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The 3 rd MCM of COST 289 : TU Košice , October 30 - 31 , 2003

Technical University of Ko šice, Slovakia. Department of Electronics and Multimedial Communications. Technical University of Ko š ice. Park Komenskeho 13 , 041 20 Ko š ice. THE PIECE-WISE LINEAR. S lovak Republic. MICROSTATISTIC. e-mail: Dusan.Kocur@tuke.sk. MULTI-USER RECEIVE R.

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The 3 rd MCM of COST 289 : TU Košice , October 30 - 31 , 2003

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  1. Technical University of Košice, Slovakia Department of Electronics and Multimedial Communications Technical University of Košice Park Komenskeho 13 , 041 20 Košice THE PIECE-WISE LINEAR Slovak Republic MICROSTATISTIC e-mail: Dusan.Kocur@tuke.sk MULTI-USER RECEIVER The 3rd MCM of COST 289: TU Košice, October30-31, 2003 1 of 27 Dušan Kocur, Jana Čížová, Stanislav Marchevský

  2. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 2 of 27 CONTENT • multi-user detection receiver (MUD), • motivation for new MUD design, • single-channel conventional microstatistic filter (CMF), • multi-channel CMF (M-CMF): structure, • M-CMF: design procedure, • microstatistic MUD (MSF-MUD), • computer experiments, • conclusions.

  3. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 3 of 27 1. MULTI-USER DETECTION RECEIVER (MUD) • MUD refers to the process of demodulating one or more user data streams from a non-orthogonal multiplex based on knowledge on spreading codes (signature sequence), timing, phases and received amplitudes of all users, • optimum receiver: makes decisions by selecting the transmitted sequence to minimize the sequence error probability (maximum likelihood sequence detection, ML).

  4. Technical University of Košice, Slovakia BMF MF-1 MF-2 VITERBI DECISION ALGORITHM MF-M The 3rd MCM of COST 289: TU Košice, October30-31, 2003 4 of 27 bank of matched filters MF-k: the k-th matched filter Fig. 1. Base-band optimum receiver

  5. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 5 of 27 2. MOTIVATIONFOR NEW MUD DEVELOPMENT • high performance gains of optimum receiver: achieved at the cost of extremely high degree of complexity, • solution: suboptimum receivers, • suboptimum receiver principle: mostly replacing Viterbi decision algorithm with a reduced complexity algorithm.

  6. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 6 of 27 • suboptimum receiver examples: • BMF receivers, • decorrelating MUD receivers (D-MUD), • minimum mean square error MUD receivers (MMSE-MUD), • non-linear single-stage MUD receivers (NSS-MUD); e.g neural network or Volterra filter based MUD receivers, • non-linear multi-stage MUD receivers: serial or parallel interference cancellation (SIC, PIC receivers), etc.

  7. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 7 of 27 • NSS-MUD principle:the output of the NSS-MUD is taken as the sign of the multi-channel non-linear transformation of the outputs of the BMF, • basic idea for a new NSS-MUD design: application of M-CMF in order to dothe multi-channel non-linear transformation of the outputs of the BMF. WHY TO DO IT?

  8. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 8 of 27 • boundary of decision regions: • optimum receiver: non-linear, • suboptimum receivers: approximation of boundary of decision region of optimum receiver, • linear suboptimum receivers: linear approximation (e.g. BMF receiver , MMSE-MUD, D-MUD), • non-linear suboptimum receivers: non-linear approximation.

  9. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 9 of 27 • intention: to design of the receiver with piece-wise linear approximation of boundary of the decision region, • why (1): this approximation should provide better results than that of linear approximation, • why (2): this approximation should provide less complex implementation than that of non-linear suboptimum receiver.

  10. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 10 of 27 3. SINGLE-CHANNEL CONVENTIONAL MICROSTATISTIC FILTER (CMF) • minimum mean-square non-linear estimator, • the desired signal (filter output) is given by a linear combination of signal samples obtained by a threshold decomposition of the input signal of the filter, • CMF: the piece-wise linear system, • CMF structure (Fig.2): threshold decomposer (TD) + multi-channel Wiener filter (M-WF) + constant term.

