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Jie Xu

Publications and source records attributed to Jie Xu.

At least 307 records · Page 17Linked to original sources
arXiv2023

Integrating Sensing, Communication, and Power Transfer: Multiuser Beamforming Design

In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer.

cs.IT↗
arXiv2023

Sensing-Assisted Sparse Channel Recovery for Massive Antenna Systems

This correspondence presents a novel sensing-assisted sparse channel recovery approach for massive antenna wireless communication systems. We focus on a fundamental configuration with one massive-antenna base station (BS) and one single-antenna communication user (CU). The wireless channel exhibits sparsity and consists of multiple paths associated with scatterers detectable via radar sensing. Under this setup, the BS first sends downlink pilots to the CU and concurrently receives the echo pilot signals for sensing the surrounding scatterers. Subsequently, the CU sends feedback information on its received pilot signal to the BS. Accordingly, the BS determines the sparse basis based on the sensed scatterers and proceeds to recover the wireless channel, exploiting the feedback information based on advanced compressive sensing (CS) algorithms. Numerical results show that the proposed sensing-assisted approach significantly increases the overall achievable rate than the conventional design relying on a discrete Fourier transform (DFT)-based sparse basis without sensing, thanks to the reduced training overhead and enhanced recovery accuracy with limited feedback.

cs.IT↗
arXiv2023

Fully-Passive versus Semi-Passive IRS-Enabled Sensing: SNR and CRB Comparison

This paper investigates the sensing performance of two intelligent reflecting surface (IRS)-enabled non-line-of-sight (NLoS) sensing systems with fully-passive and semi-passive IRSs, respectively. In particular, we consider a fundamental setup with one base station (BS), one uniform linear array (ULA) IRS, and one point target in the NLoS region of the BS. Accordingly, we analyze the sensing signal-to-noise ratio (SNR) performance for a target detection scenario and the estimation Cramér-Rao bound (CRB) performance for a target's direction-of-arrival (DoA) estimation scenario, in cases where the transmit beamforming at the BS and the reflective beamforming at the IRS are jointly optimized. First, for the target detection scenario, we characterize the maximum sensing SNR when the BS-IRS channels are line-of-sight (LoS) and Rayleigh fading, respectively. It is revealed that when the number of reflecting elements $N$ equipped at the IRS becomes sufficiently large, the maximum sensing SNR increases proportionally to $N^2$ for the semi-passive-IRS sensing system, but proportionally to $N^4$ for the fully-passive-IRS counterpart. Then, for the target's DoA estimation scenario, we analyze the minimum CRB performance when the BS-IRS channel follows Rayleigh fading. Specifically, when $N$ grows, the minimum CRB decreases inversely proportionally to $N^4$ and $N^6$ for the semi-passive and fully-passive-IRS sensing systems, respectively. Finally, numerical results are presented to corroborate our analysis across various transmit and reflective beamforming design schemes under general channel setups. It is shown that the fully-passive-IRS sensing system outperforms the semi-passive counterpart when $N$ exceeds a certain threshold. This advantage is attributed to the additional reflective beamforming gain in the IRS-BS path, which efficiently compensates for the path loss for a large $N$.

eess.SP↗
arXiv2023

An Overview on IEEE 802.11bf: WLAN Sensing

With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent requirements for emerging sensing applications. To resolve this issue, a new Task Group (TG), namely IEEE 802.11bf, has been established by the IEEE 802.11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications. This paper provides a comprehensive overview on the up-to-date efforts in the IEEE 802.11bf TG. First, we introduce the definition of the 802.11bf amendment and its formation and standardization timeline. Next, we discuss the WLAN sensing use cases with the corresponding key performance indicator (KPI) requirements. After reviewing previous WLAN sensing research based on communication-oriented WLAN standards, we identify their limitations and underscore the practical need for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the WLAN sensing framework and procedure used for measurement acquisition, by considering both sensing at sub-7GHz and directional multi-gigabit (DMG) sensing at 60 GHz, respectively, and address their shared features, similarities, and differences. In addition, we present various candidate technical features for IEEE 802.11bf, including waveform/sequence design, feedback types, as well as quantization and compression techniques. We also describe the methodologies and the channel modeling used by the IEEE 802.11bf TG for evaluation. Finally, we discuss the challenges and future research directions to motivate more research endeavors towards this field in details.

