# Joint Antenna Array Mode Selection and User Assignment for Full-Duplex MU-MISO Systems

## TL;DR

This paper proposes a joint antenna mode selection and user assignment strategy for full-duplex MU-MISO systems, effectively managing interference and improving sum rate performance with guaranteed convergence.

## Contribution

It introduces a novel iterative algorithm for joint antenna mode and user assignment optimization, addressing non-convex challenges in full-duplex multiuser MISO systems.

## Key findings

- The proposed algorithms converge quickly.
- They significantly outperform existing interference management schemes.
- The robust algorithm maintains performance under channel uncertainty.

## Abstract

This paper considers a full-duplex (FD) multiuser multiple-input single-output system where a base station simultaneously serves both uplink (UL) and downlink (DL) users on the same time-frequency resource. The crucial barriers in implementing FD systems reside in the residual self-interference and co-channel interference. To accelerate the use of FD radio in future wireless networks, we aim at managing the network interference more effectively by jointly designing the selection of half-array antenna modes (in the transmit or receive mode) at the BS with time phases and user assignments. The first problem of interest is to maximize the overall sum rate subject to quality-of-service requirements, which is formulated as a highly non-concave utility function followed by non-convex constraints. To address the design problem, we propose an iterative low-complexity algorithm by developing new inner approximations, and its convergence to a stationary point is guaranteed. To provide more insights into the solution of the proposed design, a general max-min rate optimization is further considered to maximize the minimum per-user rate while satisfying a given ratio between UL and DL rates. Furthermore, a robust algorithm is devised to verify that the proposed scheme works well under channel uncertainty. Simulation results demonstrate that the proposed algorithms exhibit fast convergence and substantially outperform existing schemes.