Time-Modulated Intelligent Reflecting Surface for Waveform Security

We consider an OFDM transmitter aided by an intelligent reflecting surface (IRS) and propose a novel approach to enhance waveform security by employing time modulation (TM) at the IRS side. By controlling the periodic TM pattern of the IRS elements, the system is designed to preserve communication information towards an authorized recipient and scramble the information towards all other directions. We introduce two modes of TM pattern control: the linear mode, in which we design common TM parameters for entire rows or columns of the IRS, and the planar mode, where we design TM parameters for each individual IRS unit. Due to the required fewer switches, the linear mode is easier to implement as compared to the planar mode. However, the linear model results in a beampattern that has sidelobes, over which the transmitted information is not sufficiently scrambled. We show that the sidelobes of the linear mode can be suppressed by exploiting the high diversity available in that mode.

Enhance Security of Time-Modulated Array-Enabled Directional Modulation by Introducing Symbol Ambiguity

In this paper, if the time-modulated array (TMA)-enabled directional modulation (DM) communication system can be cracked is investigated and the answer is YES! We first demonstrate that the scrambling data received at the eavesdropper can be defied by using grid search to successfully find the only and actual mixing matrix generated by TMA. Then, we propose introducing symbol ambiguity to TMA to defend the defying of grid search, and design two principles for the TMA mixing matrix, i.e., rank deficiency and non-uniqueness of the ON-OFF switching pattern, that can be used to construct the symbol ambiguity. Also, we present a feasible mechanism to implement these two principles. Our proposed principles and mechanism not only shed light on how to design a more secure TMA DM system theoretically in the future, but also have been validated to be effective by bit error rate measurements.

How secure is the time-modulated array-enabled ofdm directional modulation?

Time-modulated arrays (TMA) transmitting orthogonal frequency division multiplexing (OFDM) waveforms achieve physical layer security by allowing the signal to reach the legitimate destination undistorted, while making the signal appear scrambled in all other directions. In this paper, we examine how secure the TMA OFDM system is, and show that it is possible for the eavesdropper to defy the scrambling. In particular, we show that, based on the scrambled signal, the eavesdropper can formulate a blind source separation problem and recover data symbols and TMA parameters via independent component analysis (ICA) techniques. We show how the scaling and permutation ambiguities arising in ICA can be resolved by exploiting the Toeplitz structure of the corresponding mixing matrix, and knowledge of data constellation, OFDM specifics, and the rules for choosing TMA parameters. We also introduce a novel TMA implementation to defend the scrambling against the eavesdropper.

A Secure Dual-Function Radar Communication System via Time-Modulated Arrays

Dual-function radar-communication (DFRC) systems offer high spectral, hardware and power efficiency, as such are prime candidates for 6G wireless systems. DFRC systems use the same waveform for simultaneously probing the surroundings and communicating with other equipment. By exposing the communication information to potential targets, DFRC systems are vulnerable to eavesdropping. In this work, we propose to mitigate the problem by leveraging directional modulation (DM) enabled by a time-modulated array (TMA) that transmits OFDM waveforms. DM can scramble the signal in all directions except the directions of the legitimate user. However, the signal reflected by the targets is also scrambled, thus complicating the extraction of target parameters. We propose a novel, low-complexity target estimation method that estimates the target parameters based on the scrambled received symbols. We also propose a novel method to refine the obtained target estimates at the cost of increased complexity. With the proposed refinement algorithm, the proposed DFRC system can securely communicate with users while having high-precision sensing functionality.

DFRC with Improved Communication-Sensing Trade-off via Private Subcarrier Permutations and Pairing with Antennas

Dual function radar communication (DFRC) systems can achieve significant improvements in spectrum efficiency, system complexity and energy efficiency, and are attracting a lot of attention for next generation wireless system design. This paper considers DFRC systems using MIMO radar with a sparse transmit array, transmitting OFDM waveforms, and assigning shared and private subcarriers to active transmit antennas. Subcarrier sharing allows antennas to modulate data symbols onto the same subcarriers and enables high communication rate, while the use of private subcarriers trades-off communication rate for sensing performance by enabling the formulation of a virtual array with larger aperture than the physical receive array. We propose to exploit the permutation of private subcarriers among the available subcarriers and the pairing between active antennas and private subcarriers to recover some of the communication rate loss. Exploiting the $1$-sparse property of private subcarriers, we also propose a low complexity algorithm to identify private subcarriers and detect the antenna-subcarrier pairing.

Phased Array With Improved Beamforming Capability via Use of Double Phase Shifters

The passive electronically scanned array (PESA) is widely used due to its simple structure and low cost. {Its antenna weights have unit modulus and thus, only the weights phases can be controlled. PESA has limited degrees of freedom for beampattern design, where only the direction of the main beam can be controlled.} In this paper we propose a novel way to improve the beamforming capability of PESA by endowing it with more degrees of freedom via the use of double phase shifters (DPS). By doing so, both the magnitude and the phase of the antenna weights can be controlled, allowing for more flexibility in beampattern design. We also take into account the physical resolution limitation of phase shifters, and propose a method to approximate a given complex beamformer using DPS. Simulation results indicate significant beamforming improvement even at low phase resolution.

