This study focuses on the latest research advancements in the field of semantic communication. Traditional communication systems prioritize the transmission of raw data, whilst semantic communication emphasizes conveying the meaning represented by the data. However, the extracted semantic information is often ambiguous and subject to subjective evaluation. To address this problem, this study proposes a model that combines a convolutional neural network (CNN) with a Transformer, called DeepSC‐CT. The model utilizes a CNN to extract semantic information from the data, followed by a Transformer model to capture spatial relationships and contextual information within the semantic content. We utilize federated learning to train the model and propose an adaptive aggregation algorithm to accelerate the convergence process. Moreover, we expand the single‐modality semantic communication model to encompass multiple modalities, such as texts, audio, and images. Furthermore, this study introduces a learnable position‐encoding method for the Transformer. The experimental results and visual effects of audio and image restoration demonstrate that the proposed method exhibits impressive performance and that the proposed model shows robust data restoration capabilities under various signal‐to‐noise ratio conditions.

The channel estimation technique is crucial for the development of wireless communication systems. By accurately estimating the channel state, transmission parameters such as power allocation, modulation schemes, and encoding strategies can be optimized to maximize system capacity and transmission rate. In this paper, we propose a hybrid deep learning model for channel estimation in multiple‐input multiple‐output (MIMO) wireless communication system. By combining the advantages of convolutions and gated recurrent units (GRUs), the generalization capability of deep learning models across various wireless communication scenarios can be fully utilized. Furthermore, a series of regularization techniques such as data augmentation and structural complexity constraints have been introduced to avoid overfitting problems. The stochastic gradient descent (SGD) based on error backpropagation is used to iteratively train the model to convergence. During the simulation process, we have validated the effectiveness of the hybrid deep learning model on two wireless channel conditions, including quasi‐static block fading and time‐varying fading condition. All the samples are generated offline with SNRs from 10 to 40 dB with a step size of 5 dB. The comparison results with a series of conventional methods and deep learning models have proven the effectiveness of the proposed method.

As an essential technology for spectrum sensing and dynamic spectrum access, automatic modulation classification (AMC) is a critical step in intelligent wireless communication systems, aiming at automatically recognizing the modulation schemes of received signals. In practice, AMC is challenging due to the influence of communication environment and signal parameters, such as unknown channels, noise, symbol rate, signal length, and sampling frequency. In this survey, we investigated a series of typical AMC methods, including key technology, performance comparisons, advantages, challenges, and future key development directions. According to the methodology and processing flow, AMC methods are divided into three categories: likelihood‐based (Lb) methods, feature‐based (Fb) methods, and deep learning methods. The technical details of various types of methods are introduced and discussed, such as likelihood distributions, artificial features, classifiers, and network structures. Then, extensive experimental results of state‐of‐the‐art AMC methods on public or simulated datasets are compared and analyzed. Despite the achievements that have been made, there are still limitations of the individual methods, including generalization capability, reasoning efficiency, model complexity, and robustness. In the end, we summarized the severe challenges faced by AMC and key future research directions.

Cover Caption: The cover image is based on the Research Article MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification by Qinghe Zheng et al., https://doi.org/10.1002/int.22586.

Nowadays, radio broadcasting plays an important role in people's daily life. However, unauthorized broadcasting stations may seriously interfere with normal broadcastings and further disrupt the management of civilian spectrum resources. Since they are easily hidden in the spectrum and are essentially the same as normal signals, it still remains challenging to automatically and effectively identify unauthorized broadcastings in complicated electromagnetic environments. In this paper, we introduce the manifold regularization-based deep convolutional autoencoder (MR-DCAE) model for unauthorized broadcasting identification. The specifically designed autoencoder (AE) is optimized by entropy-stochastic gradient descent, then the reconstruction errors in the testing phase can be adopted to determine whether the received signals are authorized. To make this indicator more discriminative, we design a similarity estimator for manifolds spanning various dimensions as the penalty term to ensure their invariance during the back-propagation of gradients. In theory, the consistency degree between discrete approximations in the manifold regularization (MR) and the continuous objects that motivate them can be guaranteed under an upper bound. To the best of our knowledge, this is the first time that MR has been successfully applied in AE to promote cross-layer manifold invariance. Finally, MR-DCAE is evaluated on the benchmark data set AUBI2020, and comparative experiments show that it achieves state-of-the-art performance. To help understand the principle behind MR-DCAE, convolution kernels and activation maps of test signals are both visualized. It can be observed that the expert knowledge hidden in normal signals can be extracted and emphasized, rather than simple overfitting.