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Shengchao SHI Guangxia LI Zhiqiang LI Bin GAO Zhangkai LUO
Broadband satellites, operating at Ka band and above, are playing more and more important roles in future satellite networks. Meanwhile, rain attenuation is the dominant impairment in these bands. In this context, a dynamic power allocation scheme based on rain attenuation prediction is proposed. By this scheme, the system can dynamically adjust the allocated power according to the time-varying predicted rain attenuation. Extensive simulation results demonstrate the improvement of the dynamic scheme over the static allocation. It can be concluded that the allocated capacities match the traffic demands better by introducing such dynamic power allocation scheme and the waste of power resources is also avoided.
Hongwei YANG Chen HE Hongwen ZHU Wentao SONG
Investigations into the suitability of artificial neural network for the prediction of rain attenuation based on radio, meteorological and geographical data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adaptive learning from the measurement are made. Rain attenuation prediction with the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evolve the artificial neural network rain attenuation model obtained both in architecture and weight, and an optimal rain attenuation model with simpler architecture and better prediction accuracy based on EPNet-evolved artificial neural network is obtained. Compared with the ITU-R model, the EPNet-evolved artificial neural network model of rain attenuation proposed in this paper improves the accuracy of rain attenuation prediction and creates a novel way to predict rain attenuation.