Handover Mechanism in 5G mmwave Band

Spoorthi, G P; B, Akkamahadevi M

doi:10.1109/rteict46194.2019.9016943

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…Handover is a critical element of mobility management, with its main function being to facilitate seamless movement of UEs between serving and target cells without interruption. The characteristic of the mmWave channel, which is highly intermittent, adds further complexity to handover management [22][23][24][25][26]. For example, in NSA DC networks, the presence of a substantial number of base stations with high intermittency demands UEs to perform frequent HOs and leads to a higher number of HOs.…”

Section: G Non-standalone Dual Connectivity Networkmentioning

confidence: 99%

“…The fixed HCPs settings proved to be effective in the conventional LTE network scenarios, where there was no coverage overlapping and handovers usually occurred at cell edges. However, in dense deployments and small cells such as NSA DC networks, the static or fixed approach becomes impractical, as UEs with changing requirements may frequently move into areas where the cell coverage of numerous base stations overlaps [23][24][25][26].…”

Section: Handover Management In 5g Nsa DC Networkmentioning

confidence: 99%

“…Alternatively, rather than employing standardised or fixed HCPs throughout the scenario, NSA DC and dense networks demand a more diverse and personalised approach, with parameter settings for HCPs adjusted intelligently for each UE by considering its unique requirements [26]. For instance, as UEs move from serving to target base stations, they might pass through multiple cells and encounter varying obstacles, resulting in both early and delayed handovers.…”

Section: Handover Management In 5g Nsa DC Networkmentioning

confidence: 99%

“…The selection of SINR for channel evaluation is based on its capability to offer a more comprehensive channel measurement of wireless communication link quality compared to RSRP and RSRQ. While RSRP only indicates the received signal power and RSRQ reflects the received signal quality, the SINR considers both the interference and noise levels in addition to the signal power [24][25][26].…”

Section: Proposed Smart Handover Strategy (Shs) Mechanismmentioning

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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Section: G Non-standalone Dual Connectivity Networkmentioning

confidence: 99%

Section: Handover Management In 5g Nsa DC Networkmentioning

confidence: 99%

Section: Handover Management In 5g Nsa DC Networkmentioning

confidence: 99%

Section: Proposed Smart Handover Strategy (Shs) Mechanismmentioning

confidence: 99%

See 2 more Smart Citations

A Smart Handover Strategy for 5G mmWave Dual Connectivity Networks

Gannapathy,

Nordin,

Abdullah

et al. 2023

IEEE Access

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“…The main challenges encountered are namely due to (1) handling the handover/path switch in the event of rapid fluctuation of signal, (2) requiring severe scanning of channel quality, (3) consuming more time, and (4) causing high latency. In addition, the previous study revealed that unnecessary handover, even for fixed UEs, is the major drawback for higher frequency networks [10]. In addition, a lack of dynamic configuration at the handover margin (HOM) and TTT parameters often leads to inefficient handovers, which result in higher signaling overheads and performance degradation .…”

Section: Introductionmentioning

confidence: 99%

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

Gannapathy

Nordin

Abu-Samah

et al. 2023

Sensors

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Fifth Generation (5G) signals using the millimeter wave (mmWave) spectrums are highly vulnerable to blockage due to rapid variations in channel link quality. This can cause the devices or User Equipment (UE) to suffer from connection failure. In a dual connectivity (DC) network, the channel’s intermittency issues were partially solved by maintaining the UE’s connectivity to primary (LTE advanced stations) and secondary (5G mmWave stations) simultaneously. Even though the dual-connected network performs excellently in maintaining connectivity, its performance drops significantly due to the inefficient handover from one 5G mmWave station to another. The situation worsens when UE travels a long distance in a highly dense obstacle environment, which requires multiple ineffective handovers that eventually lead to performance degradation. This research aimed to propose an Adaptive TTT Handover (ATH) mechanism that deals with unpredictable 5G mmWave wireless channel behaviors that are highly intermittent. An adaptive algorithm was developed to automatically adjust the handover control parameters, such as Time-to-Trigger (TTT), based on the current state of channel condition measured by the Signal-to-Interference-Noise Ratio (SINR). The developed algorithm was tested under a 5G mmWave statistical channel model to represent a time-varying channel matrix that includes fading and the Doppler effect. The performance of the proposed handover mechanism was analyzed and evaluated in terms of handover probability, latency, and throughput by using the Network Simulator 3 tool. The comparative simulation result shows that the proposed adaptive handover mechanism performs excellently compared to conventional handovers and other enhancement techniques.

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