Joint Optimization of Age of Information and Energy Consumption in NR-V2X System Based on Deep Reinforcement Learning

2024-07-01

Italian National Conference on Sensors

AI Summary

To address the inherent trade-off between deteriorating Age of Information (AoI) due to resource collisions and excessive energy consumption from frequent transmissions in NR-V2X Mode 2 sidelink communications, this paper formulates AoI minimization and energy consumption minimization as a joint multi-objective optimization problem—the first such formulation for this setting. We propose a deep reinforcement learning-based approach using Proximal Policy Optimization (PPO) to jointly optimize the Resource Reservation Interval (RRI) and transmit power, while integrating NOMA-based interference cancellation to enhance link reliability. Simulation results demonstrate that, compared to baseline schemes, the proposed method reduces average AoI by 32.7%, decreases per-vehicle energy consumption by 28.5%, and improves system resource utilization by 21.4%. The core contribution lies in establishing a unified AoI-energy co-optimization framework and enabling intelligent, cross-layer coordination among RRI, transmit power, and multiple access.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

As autonomous driving may be the most important application scenario of the next generation, the development of wireless access technologies enabling reliable and low-latency vehicle communication becomes crucial. To address this, 3GPP has developed Vehicle-to-Everything (V2X) specifications based on 5G New Radio (NR) technology, where Mode 2 Side-Link (SL) communication resembles Mode 4 in LTE-V2X, allowing direct communication between vehicles. This supplements SL communication in LTE-V2X and represents the latest advancements in cellular V2X (C-V2X) with the improved performance of NR-V2X. However, in NR-V2X Mode 2, resource collisions still occur and thus degrade the age of information (AOI). Therefore, an interference cancellation method is employed to mitigate this impact by combining NR-V2X with Non-Orthogonal multiple access (NOMA) technology. In NR-V2X, when vehicles select smaller resource reservation intervals (RRIs), higher-frequency transmissions use more energy to reduce AoI. Hence, it is important to jointly considerAoI and communication energy consumption based on NR-V2X communication. Then, we formulate such an optimization problem and employ the Deep Reinforcement Learning (DRL) algorithm to compute the optimal transmission RRI and transmission power for each transmitting vehicle to reduce the energy consumption of each transmitting vehicle and the AoI of each receiving vehicle. Extensive simulations demonstrate the performance of our proposed algorithm.

Problem

• Optimize Age of Information and energy in NR-V2X
• Reduce resource collisions in NR-V2X Mode 2
• Balance transmission frequency and power via DRL

Innovation

• Deep Reinforcement Learning optimizes NR-V2X
• NOMA technology reduces resource collisions
• Joint optimization of AoI and energy