Channel-Correlation-Based Access Point Selection and Pilot Power Allocation for Cell-Free Massive MIMO

TL;DR

This paper introduces a novel dynamic AP selection and pilot power allocation framework for cell-free massive MIMO systems, improving spectral efficiency and reducing interference through correlation-based clustering and optimized pilot power strategies.

Contribution

It presents a hierarchical correlation-based clustering algorithm for AP grouping and a weighted sum-rate maximization approach for pilot power allocation, enhancing scalability and performance.

Findings

Significant spectral efficiency gains demonstrated.

Faster convergence compared to benchmark schemes.

Effective in high-density multi-user scenarios.

Abstract

This paper proposes a dynamic access point (AP) selection and pilot power allocation (DAPPA) framework for uplink cell-free massive multiple-input multiple-output (CFmMIMO) systems, aiming to mitigate inter-user interference and improve overall spectral efficiency (SE). A hierarchical correlation-based clustering algorithm is developed to group APs according to their channel correlation, enabling each user to be associated with APs that simultaneously provide strong channel gains and low mutual correlation. This association ensures reliable connectivity, maximizes coherent combining gains, and reduces inter-user interference, while also allowing the number of AP clusters to be adjusted flexibly, without the need to reorganize the network completely. By maintaining links to low-correlated APs, the proposed scheme reduces the need for frequent channel state information (CSI) estimation and minimizes network-wide update overhead. To enhance scalability, a user-capacity constraint per AP is incorporated, preventing hardware overload and alleviating the effects of pilot reuse. Furthermore, an effective pilot power allocation strategy is introduced to boost the signal-to-interference-plus-noise ratio (SINR) during channel training. This is formulated as a weighted sum-rate maximization (WSRM) problem and solved iteratively using a quadratic transform, which enables efficient optimization while ensuring fairness and high-quality service across all users. Numerical results demonstrate that the proposed method delivers significant SE gains, maintains performance in high-density multi-user scenarios, and converges faster than benchmark schemes.