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This paper develops an edge-device collaborative Generative Semantic Communications (Gen SemCom) framework leveraging pre-trained Multi-modal/Vision Language Models (M/VLMs) for ultra-low-rate semantic communication via textual prompts. The proposed framework optimizes the use of M/VLMs on the wireless edge/device to generate high-fidelity textual prompts through visual captioning/question answering, which are then transmitted over a wireless channel for SemCom. Specifically, we develop a multi-user Gen SemCom framework using pre-trained M/VLMs, and formulate a joint optimization problem of prompt generation offloading, communication and computation resource allocation to minimize the latency and maximize the resulting semantic quality. Due to the nonconvex nature of the problem with highly coupled discrete and continuous variables, we decompose it as a two-level problem and propose a low-complexity swap/leaving/joining (SLJ)-based matching algorithm. Simulation results demonstrate significant performance improvements over the conventional semanticunaware/non-collaborative offloading benchmarks.

The escalating interests on underwater exploration/reconnaissance applications have motivated high-rate data transmission from underwater to airborne relaying platforms, especially under high-sea scenarios. Thanks to its broad bandwidth and superior confidentiality, Optical wireless communication has become one promising candidate for water-air transmission. However, the optical signals inevitably suffer from deviations when crossing the highly-dynamic water-air interfaces in the absence of relaying ships/buoys. To address the issue, this article proposes one novel beam alignment strategy based on deep reinforcement learning (DRL) for water-air direct optical wireless communications. Specifically, the dynamic water-air interface is mathematically modeled using sea-wave spectrum analysis, followed by characterization of the propagation channel with ray-tracing techniques. Then the deep deterministic policy gradient (DDPG) scheme is introduced for DRL-based transceiving beam alignment. A logarithm-exponential (LE) nonlinear reward function with respect to the received signal strength is designed for high-resolution rewarding between different actions. Simulation results validate the superiority of the proposed DRL-based beam alignment scheme., Comment: 6 pages, 6 figures, published to ICCC 2024

We report the discovery of a hyperluminous type 1 quasar (eFEDS J082826.9-013911; eFEDSJ0828-0139) at $z_{\rm spec}$ = 1.622 with a super-Eddington ratio ($\lambda_{\rm Edd}$). We perform the optical spectroscopic observations with KOOLS-IFU on the Seimei Telescope. The black hole mass ($M_{\rm BH}$) based on the single-epoch method with MgII $\lambda$2798 is estimated to be $M_{\rm BH} = (6.2 \pm 1.2) \times 10^8$ $M_{\odot}$. To measure the precise infrared luminosity ($L_{\rm IR}$), we obtain submillimeter data taken by SCUBA-2 on JCMT and conduct the spectral energy distribution analysis with X-ray to submillimeter data. We find that $L_{\rm IR}$ of eFEDSJ0828-0139 is $L_{\rm IR} = (6.8 \pm 1.8) \times 10^{13}$ $L_{\odot}$, confirming the existence of a hypeluminous infrared galaxy (HyLIRG). $\lambda_{\rm Edd}$ is estimated to be $\lambda_{\rm Edd} = 3.6 \pm 0.7$, making it one of the quasars with the highest BH mass accretion rate at cosmic noon., Comment: 9 pages, 3 figures, and 3 tables, accepted for publication in PASJ

