In the coming 6G communications, network densification, high throughput, positioning accuracy, energy efficiency, and many other key performance indicator requirements are becoming increasingly strict. In the future, how to improve work efficiency while saving costs is one of the foremost research directions in wireless communications. Being able to learn from experience is an important way to approach this vision. Transfer learning (TL) encourages new tasks/domains to learn from experienced tasks/domains for helping new tasks become faster and more efficient. TL can help save energy and improve efficiency with the correlation and similarity information between different tasks in many fields of wireless communications. Therefore, applying TL to future 6G communications is a very valuable topic. TL has achieved some good results in wireless communications. In order to improve the development of TL applied in 6G communications, this article performs a comprehensive review of the TL algorithms used in different wireless communication fields, such as base stations/access points switching, indoor wireless localization and intrusion detection in wireless networks, etc. Moreover, the future research directions of mutual relationship between TL and 6G communications are discussed in detail. Challenges and future issues about integrate TL into 6G are proposed at the end. This article is intended to help readers understand the past, present, and future between TL and wireless communications.

With the development of the Internet of Things (IoT), the IoT devices are increasing day by day, resulting in increasingly scarce spectrum resources. At the same time, many IoT devices are facing inevitable malicious attacks. The cognitive Radio-enabled IoT (CR-IoT) is proposed as an effective method for spectrum resource allocation and risk monitoring in the IoT. The signal detection and modulation recognition are the key technologies for CR-IoT, addressing the problem of multisignal detection and automatic modulation classification (AMC) is one of the prerequisites for realizing secure dynamic spectrum access. Based on sliding window and deep learning (DL), this study proposes a multisignal frequency domain detection and recognition method. The frequency spectrum of the time-domain overlapping signal is obtained through the fast Fourier transform (FFT), and the frequency spectrum is segmented based on the signal energy detection method. Finally a complex convolutional neural network (CNN) is constructed for the identification of signal spectrum information. The proposed method can recognize 264 time-domain aliasing and frequency-closed signals with an accuracy of 97.3% under the influence of −2 dB corresponding to the noise of the calibration signal. In addition, the proposed method eliminates the influence of bandwidth, which can effectively detect and recognize the signal types of each component in the frequency band. This method has wide applicability and provides an effective scheme for the IoT cognitive technology.

With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

To give full play to the high efficiency and parallelism of multi-processor systems, the fireworks algorithm (FWA) is improved, and a multi-processor task scheduling algorithm based on improved FWA, named IMFWA, is proposed. IMFWA maps continuous space to discrete space by designing the fireworks location coding method, improves the Gaussian mutation process, and sets adaptive dimensions to accelerate the convergence speed of the algorithm. At the same time, in order to reduce the time complexity of the algorithm and shorten the time finding the optimal task scheduling sequence, the fitness-based tournament selection strategy is used instead of the rule based on Euclidean distance. Finally, IMFWA is compared with the basic fireworks algorithm and the genetic algorithms on the Matlab platform for performance analysis. The results show that the IMFWA has advantages in the convergence speed, and the negative impact of the number of tasks is also lower than the fireworks algorithm and genetic algorithm.