Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks

Hwang, Weng-Sing; Cheng, Teng-Yu; Wu, Yan-Jing; Cheng, Ming-Hua

doi:10.3390/electronics11203278

Cited by 24 publications

(10 citation statements)

References 26 publications

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“…These algorithms are described as follows: SBH‐based handover algorithm for single connectivity (SBH‐SC): After triggering the A2 event, priority is given to selecting the mmWave BS with the maximum SINR. Fuzzy logic‐based handover algorithm for single connectivity (FL‐SC): Handover is achieved by dynamically adjusting the handover margin (HOM) and time to trigger (TTT) of A3 events using fuzzy logic 30 Q‐learning‐based handover algorithm for single connectivity (QL‐SC): After the A2 event is triggered, the target node is selected based on the Q table. …”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

“…Fuzzy logic-based handover algorithm for single connectivity (FL-SC): Handover is achieved by dynamically adjusting the handover margin (HOM) and time to trigger (TTT) of A3 events using fuzzy logic. 30 3. Q-learning-based handover algorithm for single connectivity (QL-SC): After the A2 event is triggered, the target node is selected based on the Q table.…”

Section: Comparative Algorithms and Performance Metricsmentioning

confidence: 99%

“…In Reference 29, a novel hierarchical SDN architecture is proposed, effectively addressing the network control problems and practical challenges of mmWave transmission. References 30 and 31 also employ adaptive handover parameter optimization algorithms, utilizing current UE state information as input to a fuzzy logic controller to dynamically adjust handover parameters for each terminal. In References 32 and 33, the target BS is directly selected using deep reinforcement learning and contextual multi‐armed bandit methods, respectively, when the handover condition is triggered.…”

Section: Related Workmentioning

confidence: 99%

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Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Wang,

2023

Trans Emerging Tel Tech

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Millimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.

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Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Section: Comparative Algorithms and Performance Metricsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Wang,

2023

Trans Emerging Tel Tech

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“…In 2022, a similar study was conducted by Hwang et al [28], in which the author introduced an inventive adaptive handover approach known as FLDHDT. This approach dynamically adapts HCPs settings, particularly TTT and HM, by considering two crucial parameters: SINR and UE velocity.…”

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

confidence: 94%

“…This leads to considerable interference due to the beamforming vectors associated with each link. An in-depth review of recent articles [28][31] [37][38] that utilise SINR for measuring channel quality in handover management has been conducted. These works showcase the latest advancements in the field.…”

Section: Proposed Smart Handover Strategy (Shs) Mechanismmentioning

confidence: 99%

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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A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets

Ben Ali,

Zarai

2023

Lecture Notes in Electrical Engineering

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Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks

Cited by 24 publications

References 26 publications

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

A Smart Handover Strategy for 5G mmWave Dual Connectivity Networks

A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets

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