As cloud applications and 5G use cases increase the demand for additional spectrum, there has emerged interest in spectrum sharing between terrestrial and satellite (SAT) networks. This paper assesses Dynamic Exclusion Zones (DEZ) and Power Control for spectrum coexistence between 5G Networks and SAT Digital Broadcasting Stations (DVB-S2). Relying on a baseband Software Defined Radio (SDR) based emulation on the COSMOS testbed, we propose a spectrum sharing control model that leverages a joint Dynamic Radio Resource Management (RRM) algorithm (Power Control with DEZ) for optimized control. Furthermore, the model combines RRM strategies with artificially generated atmospheric attenuation and path losses for robust control in a standard communication environment. Evaluating our implementation with a ETSI Rural Macro case study, we emphasize our system’s effective power control to preserve DVB-S2 Receiver Packet Error Rate (PER) performance, while maintaining a satisfactory 5G network PER. Against varying weather conditions, our results reveal joint RRM strategies can restore link performance within 73% to 99% of the ideal (no-interference) in a single base station (BS) interference scenario and 82% of the ideal against multiple BS conditions.

This paper presents the broadband communication capability of a millimeter-wave (mmWave) ultrawideband tightly coupled array (TCA) antenna based on high-density interconnect (HDI) antenna-in-package (AiP) technology. The essential antenna array elements are crafted using glass-based integrated passive devices (IPDs) and are subsequently flip-chipped on a multilayered HDI printed circuit board (PCB). The antenna features a wide impedance bandwidth from Ka- to W-band, suiting applications that demand a large signal bandwidth.

Speech eavesdropping has long been an important threat to the privacy of individuals and enterprises. Recent research has shown the possibility of deriving private speech information from sound-induced vibrations. Acoustic signals transmitted through a solid medium or air may induce vibrations upon solid surfaces, which can be picked up by various sensors (e.g., motion sensors, high-speed cameras and lasers), without using a microphone. To date, these threats are limited to scenarios where the sensor is in contact with the vibration surface or at least in the visual line-of-sight.

Recognizing the low-cost advantage of phased arrays, we investigate the improvement of the beamforming capability of a phased array via the use of double-phase shifters (DPS), i.e., each antenna is fed with the sum of the outputs of two phase shifters. The use of DPS allows for the manipulation of both the magnitude and phase of the signal transmitted by each antenna, thus enabling the formation of a flexible beam toward the desired target while suppressing the energy radiated toward unwanted directions. By sequentially steering the mainbeam to each target, while nulling other targets, one can monitor the vital signals of multiple targets with low inter-target interference. This is achieved at only a modest increase in the cost of a phased array due to the addition of phase shifters. The performance of the proposed approach is demonstrated via a prototype DPS phased array with 4 Vivaldi antennas using 8 commercial phase shifters and transmitting a continuous-wave RF signal at center frequency of 2.2 GHz.

Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in realizing intelligent applications across various domains. However, as the model size of deep neural networks rapidly increases, implementing large-scale RFNN in practice requires an extensive number of RF interferometers and consumes a substantial amount of energy. To address this challenge, we propose to utilize low-rank decomposition to transform a large-scale RFNN into a compact RFNN while almost preserving its accuracy. Specifically, we develop a Tensor-Train RFNN (TT-RFNN) where each layer comprises a sequence of low-rank third-order tensors, leading to a notable reduction in parameter count, thereby optimizing RF interferometer utilization in comparison to the original large-scale RFNN. Additionally, considering the inherent physical errors when mapping TT-RFNN to RF device parameters in real-world deployment, from a general perspective, we construct the Robust TT-RFNN (RTT-RFNN) by incorporating a robustness solver on TT-RFNN to enhance its robustness. To adapt the RTT-RFNN to varying requirements of reshaping operations, we further provide a reconfigurable reshaping solution employing RF switch matrices. Empirical evaluations conducted on MNIST and CIFAR-10 datasets show the effectiveness of our proposed method.

Speech privacy leakage has long been a public concern. Existing non-microphone-based eavesdropping attacks rely on physical contact or line-of-sight between the sensor (e.g., a motion sensor or a radar) and the victim sound source. In this poster, we investigate a new form of attack that remotely elicits speech from minute surface vibrations upon common room objects (e.g., paper bags, plastic storage bin) via mmWave sensing. We design and implement a highresolution software-defined phased-MIMO radar that integrates transmit beamforming, virtual array, and receive beamforming. The proposed system enhances sensing directivity by focusing all the mmWave beams toward a target room object. We successfully demonstrate such an attack by developing a deep speech recognition scheme grounded on unsupervised domain adaptation. Without prior training on the victim's data, our attack can achieve a high success rate of over 90% in recognizing simple digits.

Speech privacy leakage has long been a public concern. Through speech eavesdropping, an adversary may steal a user's private information or an enterprise's financial/intellectual properties, leading to catastrophic consequences. Existing non-microphone-based eavesdropping attacks rely on physical contact or line-of-sight between the sensor (e.g., a motion sensor or a radar) and the victim sound source. In this poster, we discover a new form of speech eavesdropping attack that senses minor speech-induced vibrations upon common room objects using mmWave. By integrating phasedarray and multiple-input and multiple-output (MIMO) on a single mmWave transceiver, our attack can capture and fuse micrometerlevel vibrations upon the surfaces of multiple objects to reveal speech content in a remote and non-line-of-sight fashion. We successfully demonstrate such an attack by developing a deep speech recognition scheme grounded on unsupervised domain adaptation. Without prior training on the victim's data, our attack can achieve a high success rate of over 90% in recognizing simple speech content.

Novel transmission schemes, enabled by recent advances in the fields of metamaterial (MTM), leaky-wave antenna (LWA) and directional modulation (DM), are proposed for enhancing the physical layer (PHY) security. MTM-LWAs, which offer compact, integrated, and cost-effective alternatives to the classic phased-array architectures, are particularly of interest for emerging wireless communication systems including Internet-of-Things. The proposed secure schemes are devised to accomplish the functionalities of directional modulation (DM) transmitters for orthogonal frequency-division multiplexing (OFDM) and non-contiguous OFDM transmissions, while enjoying the implementation benefits of MTM-LWAs. Specifically, transmitter architectures based on the idea of time-modulated MTM-LWA have been put forth as a promising solution for PHY security for the first time. The PHY security for the proposed schemes are investigated from the point of view of both passive and active attacks where an adversary aims to decode secret information and feed spurious data to the legitimate receiver, respectively. Numerical simulations reveal that even when the adversary employs sophisticated state-of-the-art deep learning based attacks, the proposed transmission schemes are resistant to these attacks and reliably guarantee system security.