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Hybrid Spectrum Access and Power Allocation Based On Improved Hopfield Neural Networks

机译:基于改进的Hopfield神经网络的混合谱址和功率分配

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This paper aims to solve the optimization power allocation problem based on cognitive radio network system. We propose a Hybrid Spectrum Access (HSA) method which considers the total transmit power constraint, the peak power constraint and the primary users' tolerance. In order to solve this combinational optimization problem and achieve the global optimal solution, we derived a Simulated Annealing-Hopfield neural networks (SA-HNN). The simulation results of the optimized ergodic capacity shows that the proposed optimization problem can be solved more efficiently and better by SA-HNN than HNN or Simulated Annealing (SA), and the proposed HSA method by SA-HNN can achieve a better ergodic capacity than the traditional methods.
机译:本文旨在解决基于认知无线电网络系统的优化功率分配问题。我们提出了一种混合谱址(HSA)方法,其考虑总发射功率约束,峰值功率约束和主要用户的容差。为了解决这一组合优化问题并实现全局最优解决方案,我们派生了模拟退火 - Hopfield神经网络(SA-HNN)。优化的ergodic能力的仿真结果表明,通过SA-HNN比HNN或模拟退火(SA)更有效地求解所提出的优化问题,并且SA-HNN的提出的HSA方法可以实现更好的符号能力传统方法。

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