Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum $2$ dB and $19$ times for various M-MIMO system configurations.
翻译:超可靠低时线通信(URLLC)的超可靠和处理性强要求非常严格,使得线性大型多投入-多输出接收器的设计变得非常具有挑战性;最近,巴伊西亚概念被用来提高最低中位方-中程(MMSE)线性接收器的检测可靠性;然而,由于MMSE计划中矩阵反转操作的指数复杂性,悬浮处理时间是一个重大关切问题。本文件提议采用一个迭代MIMIM接收器,该接收器采用巴伊西亚概念和平行干扰取消(PIC)办法,称为线性巴伊斯学习接收器,具有线性复杂性,因为采用最大比率结合(MRC)和决定统计组合(DSC)办法,以避免矩阵反转操作。模拟结果表明,拟议的接收器的位式拉通处理性优于MMSE和最佳巴伊斯货币接收器的性能,最低比MMSE和各种MMMMM-MM-MM-MM-MM-MMM-MMM-MM-M-MM-M-MM-Ms-Ms-Ms-Ms-Ms-Ms-Ms-M-M-M-Ms-Ms-Ms-Ms-M-Ms-Ms-Ms-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M-M