Best Server SINR Models for Single- and Multi-Point Transmission in Wireless Networks

Analytical models enable accurate and quick assessment of performance metrics in network planning and system design. In wireless networks, the signal-to-interference-and-noise ratio (SINR) is of key importance since other metrics, such as throughput and capacity, strongly depend on the SINR. In this work, we characterize the SINR by a composition of log-normal random variables describing shadow fading and propose a comprehensive framework for modeling SINR distributions at specific user locations. In contrast to existing works, we include shadowing cross-correlation, noise power, and the best server policy in a single framework. Especially, the best server policy, which captures the influence of shadowing on the selection of the serving base station, is frequently neglected in analytical models. Moreover, we put forward SINR models for non-coherent joint transmission in dynamic multi-point networks. Finally, numerical evaluations show the applicability of the models but also reveal the limits of them.