ML Optimizes 5G Handover for Seamless Connectivity

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Aug 21 2024

In a paper published in the journal Electronics, researchers used machine learning (ML) models to explore intelligent handover (HO) optimization strategies for next-generation 5G networks.

They addressed challenges posed by network densification and strict 5G criteria, focusing on seamless communication and quality-of-service issues. The study reviewed the current state of cellular networks, mobility, and HO management and discussed future research directions and challenges.

Background

Past work has highlighted the increasing demands on connectivity and bandwidth due to the rapid growth of data-intensive applications and Internet of Things (IoT) devices. The emergence of 5G networks, with enablers like network densification and mm-wave communications, has intensified challenges in HO, affecting quality of service (QoS) through service disruptions and decreased throughput. Recent studies have focused on using ML to optimize HO management, addressing the complexities of modern cellular networks and the stricter requirements of 5G.

HO Optimization Techniques

HO in cellular networks is classified into two main types based on techniques: connect before break (CBB) or soft HO, where a continuous connection is maintained during the transition, and break before connect (BBC) or hard HO, where the existing connection is broken before establishing a new one. These methods are crucial for managing the increased network complexity in 5G environments, where horizontal HOs (within the same network) and vertical HOs (between different networks) are common.

Past research has explored various HO management strategies, including ML applications, to optimize these processes. Studies have reviewed the role of ML in self-organizing networks, mobility predictions, and HO management, particularly within the context of 5G and emerging 6G technologies. However, while ML's potential in enhancing HO management has been acknowledged, many studies still need to fully delve into specific ML applications or the use of visual data in this context, leaving room for further exploration in future research.

5G HO Management

Analysts divided HO management in 5G networks into two categories: inter–intra-frequency-based and inter–intra-radio access technology (RAT)-based HOs. Inter–intra-frequency HO involves transitions within the same or different frequencies. Intra-frequency HO occurs when a user equipment (UE) moves to a new cell using the same frequency.

In contrast, inter-frequency HO happens when a switch is made to a different carrier frequency. The process relies on specific events, like A3 and A6 for intra-frequency and A4 and A5 for inter-frequency, which can result in challenges like decreased throughput and increased energy consumption due to the need for measurement gaps.

Inter–intra-RAT-based HO cover transitions within the same RAT (intra-RAT) or between different RATs (inter-RAT). Intra-RAT HO, or horizontal HOs, keeps the UE connected to the network by selecting the strongest signal within the same RAT. Conversely, inter-RAT HO, or vertical HO, involves switching between RATs, such as cellular to wireless local area network (WLAN), considering user accessibility and service type. Centralized architectures for inter-RAT HO can reduce interruption times and enhance user experience. At the same time, ML models optimize HO management by predicting and adjusting user demands in real time.

Optimizing 5G HO

HO management in 5G networks, especially in mm-wave and THz-wave bands, presents significant challenges due to the frequent HOs necessitated by the small coverage areas of these high-frequency base stations (BSs). Traditional HO methods based on parameters like signal-to-noise ratio or received signal strength are insufficient in these bands because of high path loss and susceptibility to line-of-sight (LOS) occlusion.

ML approaches, however, offer potential solutions by minimizing computational overhead, delays, and frequent HOs. ML models like deep reinforcement learning (DRL), support vector machines (SVMs), and convolutional neural networks (CNNs) can optimize BS selection and HO processes by predicting user demands and ensuring resource availability before HO, thus enhancing network performance and reducing interruptions. These techniques are particularly crucial in ultra-dense network (UDN) designs, where the high frequency of HOs can lead to increased signaling overhead and potential HO failures, especially in scenarios involving high-speed mobility or large numbers of devices.

The complexity of HO management in 5G is further exacerbated by issues such as interoperability across different radio access technologies, the need for seamless and fast HOs, and the challenges posed by ultra-high mobility environments like high-speed rail systems.

To address these challenges, researchers must generalize ML models effectively to handle diverse environmental conditions and develop strategies for decentralized deployment that balance global network accuracy with localized optimization. Future research must focus on creating sophisticated models that reduce unnecessary HOs, improve load balancing across cells, and ensure the security and privacy of user data while maintaining the high accuracy required for 5G networks.

Conclusion

To sum up, this study reviewed the current state of cellular communication networks and ML integration into HO management. It highlighted the shift from traditional methods to ML-assisted approaches, including supervised, unsupervised, and RL, and examined the latest research and challenges in this area. The study introduced a novel taxonomy linking data sources to visual data-assisted HO strategies and discussed the potential for intelligent HO management in crises.