The integration of unmanned aerial vehicles (UAVs) into cellular networks presents significant mobility management challenges, primarily due to frequent handovers caused by probabilistic line-of-sight conditions with multiple ground base stations (BSs). To tackle these challenges, reinforcement learning (RL)-based methods, particularly deep Q-networks (DQN), have been employed to optimize handover decisions dynamically. However, a major drawback of these learning-based approaches is their black-box nature, which limits interpretability in the decision-making process. This paper introduces an explainable AI (XAI) framework that incorporates Shapley Additive Explanations (SHAP) to provide deeper insights into how various state parameters influence handover decisions in a DQN-based mobility management system. By quantifying the impact of key features such as reference signal received power (RSRP), reference signal received quality (RSRQ), buffer status, and UAV position, our approach enhances the interpretability and reliability of RL-based handover solutions. To validate and compare our framework, we utilize real-world network performance data collected from UAV flight trials. Simulation results show that our method provides intuitive explanations for policy decisions, effectively bridging the gap between AI-driven models and human decision-makers.

Mobility performance has been a key focus in cellular networks up to 5G. To enhance handover (HO) performance, 3GPP introduced Conditional Handover (CHO) and Layer 1/Layer 2 Triggered Mobility (LTM) mechanisms in 5G. While these reactive HO strategies address the trade-off between HO failures (HOF) and ping-pong effects, they often result in inefficient radio resource utilization due to additional HO preparations. To overcome these challenges, this article proposes a proactive HO framework for mobility management in O-RAN, leveraging user-cell link predictions to identify the optimal target cell for HO. We explore various categories of Graph Neural Networks (GNNs) for link prediction and analyze the complexity of applying them to the mobility management domain. Two GNN models are compared using a real-world dataset, with experimental results demonstrating their ability to capture the dynamic and graph-structured nature of cellular networks. Finally, we present key insights from our study and outline future steps to enable the integration of GNN-based link prediction for mobility management in 6G networks.

High-speed railway (HSR) communications are pivotal for ensuring rail safety, operations, maintenance, and delivering passenger information services. The high speed of trains creates rapidly time-varying wireless channels, increases the signaling overhead, and reduces the system throughput, making it difficult to meet the growing and stringent needs of HSR applications. In this article, we explore artificial intelligence (AI)-based beam-level and cell-level mobility management suitable for HSR communications, including the use cases, inputs, outputs, and key performance indicators (KPI)s of AI models. Particularly, in comparison to traditional down-sampling spatial beam measurements, we show that the compressed spatial multi-beam measurements via compressive sensing lead to improved spatial-temporal beam prediction. Moreover, we demonstrate the performance gains of AI-assisted cell handover over traditional mobile handover mechanisms. In addition, we observe that the proposed approaches to reduce the measurement overhead achieve comparable radio link failure performance with the traditional approach that requires all the beam measurements of all cells, while the former methods can save 50% beam measurement overhead.

Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks (HLWNets) are an emerging indoor wireless communication paradigm, which combines the advantages of the capacious optical spectra of LiFi and ubiquitous coverage of WiFi. Meanwhile, load balancing (LB) becomes a key challenge in resource management for such hybrid networks. The existing LB methods are mostly network-centric, relying on a central unit to make a solution for the users all at once. Consequently, the solution needs to be updated for all users at the same pace, regardless of their moving status. This would affect the network performance in two aspects: i) when the update frequency is low, it would compromise the connectivity of fast-moving users; ii) when the update frequency is high, it would cause unnecessary handovers as well as hefty feedback costs for slow-moving users. Motivated by this, we investigate user-centric LB which allows users to update their solutions at different paces. The research is developed upon our previous work on adaptive target-condition neural network (ATCNN), which can conduct LB for individual users in quasi-static channels. In this paper, a deep neural network (DNN) model is designed to enable an adaptive update interval for each individual user. This new model is termed as mobility-supporting neural network (MSNN). Associating MSNN with ATCNN, a user-centric LB framework named mobility-supporting ATCNN (MS-ATCNN) is proposed to handle resource management and mobility management simultaneously. Results show that at the same level of average update interval, MS-ATCNN can achieve a network throughput up to 215\% higher than conventional LB methods such as game theory, especially for a larger number of users. In addition, MS-ATCNN costs an ultra low runtime at the level of 100s $\mu$s, which is two to three orders of magnitude lower than game theory.

Reinforcement learning (RL) has shown promise in creating complex logic in controlled settings. On the other hand, what are the prospects for using RL in a more complicated context like telecom networks? Let's learn the basics first. What is reinforcement learning, and how does it work? In machine learning, the three methodologies are reinforcement learning (RL), supervised learning, and unsupervised learning.

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 ultra-dense 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.

The entire way people and goods travel from point A to point B is changing, driven by a series of converging technological and social trends: the rapid growth of carsharing and ridesharing; the increasing viability of electric and alternative powertrains; new, lightweight materials; and the growth of connected and, ultimately, autonomous vehicles. The result is the emergence of a new ecosystem of mobility that could offer faster, cheaper, cleaner, safer, more efficient, and more customized travel. While uncertainty abounds, in particular about the speed of the transition, a fundamental shift is driving a move away from personally owned, driver-driven vehicles and toward a future mobility system centered around (but not exclusively composed of) driverless vehicles and shared mobility. The shift will likely affect far more than automakers--industries from insurance and health care to energy and media should reconsider how they create value in this emerging environment. We believe a series of technological and social forces, including the emergence of connected, electric, and autonomous vehicles and shifting attitudes toward mobility, are likely to profoundly change the way people and goods move about.