Power Allocation for D2D Communications Using Max-Min Message-Passing Algorithm

The approach of factor-graphs (FGs) is applied in the context of power control and user pairing in Device-to-Device (D2D) communications as an effective underlay concept in wireless cellular networks. D2D communications can increase the spectral efficiency of wireless cellular networks by establishi...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 69; no. 8; pp. 8443 - 8458
Main Authors: Rashed, Salma Kazemi, Asvadi, Reza, Rajabi, Siavash, Ghorashi, Seyed Ali, Martini, Maria G.
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Cellular communication
Cellular networks
Complexity
Complexity theory
Convergence
Device-to-device (D2D) communications
Device-to-device communication
factor-graph (FG)
Game theory
Games
Interference
Max-Min
Message passing
Power control
Power management
Resource management
Wireless communications
Wireless networks
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:The approach of factor-graphs (FGs) is applied in the context of power control and user pairing in Device-to-Device (D2D) communications as an effective underlay concept in wireless cellular networks. D2D communications can increase the spectral efficiency of wireless cellular networks by establishing a direct link between devices with limited help from the evolved node base stations (eNBs). A well-designed user pairing and power allocation scheme with low complexity can remarkably improve the system's performance. In this paper, a simple and distributed FG based approach is utilized for power control and user pairing implementation in an underlay cellular network with D2D communications. A max-min criterion is proposed to maximize the minimum rate of all active users in the network, including the cellular and multiple D2D co-channel links in the uplink direction. An associated message-passing (MP) algorithm is presented to distributedly solve the resultant NP-hard maximization problem, with a guaranteed convergence compared to game theoretic and Q-learning based methods. The complexity and convergence of the proposed method is analyzed and numerical results confirm that the proposed scheme outperforms alternative algorithms in terms of complexity, while keeping the sum-rate of users nearly the same as centralized counterpart methods.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.2995534