Improving Physical Layer Group Key Generation Efficiency in 5G Wireless Networks
| dc.contributor.advisor | Zeng, Kai | |
| dc.contributor.author | Jiao, Long | |
| dc.creator | Jiao, Long | |
| dc.date | 2022-05-06 | |
| dc.date.accessioned | 2023-02-16T12:36:13Z | |
| dc.date.available | 2023-02-16T12:36:13Z | |
| dc.description.abstract | In this thesis, we investigate the scheme to improve the group secret key generation efficiency in 5G mmWave Massive MIMO networks by enhancing the efficiency of channel probing for group key generation. A new channel probing strategy for star-topology networks group key generation is proposed, which focuses on multiplexing of downlink probing signals to perform the downlink channel probing concurrently. The hybrid precoder has been considered in this scenario to mitigate the inter-group interference, which includes a analog precoder and baseband precoder. To further balance the group key rates, a genetic algorithm (GA) based power allocation algorithm is developed to allocate more power to the nodes with unfavorable channel conditions. What's more, we propose a scheme to estimate group key rates based on the maximum likelihood estimator (MLE) so that we can estimate the group key rates based on the probing samples. Various numerical results are provided including the group key rates and bits disagreement ratio (BDR). The numerical results show that the GA-based downlink channel probing scheme can increase the efficiency of channel probing and have higher group key rates compared with the existing channel probing schemes. When the SNR is 25dB, the key rates of GA-based power allocation scheme are 20% higher than the scheme with the conventional channel probing strategy. | |
| dc.identifier.uri | https://hdl.handle.net/1920/13059 | |
| dc.language.iso | en | |
| dc.subject | Physical layer security | |
| dc.subject | 5G | |
| dc.subject | MmWave | |
| dc.title | Improving Physical Layer Group Key Generation Efficiency in 5G Wireless Networks | |
| dc.type | Thesis | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | Master of Science in Electrical Engineering |