The application of stochastic geometry to the analysis of wireless networks is shown to be propelled by (i) a clean separation of time scales, (ii) abstraction of small-scale effects via ergodicity, and (iii) an interference model reflecting the receiver's lack of knowledge of how individual interference terms are faded. These steps render the analysis simpler and more precise, and more amenable to incorporating subsequent features. Specifically, the paper presents easy-to-evaluate expressions for ...
The application of stochastic geometry to the analysis of wireless networks is shown to be propelled by (i) a clean separation of time scales, (ii) abstraction of small-scale effects via ergodicity, and (iii) an interference model reflecting the receiver's lack of knowledge of how individual interference terms are faded. These steps render the analysis simpler and more precise, and more amenable to incorporating subsequent features. Specifically, the paper presents easy-to-evaluate expressions for the ergodic spectral efficiency of cellular networks with single-user multiple-input multiple-output (MIMO).
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