2019/11/06 by Michal Yemini, Andrea Goldsmith, Yemini, Michal +1
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cooperative Communication and Network Coding #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Signal Processing (eess.SP) #Wireless Networks and Protocols #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1911.03436
openalex publication_date 2019/11/06 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
This work proposes a new resource allocation optimization and network\nmanagement framework for wireless networks using neighborhood-based\noptimization rather than fully centralized or fully decentralized methods. We\npropose hierarchical clustering with a minimax linkage criterion for the\nformation of the virtual cells. Once the virtual cells are formed, we consider\ntwo cooperation models: the interference coordination model and the coordinated\nmulti-point decoding model. In the first model base stations in a virtual cell\ndecode their signals independently, but allocate the communication resources\ncooperatively. In the second model base stations in the same virtual cell\nallocate the communication resources and decode their signals cooperatively. We\naddress the resource allocation problem for each of these cooperation models.\nFor the interference coordination model this problem is an NP-hard\nmixed-integer optimization problem whereas for the coordinated multi-point\ndecoding model it is convex. Our numerical results indicate that proper design\nof the neighborhood-based optimization leads to significant gains in sum rate\nover fully decentralized optimization, yet may also have a significant sum rate\npenalty compared to fully centralized optimization. In particular,\nneighborhood-based optimization has a significant sum rate penalty compared to\nfully centralized optimization in the coordinated multi-point model, but not\nthe interference coordination model.\n