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

TL;DR

This work tackles the challenge of scalable resource management in XL-MIMO where optimizing instantaneous CSI is prohibitive. It derives closed-form deterministic uplink SINR expressions for both centralized and distributed operation under spatially correlated Rician fading with MMSE channel estimation, depending only on long-term statistics. Building on these results, it develops statistical-CSI based algorithms for joint subarray selection, user scheduling, and pilot assignment to maximize the minimum spectral efficiency while exploiting visibility-region sparsity for more aggressive pilot reuse. Numerical results validate the accuracy of the SINR approximations and show substantial fairness and throughput gains compared to baselines with explicit reductions in complexity. The framework offers a practical pathway to scalable 6G XL-MIMO designs and can be extended to multi-cell, RIS-enhanced, and near-field 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.

Figures (6)

• Figure 1: Mean SE versus number of selected subarrays $L_k$.
• Figure 2: MNAE of the global SE approximation for distributed operation versus subarray antennas.
• Figure 3: MNAE of the deterministic SE approximations vs. number of UEs, contrasting ergodic and asymptotic approximations under varying spatial correlation and pilot reuse scenarios.
• Figure 4: MNAE of the asymptotic SE approximation versus subarray antennas ($M$).
• Figure 5: Minimum per-user SE versus number of scheduled UEs.

Theorems & Definitions (13)

• Proposition 1: Global SINR Approximation and Optimal Weights
• proof
• Theorem 1: Ergodic SINR
• proof
• Corollary 1
• proof
• Theorem 2: Asymptotic SINR
• proof
• Corollary 2
• proof