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

This research project investigates a novel look-up table (LUT)-based multi-user detection (MUD) framework designed to emulate the approximate message passing (AMP) algorithm for uplink signal detection in massive multi-user multi-input multi-output (MU-MIMO) systems. Standard double-precision AMP implementations suffer from high power consumption and processing latency as system scales expand. To address these computational bottlenecks, this research analyzes a referential AMP detector constructed from hierarchically cascaded, low-complexity LUTs that exchange integer-valued messages over a factor graph. The methodology tracks the discrete distribution of the LUT outputs across multiple layers and dynamically determines non-uniform quantization thresholds using an unsupervised learning approach. Specifically, the Lloyd-Max clustering algorithm, initialized via the k-means++ method, is

utilized to optimize thresholds at each hierarchical level, thereby minimizing performance degradation from quantization errors. Through comprehensive simulation and modeling support, the project evaluates the bit error rate (BER), convergence rate, and computational complexity of the proposed LUT-AMP detector against conventional state-of-the-art detection schemes under varying channel conditions and antenna configurations.

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