An automatic method for classifying frequency shift keying (FSK), minimum shift keying (MSK), phase shift keying (PSK), quadrature amplitude modulation (QAM), and orthogonal frequency division multiplexing (OFDM) is proposed by simultaneously using normality test, spectral analysis, and geometrical characteristics of in-phase-quadrature (I-Q) constellation diagram. Since the extracted features are unique for each modulation, they can be considered as a fingerprint of each modulation. We show that the proposed algorithm outperforms the previously published methods in terms of signal-to-noise ratio (SNR) and success rate. For example, the success rate of the proposed method for 64-QAM modulation at SNR=11 dB is 99%. Another advantage of the proposed method is its wide SNR range; such that the probability of classification for 16-QAM at SNR=3 dB is almost 1. The proposed method also provides a database for geometrical features of I-Q constellation diagram. By comparing and correlating the data of the provided database with the estimated I-Q diagram of the received signal, the processing gain of 4 dB is obtained. Whatever can be mentioned about the preference of the proposed algorithm are low complexity, low SNR, wide range of modulation set, and enhanced recognition at higher-order modulations.
This paper introduces an algorithm for beamforming systems by the aid of multidimensional harmonic retrieval (MHR). This algorithm resolves problems, removes limitations of sampling and provides a more robust beamformer. A new sample space is created that can be used for estimating weights of a new beamforming called spatial-harmonics retrieval beamformer (SHRB). Simulation results show that SHRB has a better performance, accuracy, and applicability and more powerful eigenvalues than conventional beamformers. A simple mathematical proof is provided. By changing the number of harmonics, as a degree of freedom that is missing in conventional beamformers, SHRB can achieve more optimal outputs without increasing the number of spatial or temporal samples. We will demonstrate that SHRB offers an improvement of 4 dB in signal to noise ratio (SNR) in bit error rate (BER) of over conventional beamformers. In the case of direction of arrival (DOA) estimation, SHRB can estimate the DOA of the desired signal with an SNR of −25 dB, when conventional methods cannot have acceptable response.
In this study, we propose a novel machine learning based algorithm to improve the performance of beyond 5 generation (B5G) wireless communication system that is assisted by Orthogonal Frequency Division Multiplexing (OFDM) and Non-Orthogonal Multiple Access (NOMA) techniques. The non-linear soft margin support vector machine (SVM) problem is used to provide an automatic modulation classifier (AMC) and a signal power to noise and interference ratio (SINR) estimator. The estimation results of AMC and SINR are used to reassign the modulation type, codding rate, and transmit power throughout the frames of eNode B connections. The AMC success rate versus SINR, total power consuming, and sum capacity are evaluated for OFDM-NOMA assisted 5G system. In comparison to recently published methods, our results show that the success rate improves. The suggested method directly senses the physical channel because it computes the SINR and codding rate of received signal just after the signal is detected by successive interference cancellation (SIC). Hence, because of this direct sense, this algorithm can really decrease occupied symbols (overhead signaling) for channel quality information (CQI) in network communication signaling. The results also prove that the proposed algorithm reduces the total power consumption and increases the sum capacity during the eNode B connections. Simulation results compared to other algorithms show more successful AMC, efficient SINR estimator, easier practical implantation, less overhead signaling, less power consumption, and more capacity achievement.
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