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.