An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks

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.

An XGB-Based Reliable Transmission Method in the mMTC Scenarios

Massive machine-type communications (mMTCs) for Internet of things are being developed thanks to the fifth-generation (5G) wireless systems. Narrowband Internet of things (NB-IoT) is an important communication technology for machine-type communications. It supports many different protocols for communication. The reliability and performance of application layer communication protocols are greatly affected by the retransmission time-out (RTO) algorithm. In order to improve the reliability and performance of machine-type communications, this study proposes a novel RTO algorithm UDP-XGB based on the user datagram protocol (UDP) and NB-IoT. It combines traditional algorithms with machine learning. The simulation results show that real round-trip time (RTT) is close to the RTO, which is obtained by this algorithm, and the reliability and performance of machine-type communications have improved.

Analysis and evaluation of the effectiveness of methods for ensuring the quality of service for software-defined networks of the standard 5G/IMT-2020

The quality of service (QoS) in networking is the process of managing network resources to reduce packet loss and to lower network jitter and latency. QoS has been widely used in traditional network and can also be implemented in the 5G standard based on a software-defined network (SDN). A traditional network carries several challenges, such as vendor dependency, the complexity of managing a large network, dynamically changing forwarding policies, and more. Software-defined networking is a new networking strategy designed to address the challenges of a traditional IP network, such as high levels of complexity and inability to adapt to the new quality of service requirements in a timely manner. The fundamental idea behind SDNs compared to the conventional networking paradigm is the creation of horizontally integrated systems through the separation of the control and the data plane while providing an increasingly sophisticated set of abstractions. Recently, various SDN-enabled QoS frameworks have emerged that offer many possibilities for network reconfiguration and high-level definition of policies. QoS requirements for 5G networks have been defined on the basis of three main categories of use cases: extreme mobile broadband (xMBB), massive machine type communications (mMTC) IoT/M2M devices, and highly reliable М2М-communication (ultra-reliable machine-type communications – uMTC). This paper analyzes and surveys the QoS based on the openflow protocol method and QoS based on open-source SDN controllers method in 5G network. In addition, we discuss various architectural issues of open-source SDN controllers network and examine their impact on the QoS. Furthermore, we outline the characteristics of the QoS parameters such as latency, availability, reliability, jitter, and bandwidth in the 5G network. Finally, the article discusses and compares parameters of the QoS in 5G determined by world’s leaders in 5G technology.

5G NR system design: a concise survey of key features and capabilities

As we enter a new era of next-generation wireless systems represented by Fifth Generation (5G) New Radio (NR) technology, it is essential to grasp the recent progress in their standardization and development. This article offers a concise survey of the 5G NR system design that aims at introducing its features according to the relevant Third Generation Partnership (3GPP) specifications. Our focus is set on the flexibility of 5G NR, which refers to its capability to support novel services and technologies, such as enhanced Mobile Broadband (eMBB) and Internet of Things (IoT) for massive Machine Type Communications (mMTC) while satisfying the underlying quality requirements. The key enablers of the 5G NR operation are scalable numerology, ultra-lean and beam-centric design, support for low latency, spectrum extension, and forward compatibility. This work summarizes these important features by studying the overall 5G architecture and the user-/control-plane protocol stacks specified by 3GPP. Furthermore, the impact of scalable numerology on system performance is discussed. Finally, we also consider open challenges and future research directions.

6G Cognitive Information Theory: A Mailbox Perspective

With the rapid development of 5G communications, enhanced mobile broadband, massive machine type communications and ultra-reliable low latency communications are widely supported. However, a 5G communication system is still based on Shannon’s information theory, while the meaning and value of information itself are not taken into account in the process of transmission. Therefore, it is difficult to meet the requirements of intelligence, customization, and value transmission of 6G networks. In order to solve the above challenges, we propose a 6G mailbox theory, namely a cognitive information carrier to enable distributed algorithm embedding for intelligence networking. Based on Mailbox, a 6G network will form an intelligent agent with self-organization, self-learning, self-adaptation, and continuous evolution capabilities. With the intelligent agent, redundant transmission of data can be reduced while the value transmission of information can be improved. Then, the features of mailbox principle are introduced, including polarity, traceability, dynamics, convergence, figurability, and dependence. Furthermore, key technologies with which value transmission of information can be realized are introduced, including knowledge graph, distributed learning, and blockchain. Finally, we establish a cognitive communication system assisted by deep learning. The experimental results show that, compared with a traditional communication system, our communication system performs less data transmission quantity and error.