Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking
Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking
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发表时间：
2004
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通讯作者：
C. Rieser;C. Bostian
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作者：
C. Rieser;C. Bostian
这项研究的重点是开发一种认知无线电，它可以在不可预见的通信环境中可靠地运行，例如灾难和应急响应社区所面临的环境。认知无线电还可能提供开辟二级或补充频谱市场的潜力，有效缓解感知到的频谱紧缩，同时为消费者提供新的有竞争力的无线服务。提出了在无线电中嵌入认知的结构和过程，包括讨论该机制如何从人类学习过程中衍生出来并映射到称为 BioCR 的数学形式主义。介绍了在硬件测试台和模拟测试台中实施和测试模型的结果，重点是可快速部署的灾难通信。研究贡献包括在无线电架构中开发受生物学启发的认知模型，提出可以使用遗传算法操作来实现该模型，开发算法框架来实现认知机制，开发用于评估认知引擎行为的认知无线电仿真工具集，并使用该工具集分析认知引擎在不同操作场景中的性能。具体来说，本研究提出并详细介绍了如何利用分布式遗传算法操作的混沌元知识搜索、优化和机器学习特性将该模型映射到与动态多级分布式存储器相结合的可计算数学框架。该系统形式主义与现有的认知无线电方法（包括传统上脆弱的人工智能方法）形成鲜明对比。开发并介绍了认知引擎架构和算法框架，包括无线信道遗传算法（WCGA）、无线系统遗传算法（WSGA）和认知系统监控器（CSM）。实验结果表明，认知引擎在不断变化的无线条件下找到了主机无线电操作参数之间的最佳权衡，而基线自适应控制器仅根据阈值增加或减少其数据速率，由于无法学习而在不需要时经常浪费可用带宽或多余功率。这种方法的局限性包括在某些情况下，由于算法参数的敏感性，引擎无法正确响应、出现答案重影、在解决方案之间来回跳动。未来的研究可以探索发动机运行的极限并调查改进的机会，包括如何最好地配置遗传算法和发动机数学以避免发动机解决方案错误。未来的研究还可能包括将认知引擎扩展到认知无线电网络，并研究对安全通信的影响。
This research focuses on developing a cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complementary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a cognitive radio simulation toolset for evaluating the behavior the cognitive engine, and using this toolset to analyze the cognitive engine's performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The cognitive engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the engine's operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and engine mathematics to avoid engine solution errors. Future research also could include extending the cognitive engine to a cognitive radio network and investigating implications for secure communications.