| Title | Boosting Secret Key Generation for IRS-Assisted Symbiotic Radio Communications |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Yang, Wang, Meng, Xu, Jing, Gong, Shimin, Hoang, Dinh Thai, Niyato, Dusit |
| Conference Name | 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) |
| Keywords | feature extraction, Human Behavior, Metrics, pubcrawl, Radio frequency, random key generation, resilience, Resiliency, Scalability, simulation, Switches, Symbiosis, Vehicular and wireless technologies, Wireless communication |
| Abstract | Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS's phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS's random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS's phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR. |
| DOI | 10.1109/VTC2021-Spring51267.2021.9448719 |
| Citation Key | liu_boosting_2021 |
Boosting Secret Key Generation for IRS-Assisted Symbiotic Radio Communications