eess.SP↗
arXiv2023

InViG: Benchmarking Interactive Visual Grounding with 500K Human-Robot Interactions

Ambiguity is ubiquitous in human communication. Previous approaches in Human-Robot Interaction (HRI) have often relied on predefined interaction templates, leading to reduced performance in realistic and open-ended scenarios. To address these issues, we present a large-scale dataset, \invig, for interactive visual grounding under language ambiguity. Our dataset comprises over 520K images accompanied by open-ended goal-oriented disambiguation dialogues, encompassing millions of object instances and corresponding question-answer pairs. Leveraging the \invig dataset, we conduct extensive studies and propose a set of baseline solutions for end-to-end interactive visual disambiguation and grounding, achieving a 45.6\% success rate during validation. To the best of our knowledge, the \invig dataset is the first large-scale dataset for resolving open-ended interactive visual grounding, presenting a practical yet highly challenging benchmark for ambiguity-aware HRI. Codes and datasets are available at: \href{https://openivg.github.io}{https://openivg.github.io}.

cs.RO↗
arXiv2023

Towards a Modular Architecture for Science Factories

Advances in robotic automation, high-performance computing (HPC), and artificial intelligence (AI) encourage us to conceive of science factories: large, general-purpose computation- and AI-enabled self-driving laboratories (SDLs) with the generality and scale needed both to tackle large discovery problems and to support thousands of scientists. Science factories require modular hardware and software that can be replicated for scale and (re)configured to support many applications. To this end, we propose a prototype modular science factory architecture in which reconfigurable modules encapsulating scientific instruments are linked with manipulators to form workcells, that can themselves be combined to form larger assemblages, and linked with distributed computing for simulation, AI model training and inference, and related tasks. Workflows that perform sets of actions on modules can be specified, and various applications, comprising workflows plus associated computational and data manipulation steps, can be run concurrently. We report on our experiences prototyping this architecture and applying it in experiments involving 15 different robotic apparatus, five applications (one in education, two in biology, two in materials), and a variety of workflows, across four laboratories. We describe the reuse of modules, workcells, and workflows in different applications, the migration of applications between workcells, and the use of digital twins, and suggest directions for future work aimed at yet more generality and scalability. Code and data are available at https://ad-sdl.github.io/wei2023 and in the Supplementary Information

cs.RO↗
arXiv2023

A second-order SO(3)-preserving and energy-stable scheme for orthonormal frame gradient flow model of biaxial nematic liquid crystals

In this paper, we present a novel second-order generalised rotational discrete gradient scheme for numerically approximating the orthonormal frame gradient flow of biaxial nematic liquid crystals. This scheme relies on reformulating the original gradient flow system into an equivalent generalised "rotational" form. A second-order discrete gradient approximation of the energy variation is then devised such that it satisfies an energy difference relation. The proposed numerical scheme has two remarkable properties: (i) it strictly obeys the orthonormal property of the tensor field and (ii) it satisfies the energy dissipation law at the discrete level, regardless of the time step sizes. We provide ample numerical results to validate the accuracy, efficiency, unconditional stability and SO(3)-preserving property of this scheme. In addition, comparisons of the simulation results between the biaxial orthonormal frame gradient flow model and uniaxial Oseen-Frank gradient flow are made to demonstrate the ability of the former to characterize non-axisymmetric local anisotropy.

math.NA↗
arXiv2023

Realization of the all-optical phase modulator, filter, splitter, and self-consistent logic gates based on assembled magneto-optical heterostructures

All-optical computing has recently emerged as a vibrant research field in response to the energy crisis and the growing demand for information processing. However, the efficiency of subwavelength-scale all-optical devices remains relatively low due to challenges such as back-scattering reflections and strict surface roughness. Furthermore, achieving multifunctionality through the reassembly of all-optical structures has thus far been rarely accomplished. One promising approach to address these issues is the utilization of one-way edge modes. In this study, we propose four types of deep-subwavelength ($\sim 10^{-2} λ_0$, where $λ_0$ is the wavelength in vacuum) all-optical functional devices: a phase modulator, a filter, a splitter, and logic gates. These devices are based on robust one-way modes but do not require an external magnetic field, which can allow for flexible assembly. In particular, we investigate a phase modulation range spanning from $-π$ to $π$, a perfect filter that divides the input port's one-way region into two output one-way regions with equal bandwidth, a multi-frequency splitter with an equal splitting ratio (e.g., 50/50), and self-consistent logic gates. We validate these theoretical findings through comprehensive full-wave numerical simulations. Our findings may find applications in minimal optical calculations and integrated optical circuits.