We report an experimental realization of a new type of topology-controlled photonic cavities in valley photonic crystals by adopting judiciously oriented mirrors to localize the valley-polarized edge states along their propagation path. By using microwave frequency- and time-domain measurements, we directly observe the strong confinement of electromagnetic energy at the mirror surface due to the extended time delay required for the valley index flipping. Moreover, we experimentally demonstrate that both the degree of energy localization and quality factors of the topology-controlled photonic cavities are determined by the valley-flipping time which is controlled by the topology of the mirror. These results extend and complement the current design paradigm of topological photonic cavities.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology in wireless communications. Simultaneously transmitting and reflecting RIS (STAR-RISs) in particular have garnered significant attention due to their dual capabilities of simultaneous transmission and reflection, underscoring their potential applications in critical scenarios within the forthcoming sixth-generation (6G) technology landscape. Moreover, full-duplex (FD) systems have emerged as a breakthrough research direction in wireless transmission technology due to their high spectral efficiency. This paper explores the application potential of STAR-RIS in FD systems for future wireless communications, presenting an innovative technology that provides robust self-interference cancellation (SIC) capabilities for FD systems. We utilize the refraction functionality of STAR-RIS enhances the transmission capacity of FD systems, while its reflection functionality is used to eliminate self interference within the FD system. We delve into the applications of two different types of STAR-RIS in FD systems and compare their performance through simulations. Furthermore, we discuss the performance differences of STAR-RIS empowered FD systems under various configurations in a case study, and demonstrate the superiority of the proposed deep learning-based optimization algorithm. Finally, we discuss possible future research directions for STAR-RIS empowered FD systems., Comment: Accepted for publication in IEEE Communications Standards Magazine

Optical bound states in the continuum (BICs), which are exotic localized eigenstates embedded in the continuum spectrum and topological polarization singularity in momentum space, have attracted great attentions in both fundamental and applied physics. Here, based on magneto-optical photonic crystal slab placed in external magnetic fields to break the time-reversal symmetry, we theoretically demonstrate magnetically tunable BICs with arbitrary polarization covering the entire Poincar\'e sphere and efficient off-{\Gamma} chiral emission of circularly polarized states. More interestingly, by further breaking the in-plane inversion symmetry of the magneto-optical photonic crystal slab to generate a pair of circularly polarized states (C point) spawning from the eliminated BICs and tuning the external magnetic field strength to move one C point to the {\Gamma} point, one at-{\Gamma} intrinsic chiral BICs with near-unity circular dichroism exceeding 0.99 and a high quality factor of 46000 owning to the preserved out-of-plane mirror symmetry can be observed. These findings may lead to a plethora of potential applications in chiral-optical effects, structured light, and tunable optical devices., Comment: 13 pages, 4 figures

We present results from Atacama Large Millimeter/submillimeter Array (ALMA) spectral line-scan observations at 3-mm and 2-mm bands of three near-infrared-dark (NIR-dark) galaxies behind two massive lensing clusters MACS J0417.5-1154 and RXC J0032.1+1808. Each of these three sources is a faint (de-lensed $S_{\text{1.2 mm}}$ $<$ 1 mJy) triply lensed system originally discovered in the ALMA Lensing Cluster Survey. We have successfully detected CO and [C I] emission lines and confirmed that their spectroscopic redshifts are $z=3.652$, 2.391, and 2.985. By utilizing a rich multi-wavelength data set, we find that the NIR-dark galaxies are located on the star formation main sequence in the intrinsic stellar mass range of log ($M_*$/$M_\odot$) = 9.8 - 10.4, which is about one order of magnitude lower than that of typical submillimeter galaxies (SMGs). These NIR-dark galaxies show a variety in gas depletion times and spatial extent of dust emission. One of the three is a normal star-forming galaxy with gas depletion time consistent with a scaling relation, and its infrared surface brightness is an order of magnitude smaller than that of typical SMGs. Since this galaxy has an elongated axis ratio of $\sim 0.17$, we argue that normal star-forming galaxies in an edge-on configuration can be heavily dust-obscured. This implies that existing deep WFC3/F160W surveys may miss a fraction of typical star-forming main-sequence galaxies due to their edge-on orientation., Comment: 23 pages, 10 figures, Submitted to ApJ

In massive multiple-input multiple-output (MIMO) systems, how to reliably acquire downlink channel state information (CSI) with low overhead is challenging. In this work, by integrating the generative pre-trained Transformer (GPT) with federated-tuning, we propose a CSI-GPT approach to realize efficient downlink CSI acquisition. Specifically, we first propose a Swin Transformer-based channel acquisition network (SWTCAN) to acquire downlink CSI, where pilot signals, downlink channel estimation, and uplink CSI feedback are jointly designed. Furthermore, to solve the problem of insufficient training data, we propose a variational auto-encoder-based channel sample generator (VAE-CSG), which can generate sufficient CSI samples based on a limited number of high-quality CSI data obtained from the current cell. The CSI dataset generated from VAE-CSG will be used for pre-training SWTCAN. To fine-tune the pre-trained SWTCAN for improved performance, we propose an online federated-tuning method, where only a small amount of SWTCAN parameters are unfrozen and updated using over-the-air computation, avoiding the high communication overhead caused by aggregating the complete CSI samples from user equipment (UEs) to the BS for centralized fine-tuning. Simulation results verify the advantages of the proposed SWTCAN and the communication efficiency of the proposed federated-tuning method. Our code is publicly available at https://github.com/BIT-ZY/CSI-GPT

