ABSTRACT
Unmanned Aerial Vehicle (UAV) enabled Mobile Edge Computing (MEC) brings the on-demand task computation services close to the user equipment (UE) by reducing the latency and enhancing the quality-of-service (QoS). However, the energy consumption remains a major issue in the system, since both mobile devices (MDs) and UAVs have limited power battery storage. Also in 5G and beyond 5G (B5G) networks, in which UEs' task requests and positions change frequently, stationary edge network implementation may increase the overall energy consumption. This article aims to minimize the overall energy consumption for MEC with Non-Orthogonal Multiple Access (NOMA) underlaying UAV systems. We have used Markov decision process (MDP) to convert the optimization problem into multi-agent reinforcement learning (MARL) problem. Then to achieve optimal policy and reduce the overall energy consumption of the system, we propose a multi-agent federated reinforcement learning (MAFRL) scheme. Simulation results show the effectiveness of the proposed scheme in reducing the overall energy consumption with respect to other state-of-art schemes.
- Ishan Budhiraja, Neeraj Kumar, Sudhanshu Tyagi, and Sudeep Tanwar. 2021. Energy Consumption Minimization Scheme for NOMA-Based Mobile Edge Computation Networks Underlaying UAV. IEEE Systems Journal 15, 4 (May 2021), 5724--5733.Google ScholarCross Ref
- Xiaowen Cao, Jie Xu, and Rui Zhang. 2018. Mobile Edge Computing for Cellular-Connected UAV: Computation Offloading and Trajectory Optimization. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). 1--5.Google Scholar
- Van Dat Tuong, Thanh Phung Truong, Anh-Tien Tran, Arooj Masood, Demeke Shumeye Lakew, Chunghyun Lee, Yunseong Lee, and Sungrae Cho. 2020. Delay-Sensitive Task Offloading for Internet of Things in Nonorthogonal Multiple Access MEC Networks. In 2020 International Conference on Information and Communication Technology Convergence (ICTC). 597--599.Google Scholar
- Xianbang Diao, Jianchao Zheng, Yuan Wu, Yueming Cai, and Alagan Anpalagan. [n. d.]. Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing. IEEE Access (Aug. [n. d.]).Google Scholar
- Zhiguo Ding, Derrick Wing Kwan Ng, Robert Schober, and H. Vincent Poor. 2018. Delay Minimization for NOMA-MEC Offloading. IEEE Signal Processing Letters 25, 12 (Dec. 2018), 1875--1879.Google ScholarCross Ref
- Yao Du, Kezhi Wang, Kun Yang, and Guopeng Zhang. 2018. Energy-Efficient Resource Allocation in UAV Based MEC System for IoT Devices. In 2018 IEEE Global Communications Conference (GLOBECOM). 1--6.Google Scholar
- Meng Hua, Yongming Huang, Yi Wang, Qingqing Wu, Haibo Dai, and Lyuxi Yang. 2018. Energy Optimization for Cellular-Connected Multi-UAV Mobile Edge Computing Systems with Multi-Access Schemes. Journal of Communications and Information Networks 3, 4 (Dec. 2018), 33--44.Google ScholarCross Ref
- Meng Hua, Yi Wang, Zhengming Zhang, Chunguo Li, Yongming Huang, and Luxi Yang. 2018. Optimal Resource Partitioning and Bit Allocation for UAV-Enabled Mobile Edge Computing. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall). 1--6.Google Scholar
- Zoubeir Mlika and Soumaya Cherkaoui. 2021. Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles. IEEE Network 35, 3 (May/June 2021), 132--138.Google ScholarCross Ref
- Liping Qian, Yuan Wu, Fuli Jiang, Ningning Yu, Weidang Lu, and Bin Lin. 2021. NOMA Assisted Multi-Task Multi-Access Mobile Edge Computing via Deep Reinforcement Learning for Industrial Internet of Things. IEEE Transactions on Industrial Informatics 17, 8 (Aug. 2021), 5688--5698.Google ScholarCross Ref
- Feng Wang, Jie Xu, and Zhiguo Ding. 2017. Optimized Multiuser Computation Offloading with Multi-Antenna NOMA. In 2017 IEEE Globecom Workshops (GC Wkshps). 1--7.Google Scholar
- Jiao Zhang, Li Zhou, Qi Tang, Edith C.-H. Ngai, Xiping Hu, Haitao Zhao, and Jibo Wei. 2019. Stochastic Computation Offloading and Trajectory Scheduling for UAV-Assisted Mobile Edge Computing. IEEE Internet of Things Journal 6, 2 (Dec. 2019), 3688--3699.Google Scholar
- Tantan Zhao, Fan Li, and Lijun He. 2022. DRL-Based Joint Resource Allocation and Device Orchestration for Hierarchical Federated Learning in NOMA-Enabled Industrial IoT. IEEE Transactions on Industrial Informatics (April 2022), 1--1.Google Scholar
- Bincheng Zhu, Kaikai Chi, Jiajia Liu, Keping Yu, and Shahid Mumtaz. 2022. Efficient Offloading for Minimizing Task Computation Delay of NOMA-Based Multiaccess Edge Computing. IEEE Transactions on Communications 70, 5 (May 2022), 3186--3203.Google ScholarCross Ref
Recommendations
NOMA-based energy efficient resource allocation in wireless energy harvesting sensor networks
AbstractNon Orthogonal Multiple Access (NOMA) and energy harvesting are two widely used concepts to improve the performance of Wireless Sensor Networks. In this paper, we consider a NOMA-based Wireless Energy Harvesting Sensor Network (WEHSN), ...
Energy management scheme for wireless powered D2D users with NOMA underlaying full duplex UAV
DroneCom '20: Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and BeyondDevice-to-Device (D2D) communications underlaying Unmanned aerial vehicle (UAV) with its mobility extend the coverage and improve the data rate. In this paper, we propose an energy management scheme for wireless powered D2D users with NOMA underlaying ...
Joint optimization of trajectory and resource allocation in cellular-connected multi-UAV MEC networks
AbstractUnmanned aerial vehicles (UAVs)-based mobile edge computing (MEC) has been introduced as a promising model for enhanced edge communications in the future of integrated air-sky-earth and sea communications. However, in UAV-assisted mobile edge ...
Comments