Abstract
Massive MIMO millimeter wave (mm-wave) system that integrates various technologies together with hundreds of antennas that supports devices together. In a mm-wave communication, the signal degrades due to its atmospheric absorption, a pencil beam is formed that is liable to attenuate due to obstacles present in between the propagation paths. Offering such a huge bandwidth a greater number of devices are interconnected. However, in order to provide a seamless connection of devices, identification of channel conditions is a need to analyze. From the channel analysis, a channel characterization is found for classifying of signal paths into Line of Sight (LoS) and Non-Line of Sight (NLoS). An energy detector is used for the signals perceiving above 10 dB. These signals are analyzed for channel conditions such as pathloss and power delay profile. In this work, independent identically distributed AWGN channel is considered. Based on which a dataset is constructed, machine learning algorithm, namely K-nearest neighbor (K-NN), is applied for efficient channel characterization into LoS and NLoS. An accuracy of 96.3 and 94.3% is obtained for pathloss, and an accuracy of 94.5 and 93.3% is obtained for power delay profile at 28 and 39 GHz, respectively.
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Prakash, V.C., Nagarajan, G., Priyavarthan, N. (2021). Channel Coverage Identification Conditions for Massive MIMO Millimeter Wave at 28 and 39 GHz Using Fine K-Nearest Neighbor Machine Learning Algorithm. In: Gopi, E.S. (eds) Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. Lecture Notes in Electrical Engineering, vol 749. Springer, Singapore. https://doi.org/10.1007/978-981-16-0289-4_12
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DOI: https://doi.org/10.1007/978-981-16-0289-4_12
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