Name
Abstract | ||
|---|---|---|
Next generation 5G wireless systems envision ultra dense networks with a huge number of heterogeneous cells. This makes the management of such heterogeneous networks (HetNets) very complex and practically impossible without any automated procedure. Self-organizing networks (SON) are expected to provide self-configuration, self-optimization, and self-healing functions for automated management of 5G wireless networks. Cell outage detection is identified as a critical problem that requires efficient self-detection process. In this letter, we first classify the 5G base stations (BSs) into four different states. Subsequently, we explore a hidden Markov model to automatically capture current states of the BSs and probabilistically estimate a cell outage. Simulation results on typical, dense 5G HetNets demonstrate that our proposed strategy is capable of predicting the state of a BS at an average of 80% accuracy, as well as correctly detecting a cell outage ~ 95% of the time. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LCOMM.2016.2517070 | IEEE Communications Letters |
Keywords | Field | DocType |
Hidden Markov models,5G mobile communication,Training,Silicon,Wireless communication,Prediction algorithms,Mathematical model | Wireless network,Base station,Wireless,Wireless systems,Computer science,Computer network,Real-time computing,Prediction algorithms,Ultra dense,Heterogeneous network,Hidden Markov model | Journal |
Volume | Issue | ISSN |
20 | 3 | 1089-7798 |
Citations | PageRank | References |
11 | 0.70 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Multazamah Alias | 1 | 11 | 0.70 |
| Navrati Saxena | 2 | 577 | 44.48 |
| Abhishek Roy | 3 | 451 | 32.21 |