  11. Technical University of Košice, Slovakia Constant term: necessary to get the unbiased estimation M-WF TD M-WF output is given by a linear combination of its input signal samples The 3rd MCM of COST 289: TU Košice, October30-31, 2003 11 of 27 Fig. 2. CMF

  12. Technical University of Košice, Slovakia TD threshold values: Threshold decomposition for positive samples: TD (1) The 3rd MCM of COST 289: TU Košice, October30-31, 2003 12 of 27 Fig. 3. Threshold decomposer

  13. Technical University of Košice, Slovakia Threshold values: The 3rd MCM of COST 289: TU Košice, October30-31, 2003 13 of 27 Fig. 4. Threshold decomposition. Example

  14. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 14 of 27 4. MULTI-CHANNEL CONVENTIONAL MICROSTATISTIC FILTER (M-CMF) • minimum mean-square non-linear estimator, • the estimations of the desired signals(filter outputs) are given by linear combinations of signal samples obtained by the threshold decomposition of the input signals of the filter.

  15. M-WF1 TD1 MULTI-CHANNEL CONVENTIONAL MICROSTATISTIC FILTER M-WF2 TD2 M-WFM TDM Fig. 5. M-CMF 15 of 27

  16. Technical University of Košice, Slovakia • M-CMF responses for : The 3rd MCM of COST 289: TU Košice, October30-31, 2003 16 of 27 5. OPTIMUM TIME-INVARIANT M-CMF DESIGN (2) • Assumptions: • the input and desired signals are stationary random processes (time-invariant filter design), • threshold values of TDs are fixed, known in advance.

  17. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 17 of 27 • estimation error: (3) • mean-square estimation error: (4) (5) (6)

  18. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 18 of 27 • because of the constant threshold values, there is the only extreme of the mean-square error represented by the global minimum given by: (7) • minimum mean-square error: (8)

  19. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 19 of 27 6. MICROSTATISTIC MULTI-USER DETECTION RECEIVER (MSF-MUD) • MSF-MUD (Fig.6) is obtained from the optimum receiver by replacing the Viterbi decision algorithm with the M-CMF, • output of MSF-MUD is taken as the sign of the non-linear transformation of the output of the BMF due to the M-CMF, • MSF-MUD is MMSE piece-wise linear receiver, • design: the same approaches as for the optimum linear MMSE-MUD.

  20. Technical University of Košice, Slovakia BMF MF-1 MF-2 M-CMF MF-M The 3rd MCM of COST 289: TU Košice, October30-31, 2003 20 of 27 Fig. 6. MSF-MUD

  21. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 21 of 27 7. COMPUTER EXPERIMENTS • Two experiments. • M=2 user base-band synchronous CDMA transmission system. • Signature waveforms: Gold sequences with the period of seven chips. • Input signal to the receiver: the sum of antipodally modulated signature waveforms embedded in AWGN. • Receivers: optimum receiver, BMF receiver, D-MUD, MMSE-MUD and MSF-MUD.

  22. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 22 of 27 • Training sequence: 3000 information bits. • Training sequence application: estimation of • M-CMF:L=2, N=0, threshold values were set experimentally. • Performance index: bit error rate (BER) vs. information signal energy per bit to noise power spectral density (Eb/No).

  23. Fig. 7. Results of the 1st experimentfor the 1st user The power of the signal at the input of the receiver produced by all users at the input of the receiver was the same. 23 of 27

  24. Fig. 8. Results of the 2nd experimentfor the 1st user The power of the signal at the input of the receiver produced by the first user was ten times smaller than that of the second user, (i.e. performance properties at near-far effect) . 24 of 27

  25. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 25 of 27 8. CONCLUSIONS • The time-invariant M-CMF was introduced. • MSF-MUD receiver structure based on M-CMF has been proposed. • Experiment 1 (Fig.7): all receivers applied in our experiments can provide almost the same results. • Experiment 2 (Fig.8): the optimum receiver: the best results, the MSF-MUD outperforms clearly the linear MUD receivers.

  26. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 26 of 27 • The simple computer simulation has shown that the MSF-MUD could outperform the other tested linear MUD receivers. • These results were achieved at the expense of the higher computational complexity of the MSF-MUD. MSF-MUD IS A PROMISING SUBOPTIMUM CDMA RECEIVERS

  27. Technical University of Košice, Slovakia The 3rd MCM of COST 289: TU Košice, October30-31, 2003 27 of 27 THANK YOU VERY MUCH FOR YOUR ATTENTION

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