Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks

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Title: Topology Adaptive Sum Rate Maximization in the Downlink of Dynamic Wireless Networks
Authors: Sugathapala, Inosha
Hanif, Muhammad Fainan
Lorenzo, Beatriz
Glisic, Savo
Juntti, Markku
Tran, Le-Nam
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Date: 15-Mar-2018
Online since: 2019-05-09T10:35:39Z
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.
Type of material: Journal Article
Publisher: IEEE
Journal: IEEE Transactions on Communications
Volume: 66
Issue: 8
Start page: 3501
End page: 3516
Copyright (published version): 2018 IEEE
Keywords: DNA networksUser associationSOCPThroughput maximizationBeamformingConvex optimizationBranch and bound algorithmExhaustive search
DOI: 10.1109/TCOMM.2018.2816071
Language: en
Status of Item: Peer reviewed
Appears in Collections:Electrical and Electronic Engineering Research Collection

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