Deep Learning-Based Wireless Channel Estimation for MIMO Uncoded Space-Time Labeling Diversity

Uncoded space-time labeling diversity (USTLD) is a space-time block coded (STBC) system with labeling diversity applied to it to increase wireless link reliability without compromising the spectral efficiency. USTLD achieves higher link reliability relative to the traditional Alamouti STBC system. This work aims to design a bandwidth-efficient and blind wireless channel estimator for the USTLD system. Traditional channel estimation techniques like the least-squares (LS) and the minimum mean squared error (MMSE) methods are generally inefficient in using the channel bandwidth. The LS and MMSE channel estimation schemes require the prior knowledge of transmitted pilot symbols and/or channel statistics, together with the receiver noise variance, for channel estimation. A neural network machine learning (NN-ML) channel estimator with transmit power-sharing is proposed to facilitate blind channel estimation for the USTLD system and to minimize the required channel estimation bandwidth utilization. We mathematically model the equivalent noise power and derive the optimal transmit power fraction that minimizes the channel estimation bandwidth utilization. The blind NN-ML channel estimator with transmit power-sharing is shown to utilize 20% of the LS and MMSE wireless channel estimators' bandwidth to achieve the same bit error rate (BER) performance for the USTLD system in the case of 16-QAM and 16-PSK modulation.