This paper investigates a practical precoding scheme for rate-splitting multiple access (RSMA) in multi-user multiple-input single-output (MU-MISO) systems where the nonlinear characteristics of power amplifiers are explicitly taken into account. To enhance performance in the presence of distortion caused by nonlinear power amplifiers, we propose an iterative accelerated gradient-based precoding scheme. The proposed precoding scheme is further integrated with a novel power scaling function that dynamically adjusts transmit power to satisfy practical power consumption constraints while maximizing an achievable sum rate. The gradient of the achievable sum rate for the considered system is derived to support efficient gradient-based optimization. We also prove that the proposed iterative precoding scheme converges to a critical point. Simulation results demonstrate that the proposed precoding scheme consistently outperforms modified conventional schemes including distortion-aware and weighted minimum mean square error beamforming, across both overloaded and underloaded scenarios. In particular, the proposed scheme demonstrates consistent performance in high-power regimes, where conventional schemes exhibit severe degradation due to distortion. Furthermore, the proposed precoding scheme exhibits faster convergence compared to the conventional benchmark.

Integrated sensing and communication (ISAC) is a promising technology for next-generation communication systems, allowing concurrent data communication and positioning. However, in the presence of blockages, ISAC does not guarantee accurate positioning due to the lack of echo signals from targets. To address this issue, we study a downlink ISAC system with an unmanned aerial vehicle (UAV) relay that decode-and-forward data signals while securing blockage-free paths. In this UAV-assisted downlink ISAC system, we aim to maximize the sum rate while ensuring a target positioning accuracy by optimizing transmit power allocation. With moving target objects in a three-dimensional (3D) environment, this optimization problem becomes non-trivial due to complicated object positioning state transitions. To relax this complexity, we derive an approximated state transition model of 3D object and thereby formulate a strictly convex optimization problem that guarantees a unique power allocation solution. Numerical results corroborate that the proposed power allocation outperforms the baseline with feedback-based beam training in terms of the achievable sum rate.

In this paper, we compare I/Q imbalance compensation schemes for massive multi-input multi-output (MIMO) system in presence of I/Q imbalance at both transmitter and receiver. Transceiver I/Q imbalance can be compensated jointly by the base station or separately by both base station and user terminals. We show that the achievable sum rate of self-compensation scheme which compensates its I/Q imbalance separately is severely deteriorated in presence of channel estimation error and the I/Q imbalance parameter estimation error. In contrast, the achievable sum rate of the joint compensation scheme with RZF precoding in the base station shows robust performance against channel estimation error and the I/Q imbalance parameter estimation error.