WO2017167370A1 - Système radio et dispositif de décodage pour une compression distribuée - Google Patents

Système radio et dispositif de décodage pour une compression distribuée Download PDF

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Publication number
WO2017167370A1
WO2017167370A1 PCT/EP2016/057039 EP2016057039W WO2017167370A1 WO 2017167370 A1 WO2017167370 A1 WO 2017167370A1 EP 2016057039 W EP2016057039 W EP 2016057039W WO 2017167370 A1 WO2017167370 A1 WO 2017167370A1
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Prior art keywords
radio
lattice
signal
compression
decoding device
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Inventor
Inaki ESTELLA AGUERRI
Meryem BENAMMAR
Abdellatif ZAIDI
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Priority to CN201680084338.7A priority Critical patent/CN109076033B/zh
Priority to PCT/EP2016/057039 priority patent/WO2017167370A1/fr
Publication of WO2017167370A1 publication Critical patent/WO2017167370A1/fr
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    • 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
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03891—Spatial equalizers
    • H04L25/03961—Spatial equalizers design criteria
    • H04L25/03968—Spatial equalizers design criteria mean-square error [MSE]

Definitions

  • the present disclosure relates to a radio system and a decoding device using lattice based distributed code.
  • the present disclosure relates to methods for distributed compression using nested lattices techniques.
  • Interference is one of the most limiting factors of communication in networks. In certain cases, its effect can be mitigated through the use of distributed network architectures, formed by distributed radio units (RU) - or remote radio units (RRUs) - and a centralized processor (CP). Interference can be mitigated by enabling the centralized processing of the signals received by multiple RRUs at the CP.
  • This topology has some other appreciable features, such as low cost deployment of BSs and flexible network utilization.
  • the RRUs implement only the radio functionalities, i.e., transmission, reception, analog-to-digital conversion (ADC) and digital-to-analog conversion (DAC), while the CP centralizes the based band processing units (BBUs).
  • Alternative architectures in Next Generation Fronthaul Interfaces (NGFI) consider RRUs which also share some base band functionalities with the BBU at the CP.
  • the cloud radio access network (C-RAN) architecture as shown for example in Fig. 1 is an example of such distributed network topology proposed for 4.5G and 5G, in which base stations (BSs), containing the RRUs, are connected to a cloud-computing central processor (CP) via finite capacity link containing the BBUs.
  • Fig. 3 shows a schematic diagram illustrating a Cloud Radio Access Setup 300.
  • the signals are forwarded to the CP 301 , which has a BBU pool 302.
  • the received signal is processed by a RRU unit 312, 322, and the IQ samples are forwarded to the BBU 303, 304.
  • FIG. 2 shows a schematic diagram illustrating a Massive Ml MO station 200.
  • the signal received at each antenna is processed at the RRU 222, 223, 232, 233 and forwarded over the CPRI links 204, 205 to a remote base band unit (BBU) pool 201 having a plurality of BBUs 202, 203
  • BBU remote base band unit
  • Fig. 4 shows a schematic diagram illustrating a multi-antenna network system according to a baseline solution 400.
  • a first path includes first transceiver 410 with BBU 41 1 , compression 412, decompression 413, CPRI link 415, second transceiver 430 with RRU 433, compression 431 and decompression 432.
  • a second path, independent from first path includes third transceiver 420 with BBU 421 , compression 422, decompression 423, CPRI link 425, fourth transceiver 440 with RRU 443, compression 441 and decompression 442.
  • independent compression 412, 431 , 422, 441 and decompression 413, 432, 423, 442 is performed for each antenna 434, 444.
  • the uplink and downlink directions are provided. Note that the two decompression and compression operations are done independently with respect to each other.
  • the signals received at the plurality of antennas are highly correlated, and consequently, so are the digitized IQ samples at the RRUs after the ADC.
  • Distributed compression can be utilized to take into account the statistical correlation between the IQ samples at the plurality of RRUs.
  • Wyner-Ziv type codes can be utilized, that consider the signals at the other RRUs as side information.
  • distributed codes are mostly theoretical and lack efficient constructions.
  • both baseline and distributed compression solutions independently design the compressor block and the decoder block. That is, the compression is designed so as to minimize the distortion between the received signals at the RUs and their decompressed versions, without taking into account the specific structure of the decoder at the CP.
