# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
###### Abstract
Table 1 summarizes the nomenclature used throughout this paper. Figure 1: Samples of device-to-device (D2D) applications. This paper main contributions can be summarized as follows:  
* A comprehensive survey about recent research activities for D2D communication will be explored accompanied with their existing conventional solutions. * The state-of-the-art ML hypothesis will be presented including its different approaches, i.e., supervised, unsupervised learning, and Reinforcement-Learning (RL). Then, ML solutions for D2D challenges will be provided showing their superior performances over the conventional counterparts. * We discuss the open research challenges and future research directions of ML based D2D networks, especially with novel ML techniques like federated learning (FL). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline D2D & Device to device & 5G & fifth generation \\ \hline BS & base station & macro BS & macro base station \\ \hline CUs & cellular users & mmwave & millimeter-wave \\ \hline ML & machine-learning & IM & Interference management \\ \hline ISM & industrial, scientific and medical & RA & resource allocation \\ \hline BF & beamforming & UAV & unmanned aerial vehicles \\ \hline V2X & vehicle to everything & NDS & neighbor discovery and selection \\ \hline MAB & multi-armed bandit & CMAB & combinatorial MAB \\ \hline
3 GPP & 3 rd generation partnership project & Prose & proximity services \\ \hline NC & network-centric & DC & device-centric \\ \hline LIA & limited interference area & COMP & coordinated multi point \\ \hline FD & full-duplex & SIC & successive interference cancellation \\ \hline DF & decode and forward & AF & amplify and forward \\ \hline HD & half-duplex & NN & neural network \\ \hline FNN & forward neural network & KNN & K-nearest neighbor \\ \hline SVM & support vector machine & DT & decision tree \\ \hline GPU & graphical processing unit & DNN & deep neural network \\ \hline RNN & recurrent neural network & mmwave & millimeter-wave \\ \hline RECOME & RElative COre MErge & GMM & Gaussian mixture model \\ \hline PCA & principal component analysis & RL & Reinforcement-Learning \\ \hline RL-LCDC &
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} RL-based latency controlled D2D \\ connectivity \\ \end{tabular} & RRM & radio resource management \\ \hline SE & spectral efficiency & SINR & signal to interference ratio \\ \hline PC & Power control & MAAC & multi-agent actor critic \\ \hline NAAC & neighbor-agent actor critic & RF & radio frequency \\ \hline HT & Hilbert transform & CV & cross-validation \\ \hline FL & Federated learning & DSGD & decentralized stochastic gradient descent \\ \hline UCB & Upper confidence bound & MOS & Minimax Optimal Stochastic Strategy \\ \hline LOS & line of sight & NLOS & non line of sight \\ \hline UAV & Unmanned Arial Vehicle & AOA & Angle of Arrival \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nomenclature. * A case study on applying multi-armed bandit (MAB) as an efficient online ML tool in enhancing the performance of mmWave NDS will be presented. In this case study, different multi armed bandit (MAB) techniques such as upper confidence bound (UCB) and minimax optimal stochastic strategy (MOSS) will be investigated to show the effectiveness of using ML tools in enhancing the average throughput performance of mmWave NDS over the existing traditional solutions, namely direct NDS and random selection. Besides, we show that such performance enhancements come with a sufficient learning convergence rate. The rest of the paper is organized as follows: Section 2 overviews the recent research directions for D2D communications. In Section 3, we summarize different ML techniques that can be generally employed for D2D network solutions. Section 4 discusses different applications of ML algorithms in various D2D scenarios. D2D challenges, future research directions, and applications are introduced in Section 5. A case study of applying MAB on mmWave D2D is presented by Section 6.