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AIRS-assisted Vehicular Networks with Rate-Splitting SWIPT Receivers: Joint Trajectory and Communication Design

Gyoungyoon Nam, Seokhyun Lee, Seongah Jeong

TL;DR

This work studies AIRS-assisted vehicular networks where vehicles employ rate-splitting SWIPT receivers. The authors jointly optimize AIRS trajectory and phase shifts, RSU transmit powers and rate allocation, and the SWIPT power-splitting ratio using an alternating optimization framework enhanced by successive convex approximation and DC techniques. The proposed AO-SCA-DC algorithm tackles a non-convex, multi-constraint problem and demonstrates superior sum-rate performance over partial optimization baselines, with insights into AIRS trajectory behavior and beam alignment. The results suggest practical benefits for dynamic V2X scenarios and point to future extensions toward integrated space-air-ground networks.

Abstract

In this correspondence, we propose to use an intelligent reflective surface (IRS) installed on unmanned aerial vehicle (UAV), referred to as aerial IRS (AIRS), for vehicular networks, where simultaneous wireless information and power transfer (SWIPT) receivers to concurrently allow information decoding (ID) and energy harvesting (EH) are equipped at the battery-limited vehicles. For efficiently supporting the multiple moving vehicles, we adopt rate-splitting multiple access (RSMA) technique. With the aim of maximizing the sum rate of vehicles, we jointly optimize trajectory and phase shift design of AIRS, transmit power and rate allocation for RSMA along with power splitting ratio for SWIPT implementation. Via simulations, the superior performances of the proposed algorithm are validated compared to the conventional partial optimizations.

AIRS-assisted Vehicular Networks with Rate-Splitting SWIPT Receivers: Joint Trajectory and Communication Design

TL;DR

This work studies AIRS-assisted vehicular networks where vehicles employ rate-splitting SWIPT receivers. The authors jointly optimize AIRS trajectory and phase shifts, RSU transmit powers and rate allocation, and the SWIPT power-splitting ratio using an alternating optimization framework enhanced by successive convex approximation and DC techniques. The proposed AO-SCA-DC algorithm tackles a non-convex, multi-constraint problem and demonstrates superior sum-rate performance over partial optimization baselines, with insights into AIRS trajectory behavior and beam alignment. The results suggest practical benefits for dynamic V2X scenarios and point to future extensions toward integrated space-air-ground networks.

Abstract

In this correspondence, we propose to use an intelligent reflective surface (IRS) installed on unmanned aerial vehicle (UAV), referred to as aerial IRS (AIRS), for vehicular networks, where simultaneous wireless information and power transfer (SWIPT) receivers to concurrently allow information decoding (ID) and energy harvesting (EH) are equipped at the battery-limited vehicles. For efficiently supporting the multiple moving vehicles, we adopt rate-splitting multiple access (RSMA) technique. With the aim of maximizing the sum rate of vehicles, we jointly optimize trajectory and phase shift design of AIRS, transmit power and rate allocation for RSMA along with power splitting ratio for SWIPT implementation. Via simulations, the superior performances of the proposed algorithm are validated compared to the conventional partial optimizations.
Paper Structure (8 sections, 15 equations, 5 figures, 1 table, 1 algorithm)

This paper contains 8 sections, 15 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System model
  3. Problem Formulation and Proposed Algorithms
  4. Joint Phase Shifter and Trajectory Optimization
  5. Joint Power and Rate Allocation Optimization
  6. PS Ratio Optimization
  7. Numerical Results
  8. Conclusions

Figures (5)

  • Figure 1: System model of AIRS-assisted vehicular networks with rate-splitting SWIPT receivers
  • Figure 2: The convergence of proposed Algorithm 1
  • Figure 3: The AIRS's optimal trajectory of obtained by proposed Algorithm 1
  • Figure 4: Sum rate versus the different number of AIRS elements
  • Figure 5: Sum rate versus the different mission time