Iterative Detection And Decoding Research Articles

Iterative EP Detection and Decoding of Polar-Coded MIMO Systems

Channel coding and multiple-input multiple-output (MIMO) antennas are two key technologies in the modern communication system. For MIMO detection, the expectation propagation (EP) detector has been proved to outperform classical and advanced solutions. Merging with the parity constraints of channel coding, a novel EP detection with linear programming (LP), named EPLP, is proposed to change the common initialization that the symbols are equiprobable by formulating an LP optimization problem. Furthermore, we apply the EPLP detector to a polar-coded MIMO system with an iterative detection and decoding (IDD) scheme. For a (128, 64) polar-coded 16 × 16 QPSK configuration, the EPLP outperforms the state-of-the-art detectors by 1 dB at the bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and the IDD scheme with the EPLP achieves 0.5 dB gain at the frame error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> with a reduced number of EP iterations.

Low computational complexity joint iterative detection and decoding without ARQ in massive MIMO systems with UAVs

Unmanned aerial vehicles (UAVs) have a wide range of military and commercial applications. UAVs play an important role in 6G networks due to their low cost and flexible positioning. UAVs can play different roles in the network and act as an air base station, as a relay, or as users in cellular networks. One of the major challenges in these systems is to increase lifespan with low energy consumption. The automatic repeat request (ARQ) protocol is used to increase throughput and obtain dependable communications for multi-input multiple-input multiple-output (MIMO) systems, even in the presence of severe propagation conditions. On the other hand, (UAV)-enabled communication system provides flexibility and reliability compared to conventional ones which have been considered in the new generation of wireless communications. Thus, using ARQ protocols in this system faces serious challenges such as increasing delay and operational implementation complexities in the receiver. Therefore, to meet the ARQ challenge in the uplink of massive MIMO systems, we have investigated utilizing joint iterative detection and decoding methods with reduced computational complexity have been proposed in a UAV-enabled communication system in the condition that users are equipped with two antennas to provide a solution to reduce energy consumption. In this structure, system performance improves as the computational complexity increases. Therefore, we look for methods that reduce complexity while maintaining system performance at an acceptable level. In this study, we seek to provide a solution that can establish a reasonable compromise between system performance and computational complexity. This aim is achieved by establishing a connection between the components of soft joint detection and decoding, linear detection with approximation methods, and sorting. The first part of the receiver is sorting the users before detection, then, in the next module, a turbo-process-based low-complexity MIMO iterative detection and decoding (IDD) algorithm, with minimum mean square error (MMSE) detector and soft channel decoder, is used. For solving the challenge of computational complexity, utilizing of approximation-based detection method is proposed. This structure works in such a way that after sorting the users, in the first iteration, a certain number of users are decoded using a hard decoding scheme after the soft detection and decoding. Therefore, these data are subtracted from the soft feedback information (prior data) to the detector, as inter user interference (IUI) and the Joint IDD module continue the iterative cycle until all users’ data are decoded.

Fast Iterative Soft-Output List Decoding of Polar Codes

Iterative detection and decoding (IDD) enjoys a higher capacity than separate detection and decoding (SDD). However, IDD requires both the detector and the decoder to support soft input and soft output. Considering the standard channel codes in 5G communication systems, the preferred decoder for polar codes, the successive cancellation list (SCL) decoder, outputs only hard decisions on the side of codeword. Yet, current soft-output polar decoders, such as belief propagation (BP) and soft cancellation (SCAN), are much inferior to the SCL decoders in terms of error correction performance. To this end, we propose a soft-output list (SOL) decoder in this paper, which considers both hypotheses of 0 and 1 for unreliable bits and keeps the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-priori</i> likelihoods for reliable bits, so that it can provide soft output and satisfactory error-rate performance at the same time. Exploiting the property of special nodes, the resulting FastSOL decoder can directly return soft messages from these nodes, thus speeding up the decoding. The error-rate performance of the FastSOL decoder enhances as the number of iterations increases in the non-IDD setup. The proposed FastSOL decoder can also be applied in the IDD system. Concatenated with a maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a-posteriori</i> detector in ten IDD loops, our decoder with a list size of eight exhibits over 1 dB gain compared to using the SCAN decoder for polar codes with length 256 and half rate, which also outperforms the state-of-the-art soft list decoder by 0.12 dB in the same settings with a higher computational complexity, but comparable decoding latency.

