Uplink Signal Detection via Look-Up Table-Based AMP for Massive MIMO Systems

Abstract

We propose a novel look-up table (LUT)-based multi-user detection (MUD) algorithm to imitate approximate message passing (AMP) for uplink signal detection in massive multi-user multi-input multi-output (MU-MIMO) systems. When AMP is implemented with double-precision arithmetic, the dramatic increase in power consumption and process latency becomes a major barrier to practical application, as the system scale expands. To circumvent this issue, we design a novel referential AMP detector composed of many small LUTs cascaded hierarchically, where only informative integer-valued messages are exchanged on a factor graph (FG). Our method tracks the discrete distribution of the LUT outputs according to the multi-layer structure, and successively determines non-uniform quantization thresholds of the LUTs, using an unsupervised learning approach. Specifically, the thresholds at each level of the hierarchy, i.e., layer, are optimized by clustering with the Lloyd-Max algorithm with initial values given by the k-means++ method, in order to minimize the performance degradation caused by quantization errors. The efficacy of the proposed referential AMP detector is confirmed by numerical simulations, which show that the proposed method significantly outperforms the state-of-the-art (SotA) in terms of the bit error rate (BER) performance in various channel models. The results also indicate the referential AMP is robust to changes in wireless channels, and then clarify the reason in terms of the algorithmic structure.

Keywords

• Massive MIMO detection
• approximate message passing
• quantization
• look-up table
• unsupervised learning