Cooperative Channel Selection With Q-Reinforcement Learning and Power Distribution in Cognitive Radio Networks

With the increasing number of wireless communication devices, there may be a shortage of non-licensed spectrum, and at the same time, licensed spectrum may be underutilized by the primary users. The utilization of licensed spectrum can be improved using cognitive radio techniques. The proposed work allows secondary users to use the correct slot period of the channel as per their need. Particle swarm optimization technique is used to optimize the resource allocation. The aim of the proposed work is to determine the optimal throughput and power of available channels between the communicating nodes and improve the routing performance by selecting the best channel. Mathematical equation is derived that represents the channel selection relationship from the Q-value, congestion throughput, and benefit value. Network simulator-2 is used to simulate the proposed work and compared with the existing work. From the simulation results, it is observed that routing performance is improved in terms of throughput, packet delivery ratio, delay, packet dropped, and normalized routing overhead.

Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique

In the past few decades, the field of image processing has seen a rapid advancement in the correlation filters, which serves as a very promising tool for object detection and recognition. Mostly, complex filter equations are used for deriving the correlation filters, leading to a filter solution in a closed loop. Selection of optimal tradeoff (OT) parameters is crucial for the effectiveness of correlation filters. This paper proposes extended particle swarm optimization (EPSO) technique for the optimal selection of OT parameters. The optimal solution is proposed based on two cost functions. The best result for each target is obtained by applying the optimization technique separately. The obtained results are compared with the conventional particle swarm optimization method for various test images belonging from different state-of-the-art datasets. The obtained results depict the performance of filters improved significantly using the proposed optimization method.

Assessment of performance metrics for fusion network

The arrangement which does not necessitate any infrastructure for doing discussion among nodes is called MANET. The direction-finding technique, a mixture of ACO (ant colony optimization) and particle swarm optimization (PSO) technique, is designed in the paper. The designed technique is judged against several alternatives of particle swarm optimization technique and ant colony optimization technique, i.e.,EDNR+PSO, AntChain Protocol, Improvised Ant Colony Routing (IACR), and ANTALG. Open-source simulator NS 2.3 is employed for carrying out the simulation. From the outcome, it has been verified that the designed technique is finest in contrast to other alternatives of PSO and ACO.Routing overheads of the designed algorithm are reduced in contrast to other methods.