physics.app-ph↗
arXiv2023

Quantum linear polynomial evaluation based on XOR oblivious transfer compatible with classical partially homomorphic encryption

XOR oblivious transfer is a universal cryptographic primitive that can be related to linear polynomial evaluation. We firstly introduce some bipartite quantum protocols for XOR oblivious transfer, which are not secure if one party cheats, and some of them can be combined with a classical XOR homomorphic encryption scheme for evaluation of linear polynomials modulo 2 with hybrid security. We then introduce a general protocol using modified versions of the XOR oblivious transfer protocols to evaluate linear polynomials modulo 2 with partial information-theoretic security. When combined with the ability to perform arbitrary quantum computation, this would lead to deterministic interactive two-party computation which is quite secure in the information-theoretic sense when the allowed set of inputs is large. For the task of classical function evaluation, although the quantum computation approach is still usable, we also discuss purely classical post-processing methods based on the proposed linear polynomial evaluation protocols.

quant-ph↗
arXiv2023

Uncertainty quantification and complex analyticity of the nonlinear Poisson-Boltzmann equation for the interface problem with random domains

The nonlinear Poisson-Boltzmann equation (NPBE) is an elliptic partial differential equation used in applications such as protein interactions and biophysical chemistry (among many others). It describes the nonlinear electrostatic potential of charged bodies submerged in an ionic solution. The kinetic presence of the solvent molecules introduces randomness to the shape of a protein, and thus a more accurate model that incorporates these random perturbations of the domain is analyzed to compute the statistics of quantities of interest of the solution. When the parameterization of the random perturbations is high-dimensional, this calculation is intractable as it is subject to the curse of dimensionality. However, if the solution of the NPBE varies analytically with respect to the random parameters, the problem becomes amenable to techniques such as sparse grids and deep neural networks. In this paper, we show analyticity of the solution of the NPBE with respect to analytic perturbations of the domain by using the analytic implicit function theorem and the domain mapping method. Previous works have shown analyticity of solutions to linear elliptic equations but not for nonlinear problems. We further show how to derive \emph{a priori} bounds on the size of the region of analyticity. This method is applied to the trypsin molecule to demonstrate that the convergence rates of the quantity of interest are consistent with the analyticity result. Furthermore, the approach developed here is sufficiently general enough to be applied to other nonlinear problems in uncertainty quantification.

math.NA↗
arXiv2023

Analytic regularity of strong solutions for the complexified stochastic non-linear Poisson Boltzmann Equation

Semi-linear elliptic Partial Differential Equations (PDEs) such as the non-linear Poisson Boltzmann Equation (nPBE) is highly relevant for non-linear electrostatics in computational biology and chemistry. It is of particular importance for modeling potential fields from molecules in solvents or plasmas with stochastic fluctuations. The extensive applications include ones in condensed matter and solid state physics, chemical physics, electrochemistry, biochemistry, thermodynamics, statistical mechanics, and materials science, among others. In this paper we study the complex analytic properties of semi-linear elliptic Partial Differential Equations with respect to random fluctuations on the domain. We first prove the existence and uniqueness of the nPBE on a bounded domain in $\mathbb{R}^3$. This proof relies on the application of a contraction mapping reasoning, as the standard convex optimization argument for the deterministic nPBE no longer applies. Using the existence and uniqueness result we subsequently show that solution to the nPBE admits an analytic extension onto a well defined region in the complex hyperplane with respect to the number of stochastic variables. Due to the analytic extension, stochastic collocation theory for sparse grids predict algebraic to sub-exponential convergence rates with respect to the number of knots. A series of numerical experiments with sparse grids is consistent with this prediction and the analyticity result. Finally, this approach readily extends to a wide class of semi-linear elliptic PDEs.

