## 7 Conclusions  
In this paper, we propose learning-based techniques for cooperative spectrum sensing and energy-efficient data protection in CRNs, by which the SUs can effectively utilize the primary channel under the constraint of limited harvested energy. We first design a new CNN-based cooperative spectrum sensing method. In this approach, the CNN is trained by using historical sensing data collected from secondary users under various environmental conditions. At the beginning of each time slot, the SUs individually perform spectrum sensing using an energy detection method, and then send the local decisions to a fusion center to make a global decision about the state of the primary user. The proposed CBCS can increase the detection probability and remarkably reduce the sensing error, which can also contribute to effective communications between the SUs and the fusion center. Regarding the proposed TLAC scheme, the SU determines its operation mode based on the remaining energy and the sensing result considering the effect of this decision on future time slots. By calculating the expected accumulated reward from the current time slot, the SU can decide to stay in idle mode to save energy for future use, or operate in transmission mode and transmit cypher data that are protected by using the AES algorithm with an appropriate key length. We then present simulation results to evaluate the performance of the proposed solutions, which show that the proposed schemes can guarantee energy-efficient data communications in cognitive radio networks.  
However, there are still some areas of the proposed learning frameworks that can be of interest for future research. First, it is possible to improve the performance of the current CNN-based cooperative spectrum sensing technique by modifying the structure of the input data. In addition, the local sensing results from \(K\) secondary users, other information such as the sensed channel SNRs, sensing duration and even the distances between the SUs and the PU can be used as the input data for the CNN to predict the state of the primary user. Those information sources could provide the CNN with more useful features for reducing the negative effect of the noise on the local sensing outcomes. Secondly, with respect to the TLAC framework, it is essential to choose good learning-rate parameters that can balance the convergence speed and the computational resource. Furthermore, the current actor-critic framework would be further extended to apply to the problems with large or continuous domains instead of discrete-time state space and action space.  
Figure 19: The number of successfully transmitted data packets according to the harvested energy, compared with the fixed key-length encryption methods.  
**Author Contributions:** All authors conceived and proposed the research idea. [PERSON] designed and performed the experiments; [PERSON] analyzed the experimental results; [PERSON] wrote the paper under the supervision of [PERSON].  
**Acknowledgments:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2015R1D1A1A09057077) as well as by the Korea government (MSIT) (2018R1A2B6001714).  
**Conflicts of Interest:** The authors declare no conflict of interest.