Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions

Abstract

The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before. Ultra-Dense Cellular Network vision consisting of cells of varying sizes with conventional and mmWave bands is being perceived as the panacea for the eminent capacity crunch. However, mobility challenges in an ultradense heterogeneous network with motley of high frequency and mmWave band cells will be unprecedented due to plurality of handover instances, and the resulting signaling overhead and data interruptions for miscellany of devices. Similarly, issues like user tracking and cell discovery for mmWave with narrow beams need to be addressed before the ambitious gains of emerging mobile networks can be realized. Mobility challenges are further highlighted when considering the 5G deliverables of multi-Gbps wireless connectivity, <1ms latency and support for devices moving at maximum speed of 500km/h, to name a few. Despite its significance, few mobility surveys exist with the majority focused on adhoc networks. This paper is the first to provide a comprehensive survey on the panorama of mobility challenges in the emerging ultra-dense mobile networks. We not only present a detailed tutorial on 5G mobility approaches and highlight key mobility risks of legacy networks, but also review key findings from recent studies and highlight the technical challenges and potential opportunities related to mobility from the perspective of emerging ultra-dense cellular networks.

A Survey of Machine Learning Applications to Handover Management in 5G and Beyond

“…The literature was also reviewed without any particular attention to ML algorithms; as such, even though some ML applications were mentioned while reviewing the state-of-the-art methods, the scope of the paper was solely HO management, not the ML applications to HO management. Similarly, an extensive review of mobility management in ultra-dense networks was given in [31]. In particular, the authors included a meticulous tutorial on the mobility management in cellular networks, followed by discussions on proactive mobility management in the next generations of cellular networks, which comprises of a brief introduction to various ML techniques.…”

“…Furthermore, the authors included an analysis of AI assistance in mobility management, where they mainly reviewed the literature by identifying the employed AI methods and use-cases. This work seems to be one of the most overlapping survey papers with our present work; however, the focus of the survey in [31] is broader as it tries to encompasses every single issue in mobility management. In our present work, on the other hand, the scope is kept limited to HO management in order to make the review more comprehensive in terms of HO management.…”

“…In our present work, on the other hand, the scope is kept limited to HO management in order to make the review more comprehensive in terms of HO management. Moreover, ML application is not the main focus in [31], whereas, in our present work, we try to exclusively analyse the integration of ML to HO management in cellular networks by discussing ML-oriented opportunities as well as challenges. Besides, visual data based HO management parts covered in our present work constitutes one of the most important novelties and contributions in this present work, as it is not available in [31] or any other mobility or HO management based survey paper in the literature.…”

Evaluation of Development Level and Technical Contribution of Recent Technologies Adopted to Meet the Challenges of 5G Wireless Cellular Networks

“…One of the most promising technologies for 5G WCSs is that of small cells (SC) which can integrate femtocells, picocells and/or microcells [87,88]. This technology has greatly contributed to the management of broadband coverage both outside and inside buildings.…”

Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication

“…These events are A1, A2, A3, A4, A5, and A6 and are used to trigger HO. They are described as follows [17]: Event A2 and A1 are activated when the UE's channel condition drops below and exceeds the configured threshold, respectively. They are also used to start and stop inter-frequency neighbour search.…”