Low-Complexity, Space Splitting-based User Selection in MU-MIMO for Massive Connectivity and AI-Native Traffic

TL;DR

This paper proposes a novel, highly scalable user selection algorithm for MU-MIMO systems that significantly reduces computational complexity while maintaining high spectral efficiency, addressing challenges in dense, latency-critical wireless scenarios.

Contribution

The paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a parallelizable method that overcomes scalability barriers in MU-MIMO user selection for massive connectivity.

Findings

SS-US reduces computational complexity by over three orders of magnitude.

SS-US achieves spectral efficiency comparable to state-of-the-art methods.

Simulation results validate SS-US across diverse configurations and conditions.

Abstract

The rise of Artificial Intelligence (AI)-driven services, machine-type communications, and massive Internet of Things (IoT) deployments is reshaping wireless traffic toward dense, uplink-oriented, bursty, and latency-critical patterns. In these regimes, Multi-User Multiple-Input Multiple-Output (MU-MIMO) is essential to support massive concurrent connectivity through spatial multiplexing. However, the need for frequent, low-latency scheduling decisions exposes fundamental scalability barriers in existing user selection approaches. The inherently combinatorial nature of MU-MIMO user selection leads computational complexity to grow rapidly with both the number of candidate users and spatial layers, rendering existing near-optimal heuristic methods impractical in dense and highly dynamic scenarios. This paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a complexity barrier-breaking, massively parallelizable method that departs from subset-based selection by constructing orthonormal spatial bases and independently matching users to spatial directions. Simulation results across diverse MIMO configurations, channel conditions, and user densities show that SS-US reduces computational complexity by over three orders of magnitude while achieving spectral efficiency comparable to state-of-the-art practical baselines.