arXiv subjects

Qinghua Guo

Publications and source records attributed to Qinghua Guo.

At least 19 recordsLinked to original sources

arXiv2026

Scalar-Wave Dispersion in Vectorial Photonic Crystals via Site-Adapted p Orbitals

Electromagnetic waves are intrinsically vectorial and require description via polarization, unlike scalar fields such as acoustic pressure or electronic wavefunctions. In three dimensions, the transversality constraint further prevents any globally smooth transverse-polarization frame at the $Γ$ point, which would apparently rule out a simple scalar band structure for three-dimensional (3D) photonic crystals. We show here that site-adapted $p$-orbitals can realize scalar-wave dispersion: the induced band representation is isomorphic to the scalar elementary band representation up to a one-dimensional character twist, so the symmetry-enforced degeneracies and compatibility relations are the same. We demonstrate this mechanism experimentally in 3D photonic meta-crystals, where the local $p$-orbital axes adapt from site to site according to symmetry. In contrast to a fixed-polarization reduction (e.g., in 2D), our construction preserves site-polarization textures while simultaneously supporting a scalar network with one amplitude per site. Thus, it offers a pathway from vectorial photonic degrees of freedom to scalar band engineering, keeping polarization as an active design knob.

physics.optics↗

arXiv2026

Exact flat bands in a 3D photonic crystal

Photonic flat bands are hard to engineer because Maxwell's equations are vectorial: transversality obstructs the localized scalar-like bases that generate destructive-interference flat bands in tight-binding models. We show that a three-dimensional metallic network of dipolar cavities joined by waveguide channels--a fully vectorial photonic crystal belonging to space group No. 224--hosts an exact scalar sector, carrying exact flat bands. The twelve-band vector problem contains one self-adaptive radial dipole axis per site whose projection is exactly the scalar four-band Hamiltonian of the same network. A microwave-scale coupled-dipole calculation confirms this scalar-vectorial duality. The result is a symmetry-based design rule for scalar-like flat bands in reciprocal vector media.

physics.optics↗

arXiv2026

Unidirectional-like Edge Transport Induced by Non-Hermitian Skin Effects

Non-Hermitian skin effects (NHSEs) enable dramatic boundary accumulation of waves, yet their experimental realization typically demands engineered nonreciprocity or spatially patterned loss. Here we demonstrate theoretically and experimentally that uniform loss provides a simple and previously overlooked mechanism for enforcing unidirectional-like edge transport in photonic crystals (PhCs) that breaks time-reversal symmetry in the presence of nonchiral edge states. Using a core cladding geometry where domains share identical Chern numbers but possess distinct bulk polarizations, we show that uniform loss activates NHSEs that reshape the spectral topology of edge bands, giving rise to point gap windings that dictate a one way propagation. Near field measurements confirm that loss converts intrinsically bidirectional interface states into a unidirectional-like circulation around the entire domain wall, showing excellent agreement with theory. Our results establish uniform loss as a universal and structurally simple route for achieving unidirectional-like wave transport.

physics.optics↗

arXiv2026

Holographic Communication via Recordable and Reconfigurable Metasurface

Holographic surface based communication technologies are anticipated to play a significant role in the next generation of wireless networks. The existing reconfigurable holographic surface (RHS)-based scheme only utilizes the reconstruction process of the holographic principle for beamforming, where the channel sate information (CSI) is needed. However, channel estimation for CSI acquirement is a challenging task in metasurface based communications. In this study, inspired by both the recording and reconstruction processes of holography, we develop a novel holographic communication scheme by introducing recordable and reconfigurable metasurfaces (RRMs), where channel estimation is not needed thanks to the recording process. Then we analyze the input-output mutual information and outage probability of the RRM-based communication system and compare it with the existing RHS based system. Our results show that, without channel estimation, the proposed scheme achieves performance comparable to that of the RHS scheme with perfect CSI, suggesting a promising alternative for future wireless communication networks.

eess.SP↗

arXiv2026

Joint Channel Sounding and Source-Channel Coding for MIMO-OFDM Systems: Deep Unified Encoding and Parallel Flow-Matching Decoding

