A Machine Learning Approach to Predicting Coverage in Random Wireless Networks

Hammouti, Hajar El; Ghogho, Mounir; Zaidi, Syed Ali Raza

doi:10.1109/glocomw.2018.8644199

Cited by 17 publications

(8 citation statements)

References 21 publications

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“…ML-based methods, on the other hand, provide more accurate results at the cost of reduced interpretability. Finally, another feature of MLbased methods is that unlike SG-based methods [21], they obliterate the need for feature extraction [2].…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

Wang,

Mondal,

Kishk

et al. 2024

IEEE Open J. Commun. Soc.

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Given the necessity of connecting the unconnected, covering blind spots has emerged as a critical task in the next-generation wireless communication network. A direct solution involves obtaining a coverage manifold that visually showcases network coverage performance at each position. Our goal is to devise different methods that minimize the absolute error between the estimated coverage manifold and the actual coverage manifold (referred to as accuracy), while simultaneously maximizing the reduction in computational complexity (measured by computational latency). Simulation is a common method for acquiring coverage manifolds. Although accurate, it is computationally expensive, making it challenging to extend to large-scale networks. In this paper, we expedite traditional simulation methods by introducing a statistical model termed line-of-sight probability-based accelerated simulation. Stochastic geometry is suitable for evaluating the performance of large-scale networks, albeit in a coarse-grained manner. Therefore, we propose a second method wherein a model training approach is applied to the stochastic geometry framework to enhance accuracy and reduce complexity. Additionally, we propose a machine learning-based method that ensures both low complexity and high accuracy, albeit with a significant demand for the size and quality of the dataset. Furthermore, we describe the relationships between these three methods, compare their complexity and accuracy as performance verification, and discuss their application scenarios.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

Wang,

Mondal,

Kishk

et al. 2024

IEEE Open J. Commun. Soc.

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show abstract

“…To the best of the authors' knowledge, there are fundamentally two lines of thought in the literature regarding the mode of interaction that should prevail between ML and SG. The first vision is based on an evolutionary interaction [414]- [416], in which ML is conceived as a separate evolved alternative to SG enabling to overcome the shortcomings of the latter and provide more accurate representation of reality. In fact, SG model-driven approach is generally governed by a tradeoff between tractability and accuracy, where tractable models are simply less accurate to reflect realistic scenarios, while precise models are hard to derive and their resulting algorithms are too complex to implement.…”

Section: B Stochastic Geometry In the Era Of Machine Learningmentioning

confidence: 99%

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

et al. 2021

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Next generation wireless networks are expected to be highly heterogeneous, multi-layered, with embedded intelligence at both the core and edge of the network. In such a context, system-level performance evaluation will be very important to formulate relevant insights into tradeoffs that govern such a complex system. Over the past decade, stochastic geometry (SG) has emerged as a powerful analytical tool to evaluate system-level performance of wireless networks and capture their tendency towards heterogeneity. However, with the imminent onset of this crucial new decade, where global commercialization of fifthgeneration (5G) is expected to emerge and essential research questions related to beyond fifth-generation (B5G) are intended to be identified, we are wondering about the role that a powerful tool like SG should play. In this paper, we first aim to track and summarize the novel SG models and techniques developed during the last decade in the evaluation of wireless networks. Next, we will outline how SG has been used to capture the properties of emerging radio access networks (RANs) for 5G/B5G and quantify the benefits of key enabling technologies. Finally, we will discuss new horizons that will breathe new life into the use of SG in the foreseeable future. For instance, using SG to evaluate performance metrics in the visionary paradigm of molecular communications. Also, we will review how SG is envisioned to cooperate with machine learning seen as a crucial component in the race towards ubiquitous wireless intelligence. Another important insight is Grothendieck toposes considered as a powerful mathematical concept that can help to solve longstanding problems formulated in SG.

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“…NN-based prediction of the coverage probability given the base station density, the propagation pathloss and the shadowing correlation model [56].…”

Section: Network Dimensioning and Planningmentioning

confidence: 99%

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

Koufos

Haloui

Dianati

et al. 2021

JSAN

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The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.

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A Machine Learning Approach to Predicting Coverage in Random Wireless Networks

Cited by 17 publications

References 21 publications

Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

Terrain-Based Coverage Manifold Estimation: Machine Learning, Stochastic Geometry, or Simulation?

New Trends in Stochastic Geometry for Wireless Networks: A Tutorial and Survey

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

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