{"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/distributed-two-tier-drl-framework-for-cell","title":"Distributed Two-tier DRL Framework for Cell-Free Network: Association, Beamforming and Power Allocation","arxiv_id":"2303.12479","date":"2023-03-22","proceeding":null,"authors":["Kaiwen Yu","Chonghao Zhao","Gang Wu","Geoffrey Ye Li"],"abstract":"Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands. In this paper, we develop a novel distributed hierarchical deep reinforcement learning (DHDRL) framework with two-tier control networks in different timescales to optimize the long-term spectrum efficiency (SE) of the downlink cell-free multiple-input single-output (MISO) network, consisting of multiple distributed access points (AP) and user terminals (UT). To realize the proposed two-tier control strategy, we decompose the optimization problem into two sub-problems, AP-UT association (AUA) as well as beamforming and power allocation (BPA), resulting in a Markov decision process (MDP) and Partially Observable MDP (POMDP). The proposed method consists of two neural networks. At the system level, a distributed high-level neural network is introduced to optimize wireless network structure on a large timescale. While at the link level, a distributed low-level neural network is proposed to mitigate inter-AP interference and improve the transmission performance on a small timescale. Numerical results show that our method is effective for high-dimensional problems, in terms of spectrum efficiency, signaling overhead as well as satisfaction probability, and generalize well to diverse multi-object problems.","url_abs":"https://arxiv.org/abs/2303.12479v2","url_pdf":"https://arxiv.org/pdf/2303.12479v2.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":"distributed-two-tier-drl-framework-for-cell","repo_url":"https://github.com/kiven-ykw/dhdrl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep 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}