  • the compression should be designed to maximize the information transmission. This falls within the information bottleneck class of problems.
  • a basic idea of the invention is to apply a novel concept for optimizing a distributed compression block for a given decoder at the CP, and implementing the solution using nested lattices.
  • a practically feasible distributed compression scheme is provided that is based on nested lattices and accounts for the specific structure of the CP decoder.
  • the presented idea provides a significant modification of the transmitted signal between RRU and CP with respect to existing modulation formats (e.g., those used in LTE), that is, in the transmission over the CPRI link.
  • the compression and decompression techniques described herein may be implemented in wireless communication networks, in particular communication networks based on mobile communication standards such as LTE, in particular LTE-A and/or OFDM.
  • the transmission and reception devices described herein may further be implemented in a base station (NodeB, eNodeB) or a mobile device (or mobile station or User Equipment (UE)).
  • the described devices may include integrated circuits and/or passives and may be manufactured according to various technologies.
  • the circuits may be designed as logic integrated circuits, analog integrated circuits, mixed signal integrated circuits, optical circuits, memory circuits and/or integrated passives.
  • Radio signals may be or may include radio frequency signals radiated by a radio transmitting device (or radio transmitter or sender) with a radio frequency lying in a range of about 3 Hz to 300 GHz.
  • the frequency range may correspond to frequencies of alternating current electrical signals used to produce and detect radio waves.
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution-A
  • the invention relates to a radio system comprising a plurality of radio units, each radio unit, comprising: a receive interface, configured to receive at least one radio signal, y k , over a multiple-input multiple-output, MIMO, radio channel;
  • compression parameters from a decoding device a compressor configured to generate a codeword signal, based on the compression parameters by encoding the at least one
  • radio signal with a lattice based distributed code configured
  • the radio units of the radio system can be at different locations and not necessarily in the same physical device. For example in the C-RAN scenario, there are various base stations (BSs) at different locations all connected to the same CP. However, in one implementation, the radio units of the radio system can be arranged in the same physical device.
  • BSs base stations
  • the radio units of the radio system can be arranged in the same physical device.
  • the decompression and compression operations can be performed with respect to each other, thereby producing a codeword that minimizes distortion and maximizes information transmission.
  • the distributed compression code and the compression parameters are known at the radio system.
  • the distributed compression code can be formed by the lattice based distributed code.
  • the compression parameters comprise parameters of a plurality of nested lattices forming the lattice based distributed code.
  • the compression parameters comprise second order moments of the plurality of nested lattices.
  • the compressor comprises: a dither module configured to add a dither to the radio signal ; a quantization module, configured to quantize the dithered radio signal based on the plurality of nested lattices; and a modulo reduction module, configured to modulo reduce the quantized dithered radio signal to generate the codeword signal, Ak.
  • the compressor is configured to compress the radio signal ⁇ k based on a lattice operation comprising a quantization and a modulo reduction.
  • the invention relates to a decoding device, comprising: a receive interface, configured to receive from a plurality of radio devices a plurality of signals, ⁇ k , over a plurality of links, each signal ⁇ k corresponding to a compressed radio signal y k , wherein the compressed radio signal y k carries a plurality of messages, ⁇ ⁇ , from a plurality of users; a decompressor, configured to decompress the plurality of signals ⁇ k with a nested lattice based distributed code to provide a plurality of
  • the decompressor comprises a plurality of lattice based reconstruction modules, configured to successively decompress the plurality of signals ⁇ k .
  • a decompression result of a reconstruction module of the plurality of lattice based reconstruction modules depends on a
  • a decompression result of a reconstruction module of the plurality of lattice based reconstruction modules depends on side information generated from decompression results of preceding reconstruction modules of the plurality of lattice based reconstruction modules.
  • This provides the advantage that the information transfer between the transmitted signal and the reconstruction at the CP can be maximized by exploiting the correlation between the received signals to generate effective side information and improve decompression.
  • the decompressor comprises an estimator, in particular a linear filtering estimator, configured to generate the side information based on estimation using the decompression results of the preceding reconstruction modules of the plurality of lattice based reconstruction modules.
  • the linear filtering estimator can be for example an optimum filter estimator, e.g. a linear minimum mean squares estimator, LMMSE.
