# Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks
###### Abstract  
Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS. D2D communication; mmWave; machine-learning applications; 5G; B5G +
Footnote †: journal: 14 January 2021  
Besides traffic offloading, the D2D concept can be exploited to enable communication in disaster situations at which the Macro BS is malfunctioned due to natural disasters such as earthquakes, floods, and typhoons, etc. The D2D concept can solve such a scenario and either connect them to the closest working ground network or identify their specific locations. Generally, D2D can be classified into in-band and out-band [1,2] based on the dedicated frequency band. In the in-band D2D networks, D2D communication is overlaid or underlaid the cellular band. However, in the out-band D2D networks, the D2D communication uses the unlicensed frequency bands, i.e., industrial, scientific and medical (ISM) bands. Although out-band D2D has the advantages of high capacity and no-interference with the cellular users (CUs), it suffers from integration/management problems due to the use of different types of interfaces, e.g., LTE and Wi-Fi. Although D2D communications provide a lot of benefits, it introduces interference to the CUs, especially for in-band schemes. In order to mitigate the interference, many power control and resources reuse algorithms have been proposed in the literature. Another attractive coexistence is the natural symbiosis between the promising millimeter wave (mmWave) band, from 30 up to 300 GHz, and D2D communications [2,3]. The fact that millimeter waves are characterized by short-range intermittent transmission comes from its fragile channel. It can be sharply directed using antenna beamforming (BF) techniques makes it a perfect candidate to coexist with D2D to create low mutual interference high data rates D2D links. Figure 1 shows some case studies of current critical D2D scenarios in real life, such as disaster management, unmanned aerial vehicles (UAVs) communications [4], vehicle to everything (V2X) applications [5], etc. On the other hand, the recent progress in machine-learning (ML) leads to employing it in various applications in communication networks, including spectrum management, intelligent RA, recovery of channel state information, modulation, detection, and mmWave BF. In this context, researchers have investigated several ML algorithms for enabling efficient D2D communication networks related to technical problems such as neighbor discovery and selection (NDS), optimal D2D pairs matching, RA, and multi-hop D2D relaying for coverage extension.