Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
  • H04B1/69—Spread spectrum techniques
  • H04B1/707—Spread spectrum techniques using direct sequence modulation
  • H04B1/7097—Interference-related aspects
  • H04B1/7103—Interference-related aspects the interference being multiple access interference
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B1/00—Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
  • H04B1/69—Spread spectrum techniques
  • H04B1/707—Spread spectrum techniques using direct sequence modulation
  • H04B1/7097—Interference-related aspects
  • H04B1/7103—Interference-related aspects the interference being multiple access interference
  • H04B1/7105—Joint detection techniques, e.g. linear detectors
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04B—TRANSMISSION
  • H04B2201/00—Indexing scheme relating to details of transmission systems not covered by a single group of H04B3/00 - H04B13/00
  • H04B2201/69—Orthogonal indexing scheme relating to spread spectrum techniques in general
  • H04B2201/707—Orthogonal indexing scheme relating to spread spectrum techniques in general relating to direct sequence modulation
  • H04B2201/70701—Orthogonal indexing scheme relating to spread spectrum techniques in general relating to direct sequence modulation featuring pilot assisted reception

Definitions

• This invention pertains to the field of digital telecommunications. More precisely, this invention relates to a mixed direct-indirect adaptation procedure applied to receiver filter.
• Fig. 1 shows a model of a typical communication system incorporating the equalization/detection techniques .
• the DS-CDMA equalizers consist in removing the InterSymbol Interference (ISI) from the data received through a telecommunications channel and the Multiple Access Interference (MAI) .
• ISI InterSymbol Interference
• MAI Multiple Access Interference
• FIG. 2a there is shown a model of a DS-CDMA baseband system
• M. Latva-aho and M. J. Juntti "LMMSE Detection for DS-CDMA Systems in Fading Channels", IEEE Transactions on Communica tions, Vol. 48, No. 2, 2000, pp 194-199 and M. J. Juntti, and M. Latva-Aho, "Multiuser Receivers for CDMA Systems in Rayleigh Fading Channels", IEEE Transactions on Vehicular Technology, Vol. 49, No. 3, May 2000, pp. 885-899 and A.O. Dahmane and D. Massicotte, "Nonlinear Multiuser Receiver for UMTS Communications", IEEE-Semiannual Vehicular Technology Conference,
• Each user's symbol is spread by its respective code sequence of length N c and denoted by s k .
• the code sequence is generated by combining OVSF codes with short scrambling codes (Verd ⁇ S., Multiuser Detection, Cambridge University Press, 1998) .
• Each user k is transmitting its data through a Rayleigh fading channel 240 of L k paths denoted by h k ⁇ t) , with maximum delay spread of ⁇ m .
• Baud spaced indexes are represented by n
• chip spaced indexes are represented by m
• user k 1 s n th transmit symbol is unless otherwise stated.
• the model used is in Baud spaced form on but can be easily extended to fractionally , spaced form.
• the k th user' s continuous time spreading waveform is
• the baseband received signal of all users is the baseband received signal of all users.
• Nb represents the number of received symbols
• a k represents the received amplitude of user k
• ⁇ (t) represents the additive Gaussian noise- with variance ⁇ ⁇ 2
• * represents linear convolution
• Equation 3 Equation 3 where L ⁇ is the number of propagation paths, h j the complex gain of the path 1 for user k at time n, ⁇ k l is the propagation delay and ⁇ t) is the Dirac pulse.
• the received signal may then be written as follows r (t) - H (b(i), p(t)) + ⁇ (t) (Equation 4) where H(») represents the channel model 270, first part of the Equation (2) .
• Yet another object of the invention is to provide a regenerated data sequence .
• an apparatus for providing a regenerated data sequence comprising a channel identification unit receiving, from a communication channel, a transmitted signal (?) and a training control sequence ( p'TM'" ) to provide a plurality of channel coefficients representative of the communication channel ( ...h k ) and a channel modeling unit filtering the plurality of channel coefficients representative of the communication channel ⁇ h v ..h k ) with a known training data sequence (X) to provide the regenerated data sequence (Y) .
