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

Haoyang Di, Xiaodong Zhu, Yulin Shao

TL;DR

This work addresses the gap between theory and practice by maximizing the average rate of a UAV-assisted decode-and-forward relay system under finite-alphabet inputs. It introduces a finite-alphabet-aware optimization (FAAO) framework that jointly designs UAV trajectory and transmitter precoders using alternating optimization and successive convex approximation. By constructing convex surrogates for the non-convex mutual information and coupling terms, FAAO achieves a locally optimal solution that aligns with discrete modulation constraints. The results demonstrate energy- and throughput-related gains over Gaussian-input designs and emphasize the importance of realistic signal models in UAV-enabled communications.

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.

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

TL;DR

This work addresses the gap between theory and practice by maximizing the average rate of a UAV-assisted decode-and-forward relay system under finite-alphabet inputs. It introduces a finite-alphabet-aware optimization (FAAO) framework that jointly designs UAV trajectory and transmitter precoders using alternating optimization and successive convex approximation. By constructing convex surrogates for the non-convex mutual information and coupling terms, FAAO achieves a locally optimal solution that aligns with discrete modulation constraints. The results demonstrate energy- and throughput-related gains over Gaussian-input designs and emphasize the importance of realistic signal models in UAV-enabled communications.

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.

Paper Structure

This paper contains 10 sections, 2 theorems, 34 equations, 3 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Methodology
  6. Optimization of the UAV trajectory
  7. Optimization of the precoders
  8. The overall optimization algorithm
  9. Simulation Results
  10. Conclusion

Key Result

Proposition 1

$F_{sj}({n})$ can be convexly approximated as where $\mathbf{w}^{(i)}({n}), \forall {n}$, denotes the trajectory solution obtained in the $(i-1)$-th iteration of the SCA procedure.

Figures (3)

  • Figure 1: The MIMO UAV-assisted DF relay system comprising a UAV, a ground BS, and a GU.
  • Figure 2: UAV trajectories of different communication period $\emph{T}$, $\emph{W}=20$ dBm.
  • Figure 3: Information rate versus $\emph{W}$ under different schemes, $\emph{T}=50$ seconds.

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2