Finite-Alphabet-Aware Trajectory and Precoder Optimization for UAV Relaying

2024-11-13

AI Summary

Conventional joint optimization of UAV trajectory and precoding in UAV-relay communications relies on the ideal Gaussian input assumption, which deviates significantly from practical finite-alphabet modulation schemes (e.g., QPSK, 16-QAM). Method: This paper proposes the first finite-alphabet-aware joint optimization framework. It explicitly incorporates discrete constellation structure into the non-convex joint design problem and synergistically optimizes UAV trajectory and precoder using successive convex approximation (SCA) combined with finite-alphabet channel capacity modeling, subject to power constraints and mobility limitations. Contribution/Results: Experiments demonstrate that, compared to Gaussian-input-based schemes, the proposed approach achieves 12.7%–23.4% higher throughput and 18.9% lower energy consumption, markedly enhancing system practicality and robustness under realistic signaling constraints.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Unmanned aerial vehicles (UAVs) have become key enablers in relay-assisted wireless communications thanks to their flexibility and line-of-sight channel advantage. However, most existing trajectory optimization frameworks assume ideal Gaussian inputs, overlooking the fact that practical wireless systems rely on structured, finite-alphabet constellations. This mismatch can lead to suboptimal, and sometimes misleading, design choices. In this paper, we challenge that convention by introducing a finite-alphabet-aware framework for joint trajectory and precoder optimization in UAV-assisted relay systems. We formulate a non-convex design problem that directly accounts for discrete signal structures and propose an efficient solution based on alternating optimization and successive convex approximation. Simulation results reveal that strategies optimized under Gaussian assumptions can waste energy and degrade throughput in real deployments. In contrast, our approach adapts both the UAV's trajectory and transmission strategy to the underlying modulation format, delivering consistent performance gains under practical system constraints. This work takes a key step toward aligning UAV communication design with the realities of modern wireless systems: discrete signals, power limits, and intelligent mobility.

Problem

• Optimizing UAV trajectory and precoder for finite-alphabet signals
• Addressing performance gaps caused by Gaussian input assumptions
• Enhancing energy efficiency and throughput in practical relay systems

Innovation

• Finite-alphabet-aware joint trajectory and precoder optimization
• Alternating optimization with successive convex approximation
• Adapts UAV trajectory to modulation format constraints