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Opportunistic Large Array Propagation Models: A Comprehensive Survey.

Farhan Nawaz1, Hemant Kumar1, Syed Ali Hassan1

  • 1School of Electrical Engineering & Computer Science (SEECS), National University of Sciences & Technology (NUST), Islamabad 44000, Pakistan.

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Summary
This summary is machine-generated.

Cooperative transmission (CT) enhances Internet-of-Things (IoT) networks using Opportunistic Large Array (OLA). New stochastic models improve OLA performance evaluation, especially in low-density networks, and introduce energy-efficient techniques for mMTC.

Keywords:
5GB5GOpportunistic Large Array (OLA)cooperative transmission (CT)massive Internet-of-Things (IoT)massive machine-type communications (mMTC)node densitypropagation modeling

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Area of Science:

  • Wireless Communications
  • Network Engineering
  • Internet of Things (IoT)

Background:

  • Massive deployments of Internet-of-Things (IoT) networks are enabled by 5G and beyond, requiring efficient communication for massive machine-type communication (mMTC) services.
  • Device-to-device (D2D) communication offers a solution for IoT by enabling nodes to form virtual antenna arrays through cooperative transmission (CT).
  • Opportunistic Large Array (OLA) is a CT technique providing efficient, reliable communication without prior coordination, suitable for mMTC.

Purpose of the Study:

  • To address the limitations of existing network models for Opportunistic Large Array (OLA) in characterizing propagation behavior and performance evaluation.
  • To introduce more accurate stochastic models, specifically quasi-stationary Markov chains, for estimating key performance metrics of OLA transmissions.
  • To provide a comprehensive survey of analytical models for OLA propagation and discuss energy-efficient OLA techniques for IoT networks.

Main Methods:

  • Review and analysis of existing literature on OLA protocols and propagation models.
  • Introduction and application of stochastic models, including quasi-stationary Markov chains, for OLA performance evaluation.
  • Exploration of energy-efficient OLA techniques and their relevance to IoT constraints.

Main Results:

  • Identified inaccuracies in widely-used OLA models for low node density networks.
  • Demonstrated the improved accuracy of stochastic models (quasi-stationary Markov chains) for OLA performance estimation.
  • Presented a comprehensive survey of OLA propagation models and introduced energy-efficient OLA strategies.

Conclusions:

  • Accurate propagation models are crucial for OLA protocol design and operation, especially in diverse network conditions.
  • Stochastic models offer a more precise approach to evaluating OLA performance in practical scenarios.
  • Future research should focus on integrating OLA with emerging technologies and optimizing energy efficiency for sustainable IoT networks.