It is predicted that by 2025, all devices will be connected to the Internet, subsequently causing the number of devices connected with the Internet to rise

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective 5 ^th generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The 3 ^rd generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.

The potential applications of deep learning (DL) to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence (AI)-based theories. DL has been extensively proposed for the MAC layer of WLANs in various research areas, such as deployment of cognitive radio and communications networks. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN (HEW) are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best channel resources with the assistance of self-scrutinized wireless channel condition inference. Such intelligence is only possible with the introduction of DL techniques in future WLANs. Therefore, in this paper, we propose reinforcement learning (RL) as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the RL models, Q-learning (QL), is used to optimize the performance of channel observation–based MAC protocols in dense wireless networks. An intelligent QL-based resource allocation (iQRA) mechanism is proposed for MAC layer channel access in dense WLANs. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed iQRA mechanism is evaluated in diverse WLAN network environments with throughput, channel access delay, and fairness as performance metrics.

Internet of Things (IoT) and named data network (NDN) are innovative technologies to meet up the future Internet requirements. NDN is considered as an enabling approach to improving data dissemination in IoT scenarios. NDN delivers in-network caching, which is the most prominent feature to provide fast data dissemination as compared to Internet protocol (IP)-based communication. The proper integration of caching placement strategies and replacement policies is the most suitable approach to support IoT networks. It can improve multicast communication which minimizes the delay in responding to IoT-based environments. Besides, these approaches are playing a most significant role in increasing the overall performance of NDN-based IoT networks. To this end, in this article, the challenges of NDN-IoT caching are identified with the aim to develop a new hybrid strategy for efficient data delivery. The proposed strategy is comparatively and extensively studied with NDN-IoT caching strategies through an extensive simulation in terms of average latency, cache hit ratio, and average stretch ratio. From the simulation findings, it is observed that the proposed hybrid strategy outperformed to achieve a higher caching performance of NDN-based IoT scenarios.

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.