• a method for providing a regenerated data sequence comprising: receiving, from a communication channel, a transmitted signal (r) and a training control sequence ( p' ra '" ) to provide a plurality of channel coefficients representative of the communication channel ( f ...h k ) ; and filtering the plurality of channel coefficients representative of the communication channel ( ) with a known training data sequence (X) to provide the regenerated data sequence (Y) .
• a embodiment of an adaptation procedure that optimizes the parameters of a receiver filters such as a Multiuser Detection (MUD) applied to Direct-Sequence Code Division Multiple Access (DS-CDMA) is disclosed.
• MOD Multiuser Detection
• DS-CDMA Direct-Sequence Code Division Multiple Access
• the adaptation consists in using two distinct data sequences transmitted through the same channel; one data sequence is transmitted as payload data and a second data sequence is transmitted as training data used to adapt the filter parameters at the receiver. Parameters of the receiver filter are adapted in presence of varying channels at the same time as the data information sequences are transmitted.
• the adaptation is realized following a mixed adaptation procedure based on a direct (without channel identification) and indirect, (with channel identification) scheme.
• the invention is described for UMTS (Universal Mobile Telecommunications System) application in cellular communications system.
• Fig. 1 is a block diagram showing a model of a typical communication system
• Fig. 2a is a block diagram showing a prior art baseband model of the DS-CDMA system
• Fig. 2b is a block diagram showing, inter alia , traffic data and a pilot
• Fig. 3 is a block diagram showing a data sequence generator apparatus comprising a channel identification unit and a channel modeling unit in accordance with one embodiment of the invention
• Fig. 4 is a block diagram showing a direct adaptation method filters receiver structure with Pilot Free for DS-CDMA systems in accordance with one embodiment of the invention
• Fig. 5 is a block diagram showing an indirect adaptation method filters receiver structure with Pilot Free for DS-CDMA systems in accordance with one embodiment of the invention
• Fig. 6 is a block diagram showing a mixed method cascade filters receiver structure for DS-CDMA systems in accordance with one embodiment of the invention
• Fig. 7 is a block diagram showing a mixed method cascade filters receiver structure with Pilot Free for DS-CDMA systems in accordance with one embodiment of the invention.
• Fig. 8 is a block diagram showing a training data sequence generator apparatus at the receiver in accordance with one embodiment of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
• a MUD filter 250 of N f dimension to which will be applied the output of the channel model has to be considered.
• the first receiver proposed for IS-95, was the Rake receiver to take advantage of the fading nature of the channel.
• the near-far problem made the receiver inefficient (Nolma H., et Toskala A., WCDMA for UMTS : Radio Access For Third Generation Mobile Communications, John Wiley & Sons LTD, 2000) .
• Linear and non-linear MUD filters 250 have been proposed in order to overcome the near-far problem.
• the algorithms are Maximum Likelihood Sequence Estimation (MLSE) for sequence detection and Maximum a-posteriori (MAP) for symbol-by- symbol detection.
• MAP Maximum a-posteriori
• the algorithms are unpractical since the complexity grows exponentially with the number of users (Verd ⁇ S., Multiuser Detection, Cambridge University Press, 1998).
• Other algorithms widely proposed are the ZF (Zero Forcing) and MMSE (Minimum Mean Square Error) which need the exact impulse response of all the users' channels (Klein S., Kaleh G.
• a MUD filter will be considered linear if a function F[»] of the equation (9) is linear in its arguments in any other case it will be treated as a non-linear one. For instance, • MUD filters based on neural networks are considered to be non-linear.
• a MUD filter is supervised if it is necessary to send a known sequence, referred to as a Training sequence generator, in order to estimate its coefficients ( b lra '" and p ⁇ ram for the transmit data and control (pilot) respectively) .
• blind MUD filters estimate its coefficients without knowledge of the sent training sequence thus increasing the bandwidth efficiency.
• a MUD filter has control component if the receive signal f is send directly at the MUD filter input, as shown for instance in Figs. 4, 6 and 7.
• a MUD filter is control (pilot) free if the signal is removed from the receive signal r and after applied at the MUD filter input, as shown for instance in Figs . 4 and 5.
