{"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/system-energy-efficient-hybrid-beamforming","title":"System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems","arxiv_id":"2001.05439","date":"2020-01-15","proceeding":null,"authors":[],"abstract":"This paper develops energy-efficient hybrid beamforming designs for mmWave\nmulti-user systems where analog precoding is realized by switches and phase\nshifters such that radio frequency (RF) chain to transmit antenna connections\ncan be switched off for energy saving. By explicitly considering the effect of\neach connection on the required power for baseband and RF signal processing, we\ndescribe the total power consumption in a sparsity form of the analog precoding\nmatrix. However, these sparsity terms and sparsity-modulus constraints of the\nanalog precoding make the system energy-efficiency maximization problem\nnon-convex and challenging to solve. To tackle this problem, we first transform\nit into a subtractive-form weighted sum rate and power problem. A compressed\nsensing-based re-weighted quadratic-form relaxation method is employed to deal\nwith the sparsity parts and the sparsity-modulus constraints. We then exploit\nalternating minimization of the mean-squared error to solve the equivalent\nproblem where the digital precoding vectors and the analog precoding matrix are\nupdated sequentially. The energy efficiency upper bound and a heuristic\nalgorithm are also examined for comparison purposes. Numerical results confirm\nthe superior performances of the proposed algorithm over benchmark\nenergy-efficiency hybrid precoding algorithms and heuristic ones.","url_abs":"http://arxiv.org/abs/2001.05439v1","url_pdf":"http://arxiv.org/pdf/2001.05439v1.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":"system-energy-efficient-hybrid-beamforming","repo_url":"https://github.com/hiroyuki-kasai/HybridPrecodingOpt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"form","task_name":"Form"},{"task_slug":"compressed-sensing","task_name":"compressed sensing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}