Some photonic crystals (PCs) with Dirac-like conical dispersions exhibit the property of double zero refractive index (that is, both epsilon and mu near zero (EMNZ)), wherein the electromagnetic waves have an infinite effective wavelength and do not experience any spatial phase change. The Dirac-like cones that support EMNZ are previously thought to present only at the center of the Brillouin zone ($\Gamma$ point) with a zero wavevector (we refer to as type-I EMNZ), which is constrained by the proportional relationship between phase refractive index and wavevector ($n=kc/\omega$). Here, we demonstrate the existence of an anomalous type-II EMNZ in PCs, which is associated with the Dirac-like point at off-$\Gamma$ points. By introducing a wave modulation approach, we theoretically elucidate its physical mechanism, and resolve the paradox of type-II EMNZ with non-zero wavevectors. We then fabricate a type-II EMNZ PC operating at the X point, and experimentally demonstrate that both its effective permittivity and permeability are zero at the Dirac-like point. Type-II EMNZ PCs exhibit a range of intriguing phenomena, including angle-selective transmission, wavefront flattening, a 180$^{\circ}$ phase shift upon transmission, and waveguiding with natural zero radiation loss. The extraordinary properties of type-II EMNZ PCs may open new avenues for the development of angle-selective optical filters, directional light sources, phase-controlled optical switches, ultracompact photonic circuits, nanolasers, and on-chip nonlinear enhancement., Comment: 38 pages, 13 figures

We present confusion-limited SCUBA-2 450-$\mu$m observations in the COSMOS-CANDELS region as part of the JCMT Large Program, SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES). Our maps at 450 and 850 $\mu$m cover an area of 450 arcmin$^2$. We achieved instrumental noise levels of $\sigma_{\mathrm{450}}=$ 0.59 mJy beam$^{-1}$ and $\sigma_{\mathrm{850}}=$ 0.09 mJy beam$^{-1}$ in the deepest area of each map. The corresponding confusion noise levels are estimated to be 0.65 and 0.36 mJy beam$^{-1}$. Above the 4 (3.5) $\sigma$ threshold, we detected 360 (479) sources at 450 $\mu$m and 237 (314) sources at 850 $\mu$m. We derive the deepest blank-field number counts at 450 $\mu$m, covering the flux-density range of 2 to 43 mJy. These are in agreement with other SCUBA-2 blank-field and lensing-cluster observations, but are lower than various model counts. We compare the counts with those in other fields and find that the field-to-field variance observed at 450 $\mu$m at the $R=6^\prime$ scale is consistent with Poisson noise, so there is no evidence of strong 2-D clustering at this scale. Additionally, we derive the integrated surface brightness at 450 $\mu$m down to 2.1 mJy to be $57.3^{+1.0}_{-6.2}$~Jy deg$^{-2}$, contributing to (41$\pm$4)\% of the 450-$\mu$m extragalactic background light (EBL) measured by COBE and Planck. Our results suggest that the 450-$\mu$m EBL may be fully resolved at $0.08^{+0.09}_{-0.08}$~mJy, which extremely deep lensing-cluster observations and next-generation submillimeter instruments with large aperture sizes may be able to achieve., Comment: 29 pages, 14 figures, accepted for publication in ApJ

Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships' advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches., Comment: Major Revision by IEEE JSAC