A Low-Complexity Double EP-Based Detector for Iterative Detection and Decoding in MIMO

We propose a new iterative detection and decoding (IDD) algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE) based IDD, as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases.

Robust Symbol Detection in Large-Scale Overloaded NOMA Systems

We a framework for the design of low-complexity and high-performance receivers for multidimensional overloaded non-orthogonal multiple access (NOMA) systems. The framework is built upon a novel compressed sensing (CS) regularized maximum likelihood (ML) formulation of the discrete-input detection problem, in which the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm is introduced to enforce adherence of the solution to the prescribed discrete symbol constellation. Unlike much of preceding literature,.g., (Assaf et al., 2020, Yeom et al., 2019, Nagahara, 2015, Naderpour and Bizaki, 2020, Hayakawa and Hayashi, 2017, Hayakawa and Hayashi, 2018, and Zeng et al., 2020), the method is not relaxed into the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm, but rather approximated with a continuous and asymptotically exact expression without resorting to parallel interference cancellation (PIC). The objective function of the resulting formulation is thus a sum of concave-over-convex ratios, which is then tightly convexified via the quadratic transform (QT), such that its solution can be obtained via the iteration of a simple closed-form expression that closely resembles that of the classic zero-forcing (ZF) receiver, making the method particularly suitable to large-scale set-ups. By further transforming the aforementioned problem into a quadratically constrained quadratic program with one convex constraint (QCQP-1), the optimal regularization parameter to be used at each step of the iterative algorithm is then shown to be the largest generalized eigenvalue of a pair of matrices which are given in closed-form. The method so obtained, referred to as the Iterative Discrete Least Square (IDLS), is then extended to address several factors of practical relevance, such as noisy conditions, imperfect channel state information (CSI), and hardware impairments, thus yielding the Robust IDLS algorithm. Simulation results show that the proposed art significantly outperforms both classic receivers, such as the linear minimum mean square error (LMMSE), and recent CS-based state-of-the-art (SotA) alternatives, such as the sum-of-absolute-values (SOAV) and the sum of complex sparse regularizers (SCSR) detectors. It is also shown via simulations that the technique can be integrated with existing iterative detection-and-decoding (IDD) methods, resulting in accelerated convergence.

Iterative List Detection and Decoding for Massive Machine-Type Communications

The main challenge of massive machine-type communications (mMTC) is the joint activity and signal detection of devices. The mMTC scenario with many devices transmitting data intermittently at low data rates and via very short packets enables its modelling as a sparse signal processing problem. In this work, we consider a grant-free system and propose a detection and decoding scheme that jointly detects activity and signals of devices. The proposed scheme consists of a list detection technique, an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm regularized activity-aware recursive least-squares algorithm, and an iterative detection and decoding (IDD) approach that exploits the device activity probability. In particular, the proposed list detection technique uses two candidate-list schemes to enhance the detection performance. We also incorporate the proposed list detection technique into an IDD scheme based on low-density parity-check codes. We derive uplink sum-rate expressions that take into account metadata collisions, interference and a variable activity probability for each user. A computational complexity analysis shows that the proposed list detector does not require a significant additional complexity over existing detectors, whereas a diversity analysis discusses its diversity order. Simulations show that the proposed scheme obtains a performance superior to existing suboptimal detectors and close to the oracle LMMSE detector.

Low Complexity Iterative Receiver With Lossless Information Transfer for Non-Binary LDPC Coded PDMA System

In this work, we first proposed a non-binary low-density parity-check (NB-LDPC) coded pattern division multiple access (PDMA) scheme with the order of the Galois field equal to the size of modulation alphabet which can avoid the symbol-to-bit or bit-to-symbol probability conversion between the detector and decoder as in binary coded system. Specifically, we considered a 4-ary LDPC over Galois field (GF(4)-LDPC) coded PDMA system with quadrature phase shift keying (QPSK) modulation. At the receiver side, Gaussian approximation based message passing (GAMP) detection algorithm instead of standard message passing (SMP) is employed to achieve a tradeoff between the computational complexity and the detection performance. When iterative detection and decoding (IDD) algorithm is used, the symbol-wise extrinsic information of the detector and GF(4)-LDPC decoder can be exchanged without information loss. At last, we proposed a symbol-wise EXIT (S-EXIT) based iterative optimization algorithm to improve the system performance. Both the S-EXIT chart based analysis and numerical simulation results show the validity of the proposed scheme above.

