An Integrated Communication and Computing Scheme for Wi-Fi Networks based on Generative AI and Reinforcement Learning (2404.13598v1)
Abstract: The continuous evolution of future mobile communication systems is heading towards the integration of communication and computing, with Mobile Edge Computing (MEC) emerging as a crucial means of implementing AI computation. MEC could enhance the computational performance of wireless edge networks by offloading computing-intensive tasks to MEC servers. However, in edge computing scenarios, the sparse sample problem may lead to high costs of time-consuming model training. This paper proposes an MEC offloading decision and resource allocation solution that combines generative AI and deep reinforcement learning (DRL) for the communication-computing integration scenario in the 802.11ax Wi-Fi network. Initially, the optimal offloading policy is determined by the joint use of the Generative Diffusion Model (GDM) and the Twin Delayed DDPG (TD3) algorithm. Subsequently, resource allocation is accomplished by using the Hungarian algorithm. Simulation results demonstrate that the introduction of Generative AI significantly reduces model training costs, and the proposed solution exhibits significant reductions in system task processing latency and total energy consumption costs.
- M. Tang and V. W. S. Wong, “Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems,” IEEE Trans. Mobile Comput., vol. 21, no. 6, pp. 1985-1997, 2022.
- L. Yang, H. Zhang, X. Li, H. Ji, and V. C. M. Leung, “A Distributed Computation Offloading Strategy in Small-Cell Networks Integrated With Mobile Edge Computing,” IEEE/ACM Trans. Netw., vol. 26, no. 6, pp. 2762-2773, 2018.
- B. Dab, N. Aitsaadi, and R. Langar, “Joint Optimization Of Offloading And Resource Allocation Scheme For Mobile Edge Computing,” in Proc. IEEE WCNC, 2019, pp. 1-7.
- T. Alfakih, M. M. Hassan, A. Gumaei, C. Savaglio, and G. Fortino, “Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA,” IEEE Access, vol. 8, pp. 54074-54084, 2020.
- N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y. Liang, and D. I. Kim, “Applications of Deep Reinforcement Learning in Communications and Networking: A Survey,” IEEE Commun. Surveys Tuts., vol. 21, no. 4, pp. 3133-3174, 2019.
- J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep Unsupervised Learning using Nonequilibrium Thermodynamics,” in Proc. ICML, 2015, pp. 2256-2265.
- H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, D. I. Kim, X. Shen, and H. V. Poor, “Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks,” CoRR, vol. abs/2304.03446.99, pp. 1-8, 2023.
- Z. Wang, J. Huang, and M. Zhou, “Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning,” in Proc. ICLR, 2022.
- P. Zhao, H. Tian, C. Qin, and G. Nie, “Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing,” IEEE Access, vol. 5, pp. 11255-11268, 2017.
- W. Zhan, C. Luo, J. Wang, G. Min, and H. Duan, “Deep Reinforcement Learning-Based Computation Offloading in Vehicular Edge Computing,” in Proc. GLOBECOM, 2019, pp. 1-6.
- H. Cao and J. Cai, “Distributed multiuser computation offloading for cloudlet-based mobile cloud computing: A game-theoretic machine learning approach,” IEEE Trans. Veh. Technol., vol. 67, no. 1, pp. 752-764, 2018.
- N. Eshraghi and B. Liang, “Joint Offloading Decision and Resource Allocation with Uncertain Task Computing Requirement,” in Proc. IEEE INFOCOM, Paris, France, Apr. 2019, pp. 1414-1422.
- J. Li, H. Gao, T. Lv, Y. Lu, “Deep Reinforcement Learning Based Computation Offloading and Resource Allocation for MEC,” in Proc. IEEE WCNC, 2018, pp. 1-6.
- J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” in Proc. NeurIPS, 2020, vol. 33, pp. 6840-6851.