Photonic Chern insulators can be implemented in gyromagnetic photonic crystals with broken time-reversal (TR) symmetry. They exhibit gapless chiral edge states (CESs), enabling unidirectional propagation and demonstrating exceptional resilience to localization even in the presence of defects or disorders. However, when two Chern insulators with opposite Chern numbers are stacked together, this one-way nature can be nullified, causing the originally gapless CESs to become gapped. Recent theoretical works have proposed achieving such a topological phase transition in condensed matter systems using antiferromagnetic thin films such as MnBi2Te4 or by coupling two quantum spin/anomalous Hall insulators, but these approaches have yet to be realized experimentally. In a bilayer gyromagnetic photonic crystal arranged in an antiferromagnetic layer configuration, our experimental observations reveal that interlayer coupling initiates a transition from a Chern insulating phase to a higher-order topological phase. This transition results in the gapping of CESs and triggers the emergence of corner states within the bandgap. The corner mode energy within the gap can be attributed to CESs interaction, forming a Jackiw-Rebbi topological domain wall mode at the corner. These states exhibit heightened resilience against defects, setting them apart from their time-reversal symmetric counterparts., Comment: 12 pages including references, 7 figures

Over-the-air computation (AirComp) is a promising technology converging communication and computation over wireless networks, which can be particularly effective in model training, inference, and more emerging edge intelligence applications. AirComp relies on uncoded transmission of individual signals, which are added naturally over the multiple access channel thanks to the superposition property of the wireless medium. Despite significantly improved communication efficiency, how to accommodate AirComp in the existing and future digital communication networks, that are based on discrete modulation schemes, remains a challenge. This paper proposes a massive digital AirComp (MD-AirComp) scheme, that leverages an unsourced massive access protocol, to enhance compatibility with both current and next-generation wireless networks. MD-AirComp utilizes vector quantization to reduce the uplink communication overhead, and employs shared quantization and modulation codebooks. At the receiver, we propose a near-optimal approximate message passing-based algorithm to compute the model aggregation results from the superposed sequences, which relies on estimating the number of devices transmitting each code sequence, rather than trying to decode the messages of individual transmitters. We apply MD-AirComp to the federated edge learning (FEEL), and show that it significantly accelerates FEEL convergence compared to state-of-the-art while using the same amount of communication resources. To support further research and ensure reproducibility, we have made our code available at https://github.com/liqiao19/MD-AirComp., Comment: IEEE Journal on Selected Areas in Communications

Reconfigurable massive multiple-input multiple-output (RmMIMO) technology offers increased flexibility for future communication systems by exploiting previously untapped degrees of freedom in the electromagnetic (EM) domain. The representation of the traditional spatial domain channel state information (sCSI) limits the insights into the potential of EM domain channel properties, constraining the base station's (BS) utmost capability for precoding design. This paper leverages the EM domain channel state information (eCSI) for antenna radiation patterns design at the BS. We develop an orthogonal decomposition method based on spherical harmonic functions to decompose the radiation pattern into a linear combination of orthogonal bases. By formulating the radiation pattern design as an optimization problem for the projection coefficients over these bases, we develop a manifold optimization-based method for iterative radiation pattern and digital precoder design. To address the eCSI estimation problem, we capitalize on the inherent structure of the channel. Specifically, we propose a subspace-based scheme to reduce the pilot overhead for wideband sCSI estimation. Given the estimated full-band sCSI, we further employ parameterized methods for angle of arrival estimation. Subsequently, the complete eCSI can be reconstructed after estimating the equivalent channel gain via the least squares method. Simulation results demonstrate that, in comparison to traditional mMIMO systems with fixed antenna radiation patterns, the proposed RmMIMO architecture offers significant throughput gains for multi-user transmission at a low channel estimation overhead., Comment: This work is being submitted to the IEEE for possible publication

With the development of technology, the chemical production process is becoming increasingly complex and large-scale, making fault detection particularly important. However, current detective methods struggle to address the complexities of large-scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long and short-term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three-layer deep learning network random trees (TDLN-trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher-level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method.