  • LMMSE linear minimum mean squares estimator
  • the LMMSE is just one example on how to design the filter to generate the side information.
  • the side information may be generated by any arbitrary function that generates the side information sequence using the plurality of previous decompression results.
  • each reconstruction module comprises a combiner, configured to combine a respective signal with the
  • the k-th combiner is based on the following lattice operation: where is the k-th signal, is the k-
  • th side information is the k-th lattice, is a k-th dither and is the k-th
  • the controller is configured to determine the compression parameters based on a decoding metric depending on the decoding operation of the decoder, the decompression operation of the decompressor and a correlation of the received signals.
  • the decoding metric depends on at least one of the following parameters: a number of the plurality of users, channel characteristics of the plurality of links, signal to noise ratios of the plurality of links, a quantization and/or statistics of a quantization error due to the nested lattice distributed code.
  • the controller is configured to maximize the decoding metric to determine optimal compression
  • the invention relates to a method to use lattice-based compression and decompression based on nested lattices for the information bottleneck problem.
  • This method provides the advantage of a modular compression block to forward the received signals with maximum information transfer.
  • the invention relates to a method to jointly design the compression parameters and decoding parameters for the aim of maximizing a given performance metric. This method provides the advantage that users can transmit with any code and any centralized decoder can be applied on the output signal of the block.
  • the invention relates to a method for successive decoding of the lattice compression codewords and effective side information generation.
  • This method provides the advantage that the compression is adjusted to the respective decoder. Therefore its implementation requires only partial update of the system.
  • the invention relates to a method to account for the correlation of the signals received at other antennas when using successive
  • This method provides the advantage that the method operates on each received signal independently without requiring the received signals to be available at the same position. Hence, the method significantly improves upon standard point to point compression.
  • Fig. 1 shows a block diagram illustrating a system model of a Cloud Radio Access Network (C-RAN) 100;
  • C-RAN Cloud Radio Access Network
  • Fig. 2 shows a schematic diagram illustrating a Massive Ml MO station 200
  • Fig. 3 shows a schematic diagram illustrating a Cloud Radio Access Setup 300
  • Fig. 4 shows a schematic diagram illustrating a multi-terminal network system according to a baseline solution 400
  • Fig. 5 shows a schematic diagram of a method 500 using decoder aware compression design according to an implementation form
  • Fig. 6 shows a schematic diagram of a multi-terminal network system 600 according to an implementation form
  • Fig. 7 shows a schematic diagram of a multi-terminal network system using lattice based compression and decompression 700 according to an implementation form
  • Fig. 8 shows a block diagram of a compressor 800 according to an implementation form
  • Fig. 9 shows a block diagram of a decompressor 900 according to an implementation form
  • Fig. 10 shows a schematic diagram of a C-RAN network 1000 according to an
  • Fig. 1 1 shows a performance diagram illustrating average throughput 1 100 versus SNR in a C-RAN network for compression with different lattice codes according to an
  • Fig. 12 shows a performance diagram illustrating average throughput 1 100 versus front- haul capacity in a C-RAN network for compression with different lattice codes according to an implementation form
  • Fig. 13 shows a performance diagram illustrating average EVM 1300 versus front-haul capacity in a C-RAN network for compression with different lattice codes according to an implementation form
  • Fig. 14 shows a performance diagram illustrating average EVM 1300 versus front-haul capacity gain in a C-RAN network for compression with different lattice codes according to an implementation form
  • Fig. 15 shows a schematic diagram of a Massive Ml MO station with dedicated CPRI links 1500 according to an implementation form
  • Fig. 16 shows a performance diagram illustrating average throughput 1600 versus SNR in a Massive Ml MO network for compression with different lattice codes according to an implementation form
  • Fig. 17 shows a performance diagram illustrating average throughput 1700 versus front- haul capacity in a Massive Ml MO network for compression with different lattice codes according to an implementation form
  • Fig. 18 shows a performance diagram illustrating average EVM 1800 versus front-haul capacity in a Massive Ml MO network for compression with different lattice codes according to an implementation form
  • Fig. 19 shows a performance diagram illustrating average EVM 1900 versus front-haul capacity gain in a Massive Ml MO network for compression with different lattice codes according to an implementation form
  • Fig. 20 shows a performance diagram illustrating average EVM 2000 versus front-haul capacity in a Massive Ml MO network with shared CPRI for compression with different lattice codes according to an implementation form
  • Fig. 21 shows a performance diagram illustrating average EVM 2000 versus SNR per user in a Massive MIMO network with different lattice codes according to an
  • Fig. 5 shows a block diagram of a method 500 using decoder aware compression design according to an implementation form.
