An Adaptive TTT Handover (ATH) Mechanism for Dual Connectivity (5G mmWave—LTE Advanced) during Unpredictable Wireless Channel Behavior

Gannapathy, Vigneswara Rao; Nordin, Rosdiadee; Abu-Samah, Asma; Abdullah, Nor Fadzilah; Ismail, Mahamod

doi:10.3390/s23094357

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…Whereas, for inter-system (3G/2G) handover, 'S1-based handover' is used [123]. Dual Connectivity is utilized in 5G networks, which use both LTE and 5G cells simultaneously to improve handover [124]. In terms of mobility management, the Centralized mobility management (CMM) [125] and Distributed mobility management (DMM) [126] methodologies are widely utilized.…”

Section: Mobility and Handover In Ai-ran 6gmentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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6G is a next-generation cellular communication technology that builds up on existing 5G networks which are currently rolled out worldwide. Through incorporation of artificial intelligence (AI) and machine learning (ML), the core 5G network is advanced into an intelligent 6G network. The 6G Artificial Intelligence Radio Access Network (AI-RAN) is anticipated to offer advanced features like reduced latency, improved bandwidth, data rates and coverage. Furthermore, AI-RAN is expected to support complex use cases such as extreme connectivity, multi-user communications and dynamic spectrum access. This paper provides a detailed survey and thorough assessment of AI-RAN's vision and state-of-the-art challenges. We first present a concise introduction to 6G AI-RAN followed by background information on the current 5G RAN and its challenges that must be overcome to implement 6G AI-RAN. The paper then examines trending research issues in AI-RAN, i.e., challenges related to spectrum allocation, network architecture, and resource management. We discuss the methods to overcome these challenges which include the adoption of advanced machine learning and edge computing technologies to boost the performance of 6G AI-RAN. We conclude by stating open research directions.

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Section: Mobility and Handover In Ai-ran 6gmentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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“…The SINR analysis is extensively utilized in the development of new handover algorithms. An adaptive algorithm for handover in 5G and LTE-Advanced networks is presented in [28]. The algorithm relies on the SINR measurements to assess the channel state information.…”

Section: Sinr Analysismentioning

confidence: 99%

An Approach for Using a Tensor-Based Method for Mobility-User Pattern Determining

Ashaev,

Safiullin,

Gaysin

et al. 2023

Inventions

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Modern mobile networks exhibit a complex heterogeneous structure. To enhance the Quality of Service (QoS) in these networks, intelligent control mechanisms should be implemented. These functions are based on the processing of large amounts of data and feature extraction. One such feature is information about user mobility. However, directly determining user mobility remains challenging. To address this issue, this study proposes an approach based on multi-linear data processing. The user mobility is proposed to determine, using the multi-linear data, about the changing of the Signal-to-Interference-plus-Noise-Ratio (SINR). SINR varies individually for each user over time, relative to the network’s base stations. It is natural to represent these data as a tensor. A tensor-based preprocessing step employing Canonical Polyadic Decomposition (CPD) is proposed to extract user mobility information and reduce the data volume. In the next step, using the DBSCAN algorithm, users are clustered according to their mobility patterns. Subsequently, users are clustered based on their mobility patterns using the DBSCAN algorithm. The proposed approach is evaluated utilizing data from Network Simulator 3 (NS-3), which simulates a portion of the mobile network. The results of processing these data using the proposed method demonstrate superior performance in determining user mobility.

