A Machine Learning Based 3D Propagation Model for Intelligent Future Cellular Networks

Masood, Usama; Farooq, Hasan; Imran, Ali

doi:10.1109/globecom38437.2019.9014187

Cited by 41 publications

(24 citation statements)

References 8 publications

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“…Some of these methods include k-nearest neighbor (KNN) [10], [11], sparse matrix or tensor processing [9], and Kriging [12], [13]. Based on the recent advance of image processing, deep learning for radio map construction was also investigated in [14]- [17]. Note that these approaches were mainly designed for 2D radio maps, and they may not be easily extended to our scenario of interest.…”

Section: A Related Workmentioning

confidence: 99%

“…2 /(2s 2 )] with a properly chosen parameter s = 55 meters and µ = i∈N (p) w(p, p (i) ) is a normalizing factor. 2) Kriging [13]: The radio map ĝ(p) is constructed based on all the measurement samples {(p (i) , y (i) )} using a similar model as in (14) and the model parameters are computed using the method in Section IV. Fig.…”

Section: A Radio Map Reconstructionmentioning

confidence: 99%

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UAV-aided Radio Map Construction for Wireless Communications and Localization

Liu¹,

Chen²

2021

Preprint

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Radio maps can be used for source localization, link performance prediction, and wireless relay planning. This paper constructs an air-to-ground radio map to predict the channel gain for each link that connects a ground terminal with a low altitude unmanned aerial vehicle (UAV). The challenge is the insufficiency of measurement samples for a radio map in full dimension, where each data point is 6-dimensional as the transmitter and the receiver each has three spatial degrees of freedom. Classical methods, such as k-nearest neighbor (KNN) and Kriging, may fail for insufficient data. This paper proposes to exploit the propagation property in the geometry of the environment to assist the radio map construction. Specifically, the radio map is built via reconstructing a virtual geometry environment. A multi-class virtual obstacle model embedded in a multi-degree channel model is developed, and a least squares problem is formulated to learn the virtual obstacle map and the model parameters. The paper investigates the partial quasiconvexity of the least squares problem, and based on that, an efficient radio map learning algorithm is developed. In addition, a data driven approach is employed to build a residual shadowing map to further improve the details of the constructed radio map. Our numerical results confirm that the proposed method significantly increases the prediction accuracy compared to a Kriging baseline, and reduces the required measurements by more than a half. When the constructed radio map is applied to received signal strength (RSS) based localization in a dense urban environment, substantial performance improvement is observed where a sub-20-meter accuracy is achieved.

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Section: A Related Workmentioning

confidence: 99%

Section: A Radio Map Reconstructionmentioning

confidence: 99%

UAV-aided Radio Map Construction for Wireless Communications and Localization

Liu¹,

Chen²

2021

Preprint

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“…• Ease of calibration through ML based models [25] from traces of real RSRP data, and then predicting accurate RSRP information on the bins where real measurement data is unavailable or has similar characteristics (terrain, BS tilt, user mobility).…”

Section: B Contributionsmentioning

confidence: 99%

“…SyntheticNET also allows importing of the detailed topographic data in various industry compliant formats for more realistic pathloss modeling. One key feature of SyntheticNET is its ability to support newly emerging AI based propagation models such as [25].…”

Section: ) Prorogation Modeling Modulementioning

confidence: 99%

SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond

et al. 2020

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The rapid evolution of cellular system design towards 5G and beyond gives rise to a need for investigation of the new features, design proposals and solutions in realistic settings for various deployments and use case scenarios. While many system level simulators for 4G and 5G exist today, there is particularly a dire need for a 3GPP compliant system level holistic and realistic simulator that can support evaluation of the plethora of AI based network automation solutions being proposed in literature. In this paper we present such a simulator developed at AI4networks Lab, called SyntheticNET. To the best of authors' knowledge, SyntheticNET is the very first python-based simulator that fully conforms to 3GPP 5G standard release 15 and is upgradable to future releases. The key distinguishing features of SyntheticNET compared to existing simulators include: 1) a modular structure to facilitate cross validation and upgrading to future releases; 2) flexible propagation modeling using measurement based, ray tracing based or AI based propagation modeling; 3) ability to import data sheet based on measurement based realistic vendor specific base station features such as antenna and energy consumption pattern; 4) support for 5G standard based adaptive numerology; 5) realistic and user-specific mobility patterns that are yielded from actual geographical maps; 6) detailed handover (HO) process implementation; and 7) incorporation of database aided edge computing. Another key feature of the SyntheticNET is the ease with which it can be used to test AI based network automation solutions. Being the first python based 5G simulator, this ease, in part stems for SyntheticNET's built-in capability to process and analyze large data sets and integrated access to Machine Learning libraries. Thus, SyntheticNET simulator offers a powerful platform for academia and industry alike to investigate not only new solutions for optimally designing, deploying and operating existing and emerging cellular networks but also for enabling AI empowered deep automation in the future. INDEX TERMS 5G, cellular networks, network simulator, flexible frame structure, mobility, edge computing.

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“…This is where Machine Learning (ML) comes into play. With machine learning, it is possible to model and map out functions that cannot be directly or mathematically interpreted in the data [1], [2]. This capability makes machine learning a promising tool to accurately capture the network dynamics due to changes in COPs even with very little and sparse experiment data.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

Shodamola

Masood

Manalastas

et al. 2020

2020 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)

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Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach.

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A Machine Learning Based 3D Propagation Model for Intelligent Future Cellular Networks

Cited by 41 publications

References 8 publications

UAV-aided Radio Map Construction for Wireless Communications and Localization

UAV-aided Radio Map Construction for Wireless Communications and Localization

SyntheticNET: A 3GPP Compliant Simulator for AI Enabled 5G and Beyond

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

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