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Optimizing the deployment of multiple AeBSs to maximize system energy efficiency is currently a prominent and actively researched topic in the AeBS-assisted edge-cloud computing network. In this paper, we deploy AeBSs using multi-agent deep reinforcement learning (MADRL). We describe the multi-AeBS deployment challenge as a decentralized partially observable Markov decision process (Dec-POMDP), taking into consideration the constrained observation range of AeBSs. The hypergraph convolution mix deep deterministic policy gradient (HCMIX-DDPG) algorithm is designed to maximize the system energy efficiency. The proposed algorithm uses the value decomposition framework to solve the lazy agent problem, and hypergraph convolutional (HGCN) network is introduced to strengthen the cooperative relationship between agents. Simulation results show that the suggested HCMIX-DDPG algorithm outperforms alternative baseline algorithms in the multi-AeBS deployment scenario.<\/jats:p>","DOI":"10.1186\/s13677-023-00556-x","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T12:02:10Z","timestamp":1701864130000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Hypergraph convolution mix DDPG for multi-aerial base station deployment"],"prefix":"10.1186","volume":"12","author":[{"given":"Haoran","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanqin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yikun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,12,6]]},"reference":[{"issue":"3","key":"556_CR1","first-page":"1","volume":"23","author":"F Wang","year":"2023","unstructured":"Wang F, Li G, Wang Y, Rafique W, Khosravi MR, Liu G, Liu Y, Qi L (2023) Privacy-aware traffic flow prediction based on multi-party sensor data with zero trust in smart city. 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