Experience level analysis for a cognitive radio engine
Experience level analysis for a cognitive radio engine
复制标题
认知无线电引擎的经验水平分析
DOI：
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发表时间：
2018
影响因子：
1.4
通讯作者：
T. Bose
中科院分区：
文献类型：
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作者：
Hamed Asadi;H. Volos;M. Marefat;T. Bose
认知无线电引擎（CE）是实现认知无线电的高级自适应算法的地方。CE是一个智能代理，它观察无线电环境并选择满足应用目标的最佳通信设置。在这个过程中，提供可靠的性能是CE面临的主要挑战之一。因此，在设计CE中最重要的问题之一是表征和可靠地预测CE在不同操作场景中的性能的能力。操作场景被定义为操作目标、信道可用性和信道质量度量的集合。在本文中，我们开发了几个性能评估和预测指标来量化不同CE算法的知识量独立的实现方法和/或其操作场景。使用这些新的指标，我们能够提供一个更准确的估计的学习过程和未来的性能，每个单独的CE算法。进行了一些基于模拟的实验。我们的研究结果表明，所提出的上下文CE算法的基础上开发的知识指标是能够提高无线通信系统的客观回报显着。实际上，上下文CE能够比具有固定探索速率的CE多递送大约10%的数据。
A cognitive radio engine (CE) is where the advanced adaptation algorithms for a cognitive radio is implemented. A CE is an intelligent agent which observes the radio environment and chooses the best communication settings that meet the application’s goal. In this process, providing reliable performance is one of the major challenges faced by a CE. Therefore, one of the most important issues in designing CEs is the ability to characterize and reliably predict performance of the CE in different operating scenarios. An operating scenario is defined as the set of the operating objective, channel availability, and channel quality metrics. In this paper, we develop several performance evaluation and prediction indices to quantify the amount of knowledge of different CE algorithms independently of the implementation approach and/or their operating scenarios. Using these new indices, we are able to provide a more accurate estimation of the learning process and future performance of each individual CE algorithm. A number of simulation-based experiments was conducted. Our results show that proposed contextual CE algorithms based on the developed knowledge indicators is able to improve the wireless communication system’s objective rewards significantly. In effect, the contextual CE is able to deliver about 10% more data than the CE with the fixed exploration rate.