{"paper":{"title":"UAV-Aided Cellular Communications with Deep Reinforcement Learning Against Jamming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Canhuang Dai, Huaiyu Dai, Liang Xiao, Xiaozhen Lu","submitted_at":"2018-05-17T07:21:37Z","abstract_excerpt":"Cellular systems are vulnerable to jamming attacks, especially smart jammers that choose their jamming policies such as the jamming channel frequencies and power based on the ongoing communication policies and network states. In this article, we present an unmanned aerial vehicle (UAV) aided cellular communication framework against jamming. In this scheme, UAVs use reinforcement learning methods to choose the relay policy for mobile users in cellular systems, if the serving base station is heavily jammed. More specifically, we propose a deep reinforcement learning based UAV relay scheme to hel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06628","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}