A gradient and double threshold-based algorithm for improved wideband spectrum sensing

There is a scarcity of wireless spectrum because of the industry’s growth spurred by the quick development of wireless communication. Future communication systems that use dynamic spectrum access and cognitive radio may be able to resolve this problem. In order to maximize spectrum use and transmission dependability, cognitive radios (CRs) adapt their settings in response to environmental inputs. This allows them to detect and take advantage of spectrum gaps. A key element of CR is spectrum sensing, which is the periodical monitoring of the spectrum of radio bands to guarantee interference-free operation. However, there are issues with spectrum sensing algorithms in noisy and heavily shadowed locations, such as the concealed primary user dilemma. Several factors need to be considered by cooperative spectrum detection algorithms, such as decision fusion methods, cooperation overheads, and sensing speed and time. The purpose of this work is to improve sensing performance and suggest. The objective of this research is to improve the performance of sensing and suggest sophisticated algorithms for basic user signal identification and wide-band sensing effectiveness. The work aims at optimizing spectrum usage, lowering computational complexity, improving detection performance, and improving noise uncertainty response by evaluating radio frequency white spaces across bands. To ensure their efficacy, the suggested algorithms are tested on hardware and software platforms.