Low-Latency Reweighted Belief Propagation Decoding for LDPC Codes
TL;DR: Simulation results show that the VFAP-BP algorithm outperforms the standard BP algorithm, and requires a significantly smaller number of iterations when decoding either general or commercial LDPC codes.
Abstract: In this paper we propose a novel message passing algorithm which exploits the existence of short cycles to obtain performance gains by reweighting the factor graph. The proposed decoding algorithm is called variable factor appearance probability belief propagation (VFAP-BP) algorithm and is suitable for wireless communications applications with low-latency and short blocks. Simulation results show that the VFAP-BP algorithm outperforms the standard BP algorithm, and requires a significantly smaller number of iterations when decoding either general or commercial LDPC codes.
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TL;DR: In this article, an average bit error probability (ABEP) analysis for maximum likelihood detection in multiuser GSM-MIMO on the uplink, where an upper bound on the ABEP was derived, and low-complexity algorithms for signal detection and channel estimation at the base station receiver based on message passing were proposed.
Abstract: Generalized spatial modulation (GSM) uses $n_{t} $ transmit antenna elements but fewer transmit radio frequency (RF) chains, $n_{rf} $ . Spatial modulation (SM) and spatial multiplexing are special cases of GSM with $n_{rf}=1$ and $n_{rf}=n_{t} $ , respectively. In GSM, in addition to conveying information bits through $n_{rf} $ conventional modulation symbols (for example, QAM), the indices of the $n_{rf} $ active transmit antennas also convey information bits. In this paper, we investigate GSM for large-scale multiuser MIMO communications on the uplink. Our contributions in this paper include: 1) an average bit error probability (ABEP) analysis for maximum-likelihood detection in multiuser GSM-MIMO on the uplink, where we derive an upper bound on the ABEP, and 2) low-complexity algorithms for GSM-MIMO signal detection and channel estimation at the base station receiver based on message passing. The analytical upper bounds on the ABEP are found to be tight at moderate to high signal-to-noise ratios (SNR) . The proposed receiver algorithms are found to scale very well in complexity while achieving near-optimal performance in large dimensions. Simulation results show that, for the same spectral efficiency, multiuser GSM-MIMO can outperform multiuser SM-MIMO as well as conventional multiuser MIMO, by about 2 to 9 dB at a bit error rate of $10^{-3} $ . Such SNR gains in GSM-MIMO compared to SM-MIMO and conventional MIMO can be attributed to the fact that, because of a larger number of spatial index bits, GSM-MIMO can use a lower-order QAM alphabet which is more power efficient.
144 citations
TL;DR: A novel strategy to improve the bit error rate (BER) performance of IDD schemes is devised, which takes into account the soft a posteriori output of the decoder in a block-fading channel when root-check LDPC codes are used.
Abstract: 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 cancelation 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.
95 citations
Cites methods from "Low-Latency Reweighted Belief Propa..."
...Recent LDPC techniques [5]–[11] that improve the coding gain and have low-complexity encoding and...
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TL;DR: This paper presents cost-effective low-rank techniques for designing robust adaptive beamforming algorithms based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method.
Abstract: This paper presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. First, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. Then, we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high-dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.
84 citations
Additional excerpts
...…[92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102], [103], [104], [105], [106], [107], [108], [109], [110], [111], [112], [114], [115], [116], [117], [118], [119],[120], [121], [122], [123], [124], [125], [126], [131], [128], [129], [130], [131], [132], [13], [134], [135], [136]....
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TL;DR: In this article, an iterative detection and decoding (IDD) algorithm with Low-Density Parity-Check (LDPC) codes for MIMO systems operating in block-fading and fast Rayleigh fading channels is proposed.
Abstract: 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 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 \textit{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.
82 citations
TL;DR: Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.
Abstract: In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and $M$ -ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.
81 citations
References
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TL;DR: The proposed cycle counting algorithm consists of integer matrix operations and its complexity grows as O(gn/sup 3/) where n=max(|U|,|W|).
Abstract: Let G=(U/spl cup/W, E) be a bipartite graph with disjoint vertex sets U and W, edge set E, and girth g. This correspondence presents an algorithm for counting the number of cycles of length g, g+2, and g+4 incident upon every vertex in U/spl cup/W. The proposed cycle counting algorithm consists of integer matrix operations and its complexity grows as O(gn/sup 3/) where n=max(|U|,|W|).