  • the method 500 to optimize the parameters is illustrated in Figure 5. It uses the inputs 501 : decompression method 502, compression method 503, system parameters 504, decoding method 505, performance metric 506 and provides the outputs 520: optimal compression parameters 521 and optimal decoding parameters 522.
  • the method 500 comprises the following steps: Characterize the
  • Effective Channel 51 1 this step characterizes the distribution of the output after decompression in terms of the compression parameters, for given lattice compression and decompression methods and system parameters (including SNR, noise distribution, channel coefficients, number or UE, number or RU); Compression Parameter
  • Optimization 512 A given a performance metric depending on the effective channel, decoding method and system parameters, e.g., mutual information, bit-error rate, the compression and decoding parameters are chosen to optimize this metric. In the embodiments below particular examples for this method are shown.
  • Fig. 6 shows a schematic diagram of a multi-terminal network system 600 according to an implementation form.
  • the multi-terminal network system 600 illustrates an end-to-end design of a lattice based distributed codes for the information bottleneck problem of maximizing the end-to-end information transfer, which takes into account the centralized decoder applied at the CP and the correlation of the signals at different RU in its design.
  • the main aspects are: Use of nested lattices for the multi-terminal information bottleneck problem, that is, the method to encode and decode for a given nested lattice code for distributed compression for the purpose of end-to-end maximization of information transfer; and a method for the joint design of the compression block and decoder structure. That implies, for example, the lattice design, effective side information generation, filtering and equalization.
  • the end-to-end multi-terminal network system 600 includes the following blocks: B1 ) Lattice Based Compression 610: Use nested lattices to compress the received signal at each RU accounting for statistical correlation with the signals at other RUs. A method is provided to compress at each RU for a given nested lattice code. B2) Lattice Based Decompression 622: Use lattice decoding at the CP to decompress the signals. We provide a method to decompress the signals successively. At each step, previous signals are used to generate an effective side information sequence, which is used in
  • B3) Centralized Decoding 626 Use a given multi-user decoder (e.g., linear filtering followed by MMSE decoding based on nearest-decoding or Zero-Forcing) to recover the messages from the decompressed signals.
  • the decompressed signals form an effective channel from which the messages are recovered.
  • Joint Parameter Design 628 Compression and decoding processing are jointly optimized based on a metric that depends both on the decoder and the compression.
  • codewords as or lattice codebooks can be used. These embodiments are denoted as
  • the Lattice Based Compression 610 according to B1 can be implemented as described in the following:
  • the lattice construction and compression method employed at each RU may be based on a lattice scheme by using a Nested Codebook construction and
  • a lattice ⁇ of dimension n is a discrete additive subgroup of It can be written in terms of a lattice Generator matrix
  • the fundamental Voronoi region V(A) is the set of point in that are closer to the zero vector than any other lattice point.
  • the reduction modulo ⁇ is defined as is a lattice quantizer.
  • the pair of lattices form a codebook of rate C k and of length
  • the second moment of each lattice can be described as and .
  • the k-th RU may compress the received signal at rate by
  • the index corresponding to the codeword may be forwarded to the CP 620.
  • a codebook is a set of codewords.
  • the number of codewords in a codebook C formed by a pair of nested lattices of dimension n is:
  • the rate of the codebook is C k , that is, it has In this case it coincides
  • the Lattice Based Compression 610 according to B1 can be implemented in one physical device including the multiple compressors 613, 614, 615, e.g. according to the Massive Ml MO station as described above with respect to Fig. 2.
  • Each of the radio units may include one or more antennas 61 1.
  • the Lattice Based Compression 610 according to B1 can be implemented as one system of physical devices coupled with respect to each other, where each physical device includes one or more of the
  • Each of the radio units may include one or more antennas 61 1.
