In this paper, a multi-static integrated sensing, identification and backscatter communication (ISIBC) system is proposed for Internet-of-Things (IoT) networks, where the backscatter devices (BDs) attached to the targets utilize ambient radio frequency (RF) signals for data transmission and multiple receivers are used for target localization. Each receiver estimates the directions of arrival (DoAs) of the incident signals from the RF source, the targets with BDs and the environmental scatterers, detects the activities of each BD, and demodulates the symbols transmitted by each active BD. With the DoA estimation and activity detection results, the receivers are required to identify the targets, i.e., associating the estimated DoAs with the corresponding targets attached with active BDs. These tasks, however, are challenging as the ambient RF signal is unknown. To tackle this issue, we propose a semi-blind generalized likelihood ratio test (GLRT) detector to estimate the activities of the BDs, which tests the homogeneity of the received signal samples over different BD symbol periods without requiring any information about the ambient RF signal and backscattering channels. A constant false alarm rate (CFAR) threshold is derived for the proposed GLRT detector. Further, we propose a novel semi-blind eigenvector-based identification method to identify the targets without using knowledge of the ambient RF signal. The eigenvector-based demodulator is proposed to demodulate the symbols transmitted by active BDs. Finally, the estimation and identification results from each receiver are utilized to jointly localize the active BDs. Extensive simulation results are presented to show the effectiveness of the proposed methods for the studied ISIBC system.

In this paper, we propose a novel integrated sensing and backscatter communication (ISABC) system in which each moving target is attached with a backscatter device (BD) to facilitate simultaneous target identification and parameter estimation. When the base station (BS) transmits signals to its desired user, each BD attached to the target transmits the target identification information to the BS via backscatter communication, which concurrently enhances the echo signal strength. The BS needs to detect the BD symbols and to estimate the target parameters using the echoes. This task, however, is challenging due to the coupling between the BD symbols and the target parameters. To address this issue, we propose a novel iterative detection and estimation (IDE) framework, which involves the following two processes alternately: 1) Utilizing a modified maximum likelihood (ML) estimator to perform parameter estimation with the detected BD symbols; 2) Employing the ML detector to detect the BD symbols with the estimated target parameters. Since the inter-carrier interference (ICI) of the OFDM signal is independent of the BD symbols, but contains the delay and Doppler shift information, we develop a target parameter initialization method using such ICI component to improve the performance of the proposed IDE scheme. Moreover, the closed-form Miller-Chang bound is derived to demonstrate the theoretical performance for the target parameter estimation. Finally, simulation results are provided to validate the effectiveness of the proposed designs.

In this paper, we propose a novel frequency-modulated continuous wave (FMCW)-enabled integrated sensing, identification, and backscatter communication (ISIBC) system design for 6G Internet of Things (IoT). In this system, during the sensing stage, the base station (BS) emits the FMCW signal, and from the echo signal, it estimates the angles of arrival (AoAs) and ranges of multiple targets to obtain their positions while simultaneously identifying these targets through detecting the symbols transmitted by the attached backscatter devices (BDs). In particular, we first formulate precise models for the echo signal and the discrete beat signal at the uniform linear array (ULA). To eliminate the inter-symbol interference (ISI), we propose a zero-padded BD symbol pattern. Subsequently, by leveraging the proposed BD symbol pattern and a truncation operation, we further reformulate the discrete beat signal model into an explicit third-order tensor model. Next, building on the reformulated tensor model, we propose a canonical polyadic decomposition (CPD)-based algorithm for joint parameter estimation and BD symbol detection. Finally, numerous simulation results are provided to validate the effectiveness and excellent performance of the proposed FMCW-enabled ISIBC system design.

