{"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/towards-optimal-power-control-via-ensembling","title":"Towards Optimal Power Control via Ensembling Deep Neural Networks","arxiv_id":"1807.10025","date":"2018-07-26","proceeding":null,"authors":["Fei Liang","Cong Shen","Wei Yu","Feng Wu"],"abstract":"A deep neural network (DNN) based power control method is proposed, which\naims at solving the non-convex optimization problem of maximizing the sum rate\nof a multi-user interference channel. Towards this end, we first present PCNet,\nwhich is a multi-layer fully connected neural network that is specifically\ndesigned for the power control problem. PCNet takes the channel coefficients as\ninput and outputs the transmit power of all users. A key challenge in training\na DNN for the power control problem is the lack of ground truth, i.e., the\noptimal power allocation is unknown. To address this issue, PCNet leverages the\nunsupervised learning strategy and directly maximizes the sum rate in the\ntraining phase. Observing that a single PCNet does not globally outperform the\nexisting solutions, we further propose ePCNet, a network ensemble with multiple\nPCNets trained independently. Simulation results show that for the standard\nsymmetric multi-user Gaussian interference channel, ePCNet can outperform all\nstate-of-the-art power control methods by 1.2%-4.6% under a variety of system\nconfigurations. Furthermore, the performance improvement of ePCNet comes with a\nreduced computational complexity.","url_abs":"http://arxiv.org/abs/1807.10025v2","url_pdf":"http://arxiv.org/pdf/1807.10025v2.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":"towards-optimal-power-control-via-ensembling","repo_url":"https://github.com/ShenGroup/PCNet-ePCNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}