Deep Learning for Spectrum Sensing

Abstract

In cognitive radio systems, the ability to accurately detect primary user's signal is essential to secondary user in order to utilize idle licensed spectrum. Conventional energy detector is a good choice for blind signal detection, while it suffers from the well-known SNR-wall due to noise uncertainty. In this letter, we firstly propose a deep learning based signal detector which exploits the underlying structural information of the modulated signals, and is shown to achieve the state of the art detection performance, requiring no prior knowledge about channel state information or background noise. In addition, the impacts of modulation scheme and sample length on performance are investigated. Finally, a deep learning based cooperative detection system is proposed, which is shown to provide substantial performance gain over conventional cooperative sensing methods.

Blind Detection of Underwater Acoustic Communication Signals Based on Deep Learning

“…Thus, the improved SNR may be an important factor leading to a better performance in our method. Moreover, the filter (kernel) length of the convolutional layers in the proposed SDGAN and classifier (i.e., 32) is larger than those in existing DLbased methods [17], [18] (i.e., 10 and 20). Typically, a larger filter length means a better frequency response, so the convolutional layers in the proposed networks may extract better features for detection.…”

Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

“…Table 3. The results of the proposed system out performs all other algorithms presented in [23][24][25].…”

Improving Medium Access Efficiency With Intelligent Spectrum Learning

“…In [15], the machine learning algorithms were demonstrated that appreciably outperform classical signal detection methods in the 3.5-GHz band. Furthermore, Gao et al [16] proposed a deep learning-based signal detector that exploits the underlying structural information of the modulated signals. Peng et al [17] explored the transfer learning to address robustness in DL-based spectrum sensing to improve the robustness.…”

“…Substituting (9) and (12) into (8), the achieved throughput with inference errors is calculated as (15), as shown at the bottom of this page. Then, by substituting (15) into 7, the system throughput can be achieved as (16), as shown at the bottom of this page, where γ denotes the inference error rate and α is the probability that over-estimation occurs.…”