PMI predicted precoding-matrix indicator
  • a CSI reference resource is defined in time and frequency for which the reported CSI measurement may assumed to be valid.
  • a typical occasion for the reference slot in time is four slots earlier than the actual report. This allows the wireless device 22 to have some time to prepare the report.
  • the measurement restriction is extended from a single CSI-RS occasion in time as in LTE and NR to a measurement window, which can be defined by either of the following:
  • a set defined by a window in time e.g., T1 ms or T1 slots
  • a window in time e.g., T1 ms or T1 slots
  • the window length depends on the time separation(s) between the CSI-RS occasions.
  • the time separation can be derived from the periodicity, and for aperiodic CSI-RS with a burst of CSI-RS resources, the said time separation can be derived from the specified time gap (e.g., time unit) between adjacent CSI-RS occasions within the burst.
  • CSI-IM and SSB may be used for channel/interference measurements, and the measurement window may contain one or more resources for CSI- IM/SSB in addition to CSI-RS.
  • a new ReportQuantity may be introduced in the CSI-ReportConfig (as specified in, for example, 3GPP standards such as, for example, 3GPP TS 38.331), which indicates at least a predicted CSI or a predictedRSRP (for beam prediction).
  • the ReportQuantity may also mention AI/ML based prediction CSI or RSRP respectively.
  • a predicted CSI may also be configured by using the Rel-18 Type II codebook in the codebook and report configuration.
  • the CSI-ReportConfig in RRC includes a measurement restriction for channel and/or interference, e.g.:
  • timeRestrictionForChannelMeasurements_rl9 with values ⁇ configured, notConfigured, setConfigured ⁇ where setConfigured means/indicates there is a window of measurements (either in time or in the number of CSI-RS occasions) for which the wireless device 22 can assume the Tx precoder from the network node side is fixed (same spatial relation, or same QCL Type-D assumption across all CSI-RS occasions) and wireless device 22 can perform (e.g., safely perform) coherent channel combining, averaging, etc.
  • a RRC parameter timewindowforchannelmeasurements rl9 may be used to configure the wireless device 22 with the value T1 (for (a) above) or N1 (for (b) above), i.e., related to the duration of the measurement window.
  • parameters for timeRestrictionForlnterferenceMeasurements r!9 and timewindowforinterferencemeasurements r!9 may be used to adjust the interference measurement window.
  • the values of Ti or Ni related to the duration of the measurement window may be implicitly indicated.
  • FIG. 11 shows an example where multiple samples (e.g., K different occasions of CSI-RS) are indicated to the wireless device 22 for CSI or beam measurement.
  • the K different CSI-RS occasions may have the same number of CSI-RS ports, and they are to be transmitted using the same transmit precoder at the network node 16 (i.e., the wireless device 22 assumes the K different CSI- RS occasions are precoded using the same precoder.
  • the K different CSI-RS occasions may be configured in a single NZP CSI-RS resource set.
  • the K different CSI-RS occasions or the single NZP CSI-RS resource set may be aperiodically triggered via a DCI. In at least one embodiment, the K different CSI-RS occasions or the single NZP CSI-RS resource set may be activated via a MAC CE.
  • the duration of the measurement window may be implicitly defined as the time difference between the last symbol of the last CSI-RS occasion (CSI-RS K) and the first symbol of the first CSI-RS occasion (CSI- RS 7).
  • the measurement window can be considered the “enhanced CSI reference resource”, i.e., span a set of DL slots rather than a single DL slot. If the start time of the measurement window does not align with the start symbol of a DL slot, and/or the end time of the measurement window does not align with the end symbol of a DL slot, the enhanced CSI reference resource can be slightly modified to be a set of consecutive DL slots, which starts with a DL slot that contain the first CSI-RS occasion in the measurement window, and ends with a DL slot that contain the last CSI-RS occasion in the measurement window.
  • the number of occasions Ni is implicitly given by the number of CSI-RS occasions indicated to the wireless device 22 (e.g., the K different CSI- RS occasions configured in a single NZP CSI-RS resource set.
