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However, multiple satellites have overlapping coverage areas, leading to co\u2010channel interference that degrades communication system performance. Moreover, the high dynamic nature of LEO satellites makes the interference varies over time. In this paper, we analyze the receive beamforming to mitigate the complex and time\u2010varying interference in dense LEO satellite networks, and the interference mitigation is formulated as a long\u2010term data rate maximizing problem. To address this problem, a joint intelligent interference prediction and receive beamforming design algorithm is proposed. First, an interference prediction algorithm based on long short\u2010term memory (LSTM) is employed to predict the direction of arrival (DOA) information. Then, a hybrid beamforming algorithm based on deep reinforcement learning (DRL) is proposed to mitigate interference. Simulation results show that the proposed algorithm effectively improves long\u2010term data rate for users and outperforms other benchmark algorithms.<\/jats:p>","DOI":"10.1002\/sat.1563","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:41:31Z","timestamp":1742949691000},"page":"381-391","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Interference Prediction and Receive Beamforming for Dense LEO Satellite Networks"],"prefix":"10.1002","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7935-9602","authenticated-orcid":false,"given":"Xing","family":"Xin","sequence":"first","affiliation":[{"name":"School of Electronic Engineering Beijing University of Posts and Telecommunications  Beijing 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