# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 3 Results
### Efficiency on Energy Utilization
This approach considers the cooperative communications process using a Markov decision process (MDP) model to solve a single-pair optimization problem because MDP is an optimal decision process for stochastic dynamical systems based on the Markov process (MP). Since the Q-learning algorithm suffers from a low learning speed in a large state space, deep neural networks' function approximation and generalization capabilities are exploited to compensate for this limitation. As a result, to improve its operation, DQ-RSS combines deep learning with Q-learning to accelerate learning and perform optimal relay selection. According to the outage probability, a deep Q network (DQN) is trained. The channel state information (CSI) was observed for optimal relay selection among a set of candidates to participate in cooperative communication without the need for a network model prior data. To assess the Q-value of each action, the proposed deep learning network approach uses two convolutional layers and two fully connected layers. The rectified linear unit is used as the activation function in the first convolutional layer, which consists of 20 filters. The second convolutional layer employs the same non-linear rectifier and has 40 filters; 360 rectified linear units are used in the first Fc layer, whereas 180 units are used in the second [35]. Simulations were carried out to evaluate the performance of DQ-RS, using the basic parameters according to the IEEE 802.15.4 standard protocol in the 2.4 GHz frequency band. The results show that DQ-RSS outperforms the Q-RSS and random selection methods in all evaluated metrics, such as outage probability, energy consumption, and average utility of the network; for example, the outage probability of the DQ-RSS is roughly two times less than the random relay selection scheme and approximately 30% lower than the Q-RSS [35]. The authors propose, for future work, to consider the mobility of sensor nodes and complex channel models to study their impacts on actual WSN. We increasingly depend on wireless networks to perform daily tasks, which has caused a rapid expansion of networks, such as in the case of smart cities. In this context, base stations are used to control network connectivity and traffic that is more densely loaded with traffic than others. The former presents a large amount of energy wasted caused by the power consumption used in data transmission. The above leads to the death of the station and reduces the network lifetime. Therefore, managing the transmission power of the base stations to improve energy utilization efficiency is necessary. The key to enabling modern cognitive wireless networks is that base stations have self-management capabilities and dynamic adjustment. To achieve the before-mentioned goal, one of the prerequisites is that the base stations can accurately predict the wireless traffic of the network. It is, nevertheless, a complex undertaking because data traffic is highly nonlinear and complicated, characterized by temporal and spatial connections [37]. However, most existing prediction methods do not consider temporal and spatial situations in the traffic data modeling process. The preceding makes it impossible to obtain an accurate forecast of the traffic of these networks. To address this problem, in [37], a convolutional network was proposed with a mechanism (called LA-ResNet) to solve the spatial-temporal modeling and predict wireless network traffic. The residual network, the recurrent neural network, and an attention mechanism are part of the LA-ResNet mechanism [37]. The residual network is initially employed in LA-ResNet to extract the spatial properties of wireless network data. The residual network's output data are then fed into the recurrent neural network (RNN), which uses the memory unit's time-series processing capacity to record the temporal information. The attention mechanism allows the focus to be directed on the intermediate output, which ties the residual network and RNN modules together.