  • the Lattice Based Decompression 622 according to B2) can be implemented as described in the following: After receiving the K indices and remapping to the compression codewords the CP 620 successively reconstructs k starting from the
  • Effective side information generation 623 can be implemented as follows: First, given k - 1 reconstructed signals have already been decompressed as the CP 620 computes an estimate (using the linear MMSE) of each sample
  • Decompression 622 of k-th compressed signal at CP 620 can be implemented as follows: Using the effective side information sequence, the CP 620 reconstructs y k using the effective side information with the following lattice operation:
  • the Centralized Decoding 626 can be implemented as described in the following:
  • the CP 620 recovers the transmitted messages from the decompressed signal accounting for the compression noise.
  • the CP 620 may have a decoding structure of successive interference MMSE nearest neighbor receiver with noise prediction accounting for the error due to the compression.
  • This decoder 626 works as follows. Once the K compression codewords have been reconstructed, the central unit has an effective channel modeled as the CP 620 applies successive
  • the central unit performs linear MMSE estimation of X from ⁇ using the filter designed accounting for the compression noise:
  • the CP 620 decodes each message successively as follows: to decode user 1 , the CP 620 filters the decompressed signal with the first column of and decodes the first user symbol
  • the receiver uses w 1 to reduce the noise in the channel, where the first column of as:
  • MMSE filtering followed by independent nearest neighbor decoding decodes each message by applying nearest neighbor in parallel to the filtered decompressed signal:
  • the Joint Parameter design 628 according to B4) can be implemented as described in the following: In this block the method to jointly optimize the decoding structure and the compression parameters is applied.
  • the performance metric of interest is the throughput, which can be characterized as follows:
  • filtering in decoding has already been designed as the MMSE receiver, accounting for the compression noise distribution.
  • the compression parameters are designed in order to maximize the end-to-end maximum rate transmission, and are given as
  • the end-to-end function to optimize changes.
  • the decoding constrains have to be incorporated and a join designed performed. For example, if linear MMSE processing is considered, the achievable sum-rate is given by
  • N r receiver antennas per RU and a signal Y k is received.
  • Each RU then considers N r pairs of independent nested codebooks A k r and A q k r which are constructed as in the embodiment 1 described above.
  • each transformed component is independently compressed by the RU considering the signals available at the CP at the moment of decoding, as done in Embodiment 1.
  • the received successively recovers the compressed signals.
  • the k-th BS applies independent lattice compression on each component.
  • the effective channel can be characterized as
  • compression parameters may be designed jointly depending on the decoder.
  • this problem can be casted as an optimization problem over the lattice parameters and examples of its solutions are given below with respect to Fig. 15.
  • Fig. 7 shows a schematic diagram of a multi-terminal network system 700 using lattice based compression and decompression according to an implementation form.
  • the multi terminal network system 700 is an implementation of the multi-terminal network system 600 described above with respect to Fig. 6.
  • the multi-terminal network system 700 includes a radio system 710 and a decoding device 740.
  • the radio system 710 includes a plurality of radio units 71 1 , 721 , 731 transmitting signals over respective CPRI links 715, 725, 735 to the decoding device 740.
  • Each radio unit includes a receive interface 712, 722, 732, compression parameters received from the decoding device 740, a compressor 717, 727, 737 and a transmit interface, e.g. an antenna port 715, 725, 735.
  • the receive interface 712, 722, 732 is configured to receive at least one radio signal, ⁇ , over a multiple-input multiple-output,
  • the compressor 717, 727, 737 is configured to generate a codeword signal, based on the compression parameters by encoding the at least one
  • the transmit interface 715, 725, 735 is configured to transmit the codeword signal ⁇ k to the decoding device 740.
  • radio units 71 1 , 721 , 731 can be implemented in one physical device 710, e.g. according to the Massive Ml MO station as described above with respect to Fig. 2. Each of the radio units 71 1 , 721 , 731 may be coupled to one or more antennas 716, 726, 736. Alternatively, the radio units 71 1 , 721 , 731 can be implemented as a system of physical devices, where each physical device includes one or more of the radio units 71 1 , 721 , 731 , e.g. according to the Cloud Radio Access Network as described above with respect to Fig. 3. Each of the radio units 71 1 , 721 , 731 may be coupled to one or more antennas 716, 726, 736.
  • the compression parameters may comprise parameters of a plurality of nested lattices forming the lattice based distributed code.
  • the lattice based distributed code may be known at the radio system 710 and the compression parameters may comprise second order moments of the plurality of nested lattices.
