In this study, a scalable coprocessor for accelerating the Differential Evolution (DE) algorithm is presented. The coprocessor is interfaced with PowerPC embedded processor of Xilinx Virtex-5 FX70T Field Programmable Gate Array. In the proposed design, the DE algorithm module is tightly coupled with fitness function module to reduce communication and control overhead. The fixed point DE algorithm is implemented in the coprocessor whereas both fixed and floating point DE are implemented in the embedded processor. Performance of the coprocessor is evaluated by optimising benchmark functions of different complexities. The implementation results show that the coprocessor is 73.14-160.2× and 2.19-27.63× faster compared to the software execution time of the floating and fixed point algorithm respectively. As a case study, spectrum allocation problem of cognitive radio network is evaluated with the coprocessor. Results show an acceleration of 76.79-105× and 5.19-6.91× with respect to floating and fixed point DE in embedded processor. It is also observed that the application occupies 56% of BRAM, 54% of DSP48E, 16% of slice LUTs and maximum frequency of operation as 63.55 MHz in a Virtex-5 FPGA. This type of coprocessor is suitable for embedded applications where the fitness function remains unchanged.
Cognitive radio is a promising technology for efficient spectrum utilization. It explores dynamic spectrum access features while satisfying interference constraints. In this work, a joint power and spectrum allocation algorithm is proposed to maximize the cognitive network throughput while satisfying interference constraints of both primary and secondary users in the network. Evolutionary algorithms are used to solve the joint power and spectrum allocation problem. Furthermore, the algorithmic performance is compared in terms of quality of solution. And also we optimized the maximum utilization of the network and capacity of each user simultaneously by using Multi‐Objective Differential Evolution (MODE) and Nondominated Sorting Genetic Algorithm II (NSGA‐II). Simulation results show that the pareto optimal fronts provide the trade‐off solutions between total network utilization and individual sum capacity of user.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.