Download presentation

Presentation is loading. Please wait.

Published byNathan Bradford Modified over 2 years ago

1
1 Iterative Equalization and Decoding John G. Proakis COMSOC Distinguished Lecture Tour

2
2 Conventional Equalization Equalizer Decoder Soft Output From Receiver Filter Hard Output Possible Equalizer Types: Linear Equalizer Decision Feedback Equalizer (DFE) Maximum A posteriori Probability (MAP) Equalizer Soft-output Viterbi (MLSE) Equalizer Possible Decoder Types: Maximum A posteriori Probability (MAP) Decoder Viterbi (MLSE) Decoder

3
3 Turbo Principle / Turbo Coding Turbo Encoder: Parallel concatenated recursive systematic convolutional encoders Encoders separated by an interleaver d(n) b(n) DD + c s (n) c p1 (n) DD c p2 (n) d ` (n) Puncturer and P/S converter Encoder 1 Encoder Turbo Decoder: Two Soft-Input Soft-Output (SISO) decoders separated by interleavers SISO modules can be SOVA MAP Extrinsic information passed between modules SISO Decoder 1 SISO Decoder 2 x p1 xsxs x p2 L e 21 L e 12 LdLd

4
4 Serially Concatenated Systems Serially Concatenated Coding: Serial concatenated (recursive) convolutional encoders Encoders separated by an interleaver Encoder 1 d(n) c(n) b(n) Encoder 2 Coded Transmission over Multipath Channels: (recursive) convolutional encoder Interleaved bits mapped to symbols Symbols passed through a multipath channel Encoder d(n) c(n) Symbol Mapper Multipath Channel x(n) r(n)

5
5 Channel Model / Precoding Multipath Channel Model: Received signal: Rate 1/1 convolutional code D X h 1 (n) D X h 2 (n) D X h L-1 (n) X h 0 (n) + + w(n) x(n) r(n) Precoded System: Iteration gain only possible with recursive inner code (channel) [1], [2] Recursive rate 1/1 precoder is employed before transmission Most common precoder: Differential encoder D X h 1 (n) D X h 2 (n) D X h L-1 (n) X h 0 (n) + + w(n) x(n) r(n) y(n) y(n-1)

6
6 Iterative Equalization and Decoding (Turbo Equalizer) Convolutional Encoder Symbol Mapper Multipath Channel dndn cncn cncn xnxn rnrn MAP Decoder + MAP Equalizer + Channel Estimator r L D e (c) L D (c) L D (d) L E e (c) L E (c) - - Data bits are convolutionally encoded and interleaved M-ary PSK modulated signals transmitted through a multipath channel, which is treated as an encoder Received signals are jointly equalized and decoded in a turbo structure First proposed by Douillard, et.al [3], where SOVA modules are employed Bauch extended the idea by employing MAP modules [4]

7
7 Time-invariant Test Channel Impulse ResponseFrequency Response t Proakis C channel [5]

8
8 Low Complexity Alternative Equalizers: DFE and MLSE Performance of DFE and MLSE over the Proakis C channel [5] Bit error rate performance [5]

9
9 Iterative Equalization and Decoding Performance Iterative equalization and decoding with MAP modules Recursive systematic convolutional encoder with R=1/2, K=5, Time-invariant 5 tap channel with a spectral null (Proakis C [5]) Equalizer has perfect knowledge of the channel Block length 4096 Bit error rate performance of turbo equalizer [4]

10
10 Hard Iterative DFE and MAP Decoding Forward Filter Feedback Filter Symbol Detector + MAP Decoder Input from receiver filter DFE with hard input feedback Hard encoded symbols Output Data During the first pass, symbol detector output is passed to the feedback filter After the first pass, hard encoded symbol output of the decoder is used in the feedback filter

11
11 Performance of Hard Iterative DFE and MAP decoder BPSK modulation R=1/2, K=7 convolutional coding Block length 2048 Channel Proakis C

12
12 Soft Iterative DFE and MAP Decoding Forward Filter Feedback Filter Decision Device + MAP Decoder DFE with hard input feedback Soft encoded symbols Output Data Soft decisions of the decoder is combined with the soft outputs of the DFE: + Soft APP from last iteration Hard detected symbols

13
13 Histogram of DFE Output The histogram of equalizer estimated output for SNR = 12 dB The histogram of equalizer estimated output for SNR = 20 dB

14
14 Modified Soft Iterative DFE and MAP Decoding Forward Filter Feedback Filter Decision Device + MAP Decoder DFE with hard input feedback Soft encoded symbols Output Data + - Only extrinsic information is passed to the DFE from the decoder Re Im Variance Estimator + + Soft APP from last iteration Hard detected symbols Conversion to LLR

15
15 Performance of Soft Iterative DFE and MAP Decoder Recursive systematic convolutional encoder with R=1/2, K=5, Time-invariant 5 tap channel with a spectral null (Proakis C [5]) RLS updates at the DFE Block length 4096 BPSK modulation

