Non-Orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach

This article considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. We consider non-orthogonal multi-modal information dissemination, which is based on...

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Veröffentlicht in:IEEE transactions on mobile computing Jg. 23; H. 10; S. 9789 - 9803
Hauptverfasser: Al-Habob, Ahmed A., Tabassum, Hina, Waqar, Omer
Format: Magazine Article
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2024
Schlagworte:
Age-of-information (AoI)
deep reinforcement learning (DRL)
Measurement
meta deep reinforcement learning (meta-DRL)
multi-objective optimization
Optimization
Power demand
Reinforcement learning
Resource management
successive interference cancellation (SIC)
Task analysis
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ISSN:1536-1233, 1558-0660
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Zusammenfassung:This article considers minimizing the age-of-information (AoI) and transmit power consumption in a vehicular network, where a roadside unit (RSU) provides timely updates about a set of physical processes to vehicles. We consider non-orthogonal multi-modal information dissemination, which is based on superposed message transmission from RSU and successive interference cancellation (SIC) at vehicles. The formulated problem is a multi-objective mixed-integer nonlinear programming problem; thus, a Pareto-optimal front is very challenging to obtain. First, we leverage the weighted-sum approach to decompose the multi-objective problem into a set of multiple single-objective sub-problems corresponding to each predefined objective preference weight. Then, we develop a hybrid deep Q-network (DQN)-deep deterministic policy gradient (DDPG) model to solve each optimization sub-problem respective to predefined objective-preference weight. The DQN optimizes the decoding order, while the DDPG solves the continuous power allocation. The model needs to be retrained for each sub-problem. We then present a two-stage meta-multi-objective reinforcement learning solution to estimate the Pareto front with a few fine-tuning update steps without retraining the model for each sub-problem. Simulation results illustrate the efficacy of the proposed solutions compared to the existing benchmarks and that the meta-multi-objective reinforcement learning model estimates a high-quality Pareto frontier with reduced training time.
ISSN:1536-1233
1558-0660
DOI:10.1109/TMC.2024.3367166