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首页> 外文期刊>International journal of communication systems >Genetic algorithm-based scheduling in cognitive radio networks under interference temperature constraints
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Genetic algorithm-based scheduling in cognitive radio networks under interference temperature constraints

机译：干扰温度约束下认知无线电网络中基于遗传算法的调度

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摘要

The proliferation of wireless technologies and services has intensified the demand for the radio spectrum. However, the currently existing fixed spectrum assignment policy leads to an inefficient and unevenly distributed spectrum utilization. Cognitive radio paradigm has been proposed to alleviate these drawbacks by employing dynamic spectrum access (DSA) methodology. Federal Communications Commission (FCC) has proposed the interference temperature model, which enables the unlicensed users to utilize the licensed frequencies simultaneously with the licensed users as long as they conform to the interference temperature constraints. Recently, throughput and delay optimal schedulers that meet the interference temperature constraints in cognitive radio networks have been formulated in the literature. However, these schedulers have high computational complexity. In this paper, we propose genetic algorithm (GA)-based suboptimal methods addressing these throughput and delay optimal scheduling problems. The simulation results corroborate that our GA-based approach yields very close performance to the optimal solutions and operates with much lower complexity.
机译：无线技术和服务的激增加剧了对无线电频谱的需求。但是，当前存在的固定频谱分配策略导致效率低下和频谱分配不均匀。已经提出认知无线电范例以通过采用动态频谱接入（DSA）方法来减轻这些缺点。联邦通信委员会（FCC）提出了干扰温度模型，该模型使无执照的用户能够与许可用户同时使用许可频率，只要它们符合干扰温度限制。最近，在文献中已经提出了满足认知无线电网络中的干扰温度约束的吞吐量和延迟最优调度器。但是，这些调度程序具有很高的计算复杂度。在本文中，我们提出了基于遗传算法（GA）的次优方法来解决这些吞吐量和延迟最优调度问题。仿真结果证实了我们基于遗传算法的方法与最佳解决方案的性能非常接近，并且操作复杂度低得多。

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