Ronghui Peng

Approaching MIMO capacity using bitwise Markov Chain Monte Carlo detection

This paper examines near capacity performance of Markov Chain Monte Carlo (MCMC) detectors for multiple-input and multiple-output (MIMO) channels. The proposed MCMC detector (Log-MAP-tb b-MCMC) operates in a strictly bit-wise fashion and adopts Log-MAP algorithm with table look-up. When concatenated with an optimized low-density parity-check (LDPC) code, Log-MAP-tb b-MCMC can operate within 1.2-1.8 dB of the capacity of MIMO systems with 8 transmit/receive antennas at spectral efficiencies up to ¿ = 24 bits/channel use (b/ch). This result improves upon best performance achieved by turbo coded systems using list sphere decoding (LSD) detector by 2.3-3.8 dB, leading to nearly 50% reduction in the capacity gap. Detailed comparisons of the Log-MAP-tb b-MCMC with LSD based detectors demonstrate that MCMC detector is indeed the detector of choice for achieving channel capacity both in terms of performance and complexity.

Application of Nonbinary LDPC Cycle Codes to MIMO Channels

In this paper, we investigate the application of nonbinary low-density parity-check (LDPC) cycle codes over Galois field GF(q) to multiple-input multiple-output (MIMO) channels. Two types of LDPC coded systems that employ either joint or separate MIMO detection and channel decoding are considered, depending on the size of the Galois field and the modulation choice. We construct a special class of nonbinary LDPC cycle codes called the parallel sparse encodable (PSE) codes. The PSE code, consisting of a quasi-cyclic (QC) LDPC cycle code and a simple tree code, has the attractive feature that it is not only linearly encodable, but also allows parallel encoding which can reduce the encoding time significantly. We provide a systematic comparison between nonbinary coded systems and binary coded systems in both performance and complexity. Our results show that the proposed nonbinary system employing the PSE code outperforms not only the binary LDPC code specified in the 802.16e standard, but also the optimized binary LDPC code obtained using the EXIT chart methods. Through a detailed complexity analysis, we conclude that for the MIMO channel considered, the nonbinary coded systems achieve a superior performance at a receiver complexity that is comparable to that of the binary systems.

Markov Chain Monte Carlo Detectors for Channels With Intersymbol Interference

In this paper, we propose novel low-complexity soft-in soft-out (SISO) equalizers using the Markov chain Monte Carlo (MCMC) technique. We develop a bitwise MCMC equalizer (b-MCMC) that adopts a Gibbs sampler to update one bit at a time, as well as a group-wise MCMC (g-MCMC) equalizer where multiple symbols are updated simultaneously. The g-MCMC equalizer is shown to outperform both the b-MCMC and the linear minimum mean square error (MMSE) equalizer significantly for channels with severe amplitude distortion. Direct application of MCMC to channel equalization requires sequential processing which leads to long processing delay. We develop a parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that both the sequential and parallel processing MCMC equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum maximum a posteriori (MAP) equalizer. The MAP equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed MCMC equalizers grows linearly.

Design of Nonbinary Quasi-Cyclic LDPC Cycle Codes

In this paper, we study the design of nonbinary low-density parity-check (LDPC) cycle codes over Galois field GF(q). First, we construct a special class of nonbinary LDPC cycle codes with low error floors. Our construction utilizes the cycle elimination algorithm to remove short cycles in the normal graph and to select nonzero elements in the parity-check matrix to reduce the number of low-weight codewords generated by short cycles. Furthermore, we show that simple modifications of such codes are parallel sparse encodable (PSE). The PSE code, consisting of a quasi-cyclic (QC) LDPC cycle code and a simple tree code, has the attractive feature that it is not only linearly encodable, but also allows parallel encoding which can reduce the encoding time significantly. We provide a systematic comparison between nonbinary coded systems and binary coded systems. For the MIMO channel considered, our results show that the proposed nonbinary system employing the PSE code outperforms not only the binary LDPC code specified in the 802.16e standard, but also the optimized binary LDPC code obtained using the EXIT chart methods.

WLC45-2: Application of Nonbinary LDPC Codes for Communication over Fading Channels Using Higher Order Modulations

In this paper, we investigate the application of non- binary low density parity check (LDPC) codes over Galois field GF(q) for both single-input single-output (SISO) and multiple- input multiple-output (MIMO) fading channels using higher order modulations. As opposed to the widely studied binary systems that employ joint detection and channel decoding, we propose a nonbinary system where optimal signal detection is performed only once followed by channel decoding. To reduce the complexity of proposed system, we first develop a low complexity LDPC decoding algorithm over GF(q) in the logarithmic domain. We then provide a quasi-cyclic construction of nonbinary LDPC codes which not only allows linear-time encoding, but also gives comparable performance to the best known progressive edge growth (PEG) codes. Our results show that the proposed system that employs regular nonbinary LDPC codes outperforms systems using the best optimized binary irregular LDPC codes in both performance and complexity.

