Wideband cyclostationary spectrum sensing and characterization for cognitive radios

Motivated by the spectrum scarcity problem, Cognitive Radios (CRs) have been proposed as a solution to opportunistically communicate over unused spectrum licensed to Primary users (PUs). In this context, the unlicensed Secondary users (SUs) sense the spectrum to detect the presence or absence of PUs, and use the unoccupied bands without causing interference to PUs. CRs are equipped with capabilities such as, learning, adaptability, and recongurability, and are spectrum aware. Spectrum awareness comes from spectrum sensing, and it can be performed using different techniques

Robust Deep Sensing Through Transfer Learning in Cognitive Radio

We propose a robust spectrum sensing framework based on deep learning. The received signals at the secondary user's receiver are filtered, sampled and then directly fed into a convolutional neural network. Although this deep sensing is effective when operating in the same scenario as the collected training data, the sensing performance is degraded when it is applied in a different scenario with different wireless signals and propagation. We incorporate transfer learning into the framework to improve the robustness. Results validate the effectiveness as well as the robustness of the proposed deep spectrum sensing framework

Physical Layer Defenses Against Primary User Emulation Attacks

Cognitive Radio (CR) is a promising technology that works by detecting unused parts of the spectrum and automatically reconfiguring the communication system\u27s parameters in order to operate in the available communication channels while minimizing interference. CR enables efficient use of the Radio Frequency (RF) spectrum by generating waveforms that can coexist with existing users in licensed spectrum bands. Spectrum sensing is one of the most important components of CR systems because it provides awareness of its operating environment, as well as detecting the presence of primary (licensed) users of the spectrum