مقاله انگلیسی رایگان در مورد رمزگشای نماد قابل تنظیم مجدد مبتنی بر یادگیری ماشینی – الزویر 2022

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.

Introduction

     Recently, Artificial Intelligence (AI)/ML techniques are being utilized for various detection-based real-world problems. Applications such as Quadrature Amplitude Modulation (QAM), Spatial Modulation (SM), Generalized Spatial Modulation (GSM), modulation classification, Signal to Noise Power Ratio (SNR) estimation, pulse shaping, Convolutional Neural Network (CNN), and applicability in Long Term Evolution (LTE), Wireless Local Area Network (WLAN), and the next-generation Visible Light Communication (VLC) networks represent their relevancy. AI/ML techniques are significantly helpful in determining the unknown patterns and their influence on the optimizable objective function, so such applications are highly researched in various real-life scenarios. The application of AI in the existing communication systems is still an open area for research, specifically for decoding the received symbols among various modulation schemes. Some ML algorithms are employed for clustering analysis and classification purposes in such AI applications but have not been applied for symbol decoding in the existing communication systems.

     Quadrature Phase-Shift Keying (QPSK) is regarded as a prominent modulation scheme of modern communication systems, that is also being utilized for next-generation communication systems (Tan et al., 2022, Moon et al., 2022), owing to its good trade-off between higher bandwidth utilization and lower error rate. The Fifth-Generation (5G) standards organization, European Telecommunication Standards Institute (ETSI), also mentions the application of the QPSK modulation mapper in the technical specifications of 5G-New-Radio (NR) (Tg, 2018). The QPSK modulation mapper maps a pair of bits  to a modulation symbol in the complex 2-dimensional signal space,

Conclusions

     In this work, the application of an ML-based symbol decoder is proposed as a replacement to the traditional MLH decoder in a QPSK system, where both the decoders have comparable performance, but the former is reconfigurable. Since the model is based on the ML approach rather than the DL approach, it has lower complexity indicating a faster response. The model utilizes only the received symbols to extract all the required predictors. Appropriate low-complexity datasets are also generated utilizing the received symbols. After investigating the performance of nineteen standard ML classifiers, three well-known classifiers, viz: the KNB, the GNB, and the EBDT are found to be appropriate for the proposed system, for both QPSK and BPSK modulations. Additionally, the model shows a high decoding accuracy and low decoding error even for small SNR values. Overall, the test results show the appropriateness of the GNB classifier for the proposed model. The learning property, and the dataset generation mechanism together make the proposed model invariant of channel fading, thereby eliminating the complex channel estimation strategies. Further, the model supports the MIMO configuration and can simultaneously decode PD-NOMA users reducing network latency in next-generation systems where it also solves the problem of SIC.