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Distributed Solutions for Energy Efficiency Fairness in Multicell MISO Downlink
This paper aims at guaranteeing the achievable energy efficiency (EE) fairness in a multicell multiuser multiple-input single-output downlink system. The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fracti...
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Published in: | IEEE transactions on wireless communications 2017-09, Vol.16 (9), p.6232-6247 |
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container_title | IEEE transactions on wireless communications |
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creator | Nguyen, Kien-Giang Vu, Quang-Doanh Juntti, Markku Tran, Le-Nam |
description | This paper aims at guaranteeing the achievable energy efficiency (EE) fairness in a multicell multiuser multiple-input single-output downlink system. The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fractional program and as such is difficult to solve in general. Our goal is to develop decentralized algorithms for the max-min EE problem based on combining the successive convex approximation (SCA) framework and the alternating direction method of multipliers (ADMMs). Specifically, leveraging the SCA principle, we iteratively approximate the nonconvex design problem by a sequence of convex programs for which two decentralized algorithms are then proposed. In the first approach, the convex program obtained at each step of the SCA procedure is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. The convergence of the first method is analytically guaranteed but the amount of backhaul signaling can be noticeable in some realistic settings. To reduce the backhaul overhead, the second method performs an abstract version of the ADMM where only one variables update is carried out. Numerical results are provided to demonstrate the effectiveness of the two proposed decentralized algorithms. |
doi_str_mv | 10.1109/TWC.2017.2721369 |
format | article |
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The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fractional program and as such is difficult to solve in general. Our goal is to develop decentralized algorithms for the max-min EE problem based on combining the successive convex approximation (SCA) framework and the alternating direction method of multipliers (ADMMs). Specifically, leveraging the SCA principle, we iteratively approximate the nonconvex design problem by a sequence of convex programs for which two decentralized algorithms are then proposed. In the first approach, the convex program obtained at each step of the SCA procedure is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. The convergence of the first method is analytically guaranteed but the amount of backhaul signaling can be noticeable in some realistic settings. To reduce the backhaul overhead, the second method performs an abstract version of the ADMM where only one variables update is carried out. 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The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fractional program and as such is difficult to solve in general. Our goal is to develop decentralized algorithms for the max-min EE problem based on combining the successive convex approximation (SCA) framework and the alternating direction method of multipliers (ADMMs). Specifically, leveraging the SCA principle, we iteratively approximate the nonconvex design problem by a sequence of convex programs for which two decentralized algorithms are then proposed. In the first approach, the convex program obtained at each step of the SCA procedure is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. The convergence of the first method is analytically guaranteed but the amount of backhaul signaling can be noticeable in some realistic settings. To reduce the backhaul overhead, the second method performs an abstract version of the ADMM where only one variables update is carried out. Numerical results are provided to demonstrate the effectiveness of the two proposed decentralized algorithms.