# Spectral Efficiency of Full-Duplex Multiuser System: Beamforming Design, User Grouping, and Time Allocation

## TL;DR

This paper proposes a novel joint beamforming, user grouping, and time allocation method for full-duplex multiuser systems to maximize sum rate while managing interference, using a path-following algorithm with proven convergence.

## Contribution

It introduces a new optimization algorithm for joint design in full-duplex systems, addressing nonconvex constraints and interference issues.

## Key findings

- The proposed algorithm converges quickly in simulations.
- It achieves significant performance improvements over existing methods.
- The approach effectively manages residual self-interference and co-channel interference.

## Abstract

Full-duplex (FD) systems have emerged as an es- sential enabling technology to further increase the data rate of wireless communication systems. The key idea of FD 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 FD 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 compu- tational 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.