Junmei Yang

Efficient matrix inversion architecture for linear detection in massive MIMO systems

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.

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

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.

Pipelined implementations of polar encoder and feed-back part for SC polar decoder

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 feedback 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.

Pipelined belief propagation polar decoders

Due to its inherent higher parallelism over successive cancellation (SC) polar decoder, belief propagation (BP) polar decoder becomes more favorable for high throughput applications. However, most existing BP decoders suffer from low utilization. In this paper, a new updating scheme, in which both left-to-right and right-to-left messages are considered identical, is first proposed for memory reduction. By revealing the similarity between BP polar decoder and fast Fourier transform (FFT) processor, both feed-forward and feed-back pipelined BP polar decoders are proposed along with detailed processing schedules. Implementation results have shown that both proposed pipelined BP decoders achieve more than 99.8% arithmetic logical units (ALUTs) reduction and 3.40% registers & block memory reduction, together with more than 7.45% speed-up, compared to the conventional fully parallel one. The proposed design approaches can be generalized with folding technique to achieve the required balance between area and speed flexibly.

Efficient stochastic detector for large-scale MIMO

In this paper, a low-complexity stochastic belief propagation (BP) detector for large-scale MIMO is first proposed. Its efficient hardware architecture, with parallel pipeline, is presented in detail. Thanks to the stochastic approach, all arithmetic operations of the detector are implemented with simple logic structures. Several approaches which can potentially improve the detection performance are exploited. Simulation results have demonstrated that the stochastic BP detector can achieve similar detection performance compared with deterministic one for 32 × 32 MIMO system with 4-quadrature amplitude modulation (4-QAM). With the increase of antenna number, the detection performance improves at the linear expense of complexity and latency. Therefore, the proposed stochastic BP detector is suitable for large-scale MIMO system applications with good balance of detection performance and implementation complexity.

Hardware Efficient and Low-Latency CA-SCL Decoder Based on Distributed Sorting

For polar codes, cyclic redundancy check (CRC)-aided successive cancellation list (CA-SCL) decoder has attracted increasing attention from both academia and industry. In this paper, a hardware efficient and low-latency CA-SCL polar decoder based on distributed sorting is first proposed. For path metric (PM) sorting of each level, a distributed sorting (DS) algorithm is proposed to reduce the comparison complexity from

Low-Complexity Belief Propagation Detection for Correlated Large-Scale MIMO Systems

\mathcal{O}(L^{2})

Low-complexity adaptive successive cancellation list polar decoder based on relaxed sorting

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Low-latency software successive cancellation list polar decoder using stage-located copy

\mathcal{O}(L)

Efficient Hardware Architecture for Compressed Sensing with DFT Sensing Matrix

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