• the MUD filter 250 is based on two known approaches.
• a MUD filter is direct such as the MUD filter 251, shown in Fig. 6, if its coefficients are obtained from the available data.
• the MUD filter is indirect, as the MUD filter 252, shown in Fig. 5, if its coefficients are calculated on the basis of previously identified parameters (taps h ⁇ and delays ⁇ k ) of the channel # " [•] 270 by using a channel identification unit 12 such as the Correlator (Bhashyam, S., Aazhang, B., "Multiuser channel estimation and tracking for long-code CDMA systems", IEEE Transactions on communications, Volume: 50 , Issue: 7 , July 2002, pp. 1081-1090.).
• the indirect approach is usually used if the channel model is linear since channel identification unit 12 is therefore simple to implement.
• a first data sequence b is transmitted as payload data sequence .
• a second data sequence p'TM" 1 is transmitted as training control (pilot) sequence used in order to identify the parameters of the channel at the receiver using a channel identification method.
• FIG. 3 there is shown an example of a data sequence generator 10 according to one embodiment of the invention.
• the data sequence generator 10 comprises a channel identification unit 12 and a channel modeling unit 14.
• the channel identification unit 12 receives a transmitted signal r and a training control sequence p'TM"" , performs a channel identification in order to provide a plurality of channel coefficients representative of the communication channel .. .
• the channel modeling unit 14 receives the plurality of channel coefficients representative of the communication channel ..h k and a known training data sequence (X) and filters the plurality of channel coefficients representative of the communication channel /z,...z A with the known training data sequence (X) in order to provide a regenerated data sequence (Y) .
• the known training data sequence (X) may be the training control sequence p' ram (see Fig. 4).
• the channel modeling unit 14 comprises a channel control modeling unit 410 and the regenerated data sequence (Y) comprises a regenerated control sequence r p,l( " .
• An example incorporating such embodiment will be described further below.
• (X) may be the training data sequence b'TM" (see Fig. 6) .
• the channel modeling unit 14 comprises a channel data modeling 610 and the regenerated data sequence (Y) comprises a regenerated training sequence r' ram .
• An example incorporating such embodiment will be described further below.
• DSGA data sequence generator 10
• the training control (pilot) sequence p'TM'" is transmitted through the channel #[•] 270 in order to perform the training sequence of the channel identification unit 12 for all channels defined by the K users .
• the training control (pilot) sequence p' ra,n known by the receiver, is used to identify the parameters of the channel model ff(») .
• a channel identification method such as the correlator, maximum likelihood, etc. and/or following an adaptation algorithm such as the . LMS, RLS, Kalman filter, Backpropagation Neural Network, etc... is performed by the channel identification unit 12.
• the plurality of channel coefficients representative of the communication channel h i .. are identified by the channel identification unit 12
• the plurality of channel coefficients representative of the communication channel h v ..h k are sent to the channel modeling unit 14.
• a set of training data sequences (X) is generated locally at the receiver.
• the set of training data sequences (X) is used in order to generate the regenerated data sequence (Y) using the channel modeling unit 14.
• the set of training data sequences (X) is used at the receiver in order to adapt the receiver filters .
• the channel modeling unit 10 may be defined by
• the channel modeling unit 10 may be defined
• the MUD filter is designed in a mixed manner where coefficients are obtained on the basis of indirect, and direct methods as shown in Figs. 5 and 6.
• the receiver operating according to a direct adaptation method with a control (pilot) cancellation. More precisely, the receiver comprises a channel modeling unit 10 having a channel identification unit 12 and a channel control modeling unit 410.
• the receiver further comprises a control signal cancellation unit 420, a direct MUD filter 251 and a switch K 1"2 .
• control (pilot) cancellation and the adaptation of the direct MUD filter parameters • are made simultaneously by applying both the direct and indirect processes.
• the parameters of the direct MUD filter 251 and of the channel identification unit 12 are initialized.
• the training data sequence ⁇ b' mm and/or payload data sequence b, not shown, containing the information sent by all users are transmitted.
• the training data sequence p' ram is known by the receiver and the training data sequence p' ra '" is used to identify the parameters of the channel model #(•) .