In this work, we propose a deep unified (DU) encoder that embeds source information in a codeword that contains sufficient redundancy to handle both channel and source uncertainties, without enforcing an explicit pilot-data separation. At the receiver, we design a parallel flow-matching (PFM) decoder that leverages flow-based generative priors to jointly estimate the channel and the source, yielding much more efficient inference than the existing diffusion-based approaches. To benchmark performance limits, we derive the Bayesian Cramér-Rao bound (BCRB) for the joint channel and source estimation problem. Extensive simulations over block-fading MIMO-OFDM channels demonstrate that the proposed DU-PFM approach drastically outperforms the state-of-the-art methods in both channel estimation accuracy and source reconstruction quality.

eess.SP↗

arXiv2025

Blind MIMO Semantic Communication via Parallel Variational Diffusion: A Completely Pilot-Free Approach

In this paper, we propose a novel blind multi-input multi-output (MIMO) semantic communication (SC) framework named Blind-MIMOSC that consists of a deep joint source-channel coding (DJSCC) transmitter and a diffusion-based blind receiver. The DJSCC transmitter aims to compress and map the source data into the transmitted signal by exploiting the structural characteristics of the source data, while the diffusion-based blind receiver employs a parallel variational diffusion (PVD) model to simultaneously recover the channel and the source data from the received signal without using any pilots. The PVD model leverages two pre-trained score networks to characterize the prior information of the channel and the source data, operating in a plug-and-play manner during inference. This design allows only the affected network to be retrained when channel conditions or source datasets change, avoiding the complicated full-network retraining required by end-to-end methods. This work presents the first fully pilot-free solution for joint channel estimation and source recovery in block-fading MIMO systems. Extensive experiments show that Blind-MIMOSC with PVD achieves superior channel and source recovery accuracy compared to state-of-the-art approaches, with drastically reduced channel bandwidth ratio.

eess.SP↗

arXiv2025

Hybrid Liquid Neural Network-Random Finite Set Filtering for Robust Maneuvering Object Tracking

This work addresses the problem of tracking maneuvering objects with complex motion patterns, a task in which conventional methods often struggle due to their reliance on predefined motion models. We integrate a data-driven liquid neural network (LNN) into the random finite set (RFS) framework, leading to two LNN-RFS filters. By learning continuous-time dynamics directly from data, the LNN enables the filters to adapt to complex, nonlinear motion and achieve accurate tracking of highly maneuvering objects in clutter. This hybrid approach preserves the inherent multi-object tracking strengths of the RFS framework while improving flexibility and robustness. Simulation results on challenging maneuvering scenarios demonstrate substantial gains of the proposed hybrid approach in tracking accuracy.

eess.SP↗

arXiv2025

Cutting-edge 3D reconstruction solutions for underwater coral reef images: A review and comparison

Corals serve as the foundational habitat-building organisms within reef ecosystems, constructing extensive structures that extend over vast distances. However, their inherent fragility and vulnerability to various threats render them susceptible to significant damage and destruction. The application of advanced 3D reconstruction technologies for high-quality modeling is crucial for preserving them. These technologies help scientists to accurately document and monitor the state of coral reefs, including their structure, species distribution and changes over time. Photogrammetry-based approaches stand out among existing solutions, especially with recent advancements in underwater videography, photogrammetric computer vision, and machine learning. Despite continuous progress in image-based 3D reconstruction techniques, there remains a lack of systematic reviews and comprehensive evaluations of cutting-edge solutions specifically applied to underwater coral reef images. The emerging advanced methods may have difficulty coping with underwater imaging environments, complex coral structures, and computational resource constraints. They need to be reviewed and evaluated to bridge the gap between many cutting-edge technical studies and practical applications. This paper focuses on the two critical stages of these approaches: camera pose estimation and dense surface reconstruction. We systematically review and summarize classical and emerging methods, conducting comprehensive evaluations through real-world and simulated datasets. Based on our findings, we offer reference recommendations and discuss the development potential and challenges of existing approaches in depth. This work equips scientists and managers with a technical foundation and practical guidance for processing underwater coral reef images for 3D reconstruction....

