Topology adaptive sum rate maximization in the downlink of dynamic wireless networks
Sugathapala, Inosha; Hanif, Muhammad Fainan; Lorenzo , Beatriz; Glisic , Savo; Juntti, Markku; Tran, Le-Nam (2018-03-15)
I. Sugathapala, M. F. Hanif, B. Lorenzo, S. Glisic, M. Juntti and L. Tran, "Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks," in IEEE Transactions on Communications, vol. 66, no. 8, pp. 3501-3516, Aug. 2018. doi: 10.1109/TCOMM.2018.2816071
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2018112749293
Tiivistelmä
Abstract
Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users’ quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method.
Kokoelmat
- Avoin saatavuus [29694]