  • the values of Ti or Ni related to the duration of the measurement window may be explicitly indicated via DCI.
  • Codepoints in a DCI field in DCI may be associated with different values of Ti or Ni.
  • the wireless device 22 is instructed to use the value of Ti or Ni corresponding to the indicated codepoint of the DCI field.
  • This DCI may be a DCI that triggers an aperiodic CSI report, or a DCI that is different from a DCI that triggers an aperiodic CSI report.
  • the indication of the values of Ti or Ni via DCI is applicable to CSI-RS of any time domain behavior (e.g., periodic CSI-RS, semi -persistent CSI-RS, or aperiodic CSI-RS).
  • the values of Ti or Ni related to the duration of the measurement window may be explicitly indicated via a MAC CE.
  • a field in MAC CE may indicate a value of Ti or Ni.
  • the wireless device 22 is instructed to use the value of Ti or Ni corresponding to the indicated field of the MAC CE.
  • This MAC CE may be a MAC CE that activates a semi -persistent CSI report or a semi-persistent CSI-RS resource set; or a new MAC CE that is different from a MAC CE that activates either a semi -persistent CSI- RS resource set or a semi -persistent CSI report.
  • the indication of the values of Ti or Ni via MAC CE is applicable to CSI-RS of any time domain behavior (e.g., periodic CSI-RS, semi -persistent CSI-RS, or aperiodic CSI-RS)
  • the values of Ti or Ni related to the duration of the measurement window may be explicitly indicated via any combination of RRC, MAC CE, and DCI.
  • candidate values of Ti or Ni may be configured via a list in RRC where each candidate value in the list is mapped to a codepoint in a DCI field of DCI.
  • a list of values of Ti or Ni may be first RRC configured; then a subset of the list of values Ti or Ni may be activated by MAC CE, and the values activated by MAC CE are mapped to codepoints in a DCI field of DCI.
  • the set of slots used for CSI prediction or beam prediction is further specified as
  • the input to the AI/ML model can be measurements of any CSI-RS/CSI-IM/SSB whose last symbol is received no later than the CSI reference resource slot n. This may be the default operation assuming no time restriction.
  • the input to the AI/ML model are measurements of the most recent set of CSI-RS/CSI- IM/SSB whose last symbol is received up to the CSI reference resource slot n.
  • the start and/or end time of the measurement window may need to be further defined to anchor the measurement window.
  • the measurement window is anchored by the minimum computation delay between the end of the measured CSI-RS occasion and the start of the uplink channel that carries the measurement report. This is illustrated in FIG. 12.
  • the extended time restriction window can be applied in other measurement reports as well.
  • the extended time restriction window is applied to Ll-RSRP reporting.
  • the wireless device 22 derives the channel measurements for computing Ll-RSRP value reported in uplink slot n based on only the SS/PBCH or NZP CSI-RS, no later than the end time of the enhanced CSI reference resource, which is associated with the CSI resource setting.
  • the wireless device 22 derives the channel measurements for computing Ll-RSRP reported in uplink slot n based on only the occasions of SS/PBCH or NZP CSI-RS in the most recent measurement window, no later than the end time of enhanced CSI reference resource, associated with the CSI resource setting.
  • the extended time restriction window is applied to Ll- SINR reporting.
  • the wireless device 22 derives the channel measurements for computing Ll-SINR reported in uplink slot n based on only the SSB or NZP CSI-RS, no later than the end time of the enhanced the CSI reference resource, which is associated with the CSI resource setting.
  • the wireless device 22 derives the channel measurements for computing Ll-SINR reported in uplink slot n based on only the occasions of SSB or NZP CSI-RS in the most recent measurement window, no later than the end time of enhanced CSI reference resource, which is associated with the CSI resource setting.
  • the wireless device 22 derives the interference measurements for computing Ll-SINR reported in uplink slot n based on only the occasions of CSI- IM or NZP-CSLRS for interference measurement, or NZP CSI-RS for channel and interference measurement, no later than the end time of the enhanced the CSI reference resource, which is associated with the CSI resource setting.
