We introduce DenoMAE2.0, an enhanced denoising masked autoencoder designed to significantly improve representation learning for Automatic Modulation Classification (AMC) in wireless communications. Unlike standard Masked Autoencoders (MAEs), which solely reconstruct masked inputs, DenoMAE2.0 jointly performs denoising and reconstruction by incorporating a position-aware local patch classification objective. This approach enables the model to simultaneously denoise corrupted signals and accurately reconstruct missing information, effectively capturing both global context and local structural patterns crucial for modulation classification under noisy and data-scarce scenarios. Extensive experiments demonstrate that DenoMAE2.0 achieves superior denoising and classification performance, outperforming baseline methods and its predecessor, DenoMAE, particularly under extremely low Signal-to-Noise Ratios (SNR) (down to -7dB), where other approaches significantly degrade. Our method achieves state-of-the-art accuracy of 82.4%, improving upon the original DenoMAE by 1.1%, while consistently enhancing reconstruction quality across all modulation classes. Additionally, DenoMAE2.0 exhibits robust transfer learning capabilities, achieving consistent improved accuracy over a Vision Transformer (ViT) at different SNRs with a 6.13% highest gain at noise-signal equilibrium point. These results highlight the effectiveness of our dual-objective, self-supervised framework for robust AMC in challenging wireless communication environments.

Defect identification is a challenging problem in deep learning, especially in large-scale structures, such as concrete bridges and large-scale buildings, because of differences in material appearance, changing lighting, different surface markings, and the possible overlap of varying defect types. In this paper, we employ a deep learning method to address this challenge by using a hybrid network combining graph neural networks (GNN), convolutional neural networks (CNN), and feedforward neural networks (FFN). The proposed methodology is applied to the recently introduced CODEBRIM dataset, which focuses on the multilabel classification of five common concrete defects. We deviate from the conventional methods of addressing multilabel classification and utilize the combined benefits of the CNN and GNN algorithms, where CNN excels at recognizing the visual characteristics of the defects, and GNN successfully captures the relational structure of defects. The subsequent integration of the FFN helps the model solve the over-smoothing problem of the deep GNN. The findings show that learned hybrid architectures have fewer overall parameters and higher multilabel accuracy (83%) for validation when compared to popular neural architectures specifically designed to deal with defect classification.

This paper proposes a novel algorithm for allocating distributed spectrum and power resources in vehicular networks, including both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication links. The proposed algorithm utilizes imitation learning to train distributed policies that adhere to the local structure of the vehicular system while imitating a centralized policy. This approach guarantees dependable, effective, and intelligent communication and control in the future generation of vehicular communication networks. The proposed model is evaluated through Matlab simulations and machine learning experiments, which demonstrate that the proposed scheme is more effective than traditional global optimization approaches, as it has a small transmission overhead and improves network performance by using graph-based distributed mode in vehicular networks.

End-to-end learning of communications systems is a promising new paradigm for future communications, in which deep neural networks (DNNs) are implemented in the transmitter and receiver as an autoencoder architecture. However, due to DNN’s natural vulnerability to adversarial perturbations, the end-to-end communications system exhibits security and robustness issues in terms of adversarial attacks over the air. The common defensive method, known as adversarial training, is to augment training data with adversarial perturbations, but it is hard to cover all possible perturbations and also hurt the system generalization. In this paper, we propose a novel and defensive mechanism based on a generative adversarial network (GAN) framework1to achieve robust end-to-end learning of a communications system. We utilize a generative network to model a powerful adversary and enable the end-to-end communications system to combat the generative attack network via a minimax game. We show that the proposed system not only works well against white-box and black-box adversarial attacks but also possesses excellent generalization capabilities to maintain good performance under no attacks. The results also show that our GAN-based system outperforms the conventional communications system and the autoencoder communications system with/without adversarial training.

Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.

A massive multiple-input multiple-output (MIMO) system, which utilizes a large number of antennas at base stations to communicate with multiple user terminals each with a single antenna, is one of the most promising techniques for future wireless communications systems. A successful massive MIMO implementation relies on accurate channel estimation, which is typically performed through pilot sequences. However, the channel estimation performance or massive MIMO performance is limited by pilot contamination due to unavoidable reuse of pilot sequences from terminals in neighboring cells. In this paper, a semi-blind decoding method based on independent component analysis (ICA), channel energy levels, and reference bits is proposed. Specifically, the proposed decoding method uses ICA to separate and decode the received signals and to estimate the channels. The estimated channel energy is used to differentiate the in-cell signals and the neighboring cell signals, and reference bits are applied to identify a desired signal among signals within a cell. The analytical performance results of the proposed decoding method are derived. Numerical results show that the proposed semi-blind decoding method has better bit error rate performance and higher transmit efficiency than the traditional minimum mean-square error decoding method and the singular value decomposition-based decoding method.

Spectrum sharing and aggregation among authorized secondary users (A-SUs) are important tasks in operating effective cognitive radio networks. Furthermore, in protecting spectrum sharing/aggregation against unauthorized secondary users (UA-SUs), secondary user access control (SUAC) is needed, which is investigated in this paper. A jamming signal is injected to degrade the spectrum sensing performance of UA-SUs, while reliable spectrum sensing performance for A-SUs can be achieved through an oblique projection-based jamming cancellation method. An orthogonal frequency division multiplexing-based transmission model is considered in this paper. The generalized likelihood ratio test algorithm is used for both authorized and unauthorized SUs in spectrum sensing. Numerical results show the effectiveness of the proposed SUAC in degrading the spectrum sensing performance of the unauthorized SUs.

Heart disease is one of the most severe health illnesses. Developing accurate and efficient methods to diagnose heart disease is crucial in providing good heart healthcare to patients. In this paper, a data mining based technique for diagnosing heart disease is introduced, in which heart disease related patient data sets are utilized. A matrix factorization based technique for missing data reconstruction is presented. Numerical results show that recovery data sets are able to achieve reliable diagnosis or classification performance comparable to using original completed patient datasets.

Cooperative spectrum sensing improves the performance of cognitive radio (CR) networks. In cooperative sensing, there are two important phases, detecting and reporting. In the detecting phase, multiple CR users detect the presence of primary users. In the reporting phase, one or multiple channels are needed for CR users to transmit local detection results to a fusion center (FC). In this paper, a reporting channel design, based on a random access protocol, is introduced for CR users and the fusion center. Analytical and simulation evaluations are performed considering the collision and capture in the reporting channels. In addition, the rule of K out of N is implemented in FC in determining global detection results. Numerical results demonstrate the feasibility of the random access based reporting channel in implementing cooperative spectrum sensing.