Efficient Method for Three Loop MMSE-SIC based Iterative MIMO Systems

Iterative decoding is one of the promising methods to improve the performance of MIMO systems. In iterative processing channel decoder and MIMO detector share the information in order to enhance the overall system performance. However, iterative processing requires a lot of computations therefore it is considered as a computationally complex approach due to complex detection schemes involving iterative processing. There are several promising detection methods that require further improvements and they can be candidates in order to practically implement iterative processing. In this paper, the propose method to improve the efficiency of three loop-based minimum mean squared errors with soft interference cancellation (MMSE-SIC) method by reducing its complexity with a single inverse operation. Simulation results are given in order to provide detail analysis of the proposed MMSE-SIC based approach for iterative detection and decoding (IDD).

Low-Routing-Complexity Convolutional/Turbo Decoder Design for Iterative Detection and Decoding Receivers

A highly hardware-shared convolutional/turbo decoder for iterative detection and decoding (IDD) receivers is presented here. Furthermore, parallel dual-mode decoding kernels are proposed to alternatively decode the long-term evolution (LTE)-compatible convolutional codes (CCs) and turbo codes (TCs) using a parity-enhanced soft-input soft-output maximum a-posteriori algorithm. However, decoding the high-constraint-length CC results in complicated interconnections among the parallel decoding kernels and unroutable back-end chip implementation. Therefore, a minimized global route (MGR) design is proposed for the dual-mode decoder to further simplify the global data transfers and reduce the routing complexity. Using a 40-nm CMOS process, the proposed dual-mode decoder achieves a 48% area reduction compared with the individual single-mode TC and single-mode CC decoders. The global routing length of the proposed decoding kernels is reduced by 32% using the MGR design. Because of the feasible placements and routing, the proposed dual-mode decoder is implemented in an application-specific integrated circuit of 2.88-mm2 core area at the maximum operating frequency of 364 MHz. For the LTE-based IDD receiver, the proposed dual-mode decoder achieves high area efficiencies of 0.21 and 0.35 bits/mm2 for the TC and CC decoding, respectively.

A 2.4-mm2 130-mW MMSE-Nonbinary LDPC Iterative Detector Decoder for 4 $\times$ 4 256-QAM MIMO in 65-nm CMOS

Iterative detection and decoding (IDD) employs a soft-in soft-out (SISO) detector and an SISO forward error correction (FEC) decoder in an iterative loop to improve the receiver performance in multiple-input multiple-output (MIMO) wireless communications. This paper describes a 256-QAM 4 × 4 prototype IDD design made up of a minimum mean square error (MMSE) detector and a nonbinary low-density parity-check (NBLDPC) decoder with the symbol size of the NBLDPC code matched to the modulation to enhance performance. By directly translating between nonbinary symbols and constellation points, the detector-decoder interface is simplified. We present a Gb/s MMSE detector using a shortened tandem scheduling, a low-latency dual-lookup reciprocal unit, an optimized interleaved microarchitecture, and a Gb/s NBLDPC decoder with efficient internal skipping paths and memory allocation. The designs were demonstrated in a 0.7-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 1.38-Gb/s MMSE detector and a 1.7-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 1.02-Gb/s-NBLDPC decoder that are integrated in a 65-nm CMOS test chip. The chip is measured to achieve 19.2 pJ/b in detection and 20.1 pJ/b/iteration in decoding.

Knowledge‐aided iterative detection and decoding for multiuser multiple‐antenna systems

In this work, the authors assess the performance and latency of a receiver design for multiuser multiple-antenna systems using low-density parity-check (LDPC) codes with iterative detection and decoding (IDD). The proposed knowledge-aided IDD (KA-IDD) receiver employs a parallel interference cancellation detector with refined iterative processing and a reweighted belief propagation (BP) decoder. Additionally, two BP-based decoding algorithms are proposed for improved performance of the decoder. The first cycle knowledge-aided reweighted-BP decoder takes advantage of the information about the distribution of cycles in the Tanner graph and the second expansion knowledge-aided reweighted-BP decoder expands the original graph into a set of subgraphs, then locally optimises the reweighting parameters of each subgraph. Novel reweighting parameter optimisation strategies are developed for decoding regular or irregular LDPC codes. The developed schemes optimise the parameters off-line and neither of these proposed algorithms imposes extra computational complexity to the on-line decoding procedure. Furthermore, the proposed schemes reduce the decoding latency. Simulation results show that the proposed KA-IDD scheme and algorithms outperform prior art and require a reduced number of decoding iterations.

