Using deep learning technologies, the channel estimate for an orthogonal frequency division multiplexing system (OFDM) based on pilots is done in this work. To be more specific, deep learning gated recurrent unit (GRU) neural networks are used to present a new framework for channel estimation. Initially, it is trained offline using generated data sets, and thereafter it is used online to track the channel parameters, after which the data transmitted can be recovered. For the purpose of determining the performance of the proposed estimator, three alternative deep learning optimization techniques are used to test it. It is also compared to other commonly used estimators, such as least squares (LS) and minimum mean square error (MMSE). In addition, the proposed estimator is compared with two existing models. Deep learning GRU neural network-based channel state estimator, which are capable of learning and generalizing rapidly, are shown to outperform the comparable estimators when just a few pilots are available. In addition, there is no need for prior knowledge of channel statistics. So, estimating OFDM communication system channel states using the proposed estimator appears promising.

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

Artificial intelligence (AI) is capable of addressing the complexities and difficulties of fifth-generation (5G) mobile networks and beyond. In this paradigm, it is important to predict network metrics to meet future network requirements. Vehicle-to-everything (V2X) networks are promising wireless communication methods where traffic information exchange in an intelligent transportation system (ITS) still faces challenges, such as V2X communication congestion when many vehicles suddenly appear in an area. In this paper, a deep learning algorithm (DL) based on the unidirectional long short-term memory (LSTM) model is proposed to predict traffic in V2X networks. The prediction problems are studied in different cases depending on the number of packets sent per second. The prediction accuracy is measured in terms of root-mean-square error (RMSE), mean absolute percentage error (MAPE), and processing time.

This study proposes novel Long Short-Term Memory (LSTM)-based classifiers through developing the internal structure of LSTM neural networks using 26 state activation functions as alternatives to the traditional hyperbolic tangent (tanh) activation function. The LSTM networks have high performance in solving the vanishing gradient problem that is observed in recurrent neural networks. Performance investigations were carried out utilizing three distinct deep learning optimization algorithms to evaluate the efficiency of the proposed state activation functions-based LSTM classifiers for two different classification tasks. The simulation results demonstrate that the proposed classifiers that use the Modified Elliott, Softsign, Sech, Gaussian, Bitanh1, Bitanh2 and Wave as state activation functions trump the tanh-based LSTM classifiers in terms of classification accuracy. The proposed classifiers are encouraged to be utilized and tested for other classification tasks.

This study proposes an online deep learning‐based channel state estimator for OFDM wireless communication systems by employing the deep learning long short‐term memory (LSTM) neural networks. The proposed algorithm is a pilot‐assisted estimator type. The proposed estimator is initially offline trained using simulated data sets, and then it follows the channel statistics in an online deployment, where finally the transmitted data can be recovered. A comparative investigation is performed using three different optimisation algorithms for deep learning to evaluate the performance of the proposed estimator at each. The proposed estimator provides a superior performance in comparison to least square (LS) and minimum mean square error (MMSE) estimators when limited pilots are used, thanks to the outstanding learning and generalisation capabilities of deep learning LSTM neural networks. Also, it does not require any prior knowledge of channel statistics. So, the proposed estimator is promising for channel state estimation in OFDM communication systems.

This study proposes robust convolutional neural network (CNN)-based automatic modulation classification (AMC) techniques. Traditional AMCs may be classified into two types: those that rely on ML (maximum likelihood-based AMCs) and those that rely on features. Numerous studies have been conducted on feature-based automatic modulation classification techniques. The current feature-based AMCs lack generalization capability and frequently target a small group of modulation techniques. The current paper develops three different CNN-based AMCs, each with a different classification layer (CL). The adopted classification layers are mean absolute error-based CL, a sum of squared errors-based CL, and crossentropy-based CL. The developed techniques can classify the received signals without feature extraction, where they can learn the features from the transmitted signals automatically during the offline training process, thus eliminating the necessity for feature extraction. A comparison study was done for the proposed CNN-based AMCs with three optimization algorithms at two signal-to-noise ratios. The proposed AMCs attain a true classification accuracy of up to 100% depending on the optimizer and loss function-base CL.