We prove a multiplicity result for rectangular pegs that there is a generic class of smooth Jordan curves in which every curve admits two geometrically distinct similar inscribed rectangles with aspect angle in (0,\frac{\pi}{2}), based on the existence of rectangular pegs in any smooth Jordan curve, which is first proved by Greene and Lobb [GL21] and we give an alternative Floer theoretical proof in this paper. The key insight is that the rectangular peg problem is translated into finding intersection points of two Lagrangian tori. We present two distinct proofs for the multiplicity result: one involves Lagrangian Floer homology, and the other is differential topological in nature which employs a novel computation formula for the algebraic intersection number. Both rely crucially on certain generic geometric transversality of the two tori, and the correspondence between the intersection points and the inscribed rectangles. Moreover, such a generic doubling result can be further extended to cyclic quadrilateral pegs based on the existence result [GL23]. In Appendix \ref{Appendix_B}, written jointly with Urs Frauenfelder, we provide the detailed derivation of this computation formula., Comment: v5: 46 pages, major revision, add Floer theoretical proof of the existence result, acknowledgement and references updated

The discovery of photonic higher-order topological insulators (HOTIs) has significantly expanded our understanding of band topology and provided unprecedented lower-dimensional topological boundary states for robust photonic devices. However, due to the vectorial and leaky nature of electromagnetic waves, it is challenging to discover three-dimensional (3D) topological photonic systems and photonic HOTIs have so far still been limited to two dimensions (2D). Here, we report on the first experimental realization of a 3D Wannier-type photonic HOTI in a tight-binding-like metal-cage photonic crystal, whose band structure matches well with that of a 3D tight-binding model due to the confined Mie resonances. By microwave near-field measurements, we directly observe coexisting topological surface, hinge, and corner states in a single 3D photonic HOTI, as predicted by the tight-binding model and simulation results. Moreover, we demonstrate that all-order topological boundary states are self-guided even in the light cone continuum and can be exposed to air without ancillary cladding, making them well-suited for practical applications. Our work thus opens routes to the multi-dimensional robust manipulation of electromagnetic waves at the outer surfaces of 3D cladding-free photonic bandgap materials and may find novel applications in 3D topological integrated photonics devices., Comment: 23 pages,4 figures

Stable Diffusion is a popular Transformer-based model for image generation from text; it applies an image information creator to the input text and the visual knowledge is added in a step-by-step fashion to create an image that corresponds to the input text. However, this diffusion process can be corrupted by errors from the underlying hardware, which are especially relevant for implementations at the nanoscales. In this paper, the dependability of Stable Diffusion is studied focusing on soft errors in the memory that stores the model parameters; specifically, errors are injected into some critical layers of the Transformer in different blocks of the image information creator, to evaluate their impact on model performance. The simulations results reveal several conclusions: 1) errors on the down blocks of the creator have a larger impact on the quality of the generated images than those on the up blocks, while the errors on middle block have negligible effect; 2) errors on the self-attention (SA) layers have larger impact on the results than those on the cross-attention (CA) layers; 3) for CA layers, errors on deeper levels result in a larger impact; 4) errors on blocks at the first levels tend to introduce noise in the image, and those on deep layers tend to introduce large colored blocks. These results provide an initial understanding of the impact of errors on Stable Diffusion., Comment: 6 pages, 16 figures

Generative foundation AI models have recently shown great success in synthesizing natural signals with high perceptual quality using only textual prompts and conditioning signals to guide the generation process. This enables semantic communications at extremely low data rates in future wireless networks. In this paper, we develop a latency-aware semantic communications framework with pre-trained generative models. The transmitter performs multi-modal semantic decomposition on the input signal and transmits each semantic stream with the appropriate coding and communication schemes based on the intent. For the prompt, we adopt a re-transmission-based scheme to ensure reliable transmission, and for the other semantic modalities we use an adaptive modulation/coding scheme to achieve robustness to the changing wireless channel. Furthermore, we design a semantic and latency-aware scheme to allocate transmission power to different semantic modalities based on their importance subjected to semantic quality constraints. At the receiver, a pre-trained generative model synthesizes a high fidelity signal using the received multi-stream semantics. Simulation results demonstrate ultra-low-rate, low-latency, and channel-adaptive semantic communications., Comment: Accepted for publication in IEEE Wireless Communication Letters