  • the compressor may comprise a dither module 801 configured to add a dither to the radio signal y k ; a quantization module 802, configured to quantize the dithered radio signal based on the plurality of nested lattices; and a modulo reduction module 803, configured to modulo reduce the quantized dithered radio signal to generate the codeword signal, ⁇ k ., e.g. as described below with respect to Fig. 8.
  • the compressor 717, 727, 737 may be configured to compress the radio signal y k based on a lattice operation comprising a quantization and a modulo reduction.
  • the decoding device 740 includes a receive interface, a decompressor 750, a decoder 760, a controller and a transmit interface.
  • the receive interface is configured to receive from a plurality of radio units 71 1 , 721 , 731 a plurality of signals, ⁇ k , over a plurality of links. Each signal ⁇ k corresponds to a compressed radio signal y k .
  • the compressed radio signal y k carries a plurality of messages, ⁇ 1 , from a plurality of users.
  • the decompressor 750 is configured to decompress the plurality of signals ⁇ k with a nested lattice based distributed code to provide a plurality of decompressed radio signals, y k .
  • the decoder is configured to decompress the plurality of signals ⁇ k with a nested lattice based distributed code to provide a plurality of decompressed radio signals, y k .
  • the 760 is configured to decode the plurality of decompressed radio signals, y k , to recover the plurality of messages.
  • the controller is configured to determine compression parameters based on the decoder structure and based on a correlation of the received signals.
  • the transmit interface is configured to transmit the compression parameters to each of the plurality of radio units 71 1 , 721 , 731.
  • the decompressor 750 may comprise a plurality of lattice based reconstruction modules 751 , 752, configured to successively decompress the plurality of signals ⁇ k .
  • a decompression result of a reconstruction module 751 , 752 of the plurality of lattice based reconstruction modules may depend on a decompression result of a preceding reconstruction module of the plurality of lattice based reconstruction modules.
  • a decompression result of a reconstruction module 751 , 752 of the plurality of lattice based reconstruction modules may depend on side information 753, 754 generated from decompression results of preceding reconstruction modules of the plurality of lattice based reconstruction modules.
  • the decompressor 750 may comprise an optimum filter estimator, in particular a linear minimum mean squares estimator, LMMSE, configured to generate the side information based on optimum filter estimation using the decompression results of the preceding reconstruction modules of the plurality of lattice based reconstruction modules 751 , 752.
  • Each reconstruction module 751 , 752 may comprise a combiner, configured to combine a respective signal with the corresponding side information to generate the respective
  • the k-th combiner may be based on the following lattice operation mod where is the signal, is the k-th side
  • radio signal for example as described above with respect to Fig. 6.
  • the controller may be configured to determine the compression parameters based on a decoding metric depending on the decoding operation of the decoder 760, the
  • the decoding metric may depend on at least one of the following parameters: a number of the plurality of users, channel characteristics of the plurality of links, signal to noise ratios of the plurality of links, a quantization and/or statistics of a quantization error due to the nested lattice distributed code.
  • the controller may be configured to maximize the decoding metric to determine optimal compression parameters.
  • Compression may be performed at each of the K RRUs independently, whereas decompression may be performed with K decompressions, but utilizing a successive decompression.
  • the quantization codeword may be utilized, as well as the effective side information sequence y k , generated from the previously
  • decompressed signals in the previous k-1 decompressors The use of nested lattice compression and decompression allows to combine at the decompressor units the quantization codewords and the side information sequence.
  • Successive decompression at the central processor may be implemented as follows:
  • reconstructed baseband signals are used to generate a side information sequence, which is provided to the decompressor to facilitate the decompression operation of
  • the successive decompression combining utilizing the corresponding quantization codeword and the effective side information sequence procedure allows to exploit correlation, and to reduce the degradation in the reconstruction.
  • the use of distributed nested lattice codes allows constructing compression codes that can combine the side information sequence and the quantization codeword at the compressor.
  • the correlation of the signal is essentially the covariance matrix (or other
  • the covariance can be given as C
  • This statistical relation can be estimated at the CP with various methods, for example in a training or by other classical means.
  • Fig. 8 shows a block diagram of a compressor 800 according to an implementation form
  • Fig. 9 shows a block diagram of a decompressor 900 according to an implementation form.
