Radio Environment Map (REM) is an effective tool for cognitive radio spectrum management. In the process of REM construction, the accurate location of equipment with measurement capability is often difficult to obtain, and inaccurate location information will have a more serious impact on mapping. In this letter, based on variational inference, we propose a Gaussian Process with positional uncertainty (GP-PU) algorithm for constructing a REM within the range of positional uncertainty, and by using the real dataset provided by the 2019 “Huawei” Mathematical Modeling Competition, the effectiveness of the algorithm is verified. In addition, we visualize REM to demonstrate that the GP-PU algorithm can obtain favorable REM. Finally, the simulation results show that the GP-PU algorithm outperforms the traditional method in terms of continuous hierarchical probability score (CRPS) and root mean square error (RMSE).

Spectrum sensing is the premise and foundation of cognitive radio technology to solve the scarcity of spectrum resources. In this paper, a spectrum sensing method (WT-ResNet) based on wavelet transform (WT) and residual network (ResNet) is proposed for the problem of spectrum sensing of non-stationary signals with a low signal-to-noise ratio (SNR). The method uses the wavelet transform to process the signal for the characteristics of non-stationary signals, obtains the time-frequency matrix of the signal as the test statistic, and combines the residual network. The method is data-driven and does not require any a priori information about the signal. In addition, we not only test the commonly used wavelet functions but also analyze the SNR robustness and generalization ability of the algorithm. Finally, the simulation results show that the method outperforms other spectrum sensing methods. In particular, the method achieves a detection probability of 85% at SNR=-16dB when the false alarm probability is 10%.

A radio environment map (REM) is an effective spectrum management tool. With the increase in the number of mobile devices, the wireless environment changes more and more frequently, bringing new challenges to REM updates. Traditional update methods usually rely on the amount of data collected for updating without paying attention to whether the wireless environment has changed enough. In particular, a waste of computational resources results from the frequently updated REM when the wireless environment does not change much. When the wireless environment changes a lot, the REM is not updated promptly, resulting in a decrease in REM accuracy. To overcome the above problems, this work combines the Siamese neural network and an attention mechanism in computer vision and proposes an update mechanism based on the amount of wireless environmental change starting from image data. The method compares the newly collected crowdsourced data with the constructed REM in terms of similarity. It uses similarity to measure the necessity of the REM to be updated. The algorithm in this paper can achieve a controlled update by setting a similarity threshold with good controllability. In addition, the effectiveness of the algorithm in detecting changes of the wireless environment has been demonstrated by combing simulation data.

With the advent of 6G, there has been exponential growth in the trend of radio equipment, leading to challenges in electromagnetic spectrum management. Localization of radiation sources plays a pivotal role in effective spectrum management. This paper proposes a computer vision-based approach for addressing the problem of radiation source localization. The method leverages location and received signal strength information from the radio map, employing a YOLO network integrated with the AOT attention mechanism to accurately detect and locate radiation sources within the map. Furthermore, the localization results are depicted directly on the radio map. Additionally, we manually annotate a dataset specifically designed for detecting and localizing radiation sources in radio maps, followed by conducting simulation experiments using this dataset. Finally, simulation experiments are performed on different reconstructed algorithms as well as on a test set to validate their effectiveness while analyzing and illustrating their correlation with fine-grained radio maps.

In this paper, we present a new method for construction of zero correlation zone (ZCZ) sequence sets, which are optimal with respect to Tang, Fan, and Matsu-fuji bound. The method proposed is based on interleaved technique and a perfect sequence of length m(2k + 1) with m>2, k≥0 .