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Performance of the Novel Reactive Access-Barring Scheme for NB-IoT Systems Based on the Machine Learning Inference.

Anastasia Daraseliya1, Eduard Sopin1,2, Julia Kolcheva1

  • 1Department of Probability Theory and Cyber Security, Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Str., Moscow 117198, Russia.

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Summary
This summary is machine-generated.

This study introduces a machine learning-based access-barring scheme for Narrowband Internet-of-Things (NB-IoT) systems. The new method improves throughput and manages delay in low power wide area networks (LPWAN) by dynamically adjusting traffic load.

Keywords:
5GdelaymMTCoptimal resource allocationrandom access

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Area of Science:

  • Telecommunications Engineering
  • Wireless Communication Systems
  • Machine Learning Applications

Background:

  • Modern 5G+grade low power wide area network (LPWAN) technologies, like Narrowband Internet-of-Things (NB-IoT), use multi-channel slotted ALOHA for random access.
  • The random access phase in these systems suffers from low throughput and instability due to traffic fluctuations.
  • Maintaining optimal throughput is challenging because the base station (BS) lacks knowledge of the current offered traffic load.

Purpose of the Study:

  • To propose and analyze a novel reactive access-barring scheme for NB-IoT systems using machine learning (ML).
  • To demonstrate that the number of colliding user equipments (UE) at the BS can indicate traffic load.
  • To characterize the delay experienced under the proposed reactive access-barring technique.

Main Methods:

  • Utilizing ML techniques to differentiate events in the Physical Random Access Channel (PRACH) based on signal-to-noise ratio (SNR).
  • Employing XGBoost classifiers to accurately associate PRACH events with the number of competing UEs.
  • Mathematically characterizing the delay performance of the proposed scheme.

Main Results:

  • ML models achieved 0.98 accuracy in differentiating PRACH events and estimating UE competition.
  • The proposed scheme maintained a constant successful preamble transmission probability of approximately 0.3 under overloaded conditions, significantly outperforming conventional NB-IoT (less than 0.05).
  • The scheme achieved near-optimal throughput in multi-channel ALOHA by dynamically adjusting transmission probability based on traffic awareness.

Conclusions:

  • The proposed ML-based reactive access-barring scheme effectively enhances NB-IoT system performance.
  • Dynamic traffic awareness and proactive congestion control ensure bounded delay and prevent system overload.
  • This approach offers a robust solution for stable and efficient operation of LPWANs in fluctuating traffic conditions.