math.NA↗
arXiv2023

Federated Deep Multi-View Clustering with Global Self-Supervision

Federated multi-view clustering has the potential to learn a global clustering model from data distributed across multiple devices. In this setting, label information is unknown and data privacy must be preserved, leading to two major challenges. First, views on different clients often have feature heterogeneity, and mining their complementary cluster information is not trivial. Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data. To address these challenges, we propose a novel federated deep multi-view clustering method that can mine complementary cluster structures from multiple clients, while dealing with data incompleteness and privacy concerns. Specifically, in the server environment, we propose sample alignment and data extension techniques to explore the complementary cluster structures of multiple views. The server then distributes global prototypes and global pseudo-labels to each client as global self-supervised information. In the client environment, multiple clients use the global self-supervised information and deep autoencoders to learn view-specific cluster assignments and embedded features, which are then uploaded to the server for refining the global self-supervised information. Finally, the results of our extensive experiments demonstrate that our proposed method exhibits superior performance in addressing the challenges of incomplete multi-view data in distributed environments.

cs.LG↗
arXiv2023

Achievable Rate Region and Path-Based Beamforming for Multi-User Single-Carrier Delay Alignment Modulation

Delay alignment modulation (DAM) is a novel wideband transmission technique for mmWave massive MIMO systems, which exploits the high spatial resolution and multi-path sparsity to mitigate ISI, without relying on channel equalization or multi-carrier transmission. In particular, DAM leverages the delay pre-compensation and path-based beamforming to effectively align the multi-path components, thus achieving the constructive multi-path combination for eliminating the ISI while preserving the multi-path power gain. Different from the existing works only considering single-user DAM, this paper investigates the DAM technique for multi-user mmWave massive MIMO communication. First, we consider the asymptotic regime when the number of antennas Mt at BS is sufficiently large. It is shown that by employing the simple delay pre-compensation and per-path-based MRT beamforming, the single-carrier DAM is able to perfectly eliminate both ISI and IUI. Next, we consider the general scenario with Mt being finite. In this scenario, we characterize the achievable rate region of the multi-user DAM system by finding its Pareto boundary. Specifically, we formulate a rate-profile-constrained sum rate maximization problem by optimizing the per-path-based beamforming. Furthermore, we present three low-complexity per-path-based beamforming strategies based on the MRT, zero-forcing, and regularized zero-forcing principles, respectively, based on which the achievable sum rates are studied. Finally, we provide simulation results to demonstrate the performance of our proposed strategies as compared to two benchmark schemes based on the strongest-path-based beamforming and the prevalent OFDM, respectively. It is shown that DAM achieves higher spectral efficiency and/or lower peak-to-average-ratio, for systems with high spatial resolution and multi-path diversity.

cs.IT↗
arXiv2023

Near-Field 3D Localization via MIMO Radar: Cramér-Rao Bound Analysis and Estimator Design

This paper studies a near-field multiple-input multiple-output (MIMO) radar sensing system, in which the transceivers with massive antennas aim to localize multiple near-field targets in the three-dimensional (3D) space over unknown cluttered environments. We consider a spherical wavefront propagation with both channel phase and amplitude variations over different antennas. Under this setup, the unknown parameters include the 3D coordinates and complex reflection coefficients of the targets, as well as the noise and interference covariance matrix. First, by considering general transmit signal waveforms, we derive the Fisher information matrix (FIM) corresponding to the 3D coordinates and the complex reflection coefficients of the targets and accordingly obtain the Cramér-Rao bound (CRB) for the 3D coordinates. This provides a performance bound for 3D near-field target localization. For the special single-target case, we obtain the CRB in an analytical form, and analyze its asymptotic scaling behaviors with respect to the target distance and antenna size of the transceiver. Next, to facilitate practical localization, we propose two estimators to localize targets based on the maximum likelihood (ML) criterion, namely the 3D approximate cyclic optimization (3D-ACO) and the 3D cyclic optimization with white Gaussian noise (3D-CO-WGN), respectively. Numerical results validate the asymptotic CRB analysis and show that the consideration of varying channel amplitudes is vital to achieve accurate CRB and localization when the targets are close to the transceivers. It is also shown that the proposed estimators achieve localization performance close to the derived CRB under various cluttered environments, thus validating their effectiveness in practical implementation. Furthermore, it is shown that transmit waveforms have a significant impact on CRB and the localization performance.

cs.IT↗
arXiv2023

Dynamics Investigation of the quantum-control-assisted multipartite uncertainty relation in Heisenberg model with Dzyaloshinski-Moriya interaction

