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Efficient iterative soft detection based on polynomial approximation for massive MIMO

Efficient iterative soft detection based on polynomial appro...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Feng Wang Chuan Zhang Xiao Liang Zhizheng Wu Shugong Xu Xiaohu You National Mobile Communications Research Laboratory Southeast University Nanjing China Intel Collaborative Research Institutes on Mobile Networking and Computing Intel Labs Shanghai China

ISBN: (纸本)9781467376884

In massive multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) detection is near-optimal but involves large dimensional matrix inversion, which results in high complexity. To this end, Neumann series expansion (NSE) approximation, which avoids the direct computation of the matrix inversion, is recently investigated due to its low implementation complexity. Unfortunately, the complexity reduction can only be achieved well when the required number of the NSE terms L is small. To solve this problem, we proposed an iterative NSE (INSE) algorithm for MMSE detection at a manageable complexity even for large L. An approximation method based on NSE is proposed to compute the log-likelihood ratios (LLRs) for channel decoders. Both analytical and numerical results have demonstrated that, the overall complexity of the proposed soft-output MMSE-INSE algorithm is significantly reduced compared with the conventional NSE method and the Cholesky decomposition method, while keeping similar detection performance.

关键词： Approximation methods MIMO Matrix decomposition Computational complexity Approximation algorithms Uplink

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Efficient matrix inversion architecture for linear detection in massive MIMO systems

Efficient matrix inversion architecture for linear detection...

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International Conference on Digital Signal Processing (DSP)

作者： Feng Wang Chuan Zhang Junmei Yang Xiao Liang Xiaohu You Shugong Xu National Mobile Communications Research Laboratory Southeast University Nanjing China Intel Collaborative Research Institutes on Mobile Networking and Computing Intel Labs Beijing China

ISBN: (纸本)9781479980598

Resulted by the hundreds of antennas at the base-station (BS) side, the dimension of matrices involved in linear detection for massive multiple-input multiple-output (MIMO) uplink systems increases drastically. Being an indispensable part of linear detection, the matrix inversion suffers a lot from the huge matrix size of massive MIMO, and therefore becomes inefficient for realization. By achieving good tradeoff between complexity and performance, approximate matrix inversion via Neumann series now boosts one promising solution for linear detection of massive MIMO systems. In this paper, an efficient hardware architecture for approximate matrix inversion is proposed. This architecture is hardware efficient and suitable for various applications with different approximation precisions. FPGA implementation results of matrix inversion for 4 × 32 massive MIMO system have shown that the proposed architecture can achieve 56.7% higher frequency with only 58.9% hardware resources of its existing counterparts. Look-ahead transformations which can make the proposed architecture more suitable for high speed applications are also mentioned.

关键词： Computer architecture MIMO Complexity theory Approximation methods Hardware Matrix decomposition Field programmable gate arrays

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Efficient stochastic list successive cancellation decoder for polar codes

Efficient stochastic list successive cancellation decoder fo...

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IEEE International SOC Conference

作者： Xiao Liang Chuan Zhang Menghui Xu Shunqing Zhang Xiaohu You National Mobile Communications Research Laboratory Southeast University Nanjing China Intel Collaborative Research Institutes on Mobile Networking and Computing Intel Labs Shanghai China

Representing continuous values by streams of random binary bits, stochastic decoding has shown advantages in both hardware efficiency and fault tolerance, therefore has been widely adopted by iterative decoding of error correction codes such as low-density parity-check (LDPC) codes and so on. Recently, polar codes, the first codes that can provably achieve the capacity of symmetric binary-input discrete memoryless channels (B-DMCs), have drawn a lot of attentions from both academia and industry. Although, polar codes with list successive cancellation (SC) decoding can outperform several best-known LDPC codes even within high error-rate regions, the linearly increasing hardware complexity makes its efficient implementation difficult. To this end, the stochastic list SC polar decoding algorithm is proposed in this paper to provide a good tradeoff between performance and complexity. In order to increase the decoding performance of stochastic list SC polar decoder, doubling probability approach is presented. The corresponding hardware architecture is also given. The approximate doubling approach is employed to facilitate the efficient implementation. Implementation results have shown that the proposed stochastic list SC polar decoder can achieve a good trade-off between performance and complexity.

