Summary of Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-user Power Allocation, by Kangwei Qi et al.
Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation
by Kangwei Qi, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address a crucial challenge in vehicular edge computing (VEC), which enables vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. The quality of communication links between vehicles and edge devices is severely deteriorated due to obstacles like buildings, impeding the offloading process. To overcome this hurdle, the authors propose using Reconfigurable Intelligent Surfaces (RIS) to provide alternative communication pathways for vehicular communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. The paper presents an optimal scheme for local execution power, offloading power, and RIS phase-shift, taking into account random task arrivals and channel variations. To solve this problem, the authors propose a deep reinforcement learning (DRL) framework combining DDPG and MADDPG algorithms to optimize RIS phase-shift coefficients and power allocation of vehicle users (VU). The simulation results show that their proposed scheme outperforms traditional centralized DDPG, TD3, and some stochastic schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers solve a problem in vehicular edge computing. Vehicles can do complex tasks or send them to nearby devices. But if there are obstacles like buildings, it’s hard for the vehicles to communicate with those devices. The authors suggest using special surfaces that can change how they reflect signals to help vehicles communicate better. They came up with an idea for how to use these surfaces and how to make decisions about when to do tasks locally or send them away. To figure out this problem, they used a new way of learning called deep reinforcement learning. This method helps machines learn by trying different actions and seeing what works best. The results show that their solution is better than others. |
Keywords
* Artificial intelligence * Reinforcement learning
