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Automatic handover execution technique using machine learning algorithm for heterogeneous wireless networks

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Abstract

Integrating LTE sub-6 GHz and millimeter wave (mmWave) bands brings great benefit in increasing communication bandwidth, reliability, and better coverage of future smart intelligent network and its applications. However, finding the right mmWave remote radio units (RRUs) is challenging due to coverage blindness of directional beams. Further, the mmWave network depends on edge cloud deployment for satisfying low latency requirement of future smart applications. Along with, reducing energy consumption for handover execution is important due to the battery constraint of IoT (Internet of Things) device. Thus, it is important to reduce the signaling overhead of the handover process. First, this paper presents an efficient handover mechanism between LTE and mmWave; second present automatic handover execution mechanism between LTE and mmWave using a machine learning algorithm. Third presented improved XGBoost classification algorithm for predicting handover success rate using channel information collected through sampling window. Lastly, showed combining machine learning prediction model with standard handover execution model reduces signaling overhead and improves the handover success rate. The experiment is conducted by varying IoT device the result achieved shows XGBoost-based handover execution model achieves much superior performance than existing KNN-based handover execution algorithm.

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Authors and Affiliations

  1. Department of CSE, KBN College of Engineering, Kalabuargi, India

    Nishatbanu Nayakwadi & Ruksar Fatima

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  1. Nishatbanu Nayakwadi
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  2. Ruksar Fatima
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Correspondence to Nishatbanu Nayakwadi.

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Nayakwadi, N., Fatima, R. Automatic handover execution technique using machine learning algorithm for heterogeneous wireless networks. Int. j. inf. tecnol. 13, 1431–1439 (2021). https://doi.org/10.1007/s41870-021-00627-9

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  • DOI: https://doi.org/10.1007/s41870-021-00627-9

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