Research

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}
}