A Cognitive Radio Spectrum Sensing Algorithm to Improve Energy Detection at Low SNR

Abstract

Energy detection is among the most popular spectrum sensing method for spectrum sensing due its low complexity. Unfortunately, its performance is poor at low SNR. In this paper we proposed a spectrum sensing method for cognitive radio network that improves the performance of energy detection.

A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum

“…The logistic regression, SVM, and deep learning OFDM (DP-OFDM) spectrum sensing model are used for training and testing, and the error threshold Ω th is adjusted to obtain the detection probability. As described in [28], a cyclic feature detection method based on compressed sensing, such as [11] proposed the energy detection method, and [15] proposed the autocorrelation detection method. Under the condition of false alarm probability P f = 0:05, the corresponding decision thresholds are set for each of the above methods, and the above methods are simulated in the simulation platform, respectively.…”

“…As the core technology of the cognitive radio network (CRN), spectrum sensing can improve spectrum utilization and alleviate spectrum resources [8][9][10]. Traditional spectrum sensing is mainly done by mathematical methods of signal processing, including energy detection [11], matched filter detection [12], and cyclostationary feature detection [13]. [14] proposed a spectrum sensing method based on correlation detection, the correlation of cyclic prefix (CP) was used in OFDM, and the sampled data was subjected to correlation operation.…”

Broadband octagonal patch antenna for cognitive radio applications

“…It use Dynamic Spectrum Access (DSA) where users can use unused licensed spectrum from the primary users [3,4]. As a result, the spectrum usage in microwave bands can improved effectively and efficiency by using the DSA without any interference to the primary user in the used spectrum.…”