# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 3 Results
### Wireless Spectrum Management  
The explosion of internet access with wireless technologies, such as 3G, 4G, and 5G networks and wireless LANs, has caused the number of devices connected to the internet to grow without measure. As a result, the wireless spectrum is becoming an essential and scarce resource. Moreover, there are so many technologies coexisting together that the problems of interference, channel congestion, data collisions, and unbalanced spectrum  
Figure 2: Search strategy flow diagram. usage become a challenge, which drastically reduces overall network performance and user experience [25, 26]. In this sense, we present some works discussing solutions to this challenge. Mitigating channel interference within wireless networks is a significant challenge in improving spectrum usage performance. Channel interference can be reduced by optimizing the configuration of the devices in the use of the wireless channel. The authors in [27] proposed performing an effective wireless channel matching and power allocation configuration. They presented a distributed resource matching scheme based on deep reinforcement learning (DRL) in a device-to-device (D2D) network communication scenario. The scheme is called a \"Distributed Multi-user Channel and Communication Power matching algorithm\" (DMCP). The DL algorithm of DMCP is formed by a double deep Q network (DDQN), which aims to select the channel and transmission power level autonomously. This combination of learning, especially DRL, allows decision making based on historical information in real-time observations. Another point worth noting is that they propose that the base stations take over the training process, which helps to reduce the data communication delay. The work results show that the DMCP algorithm obtains good results in channel assignment and transmission power selection, which leads to improving the overall throughput and energy efficiency of the network. [PERSON] et al. [4] advocated employing deep learning to improve and regulate radio spectrum usage to address various issues related to inefficient spectrum management, utilization, and regulation that the future generation of wireless networks faces. They proposed a spectrum monitoring end-to-end learning framework and defined a generic technique for designing and implementing wireless signal classifiers. As a result, convolutional neural networks (CNN) were used to automatically extract characteristics of non-linear and more abstract wireless signals that are invariant to local spectral and temporal fluctuations and to train wireless signal classifiers. The authors presented two case studies: monitoring the radio spectrum to identify the signals in the communications channel (modulation recognition) and detecting wireless interference technologies. [PERSON] et al. [28] presented a deep learning-based solution for dealing with the problem of communication network interference. In this scenario, typical machine learning algorithms are ineffective. Because the actual interference image contains many disturbances, the same interference can take on many different shapes, making it impossible to discern the interference shape solely by extracting characteristics from the surrounding area. For spectrum interference image recognition, a deep CNN is utilized to generate an adequate classification of cell interference kinds, considerably boosting the efficiency of interference problem handling. The deep learning recognition procedure includes interference cell detection, interference type identification, and interference source location. The deep learning structure uses the picture disturbance and morphological changes as practical data input in each neuron. Different neurons represent different features of the image. The feature database is optimized by the machine self-learning system through continuous neural network iteration. As a result, the interference signature feature library approaches the real generated phase and effectively recognizes the communication network's interference.