Efficient DTCWT-TSVR algorithm for dense 5G mmWave Indoor Hotspot Communications

An efficient Dual Tree Discrete Wavelet Transform (DTDWT) based Twin Support Vector Regression (TSVR) algorithm is conceived in this article and applied to 28, 38, 60 and 73-GHz LOS (Line-of-Sight) wireless multipath transmission system in 5G Indoor Hotspot (InH) settings (empty, simple, semi-complex and complex conference rooms) under small receiver sensitivity threshold. The algorithm establishes a denoising process in the learning phase based on DTCWT which is suitable for time-series data. Additionally, the Close-In (CI) free space reference distance path loss model is analyzed and the large-scale propagation and probability distribution functions are investigated by determining the PLE (Path Loss Exponent) and the standard deviation of Shadow Factor (SF) for each indoor hotspot scenario under consideration. Performance are analyzed and evaluated in terms of Bit Error Rate (BER), Normalized Mean Squares Error (NMSE) and Root Mean Squares Error Vector Magnitude (RMS EVM).