Abstract
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.
Highlights
Uncoded Space-Time Labeling Diversity (USTLD) is a technique developed recently by [1] to increase the link reliability of space-time block coded (STBC) systems in a multiple-input multiple-output (MIMO) environment
The remainder of the paper is organized as follows: In Section II, we present the system model for the proposed blind neural network machine learning (NN-maximum likelihood (ML)) channel estimator with transmit power-sharing for USTLD MIMO and the background theory of LS and minimum mean squared error (MMSE) channel estimation
The mean squared error (MSE) performance of the NN-ML channel estimator algorithm with transmit power-sharing is very good throughout the signal-tonoise ratio (SNR) range relative to the traditional LS and MMSE channel estimation methods
Summary
Uncoded Space-Time Labeling Diversity (USTLD) is a technique developed recently by [1] to increase the link reliability of space-time block coded (STBC) systems in a multiple-input multiple-output (MIMO) environment. It uses two distinct symbol constellation mapper designs to map bitstreams to symbols. The second timeslot sends the same information symbols picked from the second constellation mapper designed using the labeling technique defined in [1] This scheme outperforms the traditional Alamouti STBC [2] system in terms of bit error rate (BER) performance as it has coding gain over the Alamouti system. They design the second and third labeling mappers using the heuristic algorithm for 256-QAM and 1024-QAM constellations since most research has developed mapper designs for lower modulation orders
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