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.
Highlights
Future wireless data traffic keeps growing, especially with recent data-hungry applications such as high definition video, virtual and augmented reality applications
Techniques based on successive interference cancellation (SIC), coordinated multi point (CoMP) and full-duplex (FD)-based self-interference cancellation were investigated in the literature to cancel the interference occurs between D2D users and cellular users (CUs)
Communication range and to rout around blockages. Another uniqueness of mmWave transmission is the use of BT, which makes the process of relay probing, i.e., exploring the candidate relays, time, and energy consuming
Summary
Future wireless data traffic keeps growing, especially with recent data-hungry applications such as high definition video, virtual and augmented reality applications. 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. 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.
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