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Adaptive multi-objective Optimization scheme for cognitive radio resource management

机译：认知无线电资源管理的自适应多目标优化方案

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摘要

Cognitive Radio is an intelligent Software Defined Radio that is capable to alter its transmission parameters according to predefined objectives and wireless environment conditions. Cognitive engine is the actuator that performs radio parameters configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance. The optimization relies on adapting radio transmission parameters to environment conditions using constrained optimization modeling called fitness functions in an iterative manner. These functions include minimizing power consumption, Bit Error Rate, delay and interference. On the other hand, maximizing throughput and spectral efficiency. Cross-layer optimization is exploited to access environmental parameters from all TCP/IP stack layers. AMOS uses adaptive Genetic Algorithm in terms of its parameters and objective weights as the vehicle of optimization. The proposed scheme has demonstrated quick response and efficiency in three different scenarios compared to other schemes. In addition, it shows its capability to optimize the performance of TCP/IP layers as whole not only the physical layer.
机译：认知无线电是一种智能的软件定义无线电，能够根据预定义的目标和无线环境条件更改其传输参数。认知引擎是通过利用优化和机器学习技术执行无线电参数配置的执行器。在本文中，我们提出了一种用于认知无线电资源管理的自适应多目标优化方案（AMOS），以改善频谱操作和网络性能。该优化依赖于使用被称为适应度函数的约束优化模型以迭代方式使无线电传输参数适应环境条件。这些功能包括最小化功耗，误码率，延迟和干扰。另一方面，最大化吞吐量和频谱效率。利用跨层优化从所有TCP / IP堆栈层访问环境参数。 AMOS在参数和目标权重方面使用自适应遗传算法作为优化的手段。与其他方案相比，所提出的方案在三种不同的情况下显示了快速响应和效率。此外，它还显示了优化整个TCP / IP层（不仅是物理层）性能的能力。

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