Joint Subarray Selection, User Scheduling, and Pilot Assignment for XL-MIMO

TL;DR

This paper introduces a novel framework for XL-MIMO that uses long-term channel statistics to optimize subarray selection, user scheduling, and pilot assignment, significantly improving fairness and throughput.

Contribution

It derives deterministic SINR expressions based on long-term statistics and develops algorithms for resource management that outperform traditional methods.

Findings

SINR approximations are highly accurate

Algorithms improve fairness among users

Significant throughput gains in crowded scenarios

Abstract

Extra-large scale MIMO (XL-MIMO) is a key technology for meeting sixth-generation (6G) requirements for high-rate connectivity and uniform quality of service (QoS); however, its deployment is challenged by the prohibitive complexity of resource management based on instantaneous channel state information (CSI). To address this intractability, this work derives novel closed-form deterministic signal-to-interference-plus-noise ratio (SINR) expressions for both centralized and distributed uplink operations. Valid for Rician fading channels with minimum mean square error (MMSE) receive combining and MMSE channel estimation, these expressions depend exclusively on long-term channel statistics, providing a tractable alternative to computationally expensive instantaneous CSI-driven optimization. Building on these results, we develop statistical-CSI-based algorithms for joint subarray selection, users scheduling, and pilot assignment, leveraging the derived SINR approximations to maximize the minimum spectral efficiency (SE) among scheduled users while preserving computational tractability. The proposed framework exploits the spatial sparsity of user equipment (UE) visibility regions (VRs) to enable more aggressive pilot reuse than is possible in conventional massive MIMO. Numerical results validate the high accuracy of the derived SINR approximations and demonstrate that the proposed algorithms significantly enhance fairness and throughput in crowded scenarios.