In complex and ever-changing electromagnetic environments, a large number of anomalous radio signals illegally occupy spectrum resources and interfere with legitimate communications. To address this challenge, this paper proposes an anomalous radio signal detection method based on an Adversarial Autoencoder (AAE). The method comprises an autoencoder and a Generative Adversarial Network. The autoencoder is used to extract time–frequency features of radio signals, generate latent feature vectors, and reconstruct the signals. By comparing the original signals to their reconstructions, anomalies can be rapidly and accurately detected via the reconstruction error. Meanwhile, the adversarial network regularizes the latent vectors produced by the encoder, forcing them to approximate a predefined prior distribution, thereby improving the model’s generative capability and enhancing the structural consistency of the latent representations. We used a Pluto SDR device to capture real-world radio data in the field, performed frequency-domain analysis, and constructed a high-resolution power spectrum time–frequency dataset for model training and testing. Experimental results show that the proposed method achieves a detection accuracy of 95.5% for anomalous radio signals, exhibiting excellent performance on multiple metrics across diverse scenarios.

Spectrum sensing is the necessary premise for implementing cognitive radio technology. The conventional wideband spectrum sensing methods mainly work with sweeping frequency and still face major challenges in performance and efficiency. This paper introduces a new wideband spectrum sensing method based on channels clustering and prediction. This method counts on the division of the wideband spectrum into uniform sub-channels, and employs a density-based clustering algorithm called Ordering Points to Identify Clustering Structure (OPTICS) to cluster the channels in view of the correlation between the channels. The detection channel (DC) is selected and detected for each cluster, and states of other channels (estimated channels, ECs) in the cluster are then predicted with Hidden Markov Model (HMM), so that all channels states of the wideband spectrum are finally obtained. The simulation results show that the proposed method could effectively improve the wideband spectrum sensing performance.

In low-resource speech recognition, the performance of the Whisper model is often limited by the size of the available training data. To address this challenge, this paper proposes a training optimization method for the Whisper model that integrates Low-Rank Adaptation (LoRA) with the Optuna hyperparameter optimization framework. This combined approach enables efficient fine-tuning and enhances model performance. A dual-metric early stopping strategy, based on validation loss and relative word error rate improvement, is introduced to ensure robust convergence during training. Experimental data were collected from three low-resource languages in Xinjiang, China: Uyghur, Kazakh, and Kyrgyz. Compared to baseline LoRA fine-tuning, the proposed optimization method reduces WER by 20.98%, 6.46%, and 8.72%, respectively, across the three languages. The dual-metric early stopping strategy effectively shortens optimization time while preventing overfitting. Overall, these results demonstrate that the proposed method significantly reduces both WER and computational costs, making it highly effective for low-resource speech recognition tasks.

In order to ease the conflict between the bandwidth demand of high-rate wireless communication and the shortage of spectrum resources, a wideband spectrum sensing method based on reconfigurable filter bank (RFB) with adjustable resolution is presented. The wideband signals are uniformly divided into multi-narrowband signals by RFB, which is designed by polyphase uniform Discrete Fourier Transform (DFT) modulation, and each sub-band is sensed by energy detection. According to the idle proportion of detected sub-bands, the number of RFB sub-bands is reset in next spectrum-sensing time. By simulating with collected wideband dataset, the influence of filter bank sub-bands number and idle state proportion on the sensing results is analyzed, and then on the basis of the trade-off between spectrum-sensing resolution and computational complexity, the optimal sub-bands number of filter bank is selected, so as to improve the detection performance and save resources.

Frequency-hopping (FH) communication is widely employed in modern wireless communication systems due to its strong resistance to interference. Accurate detection of FH signals is therefore essential for effective spectrum monitoring and reliable communication in complex electromagnetic environments. However, real-world electromagnetic environments are highly complex and dynamic, with substantial noise and multiple interfering signals coexisting. These conditions pose significant challenges to frequency-hopping signal detection, particularly in terms of low signal-to-noise ratios and co-channel interference. To address these challenges, this paper proposes a frequency-hopping signal detection method based on time–frequency cancellation and the Hough transform. The received signals are first preprocessed using time–frequency cancellation and singular value decomposition to suppress noise and fixed-frequency interference. Subsequently, the time–frequency characteristics of the preprocessed signals are extracted, and the time–frequency cancellation ratio is computed to perform an initial determination of the presence of frequency-hopping signals. To further reduce false detections caused by multiple interference sources, the Hough transform is applied to analyze the time–frequency spectrum in greater detail. By jointly exploiting the geometric and statistical characteristics of the signals, accurate detection of frequency-hopping signals is achieved. Experimental results demonstrate that the proposed method enables precise detection of frequency-hopping signals under challenging electromagnetic conditions.

Abnormal broadcast activities are becoming more and more rampant, since it is easy and cheap to achieve a radio broadcasting station. These abnormal broadcasts disseminate illegal information, which can have serious negative impacts on society. However, detecting and tracing these abnormal broadcast contents can be challenging as they are often scattered and difficult to identify. In this article, we propose a semantic-aided analysis method for broadcast content (SAAM), which is based on Latent Dirichlet Allocation (LDA) to construct a model for detecting and analyzing abnormal broadcasts. The LDA model intelligently captures the latent semantic topics implied in the broadcasting content, enabling the establishment of criteria for identifying anomaly and facilitating to investigate. The analysis method further exploits the Apriori algorithm to explore the associations between the content of abnormal broadcast and relevant organizations based on semantic topics from LDA. This allows for detailed analysis of abnormal broadcasts as well as tracing relevant organizations involved in such activities. In the experiment, the abnormal broadcast data is collected to verify the accuracy of SAAM in detecting and tracing abnormal broadcast. The experiment results show that, the accuracy rate of SAAM in detecting abnormal broadcasts is more than 0.91 and the tracing-accuracy is more than 0.8.