cs.CV↗

arXiv2025

The Future is Fluid: Revolutionizing DOA Estimation with Sparse Fluid Antennas

This paper investigates a design framework for sparse fluid antenna systems (FAS) enabling high-performance direction-of-arrival (DOA) estimation, particularly in challenging millimeter-wave (mmWave) environments. By ingeniously harnessing the mobility of fluid antenna (FA) elements, the proposed architectures achieve an extended range of spatial degrees of freedom (DoF) compared to conventional fixed-position antenna (FPA) arrays. This innovation not only facilitates the seamless application of super-resolution DOA estimators but also enables robust DOA estimation, accurately localizing more sources than the number of physical antenna elements. We introduce two bespoke FA array structures and mobility strategies tailored to scenarios with aligned and misaligned received signals, respectively, demonstrating a hardware-driven approach to overcoming complexities typically addressed by intricate algorithms. A key contribution is a light-of-sight (LoS)-centric, closed-form DOA estimator, which first employs an eigenvalue-ratio test for precise LoS path number detection, followed by a polynomial root-finding procedure. This method distinctly showcases the unique advantages of FAS by simplifying the estimation process while enhancing accuracy. Numerical results compellingly verify that the proposed FA array designs and estimation techniques yield an extended DoF range, deliver superior DOA accuracy, and maintain robustness across diverse signal conditions.

eess.SP↗

arXiv2025

Searching for topological semi-complete bandgap in elastic truss lattices

Gapless topological phases have attracted significant interest across both quantum and classical systems owing to their novel physics and promising applications. However, the search for ideal gapless topological nodes inside a clear bandgap is still lacking in elastic systems. The degenerate points are always hidden in the trivial bulk bands due to the intricate elastic modes involved. Here, we find a topological semi-complete bandgap in a three-dimensional elastic truss lattice by tuning a supporting rod, which exhibits a complete bandgap except for the inevitable topological degenerate points. Furthermore, we experimentally map the topological semi-complete bandgap and the inside nontrivial surface state arcs with a scanning laser vibrometer. The introduced scheme provides a systematic approach for the idealization of semi-complete bandgaps and thus may significantly advance the practical utility of topological phases in mechanical engineering domains.

cond-mat.mes-hall↗

arXiv2025

Photonic chiral bulk transports manipulated by boundary freedom in three-dimensional meta-crystals

In topological physics, one of the most intriguing phenomena is the presence of topological boundary states, accurately predicted by the well-established bulk-edge correspondence. For example, in three-dimensional Weyl semimetals, Fermi arcs emerge to connect projected Weyl points on the surface due to inheriting the bulk-edge correspondence from the integer quantum Hall effect. However, limited attention has been paid to exploring the reverse mechanism in topological crystals. In this study, we propose that boundaries can serve as an alternative degree of freedom to manipulate topological bulk transports. We analytically and experimentally validate our concept using a finite-thickness photonic meta-crystal that supports bulk nodal lines, with its zeroth modes exhibiting opposite chiral bulk transports under different boundary conditions. Notably, the mirror symmetry remains preserved across both configurations. These findings are applicable to other topological systems, providing new insights into systems with varied boundary conditions and offering the potential for the design of more compact and spatially efficient topological photonic devices.

cond-mat.mes-hall↗

arXiv2024

Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method.

cs.IT↗

arXiv2024

Full-Duplex ISAC-Enabled D2D Underlaid Cellular Networks: Joint Transceiver Beamforming and Power Allocation

Integrating device-to-device (D2D) communication into cellular networks can significantly reduce the transmission burden on base stations (BSs). Besides, integrated sensing and communication (ISAC) is envisioned as a key feature in future wireless networks. In this work, we consider a full-duplex ISAC- based D2D underlaid system, and propose a joint beamforming and power allocation scheme to improve the performance of the coexisting ISAC and D2D networks. To enhance spectral efficiency, a sum rate maximization problem is formulated for the full-duplex ISAC-based D2D underlaid system, which is non-convex. To solve the non-convex optimization problem, we propose a successive convex approximation (SCA)-based iterative algorithm and prove its convergence. Numerical results are provided to validate the effectiveness of the proposed scheme with the iterative algorithm, demonstrating that the proposed scheme outperforms state-of-the-art ones in both communication and sensing performance.

eess.SP↗

arXiv2024

Iterative Equalization of CPM With Unitary Approximate Message Passing

Continuous phase modulation (CPM) has extensive applications in wireless communications due to its high spectral and power efficiency. However, its nonlinear characteristics pose significant challenges for detection in frequency selective fading channels. This paper proposes an iterative receiver tailored for the detection of CPM signals over frequency selective fading channels. This design leverages the factor graph framework to integrate equalization, demodulation, and decoding functions. The equalizer employs the unitary approximate message passing (UAMP) algorithm, while the unitary transformation is implemented using the fast Fourier transform (FFT) with the aid of a cyclic prefix (CP), thereby achieving low computational complexity while with high performance. For CPM demodulation and channel decoding, with belief propagation (BP), we design a message passing-based maximum a posteriori (MAP) algorithm, and the message exchange between the demodulator, decoder and equalizer is elaborated. With proper message passing schedules, the receiver can achieve fast convergence. Simulation results show that compared with existing turbo receivers, the proposed receiver delivers significant performance enhancement with low computational complexity.