</description><subject>Algorithm design and analysis</subject><subject>alternating direction method of multipliers</subject><subject>Approximation algorithms</subject><subject>Convex functions</subject><subject>Downlink</subject><subject>Energy efficiency</subject><subject>Interference</subject><subject>max-min fractional programming</subject><subject>MISO</subject><subject>successive convex approximation</subject><subject>Wireless communication</subject><issn>1536-1276</issn><issn>1558-2248</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNo90L1OwzAUBWALgUQp7EgsfoGUazv-G1GalkqtOrSIMUpcGxmCg-xEqG9PqlZM9w7nnOFD6JHAjBDQz_v3YkaByBmVlDChr9CEcK4ySnN1ffqZyAiV4hbdpfQJY1JwPkHbuU999M3Q2wPede3Q-y4k7LqIy2DjxxGXznnjbTBHvKh9DDYl7APeDG3vjW1bvFnttnje_YbWh697dOPqNtmHy52it0W5L16z9Xa5Kl7WmWHA-0wzAAfOcM5AOkoopSJvjKrrWjSqUYoJZinJQTunCLCDFKA00QeTN9AAsCmC866JXUrRuuon-u86HisC1QmkGkGqE0h1ARkrT-eKt9b-x6UWio06f328W7A</recordid><startdate>201709</startdate><enddate>201709</enddate><creator>Nguyen, Kien-Giang</creator><creator>Vu, Quang-Doanh</creator><creator>Juntti, Markku</creator><creator>Tran, Le-Nam</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-3663-5011</orcidid><orcidid>https://orcid.org/0000-0002-9369-4990</orcidid><orcidid>https://orcid.org/0000-0002-9317-9980</orcidid></search><sort><creationdate>201709</creationdate><title>Distributed Solutions for Energy Efficiency Fairness in Multicell MISO Downlink</title><author>Nguyen, Kien-Giang ; Vu, Quang-Doanh ; Juntti, Markku ; Tran, Le-Nam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c305t-9300f0fc55307f2122264bc8aaa6b8b88363e21409ff8103d7608919dc4b0b003</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithm design and analysis</topic><topic>alternating direction method of multipliers</topic><topic>Approximation algorithms</topic><topic>Convex functions</topic><topic>Downlink</topic><topic>Energy efficiency</topic><topic>Interference</topic><topic>max-min fractional programming</topic><topic>MISO</topic><topic>successive convex approximation</topic><topic>Wireless communication</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nguyen, Kien-Giang</creatorcontrib><creatorcontrib>Vu, Quang-Doanh</creatorcontrib><creatorcontrib>Juntti, Markku</creatorcontrib><creatorcontrib>Tran, Le-Nam</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE transactions on wireless communications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nguyen, Kien-Giang</au><au>Vu, Quang-Doanh</au><au>Juntti, Markku</au><au>Tran, Le-Nam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distributed Solutions for Energy Efficiency Fairness in Multicell MISO Downlink</atitle><jtitle>IEEE transactions on wireless communications</jtitle><stitle>TWC</stitle><date>2017-09</date><risdate>2017</risdate><volume>16</volume><issue>9</issue><spage>6232</spage><epage>6247</epage><pages>6232-6247</pages><issn>1536-1276</issn><eissn>1558-2248</eissn><coden>ITWCAX</coden><abstract>This paper aims at guaranteeing the achievable energy efficiency (EE) fairness in a multicell multiuser multiple-input single-output downlink system. The design objective is to maximize the minimum EE among all base stations (BSs) subject to per-BS power constraints. This results in a max-min fractional program and as such is difficult to solve in general. Our goal is to develop decentralized algorithms for the max-min EE problem based on combining the successive convex approximation (SCA) framework and the alternating direction method of multipliers (ADMMs). Specifically, leveraging the SCA principle, we iteratively approximate the nonconvex design problem by a sequence of convex programs for which two decentralized algorithms are then proposed. In the first approach, the convex program obtained at each step of the SCA procedure is solved optimally by allowing the BSs to exchange the required information until the ADMM converges. The convergence of the first method is analytically guaranteed but the amount of backhaul signaling can be noticeable in some realistic settings. To reduce the backhaul overhead, the second method performs an abstract version of the ADMM where only one variables update is carried out. Numerical results are provided to demonstrate the effectiveness of the two proposed decentralized algorithms.</abstract><pub>IEEE</pub><doi>10.1109/TWC.2017.2721369</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-3663-5011</orcidid><orcidid>https://orcid.org/0000-0002-9369-4990</orcidid><orcidid>https://orcid.org/0000-0002-9317-9980</orcidid><oa>free_for_read</oa></addata></record> |
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source | IEEE Electronic Library (IEL) Journals |
subjects | Algorithm design and analysis alternating direction method of multipliers Approximation algorithms Convex functions Downlink Energy efficiency Interference max-min fractional programming MISO successive convex approximation Wireless communication |
title | Distributed Solutions for Energy Efficiency Fairness in Multicell MISO Downlink |
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