{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-agent-deep-reinforcement-learning-for-1","title":"Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks","arxiv_id":"1808.00490","date":"2018-08-01","proceeding":null,"authors":["Yasar Sinan Nasir","Dongning Guo"],"abstract":"This work demonstrates the potential of deep reinforcement learning\ntechniques for transmit power control in wireless networks. Existing techniques\ntypically find near-optimal power allocations by solving a challenging\noptimization problem. Most of these algorithms are not scalable to large\nnetworks in real-world scenarios because of their computational complexity and\ninstantaneous cross-cell channel state information (CSI) requirement. In this\npaper, a distributively executed dynamic power allocation scheme is developed\nbased on model-free deep reinforcement learning. Each transmitter collects CSI\nand quality of service (QoS) information from several neighbors and adapts its\nown transmit power accordingly. The objective is to maximize a weighted\nsum-rate utility function, which can be particularized to achieve maximum\nsum-rate or proportionally fair scheduling. Both random variations and delays\nin the CSI are inherently addressed using deep Q-learning. For a typical\nnetwork architecture, the proposed algorithm is shown to achieve near-optimal\npower allocation in real time based on delayed CSI measurements available to\nthe agents. The proposed scheme is especially suitable for practical scenarios\nwhere the system model is inaccurate and CSI delay is non-negligible.","url_abs":"http://arxiv.org/abs/1808.00490v3","url_pdf":"http://arxiv.org/pdf/1808.00490v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-agent-deep-reinforcement-learning-for-1","repo_url":"https://github.com/kondrasso/DQN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"q-learning","task_name":"Q-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"scheduling","task_name":"Scheduling"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}