  • the wireless device 22 derives the interference measurements for computing the Ll-SINR reported in uplink slot n based on only the occasions of CSI-IM or NZP CSI-RS for interference measurement, or NZP CSI-RS for channel and interference measurement, in the most recent measurement window, no later than the end time of enhanced CSI reference resource, which is associated with the CSI resource setting.
  • Example Al A network node 16 configured to communicate with a wireless device 22 (WD), the network node 16 configured to, and/or comprising a radio interface and/or comprising processing circuitry configured to transmit a plurality of CSI reference signals, CSI-RS, to the wireless device 22, the CSI-RS resources being distributed over a time period; and communicate with the wireless device 22 according to predicted channel state information, CSI, the predicted CSI being based on the plurality of CSI-RS and being predicted using a machine learning, ML, model.
  • CSI-RS CSI reference signals
  • Example A2 The network node 16 of Example Al, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • Example A3 The network node 16 of Example Al, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.
  • Example Bl A method implemented in a network node 16, the method comprising transmitting a plurality of CSI reference signals, CSI-RS, to the wireless device 22, the CSI-RS resources being distributed over a time period; and communicating with the wireless device 22 according to predicted channel state information, CSI, the predicted CSI being based on the plurality of CSI-RS and being predicted using a machine learning, ML, model.
  • CSI-RS CSI reference signals
  • Example B2 The method of Example Bl, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • Example B3 The method of Example Bl, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.
  • a wireless device 22 configured to communicate with a network node 16, the WD configured to, and/or comprising a radio interface and/or processing circuitry configured to predict channel state information, CSI, based on a plurality of CSI reference signal, CSI-RS, resources, the CSI-RS resources being distributed over a time period and the prediction using a machine learning, ML, model; and transmit using the predicted CSI.
  • a wireless device 22 configured to communicate with a network node 16
  • the WD configured to, and/or comprising a radio interface and/or processing circuitry configured to predict channel state information, CSI, based on a plurality of CSI reference signal, CSI-RS, resources, the CSI-RS resources being distributed over a time period and the prediction using a machine learning, ML, model; and transmit using the predicted CSI.
  • Example C2 The WD of Example Cl, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • Example C3. The WD of Example Cl, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.
  • Example DI A method implemented in a wireless device 22 (WD), the method comprising predicting channel state information, CSI, based on a plurality of CSI reference signal, CSI-RS, resources, the CSI-RS resources being distributed over a time period and the prediction using a machine learning, ML, model; and transmitting using the predicted CSI.
  • WD wireless device 22
  • Example D2 The method of Example DI, wherein the time period is defined by at least one of: a window in time that includes a plurality of CSI-RS resources based on the CSI- RS periodicity; and a window including a predetermined plurality of CSI occasions.
  • Example D3 The method of Example DI, wherein the machine learning model is configured to use a plurality of measurements based on CSI interference measurement, CSI-IM/synchronization signal block, SSB, wireless device 22 trajectory.
  • the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.
  • These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++.
  • the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer.
  • the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • LAN local area network
  • WAN wide area network
  • Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Power Engineering (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Un procédé, un système et un appareil sont divulgués. Au moins un mode de réalisation décrit un nœud de réseau. Le nœud de réseau est configuré pour indiquer, au dispositif sans fil, une fenêtre de restriction de mesure pour mesurer une pluralité de signaux de référence d'informations d'état de canal, CSI-RS, qui sont distribués sur une période. Le nœud de réseau est configuré pour recevoir, en provenance du dispositif sans fil, un rapport d'informations d'état de canal (CSI) prédites, le rapport de CSI prédites étant basé sur la mesure de la pluralité de CSI-RS pendant la fenêtre de restriction de mesure. Le nœud de réseau est configuré pour communiquer avec le dispositif sans fil sur la base du rapport de CSI prédites.
EP23783571.5A 2022-09-30 2023-09-28 Restriction de mesure pour prédiction d'informations d'état de canal (csi) Pending EP4595324A1 (fr)

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