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Enhanced machine learning approach with orthogonal frequency division multiplexing to avoid congestion in wireless communication system

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Abstract

In urban area vehicular adhoc network ascertaining appropriate channel for load balanced high data transferring, in dynamic network location is addressed by several industries and researchers using various routing algorithms. Particularly load balancing in traffic congestion and channel optimization in dynamic vehicular topology extends coverage and line-of-sight relay association issue and probability of hold-up link is high. For preventing problems instigated by traffic congestion and estimating optimistic channel for transferring high data rate without affecting network performances machine learning with orthogonal frequency division multiplexing and multi-input and multi-output (OFDM–MIMO) approach is incorporated for that takes care of load balancing and channel optimisation respectively in this paper for effective communication in vehicular network. Through miscellaneous simulations we implement Q-learning based load balancing routing (Q-LLBR) and OFDM–MIMO scheme is incorporated to achieve better performance in packet delivery ratio, latency, network utilization and signal to noise ratio are performed significantly to improve network performance in vehicular communications.

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Acknowledgements

This research is done with the financial support by the Deanship of Scientific Research at King Khalid University under research grant number (R.G.P.2/388/44).

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Authors and Affiliations

  1. College of Computing and Information Technology, Department of Computer Science, University of Bisha, Bisha, Kingdom of Saudi Arabia

    Abdulrahman Saad Alqahtani

  2. Department of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia

    Azath Mubarakali

  3. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, 641407, India

    M. Saravanan

  4. King Khalid University, Abha, Kingdom of Saudi Arabia

    Suresh Babu Changalasetty

  5. Department of CSE, Shadan Womens College of Engineering and Technology, Hyderabad, India

    Lalitha Saroja Thota

  6. Department of Electronics and Communication Engineering, CMR Institute of Technology, Bengaluru, Karnataka, 560037, India

    P. Parthasarathy

  7. Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, 603203, India

    B. Sivakumar

Authors
  1. Abdulrahman Saad Alqahtani
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  2. Azath Mubarakali
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  3. M. Saravanan
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  4. Suresh Babu Changalasetty
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  5. Lalitha Saroja Thota
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  6. P. Parthasarathy
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  7. B. Sivakumar
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Contributions

ASA and AM—initial research conduction, manuscript preparation. MS and SBC—state of art. MS—result generation and concept formulation. L—result generation and concept formulation. PP—proof of article correction and Final manuscript editing. S—proof of article correction and final manuscript editing.

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Correspondence to P. Parthasarathy.

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Alqahtani, A.S., Mubarakali, A., Saravanan, M. et al. Enhanced machine learning approach with orthogonal frequency division multiplexing to avoid congestion in wireless communication system. Opt Quant Electron 55, 913 (2023). https://doi.org/10.1007/s11082-023-05181-1

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