Modulation type is one of the most important characteristics used in signal waveform identification. In this paper, an algorithm for automatic digital modulation recognition is proposed. The proposed algorithm is verified using higher-order statistical moments (HOM) of continuous wavelet transform (CWT) as a features set. A multilayer feed-forward neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying modulation schemes and the modulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis is used to reduce the network complexity and to improve the classifier's performance. The proposed algorithm is evaluated through confusion matrix and false recognition probability. The proposed classifier is shown to be capable of recognizing the modulation scheme with high accuracy over wide signal-to-noise ratio (SNR) range over both additive white Gaussian noise (AWGN) and different fading channels.
The fifth-generation (5G) of cellular networks and beyond requires massive connectivity, high data rates, and low latency. Millimeter-wave (mmWave) communications is a key 5G enabling technology to meet these requirements thanks to its technical potentials that can be integrated with other 5G enablers such as ultra-dense networks (UDNs) and massive multiple-input-multiple-output (massive MIMO) systems. However, some technical challenges, which are mainly related to specific characteristics of mmWave propagation, must be addressed. All the aforementioned points will be discussed in this paper before presenting the different existing architectures of massive MIMO mmWave systems. This survey mainly aims at presenting a comprehensive state-of-the-art review of the channel estimation techniques associated with the different mmWave system architectures. Subsequently, we will provide a comparison among existing solutions in terms of their respective benefits and shortcomings. Finally, some open directions of research are discussed, and challenges that wait to be met are pointed out.
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