A Bayesian Regression Based LTE-R Handover Decision Algorithm for High-Speed Railway Systems

Bang, June-ho; Oh, Sehchan; Kang, Kyungran; Cho, Young-Jong

doi:10.1109/tvt.2019.2935165

Cited by 24 publications

(7 citation statements)

References 16 publications

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“…Compute Source node location and velocity (C 0 [mth Slot], V 0 [mth slot]); (5) Deploy relay node at C 0 (m + 1); (6) if Update message of relay fails then (7) Pick another Relay node from pool U i ; T update (8) Update T update � t; (9) endif (10) elseif i ϵ [1, n] then (11) Get update message from source node U 0 ; (12) Compute C i [m + 1]; (13) Synchronize Trajectory along Source node ( 14) endif (15) end for BS reply to Ui with reserve slot for U0; (7) end if (8) else if U0 also Compute BS > RSSI threshold (9) U0 send vertical handover request through Ui (10) BS allocate resource to Uo on behalf of Ui (11) Confirmation sent to relay node Ui and source node U0 (12) U0 initiate authentication (13) BS responds to authentication (14) U0 send confirmation message to relay node (15) end if ALGORITHM 2: Vertical handover using cooperative relaying.…”

Section: Relay Selection and Deploymentmentioning

confidence: 99%

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A Novel Cooperative Relaying-Based Vertical Handover Technique for Unmanned Aerial Vehicles

Haider

Gilani

Jamal

et al. 2022

Security and Communication Networks

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The real-time monitoring and autonomous decision making through unmanned aerial vehicles (UAVs) are the potential applications of future networks. Vertical handover in future networks is a mechanism to switch communication between different network access technologies like Wireless Local Area Network (WLAN), Worldwide Interoperability for Wireless Microwave Access (WiMAX), Third-Generation (3G), Fourth-Generation (4G), and Fifth-Generation (5G) mobile technologies. These technologies have significant importance in providing fast, reliable, and timely communication. However, during a vertical handover, an inadequate delay and packet loss can cause considerable disruption in maintaining communication sessions and results in intolerable end-to-end delay, disconnectivity, and poor packet delivery ratio. The proposed work addresses the vertical handover method in UAVs communication by designing a relay-based vertical handover technique. The relay UAVs is an assistant node, requiring an organized and intelligent deployment that assists in vertical handover and communication by minimizing the average packet loss and average delay from source to destination. Moreover, a multicriteria handover parameter triggering is used for seamless and more extended network coverage. Extensive simulations using S-shaped and U-shaped trajectories are designed and simulated for relay-based vertical handover performance evaluation. The results obtained show that our proposed relay-based handover method offers seamless connectivity and high-performance experienced during the vertical handover process. The extensive comparison with state-of-the-art techniques proves that the proposed method is better in terms of 18% handover success rate, 21% end-to-end delay, and 29% packet loss.

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Section: Relay Selection and Deploymentmentioning

confidence: 99%

“…In the existing literature, significant attention has been given to the handover decision algorithms [12]. e work presented in [13] focused on the false handover initiation and ensured the true need for handover.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cooperative Relaying-Based Vertical Handover Technique for Unmanned Aerial Vehicles

Haider

Gilani

Jamal

et al. 2022

Security and Communication Networks

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“…Mobile Information Systems is no novelty where an unwanted MG can be avoided through prior prediction of mmWaves' signal strength by exploiting out-of-band information that is already available. With the advancement of computing power, ML-driven algorithms are mostly proposed to overcome limitations in classical techniques [31][32][33][34][35][36][37][38][39] in such scenarios. However, the ML-based technique is only effective if it is efficiently exploited.…”

Section: Contributionsmentioning

confidence: 99%

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Majid

Shah

Marwat

et al. 2021

Mobile Information Systems

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The future high-speed cellular networks require efficient and high-speed handover mechanisms. However, the traditional cellular handovers are based upon measurements of target cell radio strength which requires frequent measurement gaps. During these measurement windows, data transmission ceases each time, while target bearings are measured causing serious performance degradation. Therefore, prediction-based handover techniques are preferred in order to eliminate frequent measurement windows. Thus, this work proposes an ultrafast and efficient XGBoost-based predictive handover technique for next generation mobile communications. The ML algorithm in general prefers 70–30% of training and test data, respectively. However, always obtaining 70% of training samples in mobile communications is challenging because the channel remains correlated within coherence time only. Therefore, collecting training samples beyond coherence time limits performance and adds delay; thus, the proposed work trains the model within coherence time where this fixed data split of 70–30% makes the model exceed coherence time. Despite the fact that the proposed model gets starved of required training samples, still there is no loss in predication accuracy. The test results show a maximum delay improvement of up to 596 ms with enhanced performance efficiency of 68.70% depending upon the scenario. The proposed model reduces delay and improves efficiency by having an appropriate training period; thus, the intelligent technique activates faster with improved accuracy and eliminates delay in the algorithm to predict mmWaves’ signal strength in contrast to actually measuring them.

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“…Machine learning (ML) applications to develop smarter handovers are numerous. The authors in [2] implement a Bayesian regression method for HO improvements in high-speed trains in South Korea, whereas authors in [3] applies K-Nearest Neighbours (KNN) for possible real-time HO decisions in vehicular networks. In [4], ML approaches are used to reduce latency and classify the best cell available for HO.…”

Section: Introductionmentioning

confidence: 99%

User-Level Handover Decision Making Based on Machine Learning Approaches

Lima,

Medeiros,

Aguiar

et al. 2022

JCIS

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This Letter covers a broad comparison of methods for classification and regression applications for a user-level handover decision making in scenarios with adverse propagation conditions involving buildings, coverage holes, and shadowing effects. The simulation campaigns are based on network simulator ns-3. The comparison encompasses classical machine learning approaches, such as KNN, SVM, and neural networks, but also state-of-the-art fuzzy logic systems and latter boosting machines. The results indicate that SVM and MLP are the most suitable for the classification of the best handover target, although fuzzy system SOFL can perform similarly with lower processing time. Additionally, for the download time estimation, LightGBM provides the smallest error with short processing time, even in hard propagation scenarios.

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A Bayesian Regression Based LTE-R Handover Decision Algorithm for High-Speed Railway Systems

Cited by 24 publications

References 16 publications

A Novel Cooperative Relaying-Based Vertical Handover Technique for Unmanned Aerial Vehicles

A Novel Cooperative Relaying-Based Vertical Handover Technique for Unmanned Aerial Vehicles

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

User-Level Handover Decision Making Based on Machine Learning Approaches

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