关键词： Maximum likelihood decoding Complexity theory Measurement Hardware Degradation Numerical simulation

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Coefficient adjustment matrix inversion approach and architecture for massive MIMO systems

Coefficient adjustment matrix inversion approach and archite...

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International Conference on ASIC

作者： Xiao Liang Chuan Zhang Shugong Xu Xiaohu You National Mobile Communications Research Laboratory Southeast University Nanjing China Intel Collaborative Research Institutes on Mobile Networking and Computing Intel Labs Shanghai China

Thanks to hundreds of antennas, spectral efficiency of massive multiple-input multiple-output (MIMO) systems has drastically increased. However, the resulting huge dimension of matrices involved in massive MIMO MMSE detection causes prohibitive complexity. Although large scale matrix inversion with Neumann approximation achieves good tradeoff between complexity and accuracy for i.i.d. massive MIMO channel, its convergency speed degrades seriously for correlated massive MIMO channel. To this end, in this paper the matrix inversion approach based on coefficient adjustment (CA), which is more adaptable to correlated channel with higher throughput, is proposed. The corresponding hardware architecture is also given. FPGA results have shown that for 4 × 32 MIMO system, the proposed architecture can achieve 69.4% higher frequency with only 49.1% hardware cost compared to Cholesky decomposition method. CA approach can also achieve 37.9% higher throughput than Neumann scheme for correlated channel on average.

关键词： MIMO Complexity theory Computer architecture Hardware Throughput Bit error rate Field programmable gate arrays

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Improved symbol-based belief propagation detection for large-scale MIMO

Improved symbol-based belief propagation detection for large...

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IEEE Workshop on Signal Processing Systems (SIPS)

作者： Junmei Yang Chuan Zhang Xiao Liang Shugong Xu Xiaohu You National Mobile Communications Research Laboratory Southeast University Nanjing China Intel Collaborative Research Institutes on Mobile Networking and Computing Intel Labs. Shanghai China

In this paper, BP detection based on belief propagation in real domain for large-scale MIMO systems is proposed. Numerical results have shown that, with quadrature phase shift keying (QPSK) modulation, this approach can show 1 dB performance improvement at the BER of 10-2, compared to conventional single-edge based BP (SE-BP) in complex domain. Based on the proposed BP detection, its symbol-based variation is also investigated for applications in large-scale MIMO systems with a high-order modulation. This symbol-based method successfully reduces computational complexity by avoiding large dimensional matrix inversion and decomposition. Since the proposed method can also shrink the constellation size, its complexity can be further reduced. Numerical simulation results and complexity comparison have demonstrated that the proposed symbol-based BP detection can show advantages in both performance and complexity compared to existing ones. Therefore, it is suitable for large-scale MIMO system applications, especially for those with high-order modulations.

关键词： MIMO Complexity theory Approximation algorithms Message passing Approximation methods Belief propagation Phase modulation

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Pipelined Implementations of Polar Encoder and Feed-Back Part for SC Polar Decoder

Pipelined Implementations of Polar Encoder and Feed-Back Par...

引用

IEEE International Symposium on Circuits and Systems

作者： Chuan Zhang Junmei Yang Xiaohu You Shugong Xu National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing China Intel Collaborative Research Institutes on Mobile Networking and Computing (ICRI-MNC) Intel Labs Beijing China

ISBN: (纸本)9781479983926

In this paper, we first reveal the similarity of polar encoder and fast Fourier transform (FFT) processor. Based on this, both feed-forward and feed-back pipelined implementations of polar encoder are proposed. It is pointed out that the feed-back part of SC polar decoder is nothing but a simplified version of polar encoder and therefore can be pipelined implemented also. Moreover, a general approach which uniformly constructs most pipelined polar encoders via folding transformation is proposed. Implementation results have shown that both proposed pipelined polar encoder architectures achieve more than 98.3% complexity reduction and more than 9.86% speed-up compared to the conventional implementation.

关键词： Polar code encoder pipelined processing folding transformation

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