EM‐based poor slot update method for MIMO IDD receiver in fast time‐varying channels

In this study, the authors propose an expectation-maximisation (EM)-based poor slot update method for multiple-input multiple-output (MIMO) iterative detection and decoding (IDD) receivers to increase their channel tracking capability in fast time-varying channels. The poor slots that trigger error propagation in a frame are selected, the channel state information is updated, and the updated recursive least squares channel tracking algorithm is executed. The accurate channel tracking capability of this new channel updating technique contributes to a 3 dB improvement in the bit error rate performance of an IDD receiver in a fast mobile environment ( > 180 km / h ). The authors also demonstrate that an ∼ 20 − 25 % complexity reduction can be achieved under various conditions.

Tight Upper Bound Performance of Full-Duplex MIMO-BICM-IDD Systems in the Presence of Residual Self-Interference

In this paper, we derive a tight upper bound on the performance of a coded full-duplex multiple-input multiple-output (MIMO)-based bidirectional transceiver. Iterative detection and decoding (IDD) are proposed to suppress the residual self-interference (SI) remaining after applying different stages of SI cancellation. IDD comprises an adaptive minimum mean-squared error filter with log-likelihood ratio demapping, while the soft decoder by using soft-in soft-out decoding utilizes the maximum a posteriori algorithm. Furthermore, bit-interleaved coded modulation is considered in the presence of additive white Gaussian noise over MIMO frequency non-selective Rayleigh fading channels. Simulation results are presented to demonstrate the bit-error rate (BER) performance as a function of the signal-to-noise ratio showing a close match to the SI-free case for the proposed system. Furthermore, we validate our results by deriving a tight upper bound on the performance of the proposed system using rate-1/2 convolutional codes together with $M$ -ary quadrature amplitude modulation, which asymptotically exhibits a close agreement with the simulated BER performance. Moreover, extrinsic information transfer chart analysis is used to investigate the convergence behavior of the proposed IDD receiver and to determine the number of iterations required for this convergence.

Large-Scale MIMO Detection Using MCMC Approach With Blockwise Sampling

In this paper, a low-complexity approach for the large-scale (underdetermined) multiple-input multiple-output (MIMO) detection is proposed using the Markov chain Monte Carlo (MCMC) algorithm in conjunction with blockwise sampling. Klein’s algorithm is employed in each sub-system to draw multidimensional samples for an MCMC detector in iterative detection and decoding (IDD). From analysis, we find that the lattice reduction (LR) technique cannot improve the performance of the proposed MCMC-based approach under low-correlated channel environment. In addition, due to blockwise sampling, the proposed method exhibits a faster convergence speed when running a Markov chain and provides a near-optimal performance for the detection of underdetermined MIMO systems. Complexity analysis and simulation results show that the proposed approach outperforms the conventional LR-based Klein randomized successive interference cancellation (SIC) detection with a relatively low complexity.

Lattice reduction‐based iterative receivers: using partial bit‐wise MMSE filter with randomised sampling and MAP‐aided integer perturbation

For iterative detection and decoding (IDD) in multiple-input multiple-output systems, the maximum a posteriori probability (MAP) detector would be ideal in terms of the performance. However, due to its high computational complexity, various suboptimal low-complexity approximate MAP detectors have been studied. In this study, a lattice reduction (LR)-based detector is considered for a near-optimal performance for IDD. The authors improve further the performance by employing a partial bit-wise minimum mean square error (MMSE) approach with randomised sampling, which has a lower complexity than that of the full bit-wise MMSE method. Moreover, the list of candidate vectors obtained by randomised sampling is extended using a MAP-aided integer perturbation algorithm for a better performance with low additional complexity. Through simulation results, it is shown that a near-optimal performance can be obtained which is better than that of the LR-based randomised successive interference cancellation and the full bit-wise MMSE methods.

Iterative Detection and Decoding Algorithms for MIMO Systems in Block-Fading Channels Using LDPC Codes

We propose iterative detection and decoding (IDD) algorithms with low-density parity-check (LDPC) codes for multiple-input multiple-output (MIMO) systems operating in block-fading and fast Rayleigh fading channels. Soft-input-soft-output minimum-mean-square-error (MMSE) receivers with successive interference cancellation are considered. In particular, we devise a novel strategy to improve the bit error rate (BER) performance of IDD schemes, which takes into account the soft a posteriori output of the decoder in a block-fading channel when root-check LDPC codes are used. A MIMO IDD receiver with soft information processing that exploits the code structure and the behavior of the log-likelihood ratios is also developed. Moreover, we present a scheduling algorithm for decoding LDPC codes in block-fading channels. Simulations show that the proposed techniques result in significant gains in terms of BER for both block-fading and fast-fading channels.

