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

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...

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Bibliographic Details
Published in:IEEE communications letters 2019-01, Vol.23 (1), p.104-107
Main Authors: Yaseen, Faisal, Masood, Usama, Hassan, Ahmad Nayyar, Naqvi, Ijaz Haider
Format: Article
Language:English
Subjects:
Cellular communication
coverage optimization
Estimation
Graph signal processing (GSP)
Health
Interpolation
Kriging
Laplace equations
network health estimation
Optimization
proximal splitting
Signal processing
Wireless communication
Wireless networks
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Summary: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 \mathcal {O}(n) compared with \mathcal {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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2018.2872942