In this paper, we introduce an analytical framework for performance analysis and design of Single-Input-Multiple-Output (SIMO) wireless systems in the presence of noise, fading, and radio frequency interference produced by randomly distributed active interferers surrounding an intended probe receiver. The framework leverages recent application of stochastic geometry and Poisson Point Processes (PPPs) theory to network interference modeling. To assess the impact of spatial dependence across multiple receive-antennas, three models of network interference are studied: i) the isotropic model, where all receive-antennas see interferers belonging to the same PPP; ii) the independent model, where each receive-antenna sees interferers belonging to an independent PPP; and iii) the mixture model, which is a superposition of isotropic and independent interferences. Depending on the fading distribution of the interferers, the Nakagami-m fading parameter of the probe link, and the number of receive-antennas, either exact or upper-bound formulas of the error probability averaged over noise, fading, and spatial interference are given. Our analysis shows that, depending on the interference model, performance can either improve or get worse with multiple antennas at the receiver. The proposed analytical methodology is applicable to single- and multi-PPPs interference environments.