Rainfall attenuation prediction model for dynamic rain fade mitigation technique considering millimeter wave communication link.
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To deliver modern day broadband services to both fixed and mobile devices, ultra-high speed wireless networks are required. Innovative services such as the Internet-of-Things (IoT) can be facilitated by the deployment of next generation telecommunication networks such as 5G technologies. The deployment of 5G technologies is envisioned as a catalyst in the alleviation of spectrum congestion experienced by current technologies. With their improved network speed, capacity and reduced communication latency, 5G technologies are expected to enhance telecommunication networks for next generation services. These technologies, in addition to using current Long Term Evolution (LTE) frequency range (600 MHz to 6 GHz), will also utilize millimetre wave bands in the range 24-86 GHz. However, these high frequencies are susceptible to signal loss under rain storms. At such high frequencies, the size of the rain drop is comparable to the wavelength of the operating signal frequency, resulting in energy loss in the form of absorption and scattering by water droplets. This study investigates the effect of intense rain storms on link performance to accurately determine and apply dynamic rain fade mitigation techniques such as the use of a combination of modulation schemes to maintain link connectivity during a rain event. The backpropagation neural network (BPNN) model is employed in this study to predict the state of the link for decision making in employment of dynamic rain fade mitigation. This prediction model was tested on all rainfall regimes including intense rain storms and initial results are encouraging. Further on, the prediction model has been tested on a rainfall event rainfall data collected over Butare (2.6078° S, 29.7368° E), Rwanda, and the results demonstrate the portability of the proposed prediction model to other regions. The evolution of R0.01 (rain rate exceeded for 0.01% of the time in an average year) parameter due to intense rain storms over the region of study is examined and detailed analysis shows that this parameter is double the proposed ITU-R value of 60 mm/h. Moreover, an investigation on the largest rain drop size present in each rain storm is carried out for different storm magnitudes. The study goes further to examine the frequency of occurrence of rain storms using the Markov chain approach. Results of this approach show that rain spikes with maximum rain rates from 150 mm/h and above (intense storms) are experienced in the region of study with probability of occurrence of 11.42%. Additionally, rain spike service times for various rain storm magnitudes are analyzed using the queueing theory technique. From this approach, a model is developed for estimation of rain cell diameter that can be useful for site diversity as a dynamic rain fade mitigation strategy. Finally, the study further investigates second-order rain fade statistics at different attenuation thresholds.