Resource allocation algorithm for UAV assisted NOMA-IRS network under hardware impairments
This paper investigates how to effectively support networks based on Non-Orthogonal Multiple Access ( NOMA ) technology with Intelligent Reflecting Surface ( IRS ) mounted on Unmanned Aerial Vehicle ( UAV ) , particularly in environments with hardware impairment, to accelerate the data transmission rate for multiple users. It introduces a holistic optimization approach, which encompasses simultaneous fine-tuning of various elements: the decoding order successive interference cancellation, the reflective settings of the IRS, the UAV geographical placement, and the power of the base station. The primary objective of these coordinated adjustments is to optimize the total transmit rate of the entire communication network. As the problem is essentially a non-convex one, this study introduces an iterative algorithm using the block coordinate descent method for optimization. The method partitions the initial non-convex problem into three distinct sub-problems. These are subsequently tackled through an integrated approach that incorporates a penalization method, semi-definite relaxation, and techniques of successive convex approximation. Our simulation results reveal the proposed algorithm enhances the sum rate of the systems compared with the NOMA scheme of random position deployment without the assistant of IRS.
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