Spectral Efficiency of Full-Duplex Multi-user System: Beamforming Design, User Grouping, and Time Allocation
- Authors
- Van-Dinh Nguyen; Nguyen, Hieu V.; Nguyen, Chuyen T.; Shin, Oh-Soon
- Issue Date
- May-2017
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Full-duplex radios; full-duplex self-interference; multiuser transmission; nonconvex programming; spectral efficiency; transmit beamforming; user grouping
- Citation
- IEEE ACCESS, v.5, pp.5785 - 5797
- Journal Title
- IEEE ACCESS
- Volume
- 5
- Start Page
- 5785
- End Page
- 5797
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/7399
- DOI
- 10.1109/ACCESS.2017.2668384
- ISSN
- 2169-3536
- Abstract
- Full-duplex (ED) systems have emerged as an essential enabling technology to further increase the data rate of wireless communication systems. The key idea of ED is to serve multiple users over the same bandwidth with a base station (BS) that can simultaneously transmit and receive the signals. The most challenging issue in designing an ED system is to address both the harmful effects of residual self-interference caused by the transmit-to-receive antennas at the BS as well as the co-channel interference from an uplink user (ULU) to a downlink user (DLU). An efficient solution to these problems is to assign the ULUs/DLUs in different groups/slots, with each user served in multiple groups. Hence, this paper studies the joint design of transmit beamformers, ULUs/DLUs group assignment, and time allocation for each group. The specific aim is to maximize the sum rate under the ULU/DLU minimum throughput constraints. The utility function of interest is a difficult nonconcave problem, and the involved constraints are also nonconvex, and so this is a computationally troublesome problem. To solve this optimization problem, we propose a new path-following algorithm for computational solutions to arrive at least the local optima. Each iteration involves only a simple convex quadratic program. We prove that the proposed algorithm iteratively improves the objective while guaranteeing convergence. Simulation results confirm the fast convergence of the proposed algorithm with substantial performance improvements over existing approaches.
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