The fifth generation (5G) mobile networks are envisioned to support the deluge of data traffic with reduced energy consumption and improved quality of service (QoS) provision. To this end, key enabling technologies, such as heterogeneous networks (HetNets), massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) techniques, have been identified to bring 5G to fruition. Regardless of the technology adopted, a user association mechanism is needed to determine whether a user is associated with a particular base station (BS) before data transmission commences. User association plays a pivotal role in enhancing the load balancing, the spectrum efficiency, and the energy efficiency of networks. The emerging 5G networks introduce numerous challenges and opportunities for the design of sophisticated user association mechanisms. Hence, substantial research efforts are dedicated to the issues of user association in HetNets, massive MIMO networks, mmWave networks, and energy harvesting networks. We introduce a taxonomy as a framework for systematically studying the existing user association algorithms. Based on the proposed taxonomy, we then proceed to present an extensive overview of the state-of-the-art in user association algorithms conceived for HetNets, massive MIMO, mmWave, and energy harvesting networks. Finally, we summarize the challenges as well as opportunities of user association in 5G and provide design guidelines and potential solutions for sophisticated user association mechanisms.

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User Association in 5G Networks: A Survey and an Outlook

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks.

A Survey of Self Organisation in Future Cellular Networks

In this paper, a survey of the literature of the past 15 years involving machine learning (ML) algorithms applied to self-organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of self-organizing networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this paper also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Inspired by swarm intelligence observed in social species, the artificial self-organized networking (SON) systems are expected to exhibit some intelligent features (e.g., flexibility, robustness, decentralized control, and self-evolution, etc.) that may have made social species so successful in the biosphere. Self-organized networks with swarm intelligence as one possible solution have attracted a lot of attention from both academia and industry. In this paper, we survey different aspects of bio-inspired mechanisms and examine various algorithms that have been applied to artificial SON systems. The existing well-known bio-inspired algorithms such as pulse-coupled oscillators (PCO)-based synchronization, ant- and/or bee-inspired cooperation and division of labor, immune systems inspired network security and Ant Colony Optimization (ACO)-based multipath routing have been surveyed and compared. The main contributions of this survey include 1) providing principles and optimization approaches of variant bio-inspired algorithms, 2) surveying and comparing critical SON issues from the perspective of physical-layer, Media Access Control (MAC)-layer and network-layer operations, and 3) discussing advantages, drawbacks, and further design challenges of variant algorithms, and then identifying their new directions and applications. In consideration of the development trends of communications networks (e.g., large-scale, heterogeneity, spectrum scarcity, etc.), some open research issues, including SON designing tradeoffs, Self-X capabilities in the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE)/LTE-Advanced systems, cognitive machine-to-machine (M2M) self-optimization, cross-layer design, resource scheduling, and power control, etc., are also discussed in this survey.

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On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches

Self-organizing network, or SON, technology, which is able to minimize human intervention in networking processes, was proposed to reduce the operational costs for service providers in future wireless systems. As a cost-effective means to significantly enhance capacity, heterogeneous deployment has been defined in the 3GPP LTEAdvanced standard, where performance gains can be achieved through increasing node density with low-power nodes, such as pico, femto, and relay nodes. The SON has great potential for application in future LTE-Advanced heterogeneous networks, also called HetNets. In this article, state-of-the-art research on self-configuring and self-optimizing HetNets are surveyed, and their corresponding SON architectures are introduced. In particular, we discuss the issues of automatic physical cell identifier assignment and radio resource configuration in HetNets based on selfconfiguring SONs. As for self-optimizing SONs, we address the issues of optimization strategies and algorithms for mobility management and energy saving in HetNets. At the end of the article, we show a testbed designed for evaluating SON technology, with which the performance gain of SON algorithms is demonstrated.

Self-configuration and self-optimization in LTE-advanced heterogeneous networks

With the rapid growth of mobile communications, deployment and maintenance of cellular mobile networks are becoming more and more complex, time consuming, and expensive. In order to meet the requirements of network operators and service providers, the telecommunication industry and international standardization bodies have recently paid intensive attention to the research and development of self-organizing networks. In this article we first introduce both the market and technological perspectives for SONs. Then we focus on the self-configuration procedure and illustrate a self-booting mechanism for a newly added evolved NodeB without a dedicated backhaul interface. Finally, mobility load balancing as one of the most important selfoptimization issues for Long Term Evolution networks is discussed, and a distributed MLB algorithm with low handover cost is proposed and evaluated.

Self-configuration and self-optimization for LTE networks

In recent years, machine-to-machine (M2M) communications, which do not need direct interactions from human beings, are booming to meet the fast-increasing requirements of datacentric wireless services and applications. Mobile cellular networks (MCN) and wireless sensor networks (WSN) are evolving from heterogeneous networks to converged networks, in order to support M2M communications. In this article, we investigate and discuss key technical challenges and opportunities for the convergence of MCN and WSN. We propose that the mobile terminals in MCN act as both sensor nodes and gateways for WSN in the converged networks. We evaluated the performance gain, and our simulation results show that better system performance, in terms of throughput, delay, and network lifetime, can be achieved in WSN by using interactive optimization with MCN.

Mobile cellular networks and wireless sensor networks: toward convergence

Method for estimating speed class of mobile terminal in multilevel cellular system

The invention discloses a method for switching from a terminal to a household base station. The method includes firstly, judging whether frequency of the household base station is changed or not after the household base station is restarted; transmitting the changed frequency value to a macro base station adjacent to or shared under the same coverage with the household base station or an adjacent household base station through an S1 interface if the frequency is changed; secondly, noticing a service base station which the terminal belongs to currently for measuring and configuring correspondingly by approach instruction information before the terminal is switched to the household base station; thirdly, noticing the terminal to measure and configure correctly by the service base station according to the updated approach instruction information; and fourthly, switching from the terminal to the target household base station according to the correct measurement and configuration. By the method for switching from the terminal to the household base station, the fact that the terminal cannot be switched to the corresponding household base station in time, caused by mistaken measurement and configuration of the macro base station to the terminal, is avoided. Switching failure is solved, switching success rate is increased and communication quality is guaranteed.

Method for switching from terminal to household base station

The invention discloses a method for coordinating mobile and reselection parameters among communities in a long term evolution (LTE) network, which comprises the steps that: (1) a source base station judges mobile parameters to be adjusted according to the own load and the load of adjacent communities, so as to calculate the variation of the mobile parameters of the source base station and a target base station to be adjusted; (2) the source base station sends a mobile parameter modification request to the target base station, and the request contains the variation of reselection parameters of the source base station and the variation of the suggested reselection parameters proposed to the target base station; (3) the target base station sends to the source base station a signaling of accepting or rejecting the mobile parameter modification request after accepting or rejecting the mobile parameter modification request; and (4) the source base station receives the signaling; and if the target base station accepts the mobile parameter modification request, then the target base station and the source base station adjust the own reselection parameters. According to the method for coordinating mobile and reselection parameters among communities in the LTE network, the times of ping-pong reselection are reduced, and the negative impact of the immediate switching which occurs when the switching and the reselection parameters are not matched on a system is reduced.

Jian Zhang

Papers

Self-configuration and self-optimization for LTE networks

Mobile cellular networks and wireless sensor networks: toward convergence

Method for estimating speed class of mobile terminal in multilevel cellular system

Method for switching from terminal to household base station

Method for coordinating mobile and reselection parameters among communities in long term evolution (LTE) network