No abstract

Summary In an energy harvesting cognitive radio network (EH‐CRN), the correspondence among the primary transmitter (PT) and primary receiver (PR) for spectrum sensing and transmission becomes ineffective as a result of insufficient SNR, finite sensing duration and interferences. We recommend designing a suitable spectrum sensing structure based on game theory for energy harvesting hybrid CRN to resolve these complications. This comprises three phases: (i) co‐operative relay approach for primary user (PU) transmission; (ii) optimal sensing interval estimation; and (iii) co‐operative energy efficient spectrum sensing and transmission. Initially, a batch of suitable secondary transmitters (STs) are identified as relay nodes for the PTs in the first phase. Next, in the second phase, a sensing interval that maximizes a utility function devised concerning average throughput, collision probability regarding secondary user (SU) and energy detector threshold is determined by applying the game theory model. Finally, co‐operative transmission is utilized in the third phase if the PR could not decode the signal from PT via STs. The presented framework is simulated using NS2, and its efficiency is estimated in terms of the parameters like transmission latency, packet loss, residual energy and possibilities of successful data delivery.

Spectrum sensing ranks amongst cognitive radio's most critical and demanding activities. For radio resource networks to use idle licensed bandwidth, secondary users need to identify the primary user's signal precisely. Cognitive radio networks (CRNs) have been proposed to improve the efficiency of spectrum use to meet the increasing demand for wireless bandwidth; they allow channel communication between secondary (unlicensed) and primary (licensed) users without interruption. A secondary user in a CRN must at all times track the presence of a primary user's signal to prevent the primary user (PU) from attempting to intervene whether the statistical variance of the PU data traffic would boost the prediction outcome provided by multiple current teaching algorithms in estimating the PU's average OFF time and the need for the best adaptive learning to predict accurate PU activities. The objective of the work has been modeled to identify the PUs signal. First, the data has been created via quadrature phase shift keying modulation and quadrature amplitude modulation by convolutional neural network (CNN). A dataset with different orthogonal frequency-division (OFDM) multiple access signals is generated.Then cyclo-stationary functions are extracted using the accumulation process fast Fourier Transform. This research aims to use glow worm swarm optimization (GWSO) to automatically check for the optimal design of CNNs without any manual work being required. Also, k-Nearest Neighbors (kNN) replaces Softmax, as it outperforms conventional CNN architectures with noisy signals. A comparison of the proposed classifier with another machine learning classifier, namely support vector machine, Decision tree (DT), clearly shows that SVM and DT are outperformed by the improved convolution neural network-glow worm swarm optimization (ICNN-GWSO) classification. Results of simulation show that the improved scheme outperforms conventional spectrum sensing systems, both in fading and non-fading settings, where efficiency is measured using measures such as chance, the total likelihood of error, and the ability to optimize data transfer possibilities.