The wide adoption of Large language models (LLMs) makes their dependability a pressing concern. Detection of errors is the first step to mitigating their impact on a system and thus, efficient error detection for LLMs is an important issue. In many settings, the LLM is considered as a black box with no access to the internal nodes; this prevents the use of many error detection schemes that need access to the model's internal nodes. An interesting observation is that the output of LLMs in error-free operation should be valid and normal text. Therefore, when the text is not valid or differs significantly from normal text, it is likely that there is an error. Based on this observation we propose to perform Concurrent Linguistic Error Detection (CLED); this scheme extracts some linguistic features of the text generated by the LLM and feeds them to a concurrent classifier that detects errors. Since the proposed error detection mechanism only relies on the outputs of the model, then it can be used on LLMs in which there is no access to the internal nodes. The proposed CLED scheme has been evaluated on the T5 model when used for news summarization and on the OPUS-MT model when used for translation. In both cases, the same set of linguistic features has been used for error detection to illustrate the applicability of the proposed scheme beyond a specific case. The results show that CLED can detect most of the errors at a low overhead penalty. The use of the concurrent classifier also enables a trade-off between error detection effectiveness and its associated overhead, so providing flexibility to a designer., Comment: 11 pages, 6 figures, 30 references

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UMMIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resolution analog-to-digital converters, and in-phase and quadrature imbalance. To overcome these challenges, this paper proposes an efficient downlink channel estimation (CE) and CSI feedback approach based on knowledge and data dual-driven deep learning (DL) networks. Specifically, we first propose a data-driven residual neural network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at user equipment (UEs), where the noise and distortion brought by imperfect hardware can be mitigated. A knowledge-driven generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) network is then developed to jointly estimate the channels by exploiting the approximately same physical angle shared by different subcarriers. In particular, two wideband redundant dictionaries (WRDs) are proposed such that the measurement matrices of the GMMV-LAMP network can accommodate the far-field and near-field beam squint effect, respectively. Finally, we propose an encoder at the UEs and a decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to compress the CSI matrix into a low-dimensional quantized bit vector for feedback, thereby reducing the feedback overhead substantially. Simulation results show that the proposed knowledge and data dual-driven approach outperforms conventional downlink CE and CSI feedback methods, especially in the case of low signal-to-noise ratios., Comment: 17 pages, 22 figures, 3 tables

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 66%., Comment: 14 pages, 10 figures

Utilizing unmanned aerial vehicles (UAVs) with edge server to assist terrestrial mobile edge computing (MEC) has attracted tremendous attention. Nevertheless, state-of-the-art schemes based on deterministic optimizations or single-objective reinforcement learning (RL) cannot reduce the backlog of task bits and simultaneously improve energy efficiency in highly dynamic network environments, where the design problem amounts to a sequential decision-making problem. In order to address the aforementioned problems, as well as the curses of dimensionality introduced by the growing number of terrestrial terrestrial users, this paper proposes a distributed multi-objective (MO) dynamic trajectory planning and offloading scheduling scheme, integrated with MORL and the kernel method. The design of n-step return is also applied to average fluctuations in the backlog. Numerical results reveal that the n-step return can benefit the proposed kernel-based approach, achieving significant improvement in the long-term average backlog performance, compared to the conventional 1-step return design. Due to such design and the kernel-based neural network, to which decision-making features can be continuously added, the kernel-based approach can outperform the approach based on fully-connected deep neural network, yielding improvement in energy consumption and the backlog performance, as well as a significant reduction in decision-making and online learning time., Comment: This paper has been accepted for publication in the IEEE Transactions on Vehicular Technology

This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment. We present a generalized model of block-sparse tensor recovery as a theoretical foundation, where concepts measuring holistic mutual incoherence property (MIP) of the measurement matrix set are defined. A representative algorithm based on the orthogonal matching pursuit (OMP) framework, called tensor generalized block OMP (T-GBOMP), is applied to the theoretical framework elaborated for analyzing both noiseless and noisy recovery conditions. Specifically, we present the exact recovery condition (ERC) and sufficient conditions for establishing it with consideration of different degrees of restriction. Reliable reconstruction conditions, in terms of the residual convergence, the estimated error and the signal-to-noise ratio bound, are established to reveal the computable theoretical interpretability based on the newly defined MIP, which we introduce. The flexibility of tensor recovery is highlighted, i.e., the reliable recovery can be guaranteed by optimizing MIP of the measurement matrix set. Analytical comparisons demonstrate that the theoretical results developed are tighter and less restrictive than the existing ones (if any). Further discussions provide tensor extensions for several classic greedy algorithms, indicating that the sophisticated results derived are universal and applicable to all these tensorized variants., Comment: Accepted by IEEE Transactions on Information Theory