  • the compressor 800 includes a dither module 801 configured to add a dither to the radio signal ; a quantization module 802, configured to quantize the dithered radio signal based on the plurality of nested lattices; and a modulo reduction module 803, configured to modulo reduce the quantized dithered radio signal to generate the codeword signal
  • the decompressor 900 includes a lattice decompression module 910 including a dither module 901 for adding a dither to the codeword and a modulo reduction module 902
  • the decompressor 900 further includes a side-information module 903 for adding 904, 905 side information to the dithered codeword or to the result of the modulo reduction operation 902.
  • Fig. 10 shows a schematic diagram of a C-RAN network 1000 according to an
  • the C-RAN network 1000 includes a central unit 1001 , a plurality of Relaying units 1002, 1003, 1004 which relay radio signals to UEs 1010, 101 1 , 1012, 1013.
  • Fig. 1 1 shows a performance diagram illustrating average throughput 1 100 versus SNR in a C-RAN network for compression with different lattice codes according to an
  • FIG. 12 shows a performance diagram illustrating average throughput 1 100 versus front-haul capacity in a C-RAN network for compression with different lattice codes according to an implementation form.
  • Figure 1 1 depicts the average throughput with respect to the SNR when the compressor uses spheric lattices 1 102, 1 103 and finite dimension lattices cubic lattice 1 104, 1 105, i.e., The Cut-Set 1 101 is
  • Figure 12 shows the achievable throughput under different fronthaul capacity values for spheric lattices 1202, 1205 and cubic lattices 1203, 1204.
  • the Cut-Set 1201 is depicted as reference.
  • Fig. 13 shows a performance diagram illustrating average EVM 1300 versus front-haul capacity in a C-RAN network for compression with different lattice codes according to an implementation form
  • Fig. 14 shows a performance diagram illustrating average EVM 1300 versus front-haul capacity gain in a C-RAN network for compression with different lattice codes according to an implementation form.
  • Figure 13 depicts the average EVM, defined as the average distortion at which the transmitted symbols can be reconstructed from the compressed signal
  • FIG. 15 shows a schematic diagram of a Massive Ml MO station with dedicated CPRI links 1500 according to an implementation form.
  • each antenna 1504 serving L users 1520, 1521 , 1522 is considered.
  • Each antenna is assigned a dedicated CPRI link 1505 which connects it to the central processor 1506.
  • each antenna 1504 is assigned a dedicated CPRI link 1505 which connects it to the central processor 1506.
  • the CP 1506 includes a joint decompression unit, i.e. a decompressor as described above with respect to Fig. 6 or 7, and a centralized decoder, i.e. a decoder as described above with respect to Fig. 6 or 7.
  • the CP 1506 uses a successive interference MMSE receiver and optimizes the Lattice parameters as in the CRAN setup, i.e. according to:
  • Fig. 16 shows a performance diagram illustrating average throughput 1600 versus SNR in a Massive Ml MO network for compression with different lattice codes according to an implementation form
  • Fig. 17 shows a performance diagram illustrating average throughput 1700 versus front-haul capacity in a Massive Ml MO network for compression with different lattice codes according to an implementation form.
  • Figure 16 depicts the huge performance improvements are possible in the Massive Ml MO setup by using the disclosed method.
  • Cut-Set 1601 is depicted as reference.
  • Figure 17 shows the achievable throughput under different fronthaul capacity values for spheric lattices 1703, 1705 and cubic lattices 1702, 1704.
  • the Cut-Set 1701 is depicted as reference.
  • Fig. 18 shows a performance diagram illustrating average EVM 1800 versus front-haul capacity in a Massive Ml MO network for compression with different lattice codes according to an implementation form
  • Fig. 19 shows a performance diagram illustrating average EVM 1900 versus front-haul capacity gain in a Massive Ml MO network for compression with different lattice codes according to an implementation form.
  • Figure 19 shows the gains in EVM achieved with the disclosed method for spheric Iattices1902 and cubic lattices 1901 with respect to point-to-point compression.
  • Fig. 20 shows a performance diagram illustrating average EVM 2000 versus front-haul capacity in a Massive Ml MO network with shared CPRI for compression with different lattice codes according to an implementation form
  • Fig. 21 shows a performance diagram illustrating average EVM 2000 versus SNR per user in a Massive Ml MO network with different lattice codes according to an implementation form.