• a channel identification algorithm such as the Correlator, Maximum Likelihood, etc. and/or following an adaptation algorithm such as the LMS, RLS, Kalman filter, Backpropagation Neural Network, etc. is used by the channel identification unit 12 in order to determine the plurality of channel coefficients representative of the communication channel l ...h k .
• the regenerated control sequence v p ' l ⁇ " at the receiver is generated using the channel control modeling unit 410 and the training control sequence p' ra "' .
• the effect of the pilot data interfere with the training data or payload data and must be cancelled.
• the cancellation is realized by a subtraction of the received data r by the regenerated control sequence r p '° using the control signal cancellation unit 420- Tne ne received data, ⁇ P ' ,0,free are provided by the control signal cancellation unit 420.
• the switch K 1-2 is in position A in order to transmit the training data sequence b' rc " n which is needed in order to perform the training sequence for all channels defined by the K users.
• the data ⁇ P ' lo,free anc j the training data sequence b tram which are known by the receiver are used in order to adapt the parameters of the direct MUD filter 251 following an adaptation algorithm such as an LMS, RLS, Backpropagation Neural Network, etc.
• a seventh step when the parameters of the direct MUD filter 251 are adapted, the switch K 1-2 is in position B.
• the payload data b containing the information are estimated with the direct MUD filter 251 using the data f P ' ,olfree j n the B position, no training data are transmitted and the direct MUD filter 251 parameters are unchanged.
• the channel parameters are tracked using the second step and the received data with pilot free are computed by applying the third and the fourth step.
• the switch K 1-2 Periodically, the switch K 1-2 is changed alternatively between positions A and B to adapt the parameters of the direct MUD filter 251 in variant channel H( «) conditions.
• Fig. 5 there is shown receiver operating according to an indirect adaptation method comprising a control (pilot) cancellation.
• the pilot cancellation is described by the following steps.
• the parameters of the channel identification unit 12 are initialized.
• the training control sequence p' ram is transmitted in order to perform the training sequence of the channel identification for all channels defined by the K users.
• the payload data b, not shown, sent by all users are transmitted.
• the training control sequence p' ram are known by the receiver and they are used to identify the parameters of the channel model
• the channel identification unit 12 using a method such as the Correlator, Maximum Likelihood, etc. and/or following an adaptation algorithm such as the LMS, RLS,
• Kalman filter, Backpropagation Neural Network, etc. are used in order to determine the plurality of channel coefficients representative of the communication channel ...h k .
• the pilot data at the receiver r 1 " 1 "' are generated using the channel control modeling unit 410 and the training control sequence p' ra '" .
• a fourth step in pilot free conditions, the effect of the control (pilot) data interferes with the payload data and must therefore be cancelled.
• the cancellation is realized by a subtraction of the received data r by the regenerated control sequence r p ' l °' using the
• control signal cancellation unit 420 provides f p,lolfree .
• the parameters of the indirect MUD filter 252 are computed.
• the payload data b containing the information are estimated with the indirect MUD filter 252 using the data f pi "" fre ⁇
• the plurality of channel coefficients representative of the communication channel h v ..h k are tracked using the second step and the received data with pilot free are computed by applying the third step and the fourth step.
• the plurality of channel coefficients representative of the communication channel h v ..h k are tracked using the second step, the third step and the last step.
• Fig. 6 there is shown a receiver operating according to a mixed adaptation method based on a channel identification unit 12 and generating a training sequence of data at the receiver (no need to transmit the training data) used in order to adapt the parameters of the direct MUD filter 251.
• the parameters of the direct MUD filter 251 and the parameters of the channel identification unit 12 are initialized.
• the training control sequence p ram ⁇ s transmitted through the channel in order to perform the training sequence of the channel identification unit 12 for all channels defined by the K users.
• the payload data • b containing the information sent by the K users are transmitted.
• the training control sequence p'TM" is known by the receiver and is used in order to identify the parameters of the channel model #(•) .