Simultaneous Monitoring of Multiple People's Vital Sign Leveraging a Single Phased-MIMO Radar

Vital sign monitoring plays a critical role in tracking the physiological state of people and enabling various health-related applications (e.g., recommending a change of lifestyle, examining the risk of diseases). Traditional approaches rely on hospitalization or body-attached instruments, which are costly and intrusive. Therefore, researchers have been exploring contact-less vital sign monitoring with radio frequency signals in recent years. Early studies with continuous wave radars/WiFi devices work on detecting vital signs of a single individual, but it still remains challenging to simultaneously monitor vital signs of multiple subjects, especially those who locate in proximity. In this paper, we design and implement a time-division multiplexing (TDM) phased-MIMO radar sensing scheme for high-precision vital sign monitoring of multiple people. Our phased-MIMO radar can steer the mmWave beam towards different directions with a micro-second delay, which enables capturing the vital signs of multiple individuals at the same radial distance to the radar. Furthermore, we develop a TDM-MIMO technique to fully utilize all transmitting antenna (TX)-receiving antenna (RX) pairs, thereby significantly boosting the signal-to-noise ratio. Based on the designed TDM phased-MIMO radar, we develop a system to automatically localize multiple human subjects and estimate their vital signs. Extensive evaluations show that under two-subject scenarios, our system can achieve an error of less than 1 beat per minute (BPM) and 3 BPM for breathing rate (BR) and heartbeat rate (HR) estimations, respectively, at a subject-to-radar distance of $1.6~m$. The minimal subject-to-subject angle separation is $40{\deg}$, corresponding to a close distance of $0.5~m$ between two subjects, which outperforms the state-of-the-art.

Remaining useful life prediction with uncertainty quantification: development of a highly accurate model for rotating machinery

Rotating machinery is essential to modern life, from power generation to transportation and a host of other industrial applications. Since such equipment generally operates under challenging working conditions, which can lead to untimely failures, accurate remaining useful life (RUL) prediction is essential for maintenance planning and to prevent catastrophic failures. In this work, we address current challenges in data-driven RUL prediction for rotating machinery. The challenges revolve around the accuracy and uncertainty quantification of the prediction, and the non-stationarity of the system degradation and RUL estimation given sensor data. We devise a novel architecture and RUL prediction model with uncertainty quantification, termed VisPro, which integrates time-frequency analysis, deep learning image recognition, and nonstationary Gaussian process regression. We analyze and benchmark the results obtained with our model against those of other advanced data-driven RUL prediction models for rotating machinery using the PHM12 bearing vibration dataset. The computational experiments show that (1) the VisPro predictions are highly accurate and provide significant improvements over existing prediction models (three times more accurate than the second-best model), and (2) the RUL uncertainty bounds are valid and informative. We identify and discuss the architectural and modeling choices made that explain this excellent predictive performance of VisPro.

Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach

Remaining useful life (RUL) refers to the expected remaining lifespan of a component or system. Accurate RUL prediction is critical for prognostic and health management and for maintenance planning. In this work, we address three prevalent challenges in data-driven RUL prediction, namely the handling of high dimensional input features, the robustness to noise in sensor data and prognostic datasets, and the capturing of the time-dependency between system degradation and RUL prediction. We devise a highly accurate RUL prediction model with uncertainty quantification, which integrates and leverages the advantages of deep learning and nonstationary Gaussian process regression (DL-NSGPR). We examine and benchmark our model against other advanced data-driven RUL prediction models using the turbofan engine dataset from the NASA prognostic repository. Our computational experiments show that the DL-NSGPR predictions are highly accurate with root mean square error 1.7 to 6.2 times smaller than those of competing RUL models. Furthermore, the results demonstrate that RUL uncertainty bounds with the proposed DL-NSGPR are both valid and significantly tighter than other stochastic RUL prediction models. We unpack and discuss the reasons for this excellent performance of the DL-NSGPR.

A Wideband Dual Function Radar Communication System With Sparse Array and OFDM Waveforms

A novel multiple-input multiple-output (MIMO) dual-function radar communication (DFRC) system is proposed. The system transmits wideband, orthogonal frequency division multiplexing (OFDM) waveforms using a small subset of the available antennas in each channel use. The proposed system assigns most carriers to antennas in a shared fashion, thus efficiently exploiting the available communication bandwidth, and a small set of subcarriers to active antennas in an exclusive fashion (private subcarriers). A novel target estimation approach is proposed to overcome the coupling of target parameters introduced by subcarrier sharing. The obtained parameters are further refined via an iterative approach, which formulates a sparse signal recovery problem based on the data of the private subcarriers. The system is endowed with beamforming capability, via waveform precoding and antenna selection. The precoding and antenna selection matrices are optimally co-designed to meet a joint sensing-communication system performance. The sparsity of the transmit array is exploited at the communication receiver to recover the transmitted information. The use of shared subcarriers enables high communication rate, while the sparse transmit array maintains low system hardware cost. The sensing problem is formulated by taking into account frequency selective fading, and a method is proposed to estimate the channel coefficients during the sensing process. The functionality of the proposed system is demonstrated via simulations.