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Turbo-NFSK: Iterative Estimation, Noncoherent Demodulation, and Decoding for Fast Fading Channels

Turbo-NFSK: Iterative Estimation, Noncoherent Demodulation, and Decoding for Fast Fading Channels. Shi Cheng and Matthew C. Valenti West Virginia University Don Torrieri U.S. Army Research Laboratory This work sponsored by the Xenotran Corporation, Glen Burnie, MD. Outline.

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Turbo-NFSK: Iterative Estimation, Noncoherent Demodulation, and Decoding for Fast Fading Channels

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  1. Turbo-NFSK: Iterative Estimation, Noncoherent Demodulation,and Decoding for Fast Fading Channels Shi Cheng and Matthew C. Valenti West Virginia University Don Torrieri U.S. Army Research Laboratory This work sponsored by the Xenotran Corporation, Glen Burnie, MD

  2. Outline • Iterative M-ary NFSK demodulation and decoding • ML estimator using EM algorithm • Performance of iterative estimation, NFSK demodulation and decoding • Methods to reduce the complexity of the estimator • Conclusions

  3. BICM-ID: Bit Interleaved CodedModulation with Iterative Decoding Binary to M-ary mapping Binary Encoder Bitwise Interleaver M-ary- modulator Complex flat-fading AWGN Soft-In Binary Decoder LLR Bit Metric Calculation Receiver front end Bitwise Deinterleaver Bitwise Interleaver Soft-Output Estimates of Coded Bits

  4. Noncoherent M-FSKUsing A Priori Probabilities • Bit LLRs are calculated based on the channel observation and the extrinsic information feedback from the decoder • Where

  5. BICM ID and CM Capacity BICM ID Performance using CDMA2000 length 6138 codeword 20 iterations, BER = 10-4 Reference: M.C.Valenti and S. Cheng, “Iterative demodulation and decoding of turbo coded M-ary noncoherent orthogonal modulation” IEEE Journal on Selected Areas in Communications, Sept. 2005. Minimum Eb/No (in dB) Code Rate R

  6. Block Fading Channel • Fading coefficient is fixed within one block. • Independent fading from block to block 1 2 3 192 ... 0 4 8 767 N =4 1 2 96 ... 0 8 767 N =8 CDMA 2000 turbo codeword rate ½ length 1530 with 16-NFSK

  7. Estimation of One Block • Channel state information is needed to calculate bit LLRs • Perform estimation of A and B independently block by block, where Soft-In Binary Decoder LLR Bit Metric Calculation Bitwise Deinterleaver Bitwise Interleaver Channel Estimator

  8. Channel Estimator with Known Transmitted Sequence • Form the log-likelihood function based on the known sequence d =[d0,d1,…,dN-1] of • Solving A and B to maximize L • Where

  9. ML Estimator with A Priori Information of the Sequence • The sequence d is never known. • Form log-likelihood function based on the a priori information of d • Too complex to find the solution to maximize this function. We resort to EM algorithm.

  10. ML Estimator using EM Algorithm 0. Select 1. 2. 3. Normalization factor Outer Iteration Inner Iteration

  11. BER of 16-ary NFSK in Rayleigh Fading Channel with Different Block Size 0 10 N=32 CDMA2000 Turbo Code, Rate ½, Length 1530 Independent Block Fading Iteration #20 N=16 N=8 -1 N=4 10 N=1 -2 10 BER -3 10 Estimator -4 Perfect CSI 10 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10 Eb/No (dB)

  12. BER of NFSK in Rayleigh Fading Channel with Different Alphabet Size 0 10 CDMA2000 Turbo Code, Rate ½, Length 1530 Data Rate = 24bits/ block M=2 M=4 Iteration #20 M=16 -1 M=64 10 Estimator -2 10 BER Perfect CSI -3 10 -4 10 4 5 6 7 8 9 10 11 12 Eb/No (dB)

  13. 0 10 N=16 N=8 N=4 -1 N=1 10 -2 10 -3 10 -4 10 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 BER of 16-ary NFSK in Rician Fading Channel (K=10dB) Estimator BER Perfect CSI CDMA2000 Turbo Code, Rate ½, Length 1530 Independent Block Fading Iteration #20 Eb/No (dB)

  14. Methods to Reduce the Complexity of the Estimator 0. Select 1. 2. 3. Making hard decision Outer Iteration Normalization factor Inner Iteration Linear approximation of F

  15. Methods to Reduce the Complexity of the Estimator 0. Select 1. 2. 3. Making hard decision Outer Iteration Linear approximation of F

  16. 1.5 Linear Approximation 1 ) x ( F 0.5 0 0 1 2 3 4 5 6 7 8 9 10 x Linear Approximation of F

  17. Complexity of Different Estimators 7 x 10 3 EM estimator CPU Cycles/BICMID iteration 16-ary NFSK, CDMA2000 turbo codeword with rate ½ and length 1530. N=4 symbols per block independent Rayleigh fading 2.5 2 Decoder 1.5 Hard Linear EM Demodulator 1 Linear EM 0.5 0 5.6 5.8 6 6.2 6.4 6.6 6.8 7 Eb/No (dB)

  18. BER Performance of Low Complexity Estimators 16-ary NFSK, CDMA2000 turbo codeword with rate ½ and length 1530. N=4 symbols per block, independent Rayleigh fading, iteration #20 BER Eb/No (dB)

  19. Conclusions • Robust noncoherent channel estimator, dealing with severe channel conditions. • The estimator works without needing to know the fading statistics model. The only requirement is the coherence time of the fading amplitude is larger than 4 symbols. • Although the estimator using the exact EM algorithm is complex, linear approximation of F(x) = I1(x)/I0(x) and hard decision of can be used to reduce the comlexity. • For 16-NFSK, when fading block size is larger than 4 symbols per block, the system has acceptable BER performance. • When there are 4 symbols per block, the BER of the iterative estimation, demodulation and decoding is about 0.6~0.8dB away from the one with perfect CSI.

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