Outer Loop Link Adaptation Based on User Multiplexing for Generalized Approximate Message Passing in Massive MIMO

This paper proposes an outer loop link adaptation (OLLA) algorithm for massive multi-user multi-input multioutput (MIMO) systems that employs uplink multi-user detection (MUD) based on generalized approximate message passing (GAMP). The contribution aims to improve uplink system throughput performance for future beyond-fifth-generation mobile communication systems by designing a novel scheduler that can select spatially multiplexed user equipment (UE) devices and their modulation and coding schemes (MCSs), considering the high detection accuracy provided by the GAMP-based MUD. To achieve this, we propose an OLLA algorithm that accurately predicts the signal-to-interference and noise power ratio (SINR) that each UE can achieve after the GAMP-based MUD. Specifically, the proposed method can dynamically optimize the scheduler according to the iterative detection characteristics of GAMP by introducing a mechanism to correct the predicted SINR separately for each combination of spatially multiplexed UEs. System-level simulation results indicate that adjusting our OLLA algorithm achieves a 50% higher throughput than the conventional OLLA algorithm when applied to GAMP.