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Online channel allocation for full-duplex device-to-device communications

Lee, Gilsoo; Saad, Walid; Bennis, Mehdi; Mehbodniya, Abolfazl; Adachi, Fumiyuki (2017-02-09)

 
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https://doi.org/10.1109/GLOCOMW.2016.7848985

Lee, Gilsoo
Saad, Walid
Bennis, Mehdi
Mehbodniya, Abolfazl
Adachi, Fumiyuki
Institute of Electrical and Electronics Engineers
09.02.2017

G. Lee, W. Saad, M. Bennis, A. Mehbodniya and F. Adachi, "Online Channel Allocation for Full-Duplex Device-to-Device Communications," 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, 2016, pp. 1-6. doi: 10.1109/GLOCOMW.2016.7848985

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© 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.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/GLOCOMW.2016.7848985
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Abstract

Full-duplex device-to-device (D2D) communications over cellular networks is a promising solution for maximizing wireless spectral efficiency. However, in practice, due to the unpredictable arrival of D2D users, the base station (BS) must smartly allocate suitable channels to arriving D2D pairs. In this paper, the problem of dynamic channel allocation is studied for full-duplex D2D networks. In particular, the goal of the proposed approach is to maximize the system sum-rate under complete uncertainty on the arrival process of D2D users. To solve this problem, a novel approach based on an online weighted bipartite matching is proposed. To find the desired solution of the channel allocation problem, a greedy online algorithm is developed to enable the BS to decide on the channel assignment for each D2D pair, without knowing any prior information on future D2D arrivals. For an illustrative case study, upper and lower bounds on the competitive ratio that compares the performance of the proposed online algorithm to that of an offline algorithm are derived. Simulation results show that the proposed online algorithm can achieve a near- optimal sum-rate with an optimality gap that is no higher than 8.3% compared to the offline, optimal solution that has complete knowledge of the system.

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