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Channel assignment and power control for D2D-enabled cellular networks

Channel assignment and power control for D2D-enabled cellular networks

Sanusi, Idayat O., Nasr, Karim M. ORCID: 0000-0002-8604-6274 and Moessner, Klaus (2019) Channel assignment and power control for D2D-enabled cellular networks. In: 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE). IEEE, pp. 225-228. ISBN 978-1728121383 (doi:https://doi.org/10.1109/iCCECE46942.2019.8941981)

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Abstract

Device-to-Device (D2D) communication systems will be a major driver for machine-type communication (MTC) in 5G networks because of their ability to facilitate the connection of heterogeneous devices. 5G networks will feature smart environments with devices having differing Quality of Service (QoS) requirements. In this paper, we formulate a channel assignment and power control optimisation problem for D2D-enabled cellular networks where users have different (QoS) requirements namely high throughput for cellular users (CUEs) and low power consumption for D2D users (DUEs). We propose a resource allocation algorithm that adopts fixed-target Signal-to-Interference-plus-Noise-Ratio (SINR) tracking for power control. In the proposed scheme, a DUE is assigned a pre-allocated cellular channel that satisfies the QoS metric of the CUE once the predefined SINR has been achieved. Numerical results show that the proposed method significantly improves CUE's throughput and DUE power savings.

Item Type: Conference Proceedings
Title of Proceedings: 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)
Uncontrolled Keywords: device-to-device (D2D) communication systems, internet of things (IoT), fifth generation (5G), SINR, power control
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 19 Sep 2020 00:20
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/26764

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