96 citations
TL;DR: A new inference algorithm, suitable for distributed processing over wireless networks, that combines the local nature of belief propagation with the improved performance of tree-reweighted belief propagation in graphs with cycles is proposed.
Abstract: In this paper, we propose a new inference algorithm, suitable for distributed processing over wireless networks. The algorithm, called uniformly reweighted belief propagation (URW-BP), combines the local nature of belief propagation with the improved performance of tree-reweighted belief propagation (TRW-BP) in graphs with cycles. It reduces the degrees of freedom in the latter algorithm to a single scalar variable, the uniform edge appearance probability ρ. We provide a variational interpretation of URW-BP, give insights into good choices of ρ, develop an extension to higher-order potentials, and complement our work with numerical performance results on three inference problems in wireless communication systems: spectrum sensing in cognitive radio, cooperative positioning, and decoding of a low-density parity-check (LDPC) code.
63 citations
"Low-Latency Reweighted Belief Propa..." refers background in this paper
...…10.1109/LCOMM.2012.12.121307 guarantee and the high-latency due to many decoding iterations are still open issues for researchers when it comes to effectively decoding LDPC codes in wireless communications applications, where a large amount of data transmission and data storage are required....
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15 Apr 2007
TL;DR: This paper studies the convergence and stability properties of the family of reweighted sum-product algorithms, a generalization of the widely used sum-Product or belief propagation algorithm, in which messages are adjusted with graph-dependent weights.
Abstract: Many signal processing applications of graphical models require efficient methods for computing (approximate) marginal probabilities over subsets of nodes in the graph. The intractability of this marginalization problem for general graphs with cycles motivates the use of approximate message-passing algorithms, including the sum-product algorithm and variants thereof. This paper studies the convergence and stability properties of the family of reweighted sum-product algorithms, a generalization of the standard updates in which messages are adjusted with graph-dependent weights. For homogenous models, we provide a complete characterization of the potential settings and message weightings that guarantee uniqueness of fixed points, and convergence of the updates. For more general inhomogeneous models, we derive a set of sufficient conditions that ensure convergence, and provide estimates of rates. These theoretical results are complemented with experimental simulations on various classes of graphs.
44 citations
"Low-Latency Reweighted Belief Propa..." refers background in this paper
...URW-BP with a uniform FAP the convergence guarantees are strengthened when the noise variance is reduced [11]....
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TL;DR: This paper studies the convergence and stability properties of the family of reweighted sum-product algorithms, a generalization of the widely used sum-Product or belief propagation algorithm, in which messages are adjusted with graph-dependent weights.
Abstract: Markov random fields are designed to represent structured dependencies among large collections of random variables, and are well-suited to capture the structure of real-world signals. Many fundamental tasks in signal processing (e.g., smoothing, denoising, segmentation etc.) require efficient methods for computing (approximate) marginal probabilities over subsets of nodes in the graph. The marginalization problem, though solvable in linear time for graphs without cycles, is computationally intractable for general graphs with cycles. This intractability motivates the use of approximate ldquomessage-passingrdquo algorithms. This paper studies the convergence and stability properties of the family of reweighted sum-product algorithms, a generalization of the widely used sum-product or belief propagation algorithm, in which messages are adjusted with graph-dependent weights. For pairwise Markov random fields, we derive various conditions that are sufficient to ensure convergence, and also provide bounds on the geometric convergence rates. When specialized to the ordinary sum-product algorithm, these results provide strengthening of previous analyses. We prove that some of our conditions are necessary and sufficient for subclasses of homogeneous models, but not for general models. The experimental simulations on various classes of graphs validate our theoretical results.
43 citations
03 Oct 2011
TL;DR: This paper extends uniformly-reweighted belief propagation to higher-order interactions and applies it to LDPC decoding, leading performance gains over BP.
Abstract: Tree-reweighted belief propagation is a message passing method that has certain advantages compared to traditional belief propagation (BP). However, it fails to outperform BP in a consistent manner, does not lend itself well to distributed implementation, and has not been applied to distributions with higher-order interactions. We propose a method called uniformly-reweighted belief propagation that mitigates these drawbacks. After having shown in previous works that this method can sub-stantially outperform BP in distributed inference with pairwise interaction models, in this paper we extend it to higher-order interactions and apply it to LDPC decoding, leading performance gains over BP.
32 citations