Energy Minimization for Federated Learning Based Radio Map Construction
This paper studies an unmanned aerial vehicle (UAV)-enabled communication network, in which the UAV acts as an air relay serving multiple ground users (GUs) to jointly construct an accurate radio map or channel knowledge maps (CKM) through a federated learning (FL) algorithm. Radio map or CKM is a s...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10662910/ |
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| author | Fahui Wu Yunfei Gao Lin Xiao Dingcheng Yang Jiangbin Lyu |
| author_facet | Fahui Wu Yunfei Gao Lin Xiao Dingcheng Yang Jiangbin Lyu |
| author_sort | Fahui Wu |
| collection | DOAJ |
| description | This paper studies an unmanned aerial vehicle (UAV)-enabled communication network, in which the UAV acts as an air relay serving multiple ground users (GUs) to jointly construct an accurate radio map or channel knowledge maps (CKM) through a federated learning (FL) algorithm. Radio map or CKM is a site-specific database that contains detailed channel-related information for specific locations. This information includes channel power gains, shadowing, interference, and angles of arrival (AoA) and departure (AoD), all of which are crucial for enabling environment-aware wireless communications. Because the wireless communication network has limited resource blocks (RBs), only a subset of users can be selected to transmit the model parameters at each iteration. Since the FL training process requires multiple transmission model parameters, the energy limitation of the wireless device will seriously affect the quality of the FL result. In this sense, the energy consumption and resource allocation have a significance to the final FL training result. We formulate an optimization problem by jointly considering user selection, wireless resource allocation, and UAV deployment, with the goal of minimizing the computation energy and wireless transmission energy. To solve the problem, we first propose a probabilistic user selection mechanism to reduce the total number of FL iterations, whereby the users who have a larger impact on the global model in each iteration are more likely to be selected. Then the convex optimization technique is utilized to optimize bandwidth allocation. Furthermore, to further save communication transmission energy, we use deep reinforcement learning (DRL) to optimize the deployment location of the UAV. The DRL-based method enables the UAV to learn from its interaction with the environment and ascertain the most energy-efficient deployment locations through an evaluation of energy consumption during the training process. Finally, the simulation results show that our proposed algorithm can reduce the total energy consumption by nearly 38%, compared to the standard FL algorithm. |
| format | Article |
| id | doaj-art-c01a70d7b6894d8faa9f60ac4cc4ebd5 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-c01a70d7b6894d8faa9f60ac4cc4ebd52025-08-20T02:53:10ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-0121248126410.1109/TMLCN.2024.345321210662910Energy Minimization for Federated Learning Based Radio Map ConstructionFahui Wu0https://orcid.org/0000-0001-8653-8079Yunfei Gao1https://orcid.org/0000-0001-8869-5625Lin Xiao2https://orcid.org/0000-0001-5608-3106Dingcheng Yang3https://orcid.org/0000-0001-5313-4481Jiangbin Lyu4https://orcid.org/0000-0001-5609-7647Information Engineering School, Nanchang University, Nanchang, ChinaSchool of Electronic Information, Wuhan University, Wuhan, ChinaInformation Engineering School, Nanchang University, Nanchang, ChinaInformation Engineering School, Nanchang University, Nanchang, ChinaSchool of Informatics, Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University, Xiamen, ChinaThis paper studies an unmanned aerial vehicle (UAV)-enabled communication network, in which the UAV acts as an air relay serving multiple ground users (GUs) to jointly construct an accurate radio map or channel knowledge maps (CKM) through a federated learning (FL) algorithm. Radio map or CKM is a site-specific database that contains detailed channel-related information for specific locations. This information includes channel power gains, shadowing, interference, and angles of arrival (AoA) and departure (AoD), all of which are crucial for enabling environment-aware wireless communications. Because the wireless communication network has limited resource blocks (RBs), only a subset of users can be selected to transmit the model parameters at each iteration. Since the FL training process requires multiple transmission model parameters, the energy limitation of the wireless device will seriously affect the quality of the FL result. In this sense, the energy consumption and resource allocation have a significance to the final FL training result. We formulate an optimization problem by jointly considering user selection, wireless resource allocation, and UAV deployment, with the goal of minimizing the computation energy and wireless transmission energy. To solve the problem, we first propose a probabilistic user selection mechanism to reduce the total number of FL iterations, whereby the users who have a larger impact on the global model in each iteration are more likely to be selected. Then the convex optimization technique is utilized to optimize bandwidth allocation. Furthermore, to further save communication transmission energy, we use deep reinforcement learning (DRL) to optimize the deployment location of the UAV. The DRL-based method enables the UAV to learn from its interaction with the environment and ascertain the most energy-efficient deployment locations through an evaluation of energy consumption during the training process. Finally, the simulation results show that our proposed algorithm can reduce the total energy consumption by nearly 38%, compared to the standard FL algorithm.https://ieeexplore.ieee.org/document/10662910/Energy efficiencyfederated learningradio mapprobability user selectionresource allocationunmanned aerial vehicle (UAV) |
| spellingShingle | Fahui Wu Yunfei Gao Lin Xiao Dingcheng Yang Jiangbin Lyu Energy Minimization for Federated Learning Based Radio Map Construction IEEE Transactions on Machine Learning in Communications and Networking Energy efficiency federated learning radio map probability user selection resource allocation unmanned aerial vehicle (UAV) |
| title | Energy Minimization for Federated Learning Based Radio Map Construction |
| title_full | Energy Minimization for Federated Learning Based Radio Map Construction |
| title_fullStr | Energy Minimization for Federated Learning Based Radio Map Construction |
| title_full_unstemmed | Energy Minimization for Federated Learning Based Radio Map Construction |
| title_short | Energy Minimization for Federated Learning Based Radio Map Construction |
| title_sort | energy minimization for federated learning based radio map construction |
| topic | Energy efficiency federated learning radio map probability user selection resource allocation unmanned aerial vehicle (UAV) |
| url | https://ieeexplore.ieee.org/document/10662910/ |
| work_keys_str_mv | AT fahuiwu energyminimizationforfederatedlearningbasedradiomapconstruction AT yunfeigao energyminimizationforfederatedlearningbasedradiomapconstruction AT linxiao energyminimizationforfederatedlearningbasedradiomapconstruction AT dingchengyang energyminimizationforfederatedlearningbasedradiomapconstruction AT jiangbinlyu energyminimizationforfederatedlearningbasedradiomapconstruction |