16
16 Performance of Soft Iterative DFE and MAP Decoder Recursive systematic convolutional encoder with R=1/2, K=5, Time-invariant 5 tap channel with a spectral null (Proakis C [5]) RLS updates at the DFE Block length 4096 QPSK modulation

17
17 Iterative Linear MMSE Equalization and Decoding SISO Linear MMSE Equalizer [6]: Known channel: Received signal: Likelihood ratio for MMSE estimator output, : Channel matrix: MMSE estimator output: where,

18
18 Iterative Linear MMSE Equalization and Decoding Steps to compute symbol estimates with the Linear MMSE equalizer: Soft output calculation assuming Gaussian distributed estimates:

19
19 Performance of SISO MMSE Linear Iterative Equalizer Recursive systematic convolutional encoder with R=1/2, K=5, Time-invariant 5 tap channel with a spectral null (Proakis C [5]) Equalizer has perfect knowledge of the channel Block length 4096

20
20 Comparison of System Performances Recursive systematic convolutional encoder with R=1/2, K=5, Time-invariant 5 tap channel with a spectral null (Proakis C [5]) BER results after 6 iterations Block length 4096

21
21 Experimental Study of Iterative Equalizers Channel Probe Training Symbols Information Symbols Dead Time

22
22 Joint MMSE Equalization and Turbo Decoding Feedback Filter MAP Decoder 1 Demapper & S/P Converter Adaptive Algorithm Decision Device x(n) ~ e -j (n) e(n) x(n) ^ y(n) xsxs x p2 L e 12 LdLd 2 (n) L e 12 Training Symbols Forward Filter +x+ x p1 MAP Decoder 2 L p2 L p1

23
23 Soft output of the DFE: RLS algorithm is used to track channel variation: Decision Feedback Equalizer (DFE) Noise variance estimate:

24
24 MAP Decoding Maximize a posteriori probability: Decision variable written in the form of log-likelihood ratio:

25
25 MAP Decoding (BCJR Algorithm) State transition probability: where channel value a priori information extrinsic information

26
26 depends on the channel trellis defined by h l (n) with 2 (L-1) states If x n-l for J

27
27 Per-Survivor Processing n-4 n-3 n-2 n-1 n Path metric: Survivor path: Discarded Paths Survivor Paths

28
28 Channel Estimator Adaptive Algorithm + h l (n) - Each survivor path has a separate channel estimator The input to the channel estimator,, is the estimates within the survivors RLS algorithm is employed

29
29 Channel Estimator Initial channel estimate is based on the correlation of the preamble RLS algorithm is employed to track the channel Noise variance estimate:

30
30 Channel impulse response estimate for transducer seven obtained using the channel probe DFE results for transducer 7. Eye Pattern - Filter coefficients PLL phase estimate - Bit error distribution Experimental Results

31
31 Experimental Results Channel impulse response estimate for transducer seven obtained using adaptive channel estimator Comparison of received signal with the estimated received signal based on the channel estimate

32
32 Experimental Results Sparse Channel with multipath delay in the order of 200 symbols Length of the DFE or channel estimator filters cannot cover the channel Sparse processing is needed

33
33 Results of DFE Turbo Decoder

34
34 Results of Iterative DFE Turbo Decoder

35
35 Results of Iterative DFE MAP Decoder

36
36 Results for Iterative Map Equalizer Turbo Decoder

37
37 Results for Iterative MAP Equalizer MAP Decoder

38
38 Conclusions Due to error propagation in the DFE, turbo decoder cannot provide performance improvement beyond the second iteration Error Floor Joint DFE and turbo decoding adds an additional loop to the system and lowers the error floor Joint channel estimator and iterative equalizer is able to decode packets with low SNR, which cannot be decoded with the DFE Tail cancellation is an effective way to reduce the computational complexity of the MAP equalizer If the channel is sparse, although the DFE filter lengths are short, the DFE is able to provide enough information to the turbo decoder A sparse DFE can be used to improve the performance of the DFE/MAP Decoder and the DFE/Turbo Decoder

39
39 References [1] S. Benedetto, et.al., Serial concatenation of interleaved codes: Design and performance analysis, IEEE Trans. Info. Theory, vol. 42, pp , April 1998 [2] I. Lee, The effect of a precoder on serially concatenated coding systems with ISI channel, IEEE Trans. Commun., pp , July 2001 [3] C. Douilard, et.al., Iterative correction of intersymbol interference: Turbo-equalization, European Transactions on Telecommunications, vol. 6, pp , Sep.-Oct [4] G. Bauch, H. Khorram, and J. Hagenauer, Iterative equalization and decoding in mobile communications systems, in Proc. European Personal Mobile Commun. Conf., pp [5] J. Proakis, Digital Communications, McGraw-Hill Inc., 2001 [6] M. Tuchler, A. Singer, and R. Koetter, Minimum mean squared error equalization using a priori information, IEEE Trans. Signal Proc., vol. 50, pp , March 2002

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google