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首页> 外文期刊>IEEE Transactions on Cognitive Communications and Networking >Cognitive Radio Algorithms Coexisting in a Network: Performance and Parameter Sensitivity
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Cognitive Radio Algorithms Coexisting in a Network: Performance and Parameter Sensitivity

机译：网络中共存的认知无线电算法：性能和参数敏感性

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This paper studies the performance of cognitive radios in a scenario where different pairs of radios adopt different cognition/decision making approaches. We want to assess: 1) if there is a category of cognitive radio algorithms that consistently outperforms the others and 2) how sensitive different algorithms are to suboptimal parameter setting. Our approach is to take a representative set of well-known classes of cognitive radio algorithms, mix and match them throughout thousands of simulations, and determine which seem to perform better. We find that choosing a cognitive radio algorithm means finding a balance between the best-case performance obtained by optimally setting all parameters, and the behavior in uncontrolled, unknown environments, where suboptimal decisions are likely to be made. The approaches we consider, namely reinforcement learning, optimization metaheuristics, multi-armed bandit solutions, and supervised learning, greatly differ in their performance. For example, schemes that are able to achieve a high throughput in our simulation study are more sensitive to suboptimally set parameters.
机译：本文研究了在不同的无线电对采用不同的认知/决策方法的情况下认知无线电的性能。我们想评估：1）是否存在一类认知无线电算法始终优于其他算法； 2）不同算法对次优参数设置的敏感度。我们的方法是采用一组代表性的知名无线电认知算法类，在数千个模拟中将它们混合并匹配，并确定哪个似乎更好。我们发现，选择认知无线电算法意味着在通过最佳设置所有参数获得的最佳情况性能与不受控制的未知环境中的行为之间进行权衡，在这种情况下可能做出次优的决策。我们考虑的方法（即强化学习，优化元启发式算法，多臂匪徒解决方案和监督学习）的性能差异很大。例如，在我们的模拟研究中能够实现高吞吐量的方案对次优化设置参数更为敏感。

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