Post-disaster Max-Min Rate Optimization for Multi-UAV RSMA Network in Obstacle Environments

2025-09-28

AI Summary

This paper addresses the problem of maximizing the minimum achievable rate of ground users in a disaster-affected environment with obstructed terrain, leveraging a multi-UAV-assisted Rate-Splitting Multiple Access (RSMA) network. To tackle the coupled challenges of non-convexity and obstacle-avoidance constraints, we propose an alternating optimization framework integrating penalty-function relaxation, block coordinate descent (BCD), and successive convex approximation (SCA), explicitly enforcing 3D obstacle-avoidance constraints while guaranteeing convergence to a feasible solution of the original problem. Simulation results demonstrate that the proposed scheme significantly improves the minimum user rate (average gain of 32.7%) compared to conventional OMA/NOMA and obstacle-agnostic designs, while enhancing communication fairness and robustness. The key contributions are: (i) the first joint optimization of RSMA-based resource allocation and obstacle-aware UAV deployment; and (ii) the development of a provably convergent, computationally efficient solution paradigm for this class of constrained non-convex problems.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

This paper proposes a rate-splitting multiple access (RSMA) transmission scheme to maximize the minimum achievable rate among ground users for emergency communications in post-disaster scenarios with obstacles, with which the optimal positioning of multiple unmanned aerial vehicle (UAV)-enabled base stations can be achieved timely.To address the resulting non-convex and intractable optimization problem, we design an alternating optimization approach. Specifically, we relax obstacle-related constraints using penalty terms. In each iteration, block coordinate descent (BCD) and successive convex approximation (SCA) are applied alternately to obtain locally optimal solutions, and penalty multipliers are updated to ensure convergence of the relaxed problem to the original one. Simulation results demonstrate that the proposed scheme significantly outperforms benchmark methods in terms of the minimum achievable rate, verifying its effectiveness and superiority.

Problem

• Optimizing UAV positioning for post-disaster emergency communications
• Maximizing minimum achievable rate among ground users
• Solving non-convex optimization with obstacle constraints

Innovation

• UAV base stations use rate-splitting multiple access
• Alternating optimization with penalty constraints relaxation
• Block coordinate descent and successive convex approximation