In this letter, we study the performance of the energy detector for opportunistic spectrum access (OSA) by a secondary user (SU) in dynamic scenarios, where a primary user (PU) switches from active to inactive at random time instances. Specifically, we model the PU activity using a two-state Markov chain and derive analytical expressions for the probabilities of detection and false alarm that explicitly consider the changes in the PU signal that may occur during the sensing interval by the SU. The expressions obtained are corroborated with numerical results obtained from simulations.

Implementation of dynamic spectrum access (DSA) in cognitive radio (CR) systems requires the unlicensed secondary users (SU) to implement spectrum sensing to monitor the activity of the licensed primary users (PU). Energy detection (ED) is one of the most widely used methods for spectrum sensing in CR systems, and in this paper we present a novel ED algorithm with an adaptive sensing threshold. The three-event ED (3EED) algorithm for spectrum sensing is considered for which an accurate approximation of the optimal decision threshold that minimizes the decision error probability (DEP) is found using Newton’s method with forced convergence in one iteration. The proposed algorithm is analyzed and illustrated with numerical results obtained from simulations that closely match the theoretical results and show that it outperforms the conventional ED (CED) algorithm for spectrum sensing.

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper outperformed alternative approaches for classifying digitally modulated signals that included conventional classifiers that employed CSP-based techniques, as well as alternative DL-based classifiers that used convolutional neural networks (CNNs) or residual networks (RESNETs) with the in-phase/quadrature (I/Q) data used for training and classification.

Spectrum sensing (SS) based on energy detection (ED) is a simple yet effective approach to detect the presence of unknown signals that are active in a specific band of frequencies. The classical ED (CED) algorithm uses the value of the energy detected in the current sensing slot as test statistic and has a fixed threshold, but improved signal detection performance is possible by modifying the test statistic and/or the detection threshold. In this paper we propose a novel ED algorithm for SS that considers a binary activity model for the signal to be detected and combines the use of an average energy test statistic with an adaptive decision threshold for improved detection performance. We present the analytical characterization of the proposed test statistic in terms of its mean and variance, and derive the expressions corresponding to the correct decision probability (CDP) and false alarm probability (FAP). Using the derived CDP and FAP expressions, we also determine the detection thresholds that yield desired values for the false alarm and missed detection probabilities. The proposed algorithm is illustrated with numerical results obtained from simulations, which confirm our theoretical findings and also show that the algorithm outperforms alternative adaptive ED algorithms.

Cooperative sensing has been shown to improve the performance of spectrum sensing in cognitive radio (CR) networks where multiple secondary users are sending local sensing information to a CR fusion centre (FC) which makes the final determination on the occupancy of a given frequency band by licensed primary users. In this study, the authors observe the use of a hidden Markov model for evaluating the performance of cooperative sensing at the FC and propose a method that uses the history of FC sensing decisions to estimate the cooperative probabilities of detection and false alarm. The proposed method enables the FC to become aware when the performance of cooperative spectrum sensing degrades without requiring knowledge of the local sensing statistics. Numerical results obtained from simulations confirm the effectiveness of the proposed method for both soft and hard combining schemes in practical scenarios with noise and/or multipath fading.

The past decade has witnessed the confluence of Intelligent Transportation Systems and Vehicular Networks that is expected to change drastically incident detection and to provide timely dissemination of traffic-related information to the traveling public. As a first step in this direction we introduce NOTICE, a secure and privacy-aware architecture for the notification of traffic incidents. Using sensor belts embedded in the roadway, traffic-related messages and advisories are carried between belts by passing cars. NOTICE moves the responsibility for making decisions about traffic-related information dissemination to the infrastructure rather than leaving those decisions with the drivers, which may have incomplete or incorrect knowledge. Extensive simulation have confirmed that NOTICE can provide “up-to-the-minute” notification of traffic-related incidents to the various participants in the traffic.