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In the current scenario, there is a drastic increase in air traffic. The air to ground communication plays a crucial role in the air traffic control system. There is a limited spectrum available for aircraft to establish a connection with the Air Traffic Controller (ATC). With air traffic growth, the available spectrum is getting more congested. This paper proposed an Advanced Squirrel Algorithm (ASA)‐trained neural network (NN) for efficient spectrum sensing for cognitive radio‐based air traffic control applications. ASA is a novel metaheuristic‐based training algorithm for an NN. With the proposed algorithm, it is possible to dynamically allocate the unused spectrum for air to ground communication between aircraft and ATC. The quantitative analysis of the proposed ASA‐NN‐based spectrum sensing is done by comparing it with the existing metaheuristic‐based NN training algorithms, namely, particle swarm optimization Gravitational Search Algorithm (PSOGSA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and artificial bee colony (ABC). Simulation‐based evaluation shows that the proposed ASA‐NN is capable of efficiently detecting the spectrum holes with high convergence rate as compared to PSOGSA‐, PSO‐, GSA‐, and ABC‐based algorithms. [ABSTRACT FROM AUTHOR]

The spectrum overcrowding is one of the prime issues faced by wireless telecommunication based applications. The network blockage causing the disconnection or call drops is another important concern. These problems, are needed to be addressed for implementing the 5G and beyond technologies. Therefore, to tackle the issues of spectrum overcrowding and network blockage simultaneously a Cognitive Radio (CR) technology based relay network is proposed in this work. The accurate detection of the primary user's signal by the cognitive radio users is the most integral functioning of the cognitive radio networks. The existing spectrum sensing using Deep Neural Network (DNN) and Convolutional Neural Network (CNN) techniques have their limitations concerned with accurate prediction and classification of vacant spectrum due to their tendency of getting jammed to the local optima. In this paper, we firstly propose a novel mutated Modified Whale Optimization Algorithm (MWOA) trained Spiking Neural Network (SNN) based spectrum sensing technique for the efficient detection of spectrum holes. Here, the weights of the SNN are trained by means of MWOA for efficiently predicting the spectrum holes. The proposed scheme exploits underlying structural information of the sensed signals via continuous wavelet transforms. The proposed scheme does not require any priori information about the channel state and is shown to achieve state of the art performance in the detection of spectrum holes. The simulation results have inferred that the proposed CR based relay model with the MWOA trained SNN based spectrum sensing has significantly improved the performance of the User Equipment (UE) in the network blockage area in terms of higher opportunistic throughput and lower BER (Bit Error Rate). The MWOA has proved to be an efficient training algorithm for SNN with the validation accuracy of 98%. • Proposed and developed Cognitive Radio based relay network with efficient spectrum sensing. • Developed a novel Modified Whale Optimization Algorithm based training for the Spiking Neural Network. • Implemented mutation and expert balance to the conventional whale optimization algorithm. • Implemented efficient real time spectrum sensing via USRPs. [ABSTRACT FROM AUTHOR]