Deep-learning-based modulation recognition methods can extract the features of signals automatically with the usage of the deep neural network (DNN). However, the background noises might lower the recognition accuracy and induce longer convergence time. In order to improve the recognition accuracy and to reduce the computational complexity, in this article, we propose to construct the dataset based on spectral correlation function (SCF), which has the special property of relatively being insensitive to background noises. Moreover, we propose to add a convolutional neural network denoising module to combat the noises for better feature extraction performances. Furthermore, in order to reduce the dataset size, we propose to use the two-dimensional (2-D) cross-section SCF profiles for the DNN. Then, we propose a DNN architecture based on the back-propagation neural network. Thanks to the noise resistant SCF feature extractions and the intelligent denoising as well as DNN-based classification design, the recognition accuracy can be improved. Simulation results show that the proposed method can effectively classify modulation schemes at the signal-to-noise ratio (SNR) as low as $-$10 dB. Moreover, the proposed intelligent system achieves higher recognition accuracy than counterpart schemes in low-SNR regions.

Chaos communications have widely been applied to provide secure and anti-jamming transmissions by exploiting the irregular chaotic behavior. However, the real-valued chaotic sequences imposed on the information induce interferences to the user data, thereby leading to reliability performance degradations. To address this issue, in this paper, we propose to utilize the intelligent and feature extraction capability of the deep neural network (DNN) to learn the transmission patterns to demodulate the received signals. In our design, we propose to construct the long short-term memory (LSTM) unit aided intelligent DNN-based deep learning (DL) demodulator for orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) systems. After learning and extracting features of information-bearing chaotic transmissions at the training stage, at the deployment stage, the received signals can be recovered efficiently and reliably. Thanks to the recursive LSTM-aided DL design, correlations between information-bearing chaotic modulated signals can be exploited to enhance reliability performances. Simulation results demonstrate with the proposed DL demodulation design, and the intelligent OFDM-DCSK system can achieve more reliable performances over additive white Gaussian noise (AWGN) channel and fading channels compared with benchmark systems.

Chaos-based communications can be applied to high-speed vehicular information transmissions thanks to the anti-jamming and anti-interference capabilities of chaotic transmissions. In traditional practical chaotic systems, reference chaotic signals are required to be delivered to remove complex chaotic synchronization circuits. However, the direct transmission of reference signals will degrade the security performances, while interferences and noises imposed on the reference signals due to imperfect channel conditions will deteriorate the reliability performances. In order to enhance the reliability and security performances over vehicular channels such as the railway channel and the channels undergoing fast fadings, in this paper, we propose a deep learning (DL) aided intelligent OFDM-DCSK transceiver. In this design, no reference chaotic signals are delivered, and we propose to utilize the time-delay neural network (TDNN) to learn the chaotic maps, followed by the long short-term memory (LSTM) units to extract and exploit the correlations between chaotic modulated signals, and multiple fully connected layers (FCLs) to estimate the user bit data. With the aid of the constructed deep neural network (DNN), after the offline neural network training, the receiver can recover the transmitted information with lower bit error rate (BER) and enhance security performances. Theoretical performance is then analyzed for the proposed intelligent transceiver. Simulation results validate the proposed design, and demonstrate that the intelligent DL-based OFDM-DCSK system can achieve better BER and security performances over fast fading and railway channels compared with the benchmark systems.

Ultra-reliable low-latency communication (URLLC) is essential for future vehicle-to-vehicle (V2V) networks to improve traffic safety and enhance driving experience. Due to the fast-varying channel caused by high mobility, guaranteeing latency and reliability performance of the V2V links is a tremendous challenge. In this paper, we propose a novel resource allocation framework to support ultra-reliable low-latency V2V communications. The proposed framework includes both large-scale and small-scale resource optimizations. The large-scale resource allocation is performed at the central base station based on large-scale channel information periodically collected from vehicles. On the other hand, the small-scale resource allocation is performed at the vehicles according to instantaneous channel and queuing information. We develop optimal solutions for both resource allocation problems. With the proposed optimal solutions, the latency performance at the occurrence of extreme events is enhanced by enabling spectrum sharing among the vehicles. Simulation results demonstrate that the proposed algorithm can effectively improve the URLLC performance compared against the benchmark algorithm.

The super augmented nested array (SANA) is a new sparse array (SA) proposed by expanding, changing the inter-element spacing (IES), and splitting the nested array (NA). The proposed SANA further enhances the sparsity of the NA to achieve lower mutual coupling (MC) and higher uniform degrees of freedom (uDOF) than the existing SAs. Specifically, the proposed SANA contains five uniform linear arrays (ULAs), and all sensor positions can be obtained from a closed-form expression. Moreover, the weight functions and achievable uDOF of the proposed SANA are analyzed in detail. It has been shown through simulations that SANA has a significant advantage over existing SAs for direction-of-arrival (DOA) estimation.

Distributed machine learning has been extensively employed in wireless systems, which can leverage abundant data distributed over massive devices to collaboratively train a high-quality global model. The research efforts of recent works have focused on improving performance (e.g., communication efficiency, energy efficiency, and scalability) of centralized architectures, which include a number of distributed devices and a server. However, centralized architectures may cause congestion at the central node, which is not applicable under some circumstances. To tackle this issue, we introduce a decentralized edge learning framework over wireless networks via unreliable device-to-device (D2D) links and improve its learning performance. The unreliable transmission caused by the channel uncertainty has a negative effect on model convergence. To enhance the performance, we formulate an optimization problem to minimize the overall model deviation under a given latency requirement by jointly optimizing the broadcast data rate and bandwidth allocation. Then, the optimal solution of broadcast data rate is derived and an algorithm for obtaining the optimal bandwidth allocation is developed. Besides, we also propose a decentralized edge learning protocol without a central server and provide the convergence analysis. Finally, extensive simulations are conducted to demonstrate the performance advantages of our proposed algorithm compared against the baseline algorithm.