{"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/machine-learning-meets-stochastic-geometry","title":"Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks","arxiv_id":"1905.00504","date":"2019-05-01","proceeding":null,"authors":["Chiranjib Saha","Harpreet S. Dhillon"],"abstract":"In wireless networks, many problems can be formulated as subset selection\nproblems where the goal is to select a subset from the ground set with the\nobjective of maximizing some objective function. These problems are typically\nNP-hard and hence solved through carefully constructed heuristics, which are\nthemselves mostly NP-complete and thus not easily applicable to large networks.\nOn the other hand, subset selection problems occur in slightly different\ncontext in machine learning (ML) where the goal is to select a subset of high\nquality yet diverse items from a ground set. In this paper, we introduce a\nnovel DPP-based learning (DPPL) framework for efficiently solving subset\nselection problems in wireless networks. The DPPL is intended to replace the\ntraditional optimization algorithms for subset selection by learning the\nquality-diversity trade-off in the optimal subsets selected by an optimization\nroutine. As a case study, we apply DPPL to the wireless link scheduling\nproblem, where the goal is to determine the subset of simultaneously active\nlinks which maximizes the network-wide sum-rate. We demonstrate that the\nproposed DPPL approaches the optimal solution with significantly lower\ncomputational complexity than the popular optimization algorithms used for this\nproblem in the literature.","url_abs":"http://arxiv.org/abs/1905.00504v1","url_pdf":"http://arxiv.org/pdf/1905.00504v1.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":"machine-learning-meets-stochastic-geometry","repo_url":"https://github.com/stochastic-geometry/DPPL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"scheduling","task_name":"Scheduling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}