HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
情報科学研究科  >
雑誌発表論文等  >

An Autonomous Learning-Based Algorithm for Joint Channel and Power Level Selection by D2D Pairs in Heterogeneous Cellular Networks

TCOM-TPS-16-0122.R2 Final.pdf1.92 MBPDF見る/開く

タイトル: An Autonomous Learning-Based Algorithm for Joint Channel and Power Level Selection by D2D Pairs in Heterogeneous Cellular Networks
著者: Asheralieva, Alia 著作を一覧する
Miyanaga, Yoshikazu 著作を一覧する
キーワード: Device-to-device communication
heterogeneous networks
interference management
reinforcement learning
resource allocation
発行日: 2016年 9月
出版者: IEEE (Institute of Electrical and Electronics Engineers)
誌名: IEEE transactions on communications
巻: 64
号: 9
開始ページ: 3996
終了ページ: 4012
出版社 DOI: 10.1109/TCOMM.2016.2593468
抄録: We study the problem of autonomous operation of the device-to-device (D2D) pairs in a heterogeneous cellular network with multiple base stations (BSs). The spectrum bands of the BSs (that may overlap with each other) comprise the sets of orthogonal wireless channels. We consider the following spectrum usage scenarios: 1) the D2D pairs transmit over the dedicated frequency bands and 2) the D2D pairs operate on the shared cellular/D2D channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plus-noise ratio requirements. We formulate this problem as a stochastic noncooperative game with multiple players (D2D pairs) where each player becomes a learning agent whose task is to learn its best strategy (based on the locally observed information) and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium. The proposed learning method is implemented in a long term evolution-advanced network and evaluated via the OPNET-based simulations. The algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations.
Rights: © © 2016 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.
資料タイプ: article (author version)
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: Asheralieva Aliia


本サイトに関するご意見・お問い合わせは repo at へお願いします。 - 北海道大学