This paper studies the performance of antenna array processing in distributed multiple access networks without power control. The interference is represented as a Poisson point process. Desired and interfering signals are subject to both path-loss fading (with an exponent greater than 2) and to independent Rayleigh fading. Using these assumptions, we derive the exact closed form expression for the cumulative distribution function of the output signal-to-interference-plus-noise ratio when optimum combining is applied. This results in a pertinent measure of the network performance in terms of the outage probability, which in turn provides insights into the network capacity gain that could be achieved with antenna array processing. We present and discuss examples of applications, as well as some numerical results. Index TermsMultiple access network, Poisson point process, shot noise process, power decay law, Rayleigh fading, MMSE linear combining, optimum combining, outage probability, and capacity.

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Microwave telecommunications links are severely influenced by the frequency and climatic parameters as refractivity gradient (dN1), geoclimatic factor (K), rain, temperature, pressure, and humidity. This influence increases with the frequency of the wave. In microwave band, recent research has shown that the geoclimatic factor (K), rain and temperature are the most important parameters affecting the performance of terrestrial links. K is an important parameter as directly proportional to the worst month outage probability P w . K depends on the refractivity gradient dN1. The dN1 depends itself on the pressure, the temperature and the humidity. In this paper, we demonstrated from the data of the terrestrial links located in Quebec, Canada, that there is a seasonal variation of the geoclimatic factor, K mes . This variation of K mes is mainly due to the variation of the humidity. The variation of the geoclimatic factor is not limited to the seasonal variation. But it is true from one year to another. IUT recommendations do not embed climate change, but assume a stationary climate. We recommend to improve data analysis and predictive models by taking into account the temporal changes.

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