This paper introduces a novel frequency-modulated continuous wave (FMCW)-enabled integrated sensing, identification, and backscatter communication (ISIBC) system to support the emerging low-altitude economy (LAE). In this system, during the sensing stage, the ground base station (GBS) utilizes the FMCW radar to estimate the range and radial velocity of the unmanned aerial vehicle (UAV), and concurrently to identify the UAV through detecting the symbols transmitted by the attached backscatter device (BD). In particular, we first formulate explicit signal models for the echo signal and the discrete beat signal. To mitigate the inter-symbol interference (ISI), we propose a zero-padded BD symbol pattern. Subsequently, the beat signal model is reformulated into a rank-1 matrix model through the proposed BD symbol pattern and the truncation operation. Next, based on the rank-1 matrix model, we propose a singular value decomposition (SVD)-based algorithm for joint parameter estimation and BD symbol detection. Finally, extensive simulation results are provided to demonstrate the effectiveness and superior performance of the proposed ISIBC system design for LAE.

In this paper, we investigate the integrated sensing and backscatter communication (ISABC) system in mobility scenarios. Specifically, the backscatter devices (BDs) are attached to the moving targets, thus enhancing the signal strength of reflected echoes and concurrently passively transmitting supplementary information, such as identification details, to the ISABC terminal through backscatter communication. The ISABC terminal aims to detect signals from the BDs while concurrently estimating target parameters, such as delays and Doppler shifts, from the backscattered signals. However, it is quite challenging to concurrently achieve parameter estimation and signal detection from the received superposition of two disparate signals emanating from the structural and antenna components of the target equipped with BD. The challenge is exacerbated coupling between the symbols of BD and the estimated parameters, alongside the intercarrier interference (ICI) induced by the Doppler shift. To address these issues, we propose a novel joint parameter estimation and signal detection scheme by alternatively performing the following two processes: 1) Utilizing a modified maximum likelihood (ML) estimation algorithm to perform off-grid ICI-aware sensing with superimposed signals. 2) Employing the generalized likelihood ratio test (GLRT) detector for demodulating the symbols of the BD. Finally, simulation results are provided to demonstrate the performance of the proposed algorithm and validate that the estimation performance can be improved in the high SNR regime.

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS focus on a single frequency band, and thus for time-division duplexing (TDD) systems, the same phase shifts can be applied to both uplink and downlink. However, for the frequency-division duplexing (FDD) mode, if the same phase shifts are applied in both uplink and downlink, the directions of the uplink RIS beams will not be aligned with those of the downlink, which will in turn cause performance degradation. To address this issue, in this paper, we investigate the practical RIS design and optimization for FDD systems. By representing the reflection coefficients of RIS with the equivalent circuit model which includes the resistor, inductor and tunable capacitor, we first provide the guidelines on the circuit design to realize 2π phase control over the two frequency bands of the FDD system. In addition, we propose a low-resolution RIS configuration scheme with two tunable modes corresponding to two capacitances, and we formulate a max-min signal-to-noise ratio (SNR) problem to maximize the minimum SNR of uplink and downlink. To solve the non-convex problem, we propose an alternating optimization algorithm to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing uplink or downlink.

In this paper, we propose a reconfigurable intelligent surface (RIS) enhanced spectrum sensing system, in which the primary transmitter is equipped with a single antenna, the secondary transmitter is equipped with multiple antennas, and the RIS is employed to reduce the required signal samples while realizing a high detection probability. Without loss of generality, we adopt the maximum eigenvalue detection approach, and propose a corresponding analytical framework based on random matrix theory, to evaluate the detection probability in the asymptotic regime. Besides, the RIS is configured with only the statistical channel state information to avoid realtime channel estimation. With the statistical configuration, the asymptotic distributions of the equivalent channel gains are derived. Then, we provide the theoretical predictions about the number of reflecting elements required to achieve a detection probability close to 1. Finally, we present the Monte-Carlo simulation results to evaluate the accuracy of the proposed asymptotic analytical framework for the detection probability and the validity of the theoretical predictions about the number of REs required to achieve a detection probability close to 1. Moreover, the simulation results show that the proposed RIS-enhanced spectrum sensing system can substantially improve the detection performance.