Adaptive Communications for Stochastic Networks

Abstract

Wireless networks have crucial needs for increasing efficiency in the spectrum usage. Adaptive communication is a key enabler for enhancing the spectral efficiency (SE) of wireless networks. This paper develops a framework for design and analysis of stochastic wireless networks, in which nodes employ diversity techniques and are randomly distributed in space. The signal-to-interference-plus-noise ratio (SINR) distribution is determined for finite and infinite stochastic networks at the output of the optimum combiner (OC). The statistics of the SINR at the OC output are used to design adaptive communication systems with diversity in the presence of small- and large-scale fading, interference, and noise. Slow adaptive modulation for any diversity order is analyzed in the presence of an interference field modeled as a Poisson point process. The results are applied to evaluate the benefits of adaptive modulation techniques with diversity for maximizing the SE in a stochastic network.