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Field programmable gate array implementation of spectrum allocation technique for cognitive radio networks

机译:认知无线电网络频谱分配技术的现场可编程门阵列实现

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摘要

Cognitive radio is an emerging technology in wireless communications for dynamically accessing under-utilized spectrum resources. In order to maximize the network utilization, vacant channels are assigned to cognitive users without interference to primary users. This is performed in the spectrum allocation (SA) module of the cognitive radio cycle. Spectrum allocation is a NP hard problem, thus the algorithmic time complexity increases with the cognitive radio network parameters. This paper addresses this by solving the SA problem using Differential Evolution (DE) algorithm and compared its quality of solution and time complexity with Particle Swarm Optimization (PSO) and Firefly algorithms. In addition to this, an Intellectual Property (IP) of DE based SA algorithm is developed and it is interfaced with PowerPC440 processor of Xilinx Virtex-5 FPGA via Auxiliary Processor Unit (APU) to accelerate the execution speed of spectrum allocation task. The acceleration of this coprocessor is compared with the equivalent floating and fixed point arithmetic implementation of the algorithm in the PowerPC440 processor. The simulation results show that the DE algorithm improves quality of solution and time complexity by 29.9% and 242.32%, 19.04% and 46.3% compared to PSO and Firefly algorithms. Furthermore, the implementation results show that the coprocessor accelerates the SA task by 76.79-105x and 5.19-6.91 x compared to floating and fixed point implementation of the algorithm in PowerPC processor. It is also observed that the power consumption of the coprocessor is 26.5 mW. (C) 2014 Elsevier Ltd. All rights reserved.
机译:认知无线电是无线通信中用于动态访问未充分利用的频谱资源的新兴技术。为了最大化网络利用率,将空闲信道分配给认知用户,而不会干扰主要用户。这是在认知无线电周期的频谱分配(SA)模块中执行的。频谱分配是一个NP难题,因此算法的时间复杂度随着认知无线电网络参数的增加而增加。本文通过使用差分进化(DE)算法解决SA问题来解决此问题,并将其解决方案的质量和时间复杂度与粒子群优化(PSO)和Firefly算法进行了比较。除此之外,还开发了基于DE的SA算法的知识产权(IP),并通过辅助处理器单元(APU)与Xilinx Virtex-5 FPGA的PowerPC440处理器接口,以加快频谱分配任务的执行速度。将此协处理器的加速与PowerPC440处理器中算法的等效浮点和定点算法实现方式进行比较。仿真结果表明,与PSO和Firefly算法相比,DE算法将解决方案的质量和时间复杂度提高了29.9%和242.32%,19.04%和46.3%。此外,实现结果表明,与PowerPC处理器中算法的浮点和定点实现相比,协处理器将SA任务加速了76.79-105x和5.19-6.91 x。还观察到协处理器的功耗为26.5 mW。 (C)2014 Elsevier Ltd.保留所有权利。

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