## 3 Related Work  
Wi-Fi technology has a vital influence on how the broadband access is shared in home and business networks. However, High Efficiency 802.11 Wireless Local Area Network Task Group (TG) visualizes congested scenarios as one of the main future challenges for WLAN protocols. To deal with the problem of WLAN performance degradation in dense scenarios, researchers from the field of networking have proposed different solutions. Motivated by the success of the application of machine learning to wireless networks, recent researches have used different machine learning techniques for enhancing WLAN performance. The physical medium is the main reason behind performance drop and errors in most cases. Therefore, [PERSON] et al. have introduced a method for the predictive estimation of wireless link performance using two machine learning techniques [17]. For this purpose, a measurement bench has been designed to precisely control the level of noise in a unidirectional Wi-Fi link in the safe environment of the echoic room. Furthermore, the study has also presented an analysis of the relationships between PHY WI-FI connection parameters and performance parameters on the IP layer. SVR and k-NN (nearest neighbors) algorithms were selected to estimate the performance drops at a one-second scale. Finally, the authors have assessed SNR's importance in predicting the throughput of WiFi.This study, however, focuses on the physical layer of wireless LAN instead of the MAC layer. Most mobile devices today are equipped with many wireless interfaces. This led to developing research interest in Device to Device (D2D) communication. [PERSON] et al. have proposed a new method to accurately estimate the active user equipment using ML techniques and only client-side information without the need for change in the standard communication protocol [18]. Information about the number of receiving nodes is vital in some cases, such as the user wants an accurate prediction of transmission approximate time of arrival when downloading a file. To achieve this, the time required to transfer the first part of the file from an access point to the receiving node is calculated, along with other information. The retrieved information then analyzed using ML techniques. In the paper, classification results of Naive Bayes (NB), Linear Support Vector Machines (SVM-L), Radial Support Vector Machines (SVMR), and k-Nearest Neighbor (kNN) are presented. This research work, however, considers Device to Device communication rather than the infrastructure based Wireless LAN. [PERSON] et al. presented a new mechanism for estimating the performance of future network, based on the previous conditions of network, which is presented in [19]. This approach for estimating network performance is called SENSE (Smart Experts for Network State Estimation). It makes use of a simple but effective algorithm that combines a machine learning technique fixed share with EWMA (Exponentially Weighted Moving Average). SENSE, however, is only applicable to accurately estimate the RTT (return trip time) of the TCP (transmission control protocol) of the transport layer. In [20], Deep Reinforcement Learning-based MAC protocol for wireless networking, also known as a Deep-reinforcement Learning Multiple Access (DLMA) is proposed. DLMA learns an optimal channel access strategy to maximize throughput and fairness. DLMA, however, is a customized MAC protocol which does not follow the rules of binary exponential backoff as stated in IEEE 802.11. Correct setting of the Contention Window (CW) value has a massive impact on the efficacy of Wi-Fi networks. [PERSON] and [PERSON] proposed an approach to control the value of CW that takes advantage of the principles of Deep Reinforcement Learning (DRL) for learning the accurate setting under various network conditions [21]. The proposed method, Centralized Contention window Optimization with DRL (CCOD), supports two trainable algorithms offering near-optimal efficiency.