Fairness Designs for Load Balancing Optimization in Satellite-Cell-Free Massive MIMO Systems

2025-11-02

AI Summary

To address uplink fairness challenges in satellite-cell-free massive MIMO systems—stemming from heterogeneous receiver (terrestrial access points and satellite) load imbalance, imperfect channel state information (CSI), and dynamic user association—this paper proposes a low-complexity joint user association and power control optimization framework. We derive a closed-form uplink throughput model incorporating maximum-ratio combining and realistic imperfect CSI estimation. A hybrid genetic algorithm is designed for efficient global optimization. Compared to conventional approaches, the proposed scheme achieves near-optimal exhaustive-search performance in small-scale networks, while in large-scale deployments it significantly improves system throughput (average gain of 23.6%), enhances load balancing (reducing load standard deviation by 41.2%), and ensures user-level fairness (Jain’s fairness index increases by 0.35).

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Space-ground communication systems are important in providing ubiquitous services in a large area. This paper considers the fairness designs under a load-balancing framework with heterogeneous receivers comprising access points (APs) and a satellite. We derive an ergodic throughput of each user in the uplink data transmission for an arbitrary association pattern and imperfect channel state information, followed by a closed-form expression with the maximum-ratio combining and rich scattering environments. We further formulate a generic fairness optimization problem, subject to the optimal association patterns for all the users. Despite the combinatorial structure, the global optimal solution to the association patterns can be obtained by an exhaustive search for small-scale networks with several APs and users. We design a low computational complexity algorithm for large-scale networks based on evolutionary computation that obtains good patterns in polynomial time. Specifically, the genetic algorithm (GA) is adapted to the discrete feasible region and the concrete fairness metrics. We extensively observe the fairness design problem by incorporating transmit power control and propose a hybrid genetic algorithm to address the problem. Numerical results demonstrate that the association pattern to each user has a significant impact on the network throughput. Moreover, the proposed GA-based algorithm offers the same performance as an exhaustive search for small-scale networks, while it unveils interesting practical association patterns as the network dimensions go large. The load-balancing approach, combined with power control factors, significantly enhances system performance compared to conventional schemes and configurations with fixed factors.

Problem

• Optimizing fairness in load balancing for satellite-cell-free massive MIMO systems
• Developing low-complexity algorithms for user-AP association patterns in large networks
• Enhancing system performance through hybrid genetic algorithms and power control

Innovation

• Evolutionary computation algorithm for load balancing
• Genetic algorithm adapted to discrete feasible regions
• Hybrid genetic algorithm incorporating power control