RSMA-Enhanced Data Collection in RIS-Assisted Intelligent Consumer Transportation Systems

2025-09-11

AI Summary

This paper investigates the joint optimization of downlink wireless power transfer and uplink data offloading in RIS-empowered intelligent vehicular transportation systems, aiming to maximize the minimum data processing volume across roadside unit (RSU) pairs. To address this, we propose a hybrid multiple-access framework integrating rate-splitting multiple access (RSMA) and time-division multiple access (TDMA). The framework jointly optimizes RIS phase shifts for both downlink and uplink, DC/RSU transmit powers, RSU computational resource allocation, and time-slot scheduling. To tackle the inherent non-convexity, we design an efficient algorithm based on alternating optimization and successive rank-one constrained relaxation. Simulation results demonstrate that the proposed scheme significantly enhances the worst-link data processing performance across diverse scenarios, outperforming existing benchmark approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Data transparency and provenance

Abstract

This paper investigates the data collection enhancement problem in a reconfigurable intelligent surface (RIS)-empowered intelligent consumer transportation system (ICTS). We propose a novel framework where a data center (DC) provides energy to pre-configured roadside unit (RSU) pairs during the downlink stage. While in the uplink stage, these RSU pairs utilize a hybrid rate-splitting multiple access (RSMA) and time-division multiple access (TDMA) protocol to transmit the processed data to the DC, while simultaneously performing local data processing using the harvested energy. Our objective is to maximize the minimal processed data volume of the RSU pairs by jointly optimizing the RIS downlink and uplink phase shifts, the transmit power of the DC and RSUs, the RSU computation resource allocation, and the time slot allocation. To address the formulated non-convex problem, we develop an efficient iterative algorithm integrating alternating optimization and sequential rank-one constraint relaxation methods. Extensive simulations demonstrate that the proposed algorithm significantly outperforms baseline schemes under diverse scenarios, validating its effectiveness in enhancing the data processing performance for intelligent transportation applications.

Problem

• Enhancing data collection in RIS-assisted intelligent transportation systems
• Maximizing minimal processed data volume via joint optimization
• Optimizing RIS phase shifts, power, computation, and time allocation

Innovation

• RSMA-TDMA hybrid protocol for data transmission
• Joint optimization of RIS phase shifts and power
• Alternating optimization with constraint relaxation algorithm