Performance Analysis of Wireless Networks with Intelligent Reflecting Surfaces

Intelligent reflecting surfaces (IRSs) have been proposed in recent years as a promising technology to enhance the quality of transmissions in high-frequency spectrum. Currently, the research on the performance of large networks with IRSs is still in its infancy. Different from the commonly-used stochastic geometry model for the study of traditional networks, where only transmitters and receivers are modeled as point processes, in an IRS network, the blockages and reflectors also need to be accounted for. In this paper, we study a bipolar network with a line segment object model, and derive the probability that an IRS can successfully reflect a signal from a transmitter to a receiver, as well as the distribution of the distance traveled by the reflected signal. With these analytic results, the signal to interference ratio (SIR) and the achievable rate are obtained in closed-form expressions. From the analysis, we can observe that IRSs have a great potential to enhance the network performance, as they are able to boost the signal power, while preventing the inter-cell interference from rising rapidly. More importantly, we find that even with a limited number of IRSs, the network can still achieve a higher achievable rate than a conventional one without IRSs.