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Zhaocheng Wang - One of the best experts on this subject based on the ideXlab platform.

  • Near-Optimal Signal Detector Based on Structured Compressive Sensing for Massive SM-MIMO
    IEEE Transactions on Vehicular Technology, 2017
    Co-Authors: Chenhao Qi, Chau Yuen, Zhaocheng Wang
    Abstract:

    Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood Detector has the high complexity, whereas state-of-the-art low-complexity Detectors for small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM Signals, we propose a low-complexity Signal Detector based on structured compressive sensing (SCS) to improve the Signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM Signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM Signals by leveraging the structured sparsity. In addition, we also propose the SM Signal interleaving to permute SM Signals in the same transmission group, whereby the channel diversity can be exploited to further improve Signal detection performance. Theoretical analysis quantifies the gain from SM Signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme.

  • near optimal Signal Detector based on structured compressive sensing for massive sm mimo
    arXiv: Information Theory, 2016
    Co-Authors: Zhen Gao, Chau Yuen, Linglong Dai, Zhaocheng Wang
    Abstract:

    Massive spatial modulation (SM)-MIMO, which employs massive low-cost antennas but few power-hungry transmit radio frequency (RF) chains at the transmitter, is recently proposed to provide both high spectrum efficiency and energy efficiency for future green communications. However, in massive SM-MIMO, the optimal maximum likelihood (ML) Detector has the prohibitively high complexity, while state-of-the-art low-complexity Detectors for conventional small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM Signals, we propose a low-complexity Signal Detector based on structured compressive sensing (SCS) to improve the Signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM Signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM Signals by leveraging the structured sparsity. In addition, we also propose the SM Signal interleaving to permute SM Signals in the same transmission group, whereby the channel diversity can be exploited to further improve the Signal detection performance. Theoretical analysis quantifies the performance gain from SM Signal interleaving, and simulation results demonstrate the near-optimal performance of the proposed scheme.

Liang Liu - One of the best experts on this subject based on the ideXlab platform.

  • energy efficient soft input soft output Signal Detector for iterative mimo receivers
    IEEE Transactions on Circuits and Systems, 2014
    Co-Authors: Liang Liu
    Abstract:

    This paper presents the VLSI design of an energy-efficient, high-throughput soft-input soft-output Signal Detector for iterative multiple-input multiple-output (MIMO) receiver. The Detector is evolved from our previously developed imbalanced fixed complexity sphere decoder and adopts several new algorithm-level techniques to exploit the available a priori information of transmitted bits. More specifically, an adaptive tree-travel control scheme, a reliability-dependent log-likelihood ratio correction method and an iteration-based hybrid node enumeration technique are proposed to provide near-optimal detection performance with much reduced computational complexity. A multi-stage parallel VLSI architecture is developed to implement the proposed algorithm with high detection throughput. Furthermore, the block-level clock gating is deployed to save power when the tree-search space is reduced, while still preserving the constant-throughput feature. As a proof of concept, we designed the iterative Detector using a 65-nm CMOS technology and conducted post-layout simulation. The core area is 0.64 mm(2) with 198.2 k gates. Working at 240-MHz clock frequency with 1.0-V voltage supply, the Detector achieves a maximum 1.44-Gbps throughput. Under frequency-selective channels, the Detector core consumes 98.5-, 127.9-, and 149.5-pJ energy per bit detection in open-loop, 2-iteration, and 4-iteration modes, respectively. (Less)

  • Area-efficient configurable high-throughput Signal Detector supporting multiple MIMO modes
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2012
    Co-Authors: Liang Liu, Johan Löfgren, Peter Nilsson
    Abstract:

    This paper presents a low-complexity, high-throughput, and configurable multiple-input multiple-output (MIMO) Signal Detector design solution targeting the emerging Long-Term-Evolution-Advanced (LTE-A) downlink. The Detector supports Signal detection of multiple MIMO modes, which are spatial-multiplexing (SM), spatial-diversity (SD), and space-division-multiple-access (SDMA). Area-efficiency is achieved by algorithm and architecture co-design where low-complexity, near-maximum-likelihood (ML) detection algorithms are proposed for these three MIMO modes respectively while keeping in mind that the operations can be reused among different modes. A parallel multistage VLSI architecture is accordingly developed that achieves high detection throughput and run-time reconfigurability. To further improve the implementation efficiency, the Detector also adopts an orthogonal-real-value-decomposition (ORVD) aided candidate-sharing technology for low-cost partial Euclidean distance calculation and a distributed interference cancelation scheme for a critical path delay reduction. The proposed multi-mode MIMO Detector has been designed using a 65-nm CMOS technology with a core area of 0.25 mm2 (the equivalent gate-count is 88.2 K), representing a 22% less hardware-resource use than the state of art in the open literature. Operating at 1.2-V supply with 165-MHz clock, the Detector achieves a 1.98 Gb/s throughput when configured to the 4 × 4 64-QAM spatial-multiplexing mode. The corresponding normalized energy consumption is 51.8 pJ per bit detection.