Markov Chain Monte Carlo: Applications to MIMO detection and channel equalization

In this paper, we present an overview of recent work on the applications of Markov Chain Monte Carlo (MCMC) techniques to both multiple-input and multiple-output (MIMO) detection and channel equalization. In the setting of MIMO detection, we have shown that, even for very large antenna systems with high spectral efficiencies of 24 bits/channel use (8 transmit and 8 receive antennas with 64 QAM modulation), the MCMC MIMO detector can bring us within 2 dB of the channel capacity with a greatly reduced complexity compared to several versions of sphere decoding based detectors. For frequency selective channels, we demonstrate that MCMC-based equalizers yield excellent performance even for severe inter-symbol-interference (ISI) channels. The MCMC equalizer achieves significant performance gain over minimum mean square error (MMSE) linear equalizer and performs closely to the optimal maximum a posteriori probability (MAP) equalizer. We will also discuss new approaches that effectively alleviate the well-known high SNR problems in existing MCMC detectors.

Low Complexity Markov Chain Monte Carlo Detector for Channels with Intersymbol Interference

In this paper, we propose a novel low complexity soft-in soft-out (SISO) equalizer using the Markov chain Monte Carlo (MCMC) technique. Direct application of MCMC to SISO equalization (reported in a previous work) results in a sequential processing algorithm that leads to a long processing delay in the communication link. Using the tool of factor graph, we propose a novel parallel processing algorithm that reduces the processing delay by orders of magnitude. Numerical results show that, both the sequential and parallel processing SISO equalizers perform similarly well and achieve a performance that is only slightly worse than the optimum SISO equalizer. The optimum SISO equalizer, on the other hand, has a complexity that grows exponentially with the size of the memory of the channel, while the complexity of the proposed SISO equalizers grows linearly.

Low-Complexity Hybrid QRD-MCMC MIMO Detection

In this paper, we propose a novel hybrid QRD- MCMC MIMO detector that combines the features of a QRD-M detector and a Markov chain Monte Carlo (MCMC) detector. The QRD-M algorithm is applied first to obtain initial estimates of the transmitted signal vector. Subsequently, the QRD-M estimate is used to initialize one of the Gibbs samplers for MCMC detection. The MCMC detection reduces the M parameter required by the QRD-M detector, while the QRD-M initialization effectively alleviates the well-known high-SNR problem in existing MCMC detectors. Performance of the QRD-M/MCMC detector is examined under both an idealized MIMO channel with perfect channel side information (CSI) and a practical IEEE 802.16e MIMO- OFDMA system with imperfect CSI. Numerical results show that, compared to the stand-alone QRD-M or MCMC detectors, the QRD-MCMC detector achieves superior performance at a reduced complexity.

Performance of channel coded noncoherent systems: modulation choice, information rate, and Markov chain Monte Carlo detection

This paper investigates performance of channel coded noncoherent systems over block fading channels. We consider an iterative system where an outer channel code is serially concatenated with an inner modulation code amenable to noncoherent detection. We emphasize that, in order to obtain near-capacity performance, the information rates of modulation codes should be close to the channel capacity. For certain modulation codes, a single-input single-output (SISO) system with only one transmit antenna may outperform a dual-input and single-output (DISO) system with two transmit antennas. This is due to the intrinsic information rate loss of these modulation codes compared to the DISO channel capacity. We also propose a novel noncoherent detector based on Markov Chain Monte Carlo (MCMC). Compared to existing detectors, the MCMC detector achieves comparable or superior performance at reduced complexity. The MCMC detector does not require explicit amplitude or phase estimation of the channel fading coefficient, which makes it an attractive candidate for high rate communication employing quadrature amplitude modulation (QAM) and for multiple antenna channels. At transmission rates of 1 ~ 1.667 bits/sec/Hz, the proposed SISO systems employing 16QAM and MCMC detection perform within 1.6-2.3 dB of the noncoherent channel capacity achieved by optimal input.

Noncoherent detection based on Markov Chain Monte Carlo methods for block fading channels

In this work we study joint channel decoding and noncoherent detection for block fading channels. We propose a novel, low-complexity noncoherent detection method based on Markov Chain Monte Carlo (MCMC). The MCMC noncoherent detector makes it possible to use large constellations such as 16 QAM and transmit at higher rates of 1 or 1.6 bits/channel use. By employing joint channel decoding and noncoherent detection, the proposed schemes achieve within 1.2-1.4 dB of the noncoherent channel capacity. Moreover, for the same transmission rates, the proposed single transmit antenna system performs 4-6 dB better than published results of the two transmit antenna systems that employ unitary space-time codes or orthogonal space-time codes