Predictive mobility management in cellular networks

Future cellular networks are expected to see enhancements that will change their operation with the advent of new technologies, such as millimetre-wave (mmWave) communications, new physical layer waveforms, and network densification, to name a few. However, despite all the benefits that these concepts will bring to future networks, other challenges will arise. One issue that has gained attention in recent years is the problem of mobility management, which is expected to become even more challenging in future networks. This occurs due to the network densification process and the short-range coverage provided by mmWaves, which will lead to more frequent and an exponential number of handovers (HOs) by end users, generating a tremendous amount of signalling which cannot be handled by conventional means. To tackle these challenges, a proactive mobility-management concept, where HO events are triggered in advance with the help of intelligent tools that are able to predict the future behaviours of users and the network have been proposed recently. Results have shown that this proactive approach helps eliminating certain steps of the HO phase, resulting in less latency and signalling overhead, leading to a better network optimisation. On the other hand, if the accuracy of the predictions is not enough, this proactive approach can provide worse results than the reactive one. As such, the accuracy of the algorithm plays a vital role in ensuring the predictive management applicable. In this chapter, both traditional and proactive mobility managements in cellular networks are presented, and a Markov-chain-based proactive HO process is proposed. Chapter Contents: • 6.1 Introduction • 6.1.1 The path towards 5G • 6.1.2 5G enablers and challenges • 6.1.3 Issues from new technologies • 6.1.4 Chapter objectives • 6.1.5 Organisation of the chapter • 6.2 Mobility management in cellular networks • 6.3 Predictive mobility management • 6.3.1 State of the art in predictive mobility management • 6.4 Advanced Markov-chain-assisted predictive mobility management • 6.4.1 Markov-chain-based mobility prediction • 6.4.2 Problem with the conventional Markov chains • 6.4.3 Introduction to 3D transition matrix • 6.4.4 Performance evaluation • 6.5 Summary • References.