# A Review of Deep Learning Applications for the Next Generation of Cognitive Networks
## 3 Results
### Wireless Spectrum Management
By increasing the efficiency of interference identification, the possibilities for improving network quality and user perception become more numerous. In [29], they proposed SL-MAC, an intelligent spectrum learning-based medium access control (MAC) protocol for future wireless local area networks (WLAN). Their proposal combines deep learning and spectrum sensing to create an intelligent medium access system that can collect more data on channel usage, for example, instead of standard spectrum sensing technologies that can simply tell whether a channel is busy or not, how many devices are sharing the spectrum, and who are they. To implement the suggested MAC, a pre-trained CNN is deployed within the access point (AP) to detect the stations (STAs) involved in collisions (when more than one user transmits request to send (RTS) packets at the same time). According to the inference results, the AP schedules the data transmissions of the users involved and obtains a collision-free channel within a period. The SL-MACprotocol can retrieve information even if packets collide, training a deep neural network offline with historical radio frequency (RF) traces and inferring STAs involved in online collisions in near real-time. The SL-MAC protocol includes three operation steps. The first step is channel contention, in which the STAs compete for the channel according to the rules of a typical multiple access channel, such as the IEEE 802.11 scheme. The second step is collision detection and identification, starting from the reception of the RTS signals that arrive at the AP, where the pre-trained CNN algorithm resides. In this case, the protocol can distinguish if the channel is free (no RF signals), when there is only one RTS signal (collision-free), and when there are several overlapping RTS signals (collision). Finally, in the third step, when there is a collision, the transmission scheduling is executed according to the inference given by the pre-trained CNN algorithm. For this, the AP broadcasts a special CTS (Clear to Send) packet that contains a field with the transmission scheduling for each STA involved in the collision. The STAs not involved are kept silent (no packet transmission) during the retransmission period marked in the CTS packet. A comparison with the conventional IEEE 802.11 protocol was made to demonstrate the superiority of the SL-MAC protocol. The authors explored the impact of the inference error on the achieved throughput and looked at the top bound of throughput gain supplied by the CNN predictor. The benefits of the SL-MAC protocol were proven through extensive simulations. As a result, network capacity is increased as channel access efficiency improves. [PERSON] et al. [30] proposed a deep neural network (DNN) strategy for predicting spectrum occupancy in the near future of unknown neighboring networks. They showed a multi-agent environment that employs RL and supervised learning approaches. Existing network schedulers can use this prediction to avoid collisions with nearby networks or other electromagnetic sources. Furthermore, unlike most existing MAC algorithms, which only strive to maximize their own network performance, this concept can change its operation to avoid cross-technology interference based on spectrum consumption by different technologies. Thus, the multi-channel access problem is studied in this paper, framed as a partially observable stochastic game in which N nodes have access to C channels in an environment where other networks use the same fraction of the spectrum. Consequently, the proposed MAC algorithm selects the channel with less predicted interference from the set of available channels to perform its data transmission. In other words, the algorithm decides when and on which channel to transmit to avoid interference between networks. Therefore, the algorithm focuses on predicting the behavior of the interfering network cluster (INC).