Compared with traditional half-duplex wireless systems, the application of emerging full-duplex (FD) technology can potentially double the system capacity theoretically. However, conventional techniques for suppressing self-interference (SI) adopted in FD systems require exceedingly high power consumption and expensive hardware. In this paper, we consider employing an intelligent reflecting surface (IRS) in the proximity of an FD base station (BS) to mitigate SI for simultaneously receiving data from uplink users and transmitting information to downlink users. The objective considered is to maximize the system weighted sum-rate by jointly optimizing the IRS phase shifts, the BS transmit beamformers, and the transmit power of the uplink users. To visualize the role of the IRS in SI cancellation, we first study a simple scenario with one downlink user and one uplink user. To address the formulated non-convex problem, a low-complexity algorithm based on successive convex approximation is proposed. For the more general case considering multiple downlink and uplink users, an efficient alternating optimization algorithm based on element-wise optimization is proposed. Numerical results demonstrate that the FD system with the proposed schemes can achieve a larger gain over the half-duplex system, and the IRS is able to achieve a balance between suppressing SI and providing beamforming gain.
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Joint Optimization for Full-Duplex Cellular Communications Via Intelligent Reflecting Surface
The implementation of full-duplex (FD) theoretically doubles the spectral efficiency of cellular communications. We propose a multiuser FD cellular network relying on an intelligent reflecting surface (IRS). The IRS is deployed to cover a dead zone while suppressing user-side self-interference (SI) and co-channel interference (CI) by carefully tuning the phase shifts of its massive low-cost passive reflection elements. To ensure network fairness, we aim to maximize the weighted minimum rate (WMR) of all users by jointly optimizing the precoding matrix of the base station (BS) and the reflection coefficients of the IRS. Specifically, we propose a low-complexity minorization-maximization (MM) algorithm for solving the subproblems of designing the precoding matrix and the reflection coefficients, respectively. Simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to realize FD cellular communications.
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- Award ID(s):
- 2030029
- PAR ID:
- 10291106
- Date Published:
- Journal Name:
- International Conf. on Acoustics Speech and Signal Processing
- Page Range / eLocation ID:
- 7888 to 7892
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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