Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks

The paper presents a game theoretic solution for distributed subchannel allocation problem in small cell networks (SCNs) analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the welfare of the SCNs, defined as the total system capacity. Altho...

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Main Authors: Shashi Shah, Somsak Kittipiyakul, Yuto Lim, Yasuo Tan
Format: Article
Language:English
Published: MDPI AG 2018-05-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/18/5/1500
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spelling doaj-cf34e40f0145459d97877ed018a3d5cc2020-11-24T23:01:13ZengMDPI AGSensors1424-82202018-05-01185150010.3390/s18051500s18051500Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell NetworksShashi Shah0Somsak Kittipiyakul1Yuto Lim2Yasuo Tan3School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology (SIIT), Thammasat University (TU), Pathum Thani 12121, ThailandSchool of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology (SIIT), Thammasat University (TU), Pathum Thani 12121, ThailandSchool of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Ishikawa 923-1292, JapanSchool of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Ishikawa 923-1292, JapanThe paper presents a game theoretic solution for distributed subchannel allocation problem in small cell networks (SCNs) analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the welfare of the SCNs, defined as the total system capacity. Although the problem can be addressed through best-response (BR) dynamics, the existence of a steady-state solution, i.e., a pure strategy Nash equilibrium (NE), cannot be guaranteed. Potential games (PGs) ensure convergence to a pure strategy NE when players rationally play according to some specified learning rules. However, such a performance guarantee comes at the expense of complete knowledge of the SCNs. To overcome such requirements, properties of PGs are exploited for scalable implementations, where we utilize the concept of marginal contribution (MC) as a tool to design learning rules of players’ utility and propose the marginal contribution-based best-response (MCBR) algorithm of low computational complexity for the distributed subchannel allocation problem. Finally, we validate and evaluate the proposed scheme through simulations for various performance metrics.http://www.mdpi.com/1424-8220/18/5/1500small cellsdistributed subchannel allocationgame theorybest-responsepotential gamesmarginal contribution
collection DOAJ
language English
format Article
sources DOAJ
author Shashi Shah
Somsak Kittipiyakul
Yuto Lim
Yasuo Tan
spellingShingle Shashi Shah
Somsak Kittipiyakul
Yuto Lim
Yasuo Tan
Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
Sensors
small cells
distributed subchannel allocation
game theory
best-response
potential games
marginal contribution
author_facet Shashi Shah
Somsak Kittipiyakul
Yuto Lim
Yasuo Tan
author_sort Shashi Shah
title Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
title_short Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
title_full Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
title_fullStr Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
title_full_unstemmed Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
title_sort marginal contribution-based distributed subchannel allocation in small cell networks
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2018-05-01
description The paper presents a game theoretic solution for distributed subchannel allocation problem in small cell networks (SCNs) analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the welfare of the SCNs, defined as the total system capacity. Although the problem can be addressed through best-response (BR) dynamics, the existence of a steady-state solution, i.e., a pure strategy Nash equilibrium (NE), cannot be guaranteed. Potential games (PGs) ensure convergence to a pure strategy NE when players rationally play according to some specified learning rules. However, such a performance guarantee comes at the expense of complete knowledge of the SCNs. To overcome such requirements, properties of PGs are exploited for scalable implementations, where we utilize the concept of marginal contribution (MC) as a tool to design learning rules of players’ utility and propose the marginal contribution-based best-response (MCBR) algorithm of low computational complexity for the distributed subchannel allocation problem. Finally, we validate and evaluate the proposed scheme through simulations for various performance metrics.
topic small cells
distributed subchannel allocation
game theory
best-response
potential games
marginal contribution
url http://www.mdpi.com/1424-8220/18/5/1500
work_keys_str_mv AT shashishah marginalcontributionbaseddistributedsubchannelallocationinsmallcellnetworks
AT somsakkittipiyakul marginalcontributionbaseddistributedsubchannelallocationinsmallcellnetworks
AT yutolim marginalcontributionbaseddistributedsubchannelallocationinsmallcellnetworks
AT yasuotan marginalcontributionbaseddistributedsubchannelallocationinsmallcellnetworks
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