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Neural network optimization for energy-optimal cooperative computing in wireless communication system

机译:无线通信系统中能量最优合作计算的神经网络优化

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摘要

Thanks to the rapidly development of optimization algorithm, more energy can be saved in the communication system when executing an application. In recent years, allocating limited resources in a cooperative manner to maximize energy efficiency in emerging sensor networks has attracted a lot of attention. In this paper, a one layer projection neural network subject to linear equalities and bound constraints is introduced and applied in mobile wireless sensor network to make it energy-efficient by reasonably allocating local and remote data sizes when processing an application within a certain period of time. Firstly, an optimal partition to minimize the total energy consumption of the local and helper sensor nodes is proposed. Then, the neural network model is described and the optimality and convergence of the proposed model are analyzed. Finally, some simulation results are given to show that the proposed algorithm is very effective to solve the energy efficient cooperative computing.
机译:由于优化算法的快速发展,在执行应用时,可以在通信系统中保存更多能量。近年来,以合作方式分配有限的资源,以最大限度地提高新兴传感器网络中的能源效率引起了很多关注。在本文中,引入了一个层投影神经网络,其受到线性平衡和束缚约束,并应用于移动无线传感器网络中,通过在一定时间段内处理应用程序时,通过合理地分配本地和远程数据大小来使其能够节能。首先,提出了最佳分区,以最小化局部和辅助传感器节点的总能量消耗。然后,描述了神经网络模型,并分析了所提出的模型的最优性和收敛性。最后,给出了一些模拟结果表明所提出的算法非常有效地解决节能协同计算。

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