Recently, Zheng constructs a quantum-control-assisted multipartite variance-based uncertainty relation, which successfully extends the conditional uncertainty relation to the multipartite case [Annalen der physik, 533, 2100014 (2021)]. We here investigate the dynamics of the new uncertainty relation in the Heisenberg system with the Dzyaloshinski-Moriya interaction. It is found that, different from entanglement, the mixedness of the system has an interesting single-valued relationship with the tightness and lower bound of the uncertainty relation. This single-valued relationship indicates that the tightness and lower bound of the uncertainty relation can be written as the functional form of the mixedness. Moreover, the single-valued relationship with the mixedness is the common nature of conditional uncertainty relations, and has no relationship with the form of the uncertainty relations. Also, the comparison between the new conditional variance-based uncertainty relation and the existing entropic one has been made.

quant-ph↗
arXiv2023

A second-order length-preserving and unconditionally energy stable rotational discrete gradient method for Oseen-Frank gradient flows

We present a second-order strictly length-preserving and unconditionally energy-stable rotational discrete gradient (Rdg) scheme for the numerical approximation of the Oseen-Frank gradient flows with anisotropic elastic energy functional. Two essential ingredients of the Rdg method are reformulation of the length constrained gradient flow into an unconstrained rotational form and discrete gradient discretization for the energy variation. Besides the well-known mean-value and Gonzalez discrete gradients, we propose a novel Oseen-Frank discrete gradient, specifically designed for the solution of Oseen-Frank gradient flow. We prove that the proposed Oseen-Frank discrete gradient satisfies the energy difference relation, thus the resultant Rdg scheme is energy stable. Numerical experiments demonstrate the efficiency and accuracy of the proposed Rdg method and its capability for providing reliable simulation results with highly disparate elastic coefficients.

math.NA↗
arXiv2023

OFVL-MS: Once for Visual Localization across Multiple Indoor Scenes

In this work, we seek to predict camera poses across scenes with a multi-task learning manner, where we view the localization of each scene as a new task. We propose OFVL-MS, a unified framework that dispenses with the traditional practice of training a model for each individual scene and relieves gradient conflict induced by optimizing multiple scenes collectively, enabling efficient storage yet precise visual localization for all scenes. Technically, in the forward pass of OFVL-MS, we design a layer-adaptive sharing policy with a learnable score for each layer to automatically determine whether the layer is shared or not. Such sharing policy empowers us to acquire task-shared parameters for a reduction of storage cost and task-specific parameters for learning scene-related features to alleviate gradient conflict. In the backward pass of OFVL-MS, we introduce a gradient normalization algorithm that homogenizes the gradient magnitude of the task-shared parameters so that all tasks converge at the same pace. Furthermore, a sparse penalty loss is applied on the learnable scores to facilitate parameter sharing for all tasks without performance degradation. We conduct comprehensive experiments on multiple benchmarks and our new released indoor dataset LIVL, showing that OFVL-MS families significantly outperform the state-of-the-arts with fewer parameters. We also verify that OFVL-MS can generalize to a new scene with much few parameters while gaining superior localization performance.

cs.CV↗
arXiv2023

Training-Free Energy Beamforming Assisted by Wireless Sensing

This paper studies the transmit energy beamforming in a multi-antenna wireless power transfer (WPT) system, in which an access point (AP) equipped with a uniform linear array (ULA) sends radio signals to wirelessly charge multiple single-antenna energy receivers (ERs). Different from conventional energy beamforming designs that require the AP to acquire the channel state information (CSI) via training and feedback, we propose a new training-free energy beamforming approach assisted by wireless radar sensing, which is implemented based on the following two-stage protocol. In the first stage, the AP performs wireless radar sensing to estimate the path gain and angle parameters of the ERs for constructing the corresponding CSI. In the second stage, the AP implements the transmit energy beamforming based on the constructed CSI to efficiently charge these ERs in a fair manner. Under this setup, first, we jointly optimize the sensing beamformers and duration in the first stage to minimize the sensing duration, while ensuring a given accuracy threshold for parameters estimation subject to the maximum transmit power constraint at the AP. Next, we optimize the energy beamformers in the second stage to maximize the minimum harvested energy by all ERs. In this approach, the estimation accuracy threshold for the first stage is properly designed to balance the resource allocation between the two stages for optimizing the ultimate energy harvesting performance. Finally, numerical results show that the proposed training-free energy beamforming design performs close to the performance upper bound with perfect CSI, and outperforms the benchmark schemes without such joint optimization and that with isotropic transmission.

cs.IT↗