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Much stringent reliability and processing latency requirements inultra-reliable-low-latency-communication (URLLC) traffic make the design oflinear massive multiple-input-multiple-output (M-MIMO) receivers becomes verychallenging. Recently, Bayesian concept has been used to increase the detectionreliability in minimum-mean-square-error (MMSE) linear receivers. However, thelatency processing time is a major concern due to the exponential complexity ofmatrix inversion operations in MMSE schemes. This paper proposes an iterativeM-MIMO receiver that is developed by using a Bayesian concept and a parallelinterference cancellation (PIC) scheme, referred to as a linear Bayesianlearning (LBL) receiver. PIC has a linear complexity as it uses a combinationof maximum ratio combining (MRC) and decision statistic combining (DSC) schemesto avoid matrix inversion operations. Simulation results show that thebit-error-rate (BER) and latency processing performances of the proposedreceiver outperform the ones of MMSE and best Bayesian-based receivers byminimum $2$ dB and $19$ times for various M-MIMO system configurations.

A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems