Visible to the public Biblio

Filters: Keyword is millimeter-wave  [Clear All Filters]
2023-02-03
Venkatesh, Suresh, Saeidi, Hooman, Sengupta, Kaushik, Lu, Xuyang.  2022.  Millimeter-Wave Physical Layer Security through Space-Time Modulated Transmitter Arrays. 2022 IEEE 22nd Annual Wireless and Microwave Technology Conference (WAMICON). :1–4.
Wireless security and privacy is gaining a significant interest due to the burgeoning growth of communication devices across the electromagnetic spectrum. In this article, we introduce the concept of the space-time modulated millimeter-wave wireless links enabling physical layer security in highspeed communication links. Such an approach does not require cryptographic key exchanges and enables security in a seamless fashion with no overhead on latency. We show both the design and implementation of such a secure system using custom integrated chips at 71-76 GHz with off-chip packaged antenna array. We also demonstrate the security metric of such a system and analyze the efficacy through distributed eavesdropper attack.
2022-02-07
Catak, Evren, Catak, Ferhat Ozgur, Moldsvor, Arild.  2021.  Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case. 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). :1–6.
6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. With the rapid developments of deep learning techniques, it is critical to take the security concern into account when applying the algorithms. While machine learning offers significant advantages for 6G, AI models’ security is normally ignored. Due to the many applications in the real world, security is a vital part of the algorithms. This paper proposes a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction using adversarial learning. The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction. We also present the adversarial learning mitigation method’s performance for 6G security in millimeter-wave beam prediction application with fast gradient sign method attack. The mean square errors of the defended model under attack are very close to the undefended model without attack.
2020-04-24
Bahman Soltani, Hooman, Abiri, Habibollah.  2018.  Criteria for Determining Maximum Theoretical Oscillating Frequency of Extended Interaction Oscillators for Terahertz Applications. IEEE Transactions on Electron Devices. 65:1564—1571.

Extended interaction oscillators (EIOs) are high-frequency vacuum-electronic sources, capable to generate millimeter-wave to terahertz (THz) radiations. They are considered to be potential sources of high-power submillimeter wavelengths. Different slow-wave structures and beam geometries are used for EIOs. This paper presents a quantitative figure of merit, the critical unloaded oscillating frequency (fcr) for any specific geometry of EIO. This figure is calculated and tested for 2π standing-wave modes (a common mode for EIOs) of two different slowwave structures (SWSs), one double-ridge SWS driven by a sheet electron beam and one ring-loaded waveguide driven by a cylindrical beam. The calculated fcrs are compared with particle-in-cell (PIC) results, showing an acceptable agreement. The derived fcr is calculated three to four orders of magnitude faster than the PIC solver. Generality of the method, its clear physical interpretation and computational rapidity, makes it a convenient approach to evaluate the high-frequency behavior of any specified EIO geometry. This allows to investigate the changes in geometry to attain higher frequencies at THz spectrum.