Chenhao Qi - One of the best experts on this subject based on the ideXlab platform.

  • Near-Optimal Signal Detector Based on Structured Compressive Sensing for Massive SM-MIMO
    IEEE Transactions on Vehicular Technology, 2017
    Co-Authors: Chenhao Qi, Chau Yuen, Zhaocheng Wang
    Abstract:

    Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood Detector has the high complexity, whereas state-of-the-art low-complexity Detectors for small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM Signals, we propose a low-complexity Signal Detector based on structured compressive sensing (SCS) to improve the Signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM Signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM Signals by leveraging the structured sparsity. In addition, we also propose the SM Signal interleaving to permute SM Signals in the same transmission group, whereby the channel diversity can be exploited to further improve Signal detection performance. Theoretical analysis quantifies the gain from SM Signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme.

Chau Yuen - One of the best experts on this subject based on the ideXlab platform.

  • Near-Optimal Signal Detector Based on Structured Compressive Sensing for Massive SM-MIMO
    IEEE Transactions on Vehicular Technology, 2017
    Co-Authors: Chenhao Qi, Chau Yuen, Zhaocheng Wang
    Abstract:

    Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood Detector has the high complexity, whereas state-of-the-art low-complexity Detectors for small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM Signals, we propose a low-complexity Signal Detector based on structured compressive sensing (SCS) to improve the Signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM Signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM Signals by leveraging the structured sparsity. In addition, we also propose the SM Signal interleaving to permute SM Signals in the same transmission group, whereby the channel diversity can be exploited to further improve Signal detection performance. Theoretical analysis quantifies the gain from SM Signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme.

  • near optimal Signal Detector based on structured compressive sensing for massive sm mimo
    arXiv: Information Theory, 2016
    Co-Authors: Zhen Gao, Chau Yuen, Linglong Dai, Zhaocheng Wang
    Abstract:

    Massive spatial modulation (SM)-MIMO, which employs massive low-cost antennas but few power-hungry transmit radio frequency (RF) chains at the transmitter, is recently proposed to provide both high spectrum efficiency and energy efficiency for future green communications. However, in massive SM-MIMO, the optimal maximum likelihood (ML) Detector has the prohibitively high complexity, while state-of-the-art low-complexity Detectors for conventional small-scale SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting the structured sparsity of multiple SM Signals, we propose a low-complexity Signal Detector based on structured compressive sensing (SCS) to improve the Signal detection performance. Specifically, we first propose the grouped transmission scheme at the transmitter, where multiple SM Signals in several continuous time slots are grouped to carry the common spatial constellation symbol to introduce the desired structured sparsity. Accordingly, a structured subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect multiple SM Signals by leveraging the structured sparsity. In addition, we also propose the SM Signal interleaving to permute SM Signals in the same transmission group, whereby the channel diversity can be exploited to further improve the Signal detection performance. Theoretical analysis quantifies the performance gain from SM Signal interleaving, and simulation results demonstrate the near-optimal performance of the proposed scheme.

Peter Nilsson - One of the best experts on this subject based on the ideXlab platform.

  • Area-efficient configurable high-throughput Signal Detector supporting multiple MIMO modes
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2012
    Co-Authors: Liang Liu, Johan Löfgren, Peter Nilsson
    Abstract:

    This paper presents a low-complexity, high-throughput, and configurable multiple-input multiple-output (MIMO) Signal Detector design solution targeting the emerging Long-Term-Evolution-Advanced (LTE-A) downlink. The Detector supports Signal detection of multiple MIMO modes, which are spatial-multiplexing (SM), spatial-diversity (SD), and space-division-multiple-access (SDMA). Area-efficiency is achieved by algorithm and architecture co-design where low-complexity, near-maximum-likelihood (ML) detection algorithms are proposed for these three MIMO modes respectively while keeping in mind that the operations can be reused among different modes. A parallel multistage VLSI architecture is accordingly developed that achieves high detection throughput and run-time reconfigurability. To further improve the implementation efficiency, the Detector also adopts an orthogonal-real-value-decomposition (ORVD) aided candidate-sharing technology for low-cost partial Euclidean distance calculation and a distributed interference cancelation scheme for a critical path delay reduction. The proposed multi-mode MIMO Detector has been designed using a 65-nm CMOS technology with a core area of 0.25 mm2 (the equivalent gate-count is 88.2 K), representing a 22% less hardware-resource use than the state of art in the open literature. Operating at 1.2-V supply with 165-MHz clock, the Detector achieves a 1.98 Gb/s throughput when configured to the 4 × 4 64-QAM spatial-multiplexing mode. The corresponding normalized energy consumption is 51.8 pJ per bit detection.