cs.IT↗

arXiv2024

Efffcient Sensing Parameter Estimation with Direct Clutter Mitigation in Perceptive Mobile Networks

In this work, we investigate sensing parameter estimation in the presence of clutter in perceptive mobile networks (PMNs) that integrate radar sensing into mobile communications. Performing clutter suppression before sensing parameter estimation is generally desirable as the number of sensing parameters can be signiffcantly reduced. However, existing methods require high-complexity clutter mitigation and sensing parameter estimation, where clutter is ffrstly identiffed and then removed. In this correspondence, we propose a much simpler but more effective method by incorporating a clutter cancellation mechanism in formulating a sparse signal model for sensing parameter estimation. In particular, clutter mitigation is performed directly on the received signals and the unitary approximate message passing (UAMP) is leveraged to exploit the common support for sensing parameter estimation in the formulated sparse signal recovery problem. Simulation results show that, compared to state-of-theart methods, the proposed method delivers signiffcantly better performance while with substantially reduced complexity.

eess.SP↗

arXiv2024

Grant Free MIMO-NOMA with Differential Modulation for Machine Type Communications

This paper considers a challenging scenario of machine type communications, where we assume internet of things (IoT) devices send short packets sporadically to an access point (AP) and the devices are not synchronized in the packet level. High transmission efficiency and low latency are concerned. Motivated by the great potential of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) in massive access, we design a grant-free MIMO-NOMA scheme, and in particular differential modulation is used so that expensive channel estimation at the receiver (AP) can be bypassed. The receiver at AP needs to carry out active device detection and multi-device data detection. The active user detection is formulated as the estimation of the common support of sparse signals, and a message passing based sparse Bayesian learning (SBL) algorithm is designed to solve the problem. Due to the use of differential modulation, we investigate the problem of non-coherent multi-device data detection, and develop a message passing based Bayesian data detector, where the constraint of differential modulation is exploited to drastically improve the detection performance, compared to the conventional non-coherent detection scheme. Simulation results demonstrate the effectiveness of the proposed active device detector and non-coherent multi-device data detector.

cs.IT↗

arXiv2024

Integrated Near Field Sensing and Communications Using Unitary Approximate Message Passing Based Matrix Factorization

Due to the utilization of large antenna arrays at base stations (BSs) and the operations of wireless communications in high frequency bands, mobile terminals often find themselves in the near-field of the array aperture. In this work, we address the signal processing challenges of integrated near-field localization and communication in uplink transmission of an integrated sensing and communication (ISAC) system, where the BS performs joint near-field localization and signal detection (JNFLSD). We show that JNFLSD can be formulated as a matrix factorization (MF) problem with proper structures imposed on the factor matrices. Then, leveraging the variational inference (VI) and unitary approximate message passing (UAMP), we develop a low complexity Bayesian approach to MF, called UAMP-MF, to handle a generic MF problem. We then apply the UAMP-MF algorithm to solve the JNFLSD problem, where the factor matrix structures are fully exploited. Extensive simulation results are provided to demonstrate the superior performance of the proposed method.

cs.IT↗

arXiv2024

Message Passing-Based Joint Channel Estimation and Signal Detection for OTFS with Superimposed Pilots

Receivers with joint channel estimation and signal detection using superimposed pilots (SP) can achieve high transmission efficiency in orthogonal time frequency space (OTFS) systems. However, existing receivers have high computational complexity, hindering their practical applications. In this work, with SP in the delay-Doppler (DD) domain and the generalized complex exponential (GCE) basis expansion modeling (BEM) for channels, a message passing-based SP-DD iterative receiver is proposed, which drastically reduces the computational complexity while with marginal performance loss, compared to existing ones. To facilitate channel estimation (CE) in the proposed receiver, we design pilot signal to achieve pilot power concentration in the frequency domain, thereby developing an SP-DD-D receiver that can effectively reduce the power of the pilot signal and almost no loss of CE accuracy. Extensive simulation results are provided to demonstrate the superiority of the proposed SP-DD-D receiver.

cs.IT↗