基于OFDMA的无线多跳协同认知无线传感器网络多目标优化选路与资源分配

A. Shahzadi, Fereshteh Atri Niasar
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引用次数: 0

摘要

在频谱约束条件下,引入了认知无线电,作为一种消除频谱不足和未使用频谱之间不平衡的技术。另一方面,传感器的使用也变得越来越广泛,因为在不久的将来将成为人类生活中不可或缺的一部分。因此本文的网络拓扑结构是基于OFDMA的无线多跳协作式认知无线传感器网络。以优化不同类型的端到端性能为目标。为了最优地求解该问题,我们首先证明了该资源最优配置问题和期望问题可以表述为一个凸优化问题,并确定了其充要条件。基于这一结论,KKT条件适用于该问题,并且在任何阶段都可以找到最优解。利用拉格朗日对偶理论,在MATLAB中提出了fmincon函数。因此,该方案收敛到全局最优解。资源分配和路径选择是一个多目标优化问题。它的一个目标是找到最优跳数,第二个目标是最大限度地提高数据传输速率。讨论了干扰温度约束的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-objective optimization route selection and resource allocation in wireless multi-hop cooperative cognitive radio sensor networks based on OFDMA
Following spectrum constraints cognitive radio is introduced, as a technology to eliminate the imbalance between the lack of spectrum and unused spectrum. On the other hand, use of sensors also become more widespread, as in the near future will be an integral part of human life. So network topology in this paper is based on a Wireless Multi-Hop cooperative Cognitive Radio Sensor Networks based on OFDMA. in the objective of optimizing different types of end-to-end performance. Aiming to solve it optimally, we first show that this problem of optimal resource allocation and hopes can be formulated as a convex optimization problem and identify its necessary and sufficient conditions. Based on this conclusion, KKT conditions apply to the issue and the optimal solution can be found at any phase. we propose fmincon function in MATLAB that uses Lagrangian duality theory. The scheme thus converges to a globally optimal solution. Resource allocation and route selection is formulated as a multi-objective optimization problem. that one of its objectives is to find the optimal amount of hops and the second is considered to maximize the data transfer rate.also the effect of Interference Temperature constraints is reviewed.
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