LDPC Coded Compressive Sensing for Joint Source-Channel Coding in Wireless Sensor Networks

Chen, Jue; Shao, Shuai; Wang, Tsang-Yi; Wu, Jwo-Yuh; Li, Chih-Peng; Ng, Soon Xin; Maunder, Robert G.; Hanzo, Lajos

doi:10.1109/tvt.2022.3212025

Cited by 2 publications

(3 citation statements)

References 45 publications

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“…Explicitly, Figure 9 is produced by generating ba artificially and then measure the MI of be . The details of generating the LDPC decoder's EXIT function are similar to the processing introduced in [32], [33]. Figure 9 shows that upon increasing the number of LDPC inner iterations I LDPC , the LDPC EXIT function moves downwards, which results in improved decoding performance.…”

Section: Simulation Resultsmentioning

confidence: 98%

Deep Learning Aided LLR Correction Improves the Performance of Iterative MIMO Receivers

Chen,

Wang,

et al. 2024

IEEE Trans. Veh. Technol.

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A Deep Learning (DL) aided Logarithmic Likelihood Ratio (LLR) correction method is proposed for improving the performance of Multiple-Input Multiple-Output (MIMO) receivers, where it is typical to adopt reduced-complexity algorithms for avoiding the excessive complexity of optimal fullsearch algorithms. These sub-optimal techniques typically express the probabilities of the detected bits using LLRs that often have values that are not consistent with their true reliability, either expressing too much confidence or not enough confidence in the value of the corresponding bits, leading to performance degradation. To circumvent this problem, a Deep Neural Network (DNN) is trained for detecting and correcting both overconfident and under-confident LLRs. We demonstrate that the complexity of employing the DL-aided technique is relatively low compared to the popular reduced-complexity receiver detector techniques since it only depends on a small number of real-valued inputs. Furthermore, the proposed approach is applicable to a wild variety of iterative receivers as demonstrated in the context of an iterative detection and decoding aided MIMO system, which uses a low-complexity Smart Ordering and Candidate Adding (SOCA) scheme for MIMO detection and Low-Density Parity Check (LDPC) codes for channel coding. We adopt Extrinsic Information Transfer (EXIT) charts for quantifying the Mutual Information (MI) and show that our DL method significantly improves the BLock Error Rate (BLER). Explicitly, we demonstrate that about 0.9 dB gain can be achieved at a BLER of 10 −3 by employing the proposed DL-aided LLR correction method, at the modest cost of increasing the complexity by 16% compared to a benchmarker dispensing with LLR correction.

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Section: Simulation Resultsmentioning

confidence: 98%

Deep Learning Aided LLR Correction Improves the Performance of Iterative MIMO Receivers

Chen,

Wang,

et al. 2024

IEEE Trans. Veh. Technol.

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“…In this case, opted for hiring 3 hidden layers and each layer has 6 neural nodes in the trained DNN. It has been shown that the DL module employed is easy to train at a low complexity and it has advantages compared to other signal processing algorithms available for correcting inconsistent LLRs such as lookup tables, which have a higher memory requirement [21].…”

Section: Mmse-ircmentioning

confidence: 99%

“…Since the MMSE-IRC detector rejects interference, there is no need for the NL-SOCA to consider interference during the detection process. Furthermore, a DL-aided LLR correction technique [21] is conceived for further improving the performance.…”

Section: Introductionmentioning

confidence: 99%

A Three-Stage-Concatenated Non-Linear MMSE Interference Rejection Combining Aided MIMO-OFDM Receiver and its EXIT-Chart Analysis

Chen,

Lu,

Wang

et al. 2024

IEEE Open J. Veh. Technol.

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The demodulation reference signal of the 5G Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) waveform has been designed for supporting Minimum Mean-Square Error-Interference Rejection Combining (MMSE-IRC) equalization, which has become the state-of-the-art, owing to its enhanced performance in the case of dense frequency reuse, which is typical in 5G. By contrast, in the 4G LTE system, typically turbo equalization techniques were used. The family of Non-Linear receiver techniques tend to be eminently suitable for tough rank-deficient scenarios, when the received signal constellation becomes linearly non-separable. Hence, we propose a novel receiver for interference-constrained MIMO-OFDM systems, relying on a linear MMSE-IRC detector intrinsically amalgamated with an additional NL equalizer. In this way, we may achieve the best of both worlds, retaining the interference rejection capability of the MMSE-IRC detector and the superior performance of the NL equalizer. Our solution circumvents the potential failure of the MMSE-IRC, when the MIMO channels' degree freedom is completely exhausted by the desired users in case the transmitter has a high number of transmission layers for example. Based on this concept, we then design a novel NL equalizer relying on the Smart Ordering and Candidate Adding (SOCA) algorithm. This reduced complexity NL detection algorithm is particularly well suited for practical hardware implementation using parallel processing at a low latency. Briefly, the proposed scheme employs the MMSE-IRC detector for mitigating the interference. It makes the first estimate of the desired user signals and then uses the SOCA detector for further decontaminating the received signals. It also generates the soft information, enabling turbo equalization, wherein iterative detector and decoder iteratively exchange their soft information. We present BLock Error Rate (BLER) results, which show that the proposed scheme can always achieve superior performance to the conventional MMSE-IRC detector at the cost of increasing the complexity. In some cases, our proposed scheme can obtain about 1.5 dB gain, at the cost of 4 times higher complexity. We demonstrate that the complexity of the SOCA detector can be reduced by adjusting its parameterization or at the cost of reducing the self-consistency of the soft information produced by the SOCA detector, which slightly erodes the BLER performance. In order to mitigate this, we propose to use Deep Learning (DL) for enhancing the accuracy of the soft information. Using this technique, we show that the MMSE-IRC-NL-SOCA detector relying on DL attains about 3 dB gain at the cost of only marginally increasing the complexity, compared to the proposed MMSE-IRC-NL-SOCA scheme.

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LDPC Coded Compressive Sensing for Joint Source-Channel Coding in Wireless Sensor Networks

Cited by 2 publications

References 45 publications

Deep Learning Aided LLR Correction Improves the Performance of Iterative MIMO Receivers

Deep Learning Aided LLR Correction Improves the Performance of Iterative MIMO Receivers

A Three-Stage-Concatenated Non-Linear MMSE Interference Rejection Combining Aided MIMO-OFDM Receiver and its EXIT-Chart Analysis

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