Resource Optimization for Integrated Terrestrial Non-Terrestrial Networks Involving IRS

Abstract

Intelligent reconfigurable surfaces (RIS) have emerged as one of the most promising and cost-effective technologies due to their high energy efficiency, extended wireless coverage, enhanced signal strength, and interference mitigation capability. This paper provides a new framework of cognitive radio-based integrated terrestrial non-terrestrial networks (ITNTNs) involving IRS. The objective is to maximize the achievable sum rate of the secondary network by simultaneously optimizing the transmission power, user association, phase shift design of IRS and 2D placement of UAVs while controlling the co-channel interference temperature to the primary network. The problem is formulated as non-convex/non-linear due to interference and decision variables which makes it NP-hard and intractable. To reduce the complexity and make the problem tractable, we first decouple it into subproblems and iteratively obtain an efficient solution. Numerical results demonstrate that the proposed optimization scheme converges within a few iterations and achieves high sum rate than the benchmark suboptimal schemes.