Effective utilization of spectrum resources is an important factor in wireless communication which reduces spectrum scarcity. Over the years, communication systems use different frequency bands, and the users are categorized into licensed and unlicensed users. Most of the wireless bands are typically licensed; as a result, accommodation of new technologies such as Internet of Things and machine to machine communication becomes difficult. So it is essential to obtain a wireless spectrum to adopt new technologies. Cognitive radio technology is introduced to improve such spectrum utilization. Reports state that most of the licensed spectrums are underutilized, and few spectrums are overutilized. Cognitive radio networks help to exploit the licensed spectrum and access the spectrum without any interference to the licensed user. Through its spectrum sensing and spectrum sharing process, cognitive radio network gains more attention in wireless communication. This research work proposed an efficient optimized spectrum sensing technique for cognitive radio networks through dragonfly optimization algorithm along with the adaptive threshold process. Proposed work performs better in terms of detection accuracy and efficiency when compared to conventional spectrum sensing schemes such as linear support vector machine and particle swarm optimization models.

中文翻译：

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一种有效的基于自适应阈值的蜻蜓优化模型，用于认知无线电网络中的协作频谱感知

频谱资源的有效利用是无线通信中减少频谱稀缺性的重要因素。多年来，通信系统使用不同的频段，用户分为授权用户和非授权用户。大多数无线频段通常都获得许可；因此，物联网和机器对机器通信等新技术的适应变得困难。因此，获得无线频谱以采用新技术至关重要。认知无线电技术被引入以提高这种频谱利用率。报告指出，大多数许可频谱未得到充分利用，很少有频谱被过度利用。认知无线电网络有助于开发许可频谱并在不对许可用户造成任何干扰的情况下访问频谱。通过其频谱感知和频谱共享过程，认知无线电网络在无线通信中获得了更多的关注。本研究工作通过蜻蜓优化算法和自适应阈值过程，提出了一种用于认知无线电网络的高效优化频谱感知技术。与线性支持向量机和粒子群优化模型等传统频谱感知方案相比，所提出的工作在检测精度和效率方面表现更好。