To cope up with drastically increasing demand for radio resources lead to raise a challenge to the wireless community. The limited radio spectrum and fixed spectrum allocation strategy have become a bottleneck for various wireless communication. Cognitive Radio (CR) technology along with potential benefits of machine learning has attracted substantial research interest especially in the context of spectrum management. However, a variety of performance attributes as objectives draw attention during the technological preparations for spectrum management such as higher spectral efficiency, lower latency, higher network capacity, and better energy efficiency as these objectives are often conflicting with each other. Hence, this paper addresses the spectrum allocation problem concerning network capacity and spectrum efficiency as conflicting objectives and model the scenario as a multi-objective optimization problem in CR networks. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) which combines the feature of evolutionary algorithm and machine learning called Non-dominated Sorting Genetic Algorithm based on Reinforcement Learning (NSGA-RL) is proposed which incorporates a self-tuning parameter approach to handle multiple conflicting objectives. The numerical findings validate the effectiveness of the proposed algorithm through the Pareto optimal set and obtain optimal solution efficiently to satisfy various requirements of spectrum allocation in CR networks.

为了应对对无线电资源的急剧增长的需求，对无线社区提出了挑战。有限的无线电频谱和固定频谱分配策略已成为各种无线通信的瓶颈。认知无线电（CR）技术以及机器学习的潜在好处已经引起了广泛的研究兴趣，尤其是在频谱管理方面。但是，在频谱管理的技术准备过程中，作为目标的各种性能属性引起了人们的注意，例如更高的频谱效率，更低的等待时间，更高的网络容量以及更好的能源效率，因为这些目标经常相互冲突。因此，本文将与网络容量和频谱效率相关的频谱分配问题作为冲突目标加以解决，并将该方案建模为CR网络中的多目标优化问题。提出了一种结合进化算法和机器学习特性的改进版本的非支配排序遗传算法-II（NSGA-II），称为基于增强学习的非支配排序遗传算法（NSGA-RL）。调整参数方法来处理多个冲突目标。数值结果通过帕累托最优集验证了所提算法的有效性，并有效地获得了最优解，以满足CR网络中频谱分配的各种要求。提出了一种结合进化算法和机器学习特性的改进版本的非支配排序遗传算法-II（NSGA-II），称为基于增强学习的非支配排序遗传算法（NSGA-RL）。调整参数方法来处理多个冲突目标。数值结果通过帕累托最优集验证了所提算法的有效性，并有效地获得了最优解，以满足CR网络中频谱分配的各种要求。提出了一种结合进化算法和机器学习特性的改进版本的非支配排序遗传算法-II（NSGA-II），称为基于增强学习的非支配排序遗传算法（NSGA-RL）。调整参数方法来处理多个冲突目标。数值结果通过帕累托最优集验证了所提算法的有效性，并有效地获得了最优解，以满足CR网络中频谱分配的各种要求。