In recent years, the usage of wireless devices and wireless service has been increased exponentially and it results in spectrum scarcity. The policies of regulatory authority employ static spectrum allocation methods and assign new spectrum band for offering new kind of services to the users. These approaches lead to poor utilization of available spectrum bands. The cognitive radio (CR) provides better solution to these problems and it mainly focuses on efficient utilization of available spectrum bands. CR network (CRN) has to adopt spectrum management techniques to assign the unused spectrum band to the CR users by following a sequence of actions such as spectrum sensing, decision and management. Spectrum sensing is a vital process in spectrum allocation. In the traditional approaches, sensing accuracy is brought down by the probabilities of misdetection and false alarm rate and hence, sensing accuracy becomes low. As a result, the Cognitive Users (CUs) face the challenge of prolonged time to complete perfect cognitive radio communication. To overcome this issue, a cooperative spectrum sensing technique with a characteristic based cluster classifier has been proposed. This classifier learns the states and their transitions in the radio frequency environment, as well as the primary user activities at regular time intervals to support the spectrum decision technique. The novelty of the work is to propose a hybrid approach which combines clustering with expected maximization (EM) algorithm and reinforcement learning (RL) techniques. This hybrid approach enhances the system performance with accurate sensing results and by identifying the optimum spectrum band through hierarchical access model using interweaving approach, energy consumption is minimized. The simulation results show that by decreasing the probabilities of error ratio, false alarm rate and missed detection, the accuracy of sensing results is improved. Further, this hybrid approach outperforms the traditional approaches in terms of probability of detection even in low SNR values.

近年来，无线设备和无线服务的使用呈指数增长，这导致频谱稀缺。监管机构的政策采用静态频谱分配方法，并分配新的频段，以向用户提供新型服务。这些方法导致对可用频谱带的不良利用。认知无线电（CR）为这些问题提供了更好的解决方案，它主要集中在有效利用可用频段上。CR网络（CRN）必须采用频谱管理技术，通过遵循一系列操作（例如频谱感知，决策和管理）将未使用的频段分配给CR用户。频谱感测是频谱分配中至关重要的过程。在传统方法中 误检测和误报率的可能性降低了传感精度，因此传感精度变低。结果，认知用户（CU）面临着时间延长才能完成完美的认知无线电通信的挑战。为了克服这个问题，已经提出了具有基于特征的聚类分类器的协作频谱感测技术。该分类器以固定的时间间隔学习射频环境中的状态及其转变以及主要用户活动，以支持频谱决策技术。这项工作的新颖性是提出一种混合方法，该方法将聚类与期望最大化（EM）算法和强化学习（RL）技术相结合。这种混合方法通过准确的传感结果增强了系统性能，并且通过使用交织方法通过分层访问模型确定最佳频谱带，从而将能耗降至最低。仿真结果表明，通过降低误码率，误报率和漏检概率，提高了检测结果的准确性。此外，即使在低SNR值下，此混合方法在检测概率方面也优于传统方法。