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.

With the wide application of deep learning (DL) across various fields, deep joint source–channel coding (DeepJSCC) schemes have emerged as a new coding approach for image transmission. Compared with traditional separated source and CC (SSCC) schemes, DeepJSCC is more robust to the channel environment. To address the limited sensing capability of individual devices, distributed cooperative transmission is implemented among edge devices. However, this approach significantly increases communication overhead. In addition, existing distributed DeepJSCC schemes primarily focus on specific tasks, such as classification or data recovery. In this paper, we explore the wireless semantic image collaborative nonorthogonal transmission for distributed edge networks, where edge devices distributed across the network extract features of the same target image from different viewpoints and transmit these features to an edge server. A two‐view distributed cooperative DeepJSCC (two‐view‐DC‐DeepJSCC) with or without information disentanglement scheme is proposed. In particular, the two‐view‐DC‐DeepJSCC with information disentanglement (two‐view‐DC‐DeepJSCC‐D) is proposed for achieving balancing performance between multitasking of image semantic communication; while the two‐view‐DC‐DeepJSCC without information disentanglement only pursues outstanding data recovery performance. Through curriculum learning (CL), the proposed two‐view‐DC‐DeepJSCC‐D effectively captures both common and private information from two‐view data. The edge server uses the received information to accomplish tasks such as image recovery, classification, and clustering. The experimental results demonstrate that our proposed two‐view‐DC‐DeepJSCC‐D scheme is capable of simultaneously performing image recovery, classification, and clustering tasks. In addition, the proposed two‐view‐DC‐DeepJSCC has better recovery performance compared to the existing schemes, while the proposed two‐view‐DC‐DeepJSCC‐D not only maintains a competitive advantage in image recovery but also has a significant improvement in classification and clustering accuracy. However, the proposed two‐view‐DC‐DeepJSCC‐D will sacrifice some image recovery performance to balance multiple tasks. Furthermore, two‐view‐DC‐DeepJSCC‐D exhibits stronger robustness across various signal‐to‐noise ratios.