Abstract
Various advanced and mission-critical applications are enabled by the emerging technologies in fifth-generation (5G) mobile communication systems. To ensure improved quality of experience (QoE) of users, 5G and beyond networks require ultra-reliable low-latency communications (URLLC). The successful realization of the URLLC entails the advent of new technological concepts. Therefore, this article presents an overview of the enabling techniques for the URLLC. Classification of the enabling techniques is done and an extensive review of the literature is presented to identify the state-of-the-art techniques, limitations, and the potential approaches for alleviating the limitations. It is observed that artificial intelligence (AI)-enabled edge computing and caching solutions are widely explored as promising techniques to effectively guarantee low latency and reliable content acquisition while reducing redundant network traffic and improving the QoE. Therefore, we present a classification of the AI-enabled edge caching solutions and discuss various mechanisms of the caching agents. In particular, we investigate the use of deep learning (DL), deep reinforcement learning (DRL), and federated learning (FL) algorithms. Subsequently, we analyze the performance of the state-of-the-art edge caching schemes and demonstrate the performance gains of FL frameworks over conventional centralized and decentralized DL and DRL frameworks. We confirm that FL edge caching is a viable mechanism in 5G and beyond networks. On the other hand, it is shown that the IEEE 802.1 time sensitive networking and the emerging IETF deterministic networking standards present effective mechanisms when deterministic networks with bounded ultra-low latency are considered. Finally, we present the open issues and opportunities for further research.
Highlights
Emerging technologies and new applications such as intelligent transport systems, industrial automation, remote patient diagnosis and surgery, smart homes, and serious gaming require ultra-reliable and low-latency communications
The techniques for low latency include the use of configurable subcarrier spacing and short transmission time interval, grant-free access, non-orthogonal multiple access (NOMA), edge computing, caching, dynamic multiplexing, latency sensitive scheduling schemes, network coding, network slicing, on-device machine learning (ML), and artificial intelligence (AI) [2], [7], [19], [21], [42], [45]-[48]
The networks are designed to operate when needed without the support of a central coordinator or with limited support for synchronization and signaling
Summary
Emerging technologies and new applications such as intelligent transport systems, industrial automation, remote patient diagnosis and surgery, smart homes, and serious gaming require ultra-reliable and low-latency communications. The services are classified as enhanced mobile broadband (eMBB), ultrareliable low-latency communications (URLLC), and massive machine-type communications (mMTC) [2], [4][7]. The URLLC provides critical communication services to support mission-critical applications requiring low-latency transmissions of small payloads with very high reliability. Near real-time connectivity is required in robotic surgery and intelligent transport systems. Both high data rates and ULL are necessary in applications such as autonomous automotive vehicles, augmented and virtual reality, and robotic applications that are essential for industrial IoT.
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