Research

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

Abstract:

In modern wireless communication systems, radio propagation modeling has always been a fundamental task in system design and performance optimization. These models are used in cellular networks and other radio systems to estimate the pathloss or the received signal strength (RSS) at the receiver or characterize the environment traversed by the signal. An accurate and agile estimation of pathloss is imperative for achieving desired optimization objectives. The state-of-the- art empirical propagation models are based on measurements in a specific environment and limited in their ability to capture idiosyncrasies of various propagation environments. To cope with this problem, ray-tracing based solutions are used in commercial planning tools, but they tend to be extremely time consuming and expensive. In this paper, we propose a Machine Learning (ML) based approach to complement the empirical or ray tracing-based models, for radio wave propagation modeling and RSS estimation. The proposed ML-based model leverages a pre-identified set of smart predictors, including transmitter parameters and the physical and geometric characteristics of the propagation environment, for estimating the RSS. These smart predictors are readily available at the network-side and need no further standardization. We have quantitatively compared the performance of several machine learning algorithms in their ability to capture the channel characteristics, even with sparse availability of training data. Our results show that Deep Neural Networks outperforms other ML techniques and provides a 25% increase in prediction accuracy as compared to state-of-the-art empirical models and a 12x decrease in prediction time as compared to ray tracing.

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Published In:

2019 IEEE Global Communications Conference (GLOBECOM) (h5-index=57)

URL:

https://ieeexplore.ieee.org/document/9014187

BibTeX:

@inproceedings{masood2019machine,
title={A Machine Learning Based 3D Propagation Model for Intelligent Future Cellular Networks},
author={Masood, Usama and Farooq, Hasan and Imran, Ali},
booktitle={2019 IEEE Global Communications Conference (GLOBECOM)},
pages={1–6},
year={2019},
organization={IEEE}
}


Deep Learning based Detection of Sleeping Cells in Next Generation Cellular Networks

Abstract:

The growing subscriber Quality of Experience demands are posing significant challenges to the mobile cellular network operators. One such challenge is the autonomic detection of sleeping cells in cellular networks. Sleeping Cell (SC) is a cell degradation problem, and a special case in Cell Outage Detection (COD) because it does not trigger any alarm due to hardware or software problems in the BS. To minimize the effect of such outages, researchers have proposed autonomous outage detection and compensation solutions. State-of-the-art SC detection depends on drive tests and subscriber complaints to identify the effected cells. However, this approach is quickly becoming unsustainable due to rising operational expenses. To address this particular issue, we employ a Deep Learning based framework which uses Minimization of Drive Tests (MDT) functionality introduced in LTE networks. In our proposed framework, MDT measurements are used to train the deep learning model. Anomalies or cell outages in the network can be then quickly detected and localized, thus significantly reducing the duty cycle of self-healing process in SON. In our simulation setup, we also quantitatively compare and demonstrate superior performance of our proposed approach with state of the art machine learning algorithm such as One Class SVM using multiple performance metrics.

Published In:

2018 IEEE Global Communications Conference (GLOBECOM) (h5-index=57)

URL:

https://ieeexplore.ieee.org/abstract/document/8647689

BibTeX:

@inproceedings{masood2018deep,
title={Deep Learning Based Detection of Sleeping Cells in Next Generation Cellular Networks},
author={Masood, Usama and Asghar, Ahmad and Imran, Ali and Mian, Adnan Noor},
booktitle={2018 IEEE Global Communications Conference (GLOBECOM)},
pages={206–212},
year={2018},
organization={IEEE}
}


Graph Signal Processing-Based Network Health Estimation for Next Generation Wireless Systems

Abstract:

In this letter, we propose a novel network health estimation technique for wireless cellular networks. The proposed scheme makes use of graph signal processing techniques to estimate network health over the entire coverage area with sparse availability of measured data. To achieve this objective, we solve an optimization problem on graph using proximal splitting method. The results show that the proposed technique outperforms existing methods such as kriging for network health estimation with an improved accuracy of more than 60% along with a reduced time complexity of O(n) compared with O(n 4 ) for kriging. Thereafter, coverage holes present in the network are found with an extremely high detection rate and extremely low false positive rate. The results, unlike kriging, are devoid of any spatial bias present in the training data.

Published In:

IEEE Communications Letters ( Volume: 23 , Issue: 1 , Jan. 2019 ) (I.F. = 3.45)

URL:

https://ieeexplore.ieee.org/abstract/document/8478364

BibTeX:

@article{yaseen2018graph,
title={Graph Signal Processing-Based Network Health Estimation for Next Generation Wireless Systems},
author={Yaseen, Faisal and Masood, Usama and Hassan, Ahmad Nayyar and Naqvi, Ijaz Haider},
journal={IEEE Communications Letters},
volume={23},
number={1},
pages={104–107},
year={2018},
publisher={IEEE}
}