Version 1
: Received: 1 November 2020 / Approved: 3 November 2020 / Online: 3 November 2020 (14:27:44 CET)
How to cite:
Khanh, N.T.V. Max-Min Rate Fairness Optimization for In-Band Full-Duplex IoT Networks: User Grouping and Power Control. Preprints2020, 2020110159. https://doi.org/10.20944/preprints202011.0159.v1
Khanh, N.T.V. Max-Min Rate Fairness Optimization for In-Band Full-Duplex IoT Networks: User Grouping and Power Control. Preprints 2020, 2020110159. https://doi.org/10.20944/preprints202011.0159.v1
Khanh, N.T.V. Max-Min Rate Fairness Optimization for In-Band Full-Duplex IoT Networks: User Grouping and Power Control. Preprints2020, 2020110159. https://doi.org/10.20944/preprints202011.0159.v1
APA Style
Khanh, N.T.V. (2020). Max-Min Rate Fairness Optimization for In-Band Full-Duplex IoT Networks: User Grouping and Power Control. Preprints. https://doi.org/10.20944/preprints202011.0159.v1
Chicago/Turabian Style
Khanh, N.T.V. 2020 "Max-Min Rate Fairness Optimization for In-Band Full-Duplex IoT Networks: User Grouping and Power Control" Preprints. https://doi.org/10.20944/preprints202011.0159.v1
Abstract
The skyrocketing growth in the number of Internet of Things (IoT) devices will certainly pose a huge traffic demand for fifth-generation (5G) wireless networks and beyond. In-band full-duplex (IBFD), which is theoretically expected to double the spectral efficiency of a half-duplex (HD) wireless channel and to connect more devices, has been considered as a promising technology to accelerate the development of IoT. To exploit the full potential of IBFD, the key challenge is how to handle network interference (including self-interference, co-channel interference and multiuser interference) more effectively. In this paper, we propose a simple yet efficient user grouping method, where a base station (BS) serves strong downlink users and weak uplink users and vice versa in different frequency bands, mitigating severe network interference. We aim to maximize a minimum rate among all users subject to bandwidth and power constraints, which is formulated as a highly nonconvex optimization problem. By leveraging inner approximation framework, we develop a very efficient iterative algorithm to solve this problem, which guarantees at least a local optimal solution. Numerical results are provided to show not only the benefit of using full-duplex raido at BS, but also the advantage of the proposed user grouping method.
Keywords
In-band full-duplex radios; full-duplex self-interference; user grouping; user fairness; Internet of Things; nonconvex programming; transmit beamforming
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
