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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.
Sensors (Basel, Switzerland)
|July 2, 2021
Summary
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.
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.
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