Cognitive radio-based Internet of Things (CR-IoTs) provide an efficient spectrum management for IoT networks with massive wireless access and data transmission needs. As one of the key technologies of CR-IoT, spectrum sensing is of great research significance. Motivated by the recent boom on applications of deep learning in wireless communications networks and IoT, several spectrum sensing methods based on deep learning have emerged. However these algorithms train the sensing models with the extracted features of received signals and require a retraining of sensing models when the number of sensing antennas changes. Thus, we develop multiple-antenna spectrum sensing methods based on convolutional neural networks (MASSnet) using the in-phase (I) and quadrature (Q) components of the signals as the input. The three schemes of MASSnet also provide the flexibility to choose between retraining the sensing models or using the obtained models for different sensing antenna configurations. Experiment results demonstrate the superior performance of the proposed methods over existing deep learning-based spectrum sensing methods in terms of probability of detection especially in very low signal-to-noise ratio (SNR) condition. Furthermore, the proposed methods have good generalization ability to new noise distribution, new fading channel, different frequency offsets, and detecting signals with a new modulation even without retraining.

Frequency Modulation (FM) broadcast signals, regarded as opportunistic signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on Received Signal Strength (RSS) for positioning, the accuracy of which needs improvement. In this paper, we introduce FM-Pnet, an end-to-end FM-based positioning method that leverages deep learning. This method utilizes the time-frequency representation of FM signals as network input, enabling automatically learning of deep features for positioning. We also propose two strategies, noise injection and enriching training samples, to enhance the model’s generalization performance over long time spans. We construct datasets for both indoor and outdoor scenarios and conduct extensive experiments to validate the performance of our proposed method. Experimental results demonstrate that FM-Pnet significantly outperforms traditional RSS-based positioning methods in terms of both positioning accuracy and stability.

Deep learning has been widely used in automatic modulation classification (AMC) recently. Most of deep learning-based AMC uses a single network model to deal with radio signals with a single input format. In this paper, we propose a hybrid heterogeneous modulation classification architecture named DeepSIG, which integrates Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Graph Neural Network (GNN) models in a single framework to process radio signals with heterogeneous input formats, i.e., in-phase (I) and quadrature (Q) sequences, images mapped from IQ signals and graphs converted from IQ signals, to extract and integrate the features from different perspectives. A fusion training mechanism is presented to train DeepSIG. We use three different radio signal datasets for simulations. Results show that our proposed DeepSIG performs the best in terms of classification accuracy compared with the three methods with single input, i.e., sequence, image or graph. The performance gain is larger in few-shot scenarios.