ML-based reconfigurable symbol decoder: An alternative for next-generation communication systems

Abstract

Modern Machine Learning (ML) techniques offer numerous opportunities to enable intelligent communication designs while addressing a wide range of problems in communication systems. A wide majority of communication systems ubiquitously employ the Maximum Likelihood (MLH) decoder in the symbol decoding process with QPSK modulation, thereby providing a non-reconfigurable solution. This work addresses the application of an ML-based reconfigurable solution for such systems. The proposed decoder can be considered a strong candidate for future communication systems, owing to its upgradable functionality, lower complexity, faster response, and reconfigurability. First, a novel low-complexity dataset for model training/testing is generated, that uses only the received symbols. Subsequently, three predictors are extracted from each of the received noisy symbols for model training/testing. The model is then trained/tested using nineteen standard ML-based classifiers, and the computations of various performance metrics indicate the suitability of Naïve Bayes (NB), and Ensemble Bagged Decision Tree (EBDT) classifiers for the model. The simulation results show that the model respectively delivers significant decoding accuracies and error rates of about 93% and 7% during testing, even for a low SNR of 5 dB. Moreover, the statistical analysis of simulation results shows the marginal superiority of the Gaussian Naïve Bayes (GNB) classifier. Further, the model reconfiguration is validated using a BPSK modulated dataset. Finally, a user-separation scheme that eliminates Successive Interference Cancellation (SIC) in the next-generation Power-Domain (PD) Non-Orthogonal Multiple Access (NOMA) networks is suggested by employing the proposed decoder.