  • both antennas share a CPRI link and that the signals at both antennas can be combined before compression.
  • the achievable rate can be determined as an optimization problem and finding the optimal compression parameters when the decoder is an MMSE-SIC decoder and an linear MMSE decoder.
  • the performance is compared with the case in which the compression parameters are chosen to minimize the distortion between the received signal and the reconstruction signal.
  • the linear transformation mapping before applying compression is chosen as the eigenvectors of matrix
  • Figure 20 and Figure 21 show the average with respect to the available fronthaul and the SNR per UE.
  • the following curves achieved by optimizing the optimizing the lattice parameters are depicted: MMSE-SIC Opt 2002, 2103: maximum information transfer assuming that there is a MMSE successive interference cancellation decoder at the CP; MMSE-SIC Dist 2003, 2102: MMSE-SIC decoder and compression designed to minimize distortion between received and reconstructed signals at the CP; MMSE Opt 2105:
  • a loss in performance can be observed if compression is not designed taking into account the received at the CP. If MMSE-SIC is used, the performance suffers if the compression is designed to minimize distortion. Similarly, a loss in performance is observed if an ML receiver is assumed for compression design while the true decoder is a suboptimal linear MMSE decoder. This loss is significant at high SNR regimes, as observed in Figure 21.
  • the present disclosure relates to the case of a compression and decoding design based on nested lattice based compression and decompression and the joint design of compression and decoding parameters to optimize a certain metric.
  • the present disclosure also supports a method to encode the lattices to account for the correlation of the signal received at different radio units.
  • a method to successively reconstruct the compressed signals, to generate an effective side information sequence, and an effective channel is used in the decoding process are also provided.
  • Degradation due to compression is also incorporated at the decoder. It has been shown that gains over the situation in which compressor and the decoder are designed independently can be achieved.
  • the present disclosure also supports a computer program product including computer executable code or computer executable instructions that, when executed, causes at least one computer to execute the performing and computing steps described herein, in particular the steps of the methods described above.
  • a computer program product may include a readable non-transitory storage medium storing program code thereon for use by a computer.
  • the program code may perform the performing and computing steps described herein, in particular the methods described above.

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  • Engineering & Computer Science (AREA)
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  • Computer Networks & Wireless Communication (AREA)
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Abstract

L'invention concerne un système radio (710) comprenant une pluralité d'unités radio (711, 721, 731), chaque unité radio comprenant : une interface de réception (712, 722, 732) configurée pour recevoir au moins un signal radio, yk, sur un canal radio à entrées multiples et sorties multiples (MIMO) ; des paramètres de compression provenant d'un dispositif de décodage (740) ; un compresseur (717, 727, 737) configuré pour générer un signal de mot de code, λk, sur la base des paramètres de compression par codage du ou des signaux radio yk avec un code distribué basé sur un réseau ; et une interface de transmission (715, 725, 735), configurée pour transmettre le signal de mot de code λk au dispositif de décodage (740).<i /> <i /> <sb /> <i /> <i /> <sb /> <sb /> L'invention concerne en outre un dispositif de décodage (740) comprenant : une interface de réception configurée pour recevoir d'une pluralité d'unités radio (711, 721, 731) une pluralité de signaux, λk, sur une pluralité de liaisons, chaque signal λk correspondant à un signal radio compressé, yk, le signal radio compressé yk portant une pluralité de messages, ω1, provenant d'une pluralité d'utilisateurs ; un décompresseur (750) configuré pour décompresser la pluralité de signaux λk avec un code distribué basé sur un réseau imbriqué pour fournir une pluralité de signaux radio décompressés, ŷk, un décodeur (760) configuré pour décoder la pluralité de signaux radio décompressés, ŷk, de façon à récupérer la pluralité de messages ; un dispositif de commande, configuré pour déterminer des paramètres de compression sur la base de la structure de décodeur et sur la base d'une corrélation des signaux reçus ; et une interface de transmission configurée pour transmettre les paramètres de compression à chacune de la pluralité d'unités radio (711, 721,731).<sb /> <sb /> <i /> <i /> <i /> <i /> <i /> <i /> <sb /> <i /> <i /> <i /> <i />
PCT/EP2016/057039 2016-03-31 2016-03-31 Système radio et dispositif de décodage pour une compression distribuée Ceased WO2017167370A1 (fr)

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