Vector Similarity Search-Based MCS Selection in Massive Multi-User MIMO-OFDM

TL;DR

This paper introduces a vector similarity search-based framework for MCS selection in massive MU-MIMO-OFDM systems, leveraging offline vector databases and GPU-accelerated ANN search for fast MI prediction, enhancing throughput.

Contribution

It presents a novel VSS-based MI prediction scheme that enables rapid, accurate MCS selection in iterative detectors, improving system performance in 5G NR settings.

Findings

Significant throughput improvements demonstrated in simulations.

Effective MI prediction with GPU-accelerated ANN search.

Enhanced translation of detection gains into system-level performance.

Abstract

This paper proposes a novel modulation and coding scheme (MCS) selection framework that integrates mutual information (MI) prediction based on vector similarity search (VSS) for massive multi-user multiple-input multiple-output orthogonal frequency-division multiplexing (MU-MIMO-OFDM) systems with advanced uplink multi-user detection (MUD). The framework performs MCS selection at the transport block (TB)-level MI and establishes the mapping from post-MUD MI to post-decoding block error rate (BLER) using a prediction function generated from extrinsic information transfer (EXIT) curves. A key innovation is the VSS-based MI prediction scheme, which addresses the challenge of analytically predicting MI in iterative detectors such as expectation propagation (EP). In this scheme, an offline vector database (VDB) stores feature vectors derived from channel state information (CSI) and average received signal-to-noise ratio (SNR), together with corresponding MI values achieved with advanced MUD. During online operation, an approximate nearest neighbor (ANN) search on graphics processing units (GPUs) enables ultra-fast and accurate MI prediction, effectively capturing iterative detection gains. Simulation results under fifth-generation new radio (5G NR)-compliant settings demonstrate that the proposed framework significantly improves both system and user throughput, ensuring that the detection gains of advanced MUD are faithfully translated into tangible system-level performance improvements.