• a channel identification method such as the Correlator, Maximum Likelihood, etc. and/or following an adaptation algorithm such as the LMS, RLS, Kalman filter, Backpropagation Neural Network, etc. is used by the channel identification unit 12 in order to identify the parameters of the channel model #(•) (the plurality of channel coefficients representative of the communication channel h v ..h k ) .
• a set of training data sequences b' rm " and training control sequence p'TM'" are generated locally at the receiver.
• the set of training data sequences b' ra '" is used in order to generate the receive data sequence r dc " a using the channel data modeling unit 610 while the training control sequence p' rara is used in order to generate the regenerated control sequence v p ' , ⁇ " using the channel control modeling unit 410.
• a summation of r dala with x p ot produces the receive signal r lram using adding unit620-
• the switches K 1_1 and K 1-2 are both in position A in order to perform the training sequence for all channels defined by the K users.
• the generated set of training data sequences b' rc "" and the receive signal r' rc "" are used in order to adapt the parameters of the direct MUD filter 251 following an adaptation algorithm such as an LMS, RLS, Neural Network Backpropagation, etc.
• a seventh step when the parameters of the direct MUD filter 251 are adapted, the switches K 1"1 and K 1"2 are both set in position B.
• the payload data b containing the information are estimated with the direct MUD filter 251 using the data f .
• switches K 1-1 and K 1-2 in position A and B are changed alternatively to repeat steps 2 to 7 in order to adapt the parameters of the direct MUD filter 251 in variant channel H(») conditions.
• a receiver operating according to a mixed adaptation method based on the same scheme as is described in Fig. 6, with a control (pilot) free signal added.
• the parameters of the direct MUD filter 251 and the parameters of the channel identification unit 12 are initialized.
• the training control sequence p' ram is transmitted through the channel in order to obtain the training sequence of the channel identification unit 12 for all channels defined by the K users.
• the payload data b, not shown, containing the information sent by the K users are transmitted.
• the training control sequence p' ram is known by the receiver and is used in order to identify the parameters of the channel model #(•) .
• a channel identification method such as the Correlator, Maximum Likelihood, etc. and/or following an adaptation algorithm such as the LMS, RLS, Kalman filter, Backpropagation Neural Network, etc. may be used by the channel identification unit 12.
• a set of training data sequence b'TM" and training control sequence p' ra '" are generated locally at the receiver.
• the set of training data sequence b' ra '" is used in order to generate the receive data sequence r da ' a using the channel data modeling unit 610 and the training control sequence p' m is used in order to generate the regenerated control sequence r p,! ⁇ " using the Channel control modeling unit 410.
• the switches K 1_1 and K 1"2 are both in position A in order to perform the training sequence for all channels defined by the K users.
• the generated set of training data sequence b ⁇ ra '" and the control sequence r' mm are used to adapt the parameters of the direct MUD filter 251 following an adaptation algorithm such as an LMS, RLS, Neural Network Backpropagation, etc.
• a set of data sequence r p ' hl is produced by the channel data modeling unit 610.
• a seventh step in control (pilot) free conditions, the effect of the control (pilot) ' data interferes with the payload data and must therefore be cancelled.
• the cancellation is performed by a subtraction of the received data r by the r p ' hl using the control signal cancellation unit 420.
• the control signal cancellation unit 420 provides the new received data r
• the switches K 1_1 and K 1-2 are both in position B.
• the payload data b containing the information are estimated with the direct MUD filter 251 using the data f t *" " .
• a ninth step concurrently performed to the seventh step, the parameters of the channel identification unit 12 are tracked using the second step and the receive data with pilot free are used in the sixth step and the seventh step.
• the switches, in position A and B are changed alternatively to repeat the steps 2 to 9 in order to adapt the parameters of the direct MUD filter 251 in variant channel H(») conditions.
• a blind adaptation procedure described above may be applied to these adaptation methods in order to increase the bandwidth efficiency.
• the present invention can be carried out as a method, can be embodied in a system, a computer readable medium or an electrical or electro- magnetical signal.

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• Engineering & Computer Science (AREA)
• Computer Networks & Wireless Communication (AREA)
• Signal Processing (AREA)
• Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
• Mobile Radio Communication Systems (AREA)

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