# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 3 Results
### Efficiency on Energy Utilization  
Power consumption is one of the most significant challenges in wireless networks since the mobile network nodes have a very scarce power supply based on batteries. This resource is crucial since the energy of each node is directly related to the network lifetime. Therefore, efficient use of this resource must be made to avoid inappropriate spending on the wireless network operation. For example, the transmission and reception of data are the functions that consume the most energy in the node. Therefore, avoiding the loss and retransmission of data promotes energy savings. There are several efforts in this regard; below, we present some works. The authors in [33] discussed the challenge of managing the energy consumed by the sensor nodes of a network under the Internet of Things paradigm. This work proposes a model that uses deep reinforcement learning (DQN) to calibrate each node sensor and reduce power consumption according to the network operating environment. Their model integrates a RL agent with a deep learning long-short-term memory (LSTM) agent. The reinforcement agent takes advantage of its ability to make observations within a changing environment and take actions accordingly. The LSTM agent takes advantage of its ability to handle time series, which can retain long sequences due to its memory cell. The RL agent's previous action input, state, and reward are taken as input vectors of the proposed architecture. They are delivered to several LSTM layers with a batch normalization layer to increase the network's stability and speed. Finally, the architecture includes a classic deep learning layer (or fully connected). In evaluations, this combination demonstrated excellent results in maximizing the energy efficiency of the IoT network under a changing operating environment, as stated by the authors. According to [PERSON] et al. [34], the fast-growing demand for wireless transmission has pushed mobile broadband to spread across numerous frequency bands. As a result, power consumption for multi-carrier information and processing is increasing proportionally, which conflicts with the energy efficiency requirements of 5G wire-free systems. This has led to the adoption of multi-carrier power amplifier (MCPA) technology, which allows many carriers to be supported by a single power amplifier. However, the authors raise an important point: how to distribute those carriers over numerous MCPAs and whether the allocation strategy should be dynamically changed. They theoretically articulated the problem of dynamic carrier allocation to MCPA due to this. Furthermore, they suggested algorithms based on convex relaxation and deep learning. The deep learning technique uses feedforward neural networks (FNN), namely a multi-layer perceptron (MLP) and a one-dimensional CNN, to approximate the non-convex function to process the one-dimensional carrier allocation problem. According to their simulation data, the convex relaxation-based approach saves more energy than the deep learning-based scheme. On the other hand, the DL-based strategy outperforms the others in terms of computing complexity. Due to the vast number and small size of sensor nodes deployed in various wireless sensor networks (WSNs) applications, the system throughput of nodes is insufficient, and energy is scarce. In addition, there are some network environments where node replacement is problematic. As a result, how to improve and extend the network life cycle is a pressing concern in today's WSNs [35]. Cooperative communication is key to improving performance and expanding network coverage [36], as it uses the broadcast characteristics of wireless systems to optimize communication between nodes in the network. A cooperative communication scheme with relay selection for WSN based on DRL, called DQ-RSS, is proposed in [35].