A Tight Upper Bound on the Performance of Iterative Detection and Decoding for Coded Full-Duplex SIMO Systems

In this letter, we investigate a coded full-duplex (FD) bidirectional transceiver based on single-input multiple-output (SIMO). Iterative detection and decoding (IDD) are proposed to mitigate the residual self-interference (SI) remaining after applying passive suppression and two stages of SI cancellation (SIC) filters in analog and digital domains. IDD comprises an adaptive minimum mean-squared error (MMSE) filter with log-likelihood ratio (LLR) demapping, while the soft-in soft-out (SISO) decoder utilizes the maximum a posteriori (MAP) algorithm. The proposed system’s performance is evaluated in the presence of additive white Gaussian noise (AWGN) over SIMO Rayleigh fading channels. Simulation results are presented to demonstrate the bit-error rate (BER) performance as a function of the signal-to-noise ratio (SNR), demonstrating a close match to the SI-free case for the proposed system. Furthermore, we validate our results by deriving a tight upper bound on the performance of the proposed system using rate-1/2 convolutional codes together with quadrature phase-shift keying (QPSK) modulation, which asymptotically exhibits a close agreement with the simulated BER performance.

Iterative group detection and decoding for large MIMO systems

Recently, a variety of reduced complexity soft-in soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-in soft-output detectors for MIMO systems due to heavy burden in computational complexity. In this paper, we propose a soft detection algorithm for MIMO systems which performs close to the full dimensional joint detection, yet offers significant complexity reduction over the existing detectors. The proposed algorithm, referred to as soft-input soft-output successive group (SSG) detector, detects a subset of symbols (called a symbol group) successively using a deliberately designed preprocessing to suppress the inter-group interference. In fact, the proposed preprocessor mitigates the effect of the interfering symbol groups successively using a priori information of the undetected groups and a posteriori information of the detected groups. Simulation results on realistic MIMO systems demonstrate that the proposed SSG detector achieves considerable complexity reduction over the conventional approaches with negligible performance loss.

An Iterative Detection and Decoding Receiver for LDPC-Coded MIMO Systems

This paper presents a high-throughput, area-efficient and energy-efficient iterative detection and decoding (IDD) receiver for low-density parity-check (LDPC)-coded multiple-input multiple-output (MIMO) systems. A layered non-resetting IDD technique is used to minimize the number of inner iterations for a required error performance. An area-efficient minimum mean-square error with parallel interference cancellation (MMSE-PIC) detector is devised to simplify matrix inversion. A detector-decoder interface that is used to exchange soft messages efficiently is proposed. Given the throughput specifications, inner and outer loops are optimally combined to maximize the error performance. The design specifications defined in the IEEE 802.11n standard are adopted as the design target. A 4 $\times$ 4 antenna configuration with BPSK, QPSK, 16-QAM modulations are realized in silicon. The designs that support 64-QAM and 256-QAM modulations are also demonstrated for comparison with prior work. Fabricated in 40 nm technology, the chip integrates 998k logic gates in 1.33 ${\rm mm}^{2}$ and achieves a maximum throughput of 794 Mb/s. The chip dissipates 135 mW at 0.9 V, achieving an energy efficiency of 170 pJ/bit.

Iterative Channel Estimation Using Virtual Pilot Signals for MIMO-OFDM Systems

The number of transmit and receive antennas in multi-input multi-output (MIMO) systems is increasing rapidly to enhance the throughput and reliability of next-generation wireless systems. Benefits of large size MIMO systems, however, can be realized only when the quality of estimated channels is ensured at the transmitter and receiver side alike. In this paper, we introduce a new decision-directed channel estimation technique dealing with pilot shortage in the MIMO-OFDM systems. The proposed channel estimator uses soft symbol decisions obtained by iterative detection and decoding (IDD) scheme to enhance the quality of channel estimate. Using the soft information from the decoders, the proposed channel estimator selects reliable data tones, subtracts interstream interferences, and performs re-estimation of the channels. We analyze the optimal data tone selection criterion, which accounts for the reliability of symbol decisions and correlation of channels between the data tones and pilot tones. From numerical simulations, we show that the proposed channel estimator achieves considerable improvement in system performance over the conventional channel estimators in realistic MIMO-OFDM scenarios.