熊海翡, 贺光辉. 基于随机计算的大规模MIMO检测算法研究与硬件实现[J]. 微电子学与计算机, 2020, 37(7): 36-41.
引用本文: 熊海翡, 贺光辉. 基于随机计算的大规模MIMO检测算法研究与硬件实现[J]. 微电子学与计算机, 2020, 37(7): 36-41.
XIONG Hai-fei, HE Guang-hui. Designand implementation of stochastic computing based massive MIMO detector[J]. Microelectronics & Computer, 2020, 37(7): 36-41.
Citation: XIONG Hai-fei, HE Guang-hui. Designand implementation of stochastic computing based massive MIMO detector[J]. Microelectronics & Computer, 2020, 37(7): 36-41.

基于随机计算的大规模MIMO检测算法研究与硬件实现

Designand implementation of stochastic computing based massive MIMO detector

  • 摘要: 为了减少大规模MIMO检测算法的复杂度以适应第五代移动通信系统的要求,本文提出了一种基于随机计算的低复杂度线性检测算法.随机计算把传统二进制数转化为一串01序列,使得复杂的计算电路能通过简单的门逻辑实现,从而大幅度的降低硬件资源消耗.通过采用基于二段分解的随机计算矩阵乘法器,检测算法的计算消耗大大降低.此外,我们通过Vivado HLS实现了基于随机计算的预处理共轭梯度算法.仿真结果表明,该算法在128×8规模的大规模MIMO系统,误比特率为10-6时,和最优检测性能误差小于0.2 dB;而FPGA结果表示,基于随机计算的检测算法不需要采用任何DSP,同时能节省20.7%的寄存器消耗.

     

    Abstract: To decrease the hardware consumption of massive MIMO systems and satisfy the requirement of the fifth-generation wireless communication system, in this paper a low-complexity linear detection algorithm based on the stochastic computing (SC) is proposed. SC transforms the traditional binary number to a sequence consisting of 0 and 1 so that the complicated computation circuit can be realized by simple gate logic, thus saves hardware cost. By adopting the proposed SC-based matrix multiplier, the hardware cost is reduced significantly. Moreover, we realized the SC-based pre-condition conjugate gradient detection algorithm through Vivado HLS. The software simulation result illustrates that the proposed SC algorithm has only 0.2 dB performance degradation compared with optimal detection at BER=10-6 for 128×8 MIMO systems. While the FPGA results show our algorithm requires no DSP and reduced the cost of flipflops by 20.7%.

     

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