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“…Developing an innovative SHS mechanism that utilises UE information, particularly SINR, to intelligently adjust HM and TTT as system outputs. Building upon previous research [19] The paper is structured into various sections to deliver a comprehensive study. In Section II, an overview of the 5G Non-Standalone (NSA) Dual Connectivity (DC) Network is presented.…”

Section: Figure 3 Handover Ping-pong Scenario At Each Gnbs Cell Edgesmentioning

confidence: 99%

“…The units for HM and TTT were in dB and ms, respectively. Figure 7 provides a visual representation of the fixed HCPs based on SINR [19] [22]. In conventional practice, the handover decision algorithm used in LTE and LTE-A systems is based on SINR/RSRP and can be represented mathematically as follows:…”

Section: Handover Management In 5g Nsa DC Networkmentioning

confidence: 99%

“…Table II provides a concise overview of the latest relevant studies and the key findings. In 2023, Gannapathy et al [19] introduced an innovative approach referred to as Adaptive TTT Handover (ATH), designed to address the challenges of inefficient handovers that often occur when UE travels long distances in densely obstructed environments. The research focuses on evaluating handover performance in the context of 5G mmWave small cell DC networks.…”

Section: Figure 7 Visual Representation Of Fixed Hcps Settings [19][22]mentioning

confidence: 99%

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A Smart Handover Strategy for 5G mmWave Dual Connectivity Networks

Gannapathy,

Nordin,

Abdullah

et al. 2023

IEEE Access

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The consideration of millimeter wave (mmWave) bands and ultra-dense deployment have emerged as essential enabling solutions in Fifth Generation (5G) networks. However, these solutions result in a significant surge in the number of handovers (HOs), leading to an increased occurrence of unnecessary handovers and a higher likelihood of dropped connectivity. Hence, designing a robust mobility technique that facilitates seamless connections to the user equipment (UE) has become crucial and challenging. One of the essential HO optimisation techniques is referred to as Mobility Robustness Optimisation (MRO), which focuses on adapting HO Control Parameters (HCPs) such as Hysteresis Margin (HM) and Time-to-Trigger (TTT). While it was originally designed for 4G and subsequently enhanced for 5G, the effectiveness of this functionality requires further refinement to meet the demands of future mobile networks. Existing MROs approaches primarily focus on UE mobility and heavily rely on metrics such as Received Signal Reference Power (RSRP) and Received Signal Reference Quality (RSRQ). However, these approaches tend to overlook critical handover challenges, including the widespread deployment of small cells, which is crucial in 5G mmWave networks, as well as issues related to interference and channel intermittency. This research proposes a Smart Handover Strategy (SHS) mechanism to autonomously fine-tune handover control parameters, including HM and TTT, by utilising real-time channel conditions assessed through the Signal-to-Interference-Noise Ratio (SINR), thereby ensuring precise parameter adjustments. The utilisation of SINR in this proposed mechanism aims to address handover challenges arising from interference, high intermittent, and unpredictable behaviours encountered in 5G mmWave wireless channels. The primary goal of this mechanism is to facilitate seamless handovers as UE transitions from one Base Station (BS) to another in a dual connectivity multi-radio network. The developed algorithm underwent testing using a 5G mmWave statistical channel model that emulates a dynamic channel matrix, including fading and the Doppler effect. The Network Simulator 3 (NS3) software was utilised to analyse and evaluate the performance of the SHS mechanism, specifically in terms of latency and throughput. The simulation results demonstrate the remarkable effectiveness of the SHS mechanism, showcasing a substantial improvement. In comparison to other handover enhancement techniques, the SHS mechanism achieves a notable increase of over 7% (38 Mbps) in PDCP throughput and a significant reduction of more than 56% (1.87 ms) in latency. Notably, when compared to fixed or traditional handover techniques in digital beamforming configurations, the SHS mechanism exhibits an impressive enhancement, with over 15% (80 Mbps) increase in PDCP throughput and a substantial reduction of more than 77% (4.85 ms) in latency.

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An Adaptive TTT Handover (ATH) Mechanism for Dual Connectivity (5G mmWave—LTE Advanced) during Unpredictable Wireless Channel Behavior

Cited by 7 publications

References 30 publications

AI-RAN in 6G Networks: State-of-the-Art and Challenges

AI-RAN in 6G Networks: State-of-the-Art and Challenges

An Approach for Using a Tensor-Based Method for Mobility-User Pattern Determining

A Smart Handover Strategy for 5G mmWave Dual Connectivity Networks

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