Information-Optimum Approximate Message Passing for Quantized Massive MIMO Detection

Abstract

We propose an information-optimum approximate message passing (AMP) for quantized massive multi-input multi-output (MIMO) signal detection. A well-known strategy for realizing low-complexity and high-accuracy massive multi-user detection (MUD) is AMP-based belief propagation (BP). However, when internal operations are conducted with double-precision arithmetic, large memory occupancy and severe processing delay are inevitable in the actual massive MIMO implementation. To address this issue, we replace all operations with a simple look-up table (LUT) search where all messages exchanged between each iteration process are unsigned integers. That is, the proposed signal detection is performed using only simple integer arithmetic. The LUT is designed offline using an information-bottleneck (IB) method, and the probability distribution of messages at each iteration step is required for determining the quantization threshold tracked by discrete density evolution (DDE). Computer simulations demonstrate the validity of the IB LUT-based AMP in terms of bit error rate (BER) performance and memory occupancy. The proposed method allows quantizing the AMP detector with fewer bits while maintaining similar performances, such as that of a typical AMP with double-precision.