# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 3 Results
### Enhanced Data Transmission  
The primary objective of any communication system is effective data transmission. However, it is not an easy task since multiple factors intervene, such as the type and amount of traffic, the communication channel congestion, etc., which makes it challenging to achieve this goal. In addition, this objective becomes a more significant challenge in the case of wireless networks. Because they are networks made up of devices with limited storage, processing, and energy capacities, with a shared transmission channel, they are unstable and prone to failures (path losses, signal fading). That is why there are several jobs focused on this goal. Below, we present some proposals that involve this objective and deep learning methods. A routing algorithm capable of detecting link-level, node-level, and sink-level failures with high accuracy and low overhead in an IoT-enabled WSN is one of the central goals proposed in [38]. To achieve this goal, the authors propose a fault tolerance multi-objective deep reinforcement learning (MO-DRL) agent embedded in each sensor node of the network to optimize a data routing algorithm. MO-DRL detects faulty nodes in the WSN and removes them from the transmission path, achieving fault-free data routing. They also suggest a mobile sink method to complete an efficient solution, which collects data from sensor nodes with better performance than a stationary one, enhancing network dependability and lifetime. The MO-DRL algorithm's multi-objective capability and support for multi-policy methods justify its use, allowing it to work with several conflicting objectives. In this work, for example, they propose minimizing message overhead, minimizing communication delay and maximizing network throughput, with all purposes contradictory to each other. In particular, the MO-DRL framework is composed of a DDQN, which consists of two DNN. The first one computes the current Q-value and updating the network parameters. The second neural network is responsible for calculating the target Q-value and periodically copying the network parameters obtained in the first neural network. Finally, the authors proposed a mobile sink node that collects data from sensor nodes through the shortest path, using the traveling salesman problem. The simulation results show that the proposal outperforms other algorithms in all the metrics evaluated. [PERSON] et al. [39] proposed using deep learning for internet traffic prediction as a key objective to guarantee the quality of service (QoS) of the network connections applications. They propose three different architectures of deep belief network (DBN) through the stacking of restricted Boltzmann machines (RBMs) to create a DNN architecture. QoS contains a set of parameters, such as error rate, transmission rate, and other physical characteristics of the network, which must be guaranteed to meet a certain level of QoS. These parameters can be measured and improved through mechanisms integrated into the network nodes. In addition, the parameters are closely related to the network traffic load. Therefore, advanced knowledge of the future traffic load becomes useful. They discover the non-linear hierarchical nature found in the time series of internet traffic data using an artificial neural network with four hidden layers in each model. First, the network's deep learning is implemented using unsupervised layer pretraining. The expected future traces of traffic load are then forecasted at the output layer, which is trained in a supervised way during the model's fine-tuning stage. The findings of its proposal revealed accurate traffic predictions while modeling traffic data patterns and stochastic features, with a test dataset RMSE of 0.028. [PERSON] et al. [40] proposed employing the DRL method, known as deep Q-learning, to develop an intelligent agent for allocating computational resources for overloaded multi-user tasks. The future 5G services and IoT paradigm requires ultra-reliable low-latency communications (URLLC), which this agent provides.