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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2402.12260 |
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| _version_ | 1866913237180612608 |
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| author | Habob, A. A. Tabassum, H. Waqar, O. |
| author_facet | Habob, A. A. Tabassum, H. Waqar, O. |
| contents | This paper 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12260 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach Habob, A. A. Tabassum, H. Waqar, O. Machine Learning This paper 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. |
| title | Non-orthogonal Age-Optimal Information Dissemination in Vehicular Networks: A Meta Multi-Objective Reinforcement Learning Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.12260 |