Visible to the public Biblio

Filters: Author is Lee, S. H.  [Clear All Filters]
2017-12-20
Viet, H. N., Kwon, K. R., Kwon, S. K., Lee, E. J., Lee, S. H., Kim, C. Y..  2017.  Implementation of GPS signal simulation for drone security using Matlab/Simulink. 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON). :1–4.
In this paper, a simulation model of digital intermediate frequency (IF) GPS signal is presented. This design is developed based on mathematical model representing the digitized IF GPS signal. In details, C/A code, navigation data and P code, and the noise models are configured some initial settings simultaneously. Simulation results show that the simulated signals share the same properties with real signals (e.g. C/A code correlation properties, and the spread spectrum). The simulated GPS IF signal data can work as input for various signal processing algorithm of GPS receivers, such as acquisition, tracking, carrier-to-noise ratio (C/No) estimation, and GPS spoofing signal generation. Particularly, the simulated GPS signal can conduct scenarios by adjust SNR values of the noise generator during simulation (e.g. signal outages, sudden changes of GPS signal power), which can be used as setup experiments of spoofing/jamming interference to UAVs for drone security applications.
2017-12-12
Lee, S. H., Wang, L., Khisti, A., Womell, G. W..  2017.  Covert communication with noncausal channel-state information at the transmitter. 2017 IEEE International Symposium on Information Theory (ISIT). :2830–2834.

We consider the problem of covert communication over a state-dependent channel, where the transmitter has non-causal knowledge of the channel states. Here, “covert” means that the probability that a warden on the channel can detect the communication must be small. In contrast with traditional models without noncausal channel-state information at the transmitter, we show that covert communication can be possible with positive rate. We derive closed-form formulas for the maximum achievable covert communication rate (“covert capacity”) in this setting for discrete memoryless channels as well as additive white Gaussian noise channels. We also derive lower bounds on the rate of the secret key that is needed for the transmitter and the receiver to achieve the covert capacity.