# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 3 Results
### Enhanced Data Transmission
The approach is based on mobile edge computing (MEC), also known as fog computing, which is used to meet the computational demands of perimeter devices that lack such resources, such as wireless devices that make up the IoT. The suggested agent has a sophisticated, dynamic policy for allocating computational resources for numerous users, and it is embedded in the MEC node. The channel quality, data packet size, and current waiting time should also beconsidered. After the offloaded data have been processed in the MEC node, the proposed mechanism selects a low downlink transmission rate to reduce the likelihood of error and enhance the successful transmission rate while staying within the downlink channels' delay limits. [PERSON] et al. [41] proposed using deep learning to optimize a congestion control algorithm in wireless networks. The algorithm uses MLP structure with one hidden layer for network congestion detection, accurately distinguishing between data congestion and wireless network error when fast retransmission by a packet loss occurs in the sender. This distinction is critical in wireless networks, deciding what type of congestion control to apply and avoiding degradation of the network's overall performance. They also propose reducing the transmission window size when packet loss occurs due to data congestion using an additive increase multiplicative decline (AIMD) algorithm. Finally, they suggest a proprietary mechanism to retransmit the lost packet solely in the event of a wireless fault. Still, they do not change the size of the current congestion window. The proposed algorithm aims to use deep learning to forecast network congestion and random packet loss. The minimum round-trip time (RTT) value and the current smoothed RTT value were utilized as inputs to the MLP structure, and the reason for packet loss was used as the output layer's response to train the algorithm. The study results reveal that the performance in the environment without wireless loss is comparable to the protocols that were tested. However, when the amount of wireless loss increases, the suggested algorithm beats the others since it determines the cause of the lost packet rather than reducing the transmission window as the other protocols do. The authors of [42] proposed an integrated method that combines a classical DNN (as multilayer perceptron) with an improved K-nearest neighbor (KNN) algorithm to deal with the indoor location problem (or location fingerprinting). The conventional KNN provides the foundation for the upgraded KNN algorithm. The original KNN algorithm, on the other hand, ignores the influence of nearby locations. The algorithm's general procedure can be broken into two sections. The Wi-Fi received signal strength indicator (RSSI) fingerprint dataset was first classified using the DNN method. The modified KNN technique is then used in the second phase to classify these alternative locations in a specific class to establish the mobile device's final position. The DNN algorithm trains the dataset in the offline stage and predicts in the online stage. The KNN algorithm classifies these favorable positions into a specific class to determine the final part of the online step. When the first phase is completed, the entire positioning scene is divided into several clusters to choose the most likely collection to which the target belongs, increasing the number of learning samples for the DNN classifier algorithm. The interference from other groups can be reduced after knowing the particular cluster, and the calculation cost of the KNN algorithm can be reduced in the second phase. The proposal's performance was compared to that of other traditional indoor location algorithms, including random forest (RF), KNN, support vector machine (SVM), and decision tree (DT), among others. The proposed algorithm outperforms the other since it takes advantage of both algorithms' strengths.