Topological polaritons characterized by light-matter interactions have become a pivotal platform in exploring new topological phases of matter. Recent theoretical advances unveiled a novel mechanism for tuning topological phases of polaritons by modifying the surrounding photonic environment (light-matter interactions) without altering the lattice structure. Here, by embedding a dimerized chain of microwave helical resonators (electric dipole emitters) in a metallic cavity waveguide, we report the pioneering observation of tunable topological phases of polaritons by varying the cavity width which governs the surrounding photonic environment and the strength of light-matter interactions. Moreover, we experimentally identified a new type of topological phase transition which includes three non-coincident critical points in the parameter space: the closure of the polaritonic bandgap, the transition of the Zak phase, and the hybridization of the topological edge states with the bulk states. These results reveal some remarkable and uncharted properties of topological matter when strongly coupled to light and provide an innovative design principle for tunable topological photonic devices., Comment: 6 pages, 4 figures

Emergent collective modes in lattices give birth to many intriguing physical phenomena in condensed matter physics. Among these collective modes, large-area modes typically feature small-level spacings, while a mode with stable frequency tends to be spatially tightly confined. Here, we theoretically propose and experimentally demonstrate a spectral-isolated photonic topological corner mode with a tunable mode area and stable frequency in a two-dimensional photonic crystal. This mode emerges from hybridizing the large-area homogeneous mode and in-gap topological corner modes. Remarkably, this large-area homogeneous mode possesses unique chirality and has a tunable mode area under the change of the mass term of the inner topological non-trivial lattice. We experimentally observe such topological large-area corner modes(TLCM) in a 2D photonic system and demonstrate the robustness by introducing disorders in the structure. Our findings have propelled the forefront of higher-order topology research, transitioning it from single-lattice systems to multi-lattice systems. They may support promising potential applications, particularly in vertical-cavity surface-emitting lasers., Comment: 5 pages, 4 figures

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space-air-ground-sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics and unique problems of NS-COM network. Afterwards, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite (StratoSat) -to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output (MIMO) and holographic MIMO, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, StratoSat-based radar detection and imaging, NS-COM assisted enhanced global navigation system, NS-COM assisted intelligent unmanned system and free space optical (FSO) communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture., Comment: 28 pages, 8 figures, 2 tables

This paper studies the affine frequency division multiplexing (AFDM)-empowered sparse code multiple access (SCMA) system, referred to as AFDM-SCMA, for supporting massive connectivity in high-mobility environments. First, by placing the sparse codewords on the AFDM chirp subcarriers, the input-output (I/O) relation of AFDM-SCMA systems is presented. Next, we delve into the generalized receiver design, chirp rate selection, and error rate performance of the proposed AFDM-SCMA. The proposed AFDM-SCMA is shown to provide a general framework and subsume the existing OFDM-SCMA as a special case. Third, for efficient transceiver design, we further propose a class of sparse codebooks for simplifying the I/O relation, referred to as I/O relation-inspired codebook design in this paper. Building upon these codebooks, we propose a novel iterative detection and decoding scheme with linear minimum mean square error (LMMSE) estimator for both downlink and uplink channels based on orthogonal approximate message passing principles. Our numerical results demonstrate the superiority of the proposed AFDM-SCMA systems over OFDM-SCMA systems in terms of the error rate performance. We show that the proposed receiver can significantly enhance the error rate performance while reducing the detection complexity.

The advent of the sixth-generation (6G) of wireless communications has given rise to the necessity to connect vast quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things for which massive access is critical. This paper surveys the most recent advances toward massive access in both academic and industry communities, focusing primarily on the promising compressive sensing-based grant-free massive access paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a compressive sensing-based grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input multiple-output systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss the key challenges and open issues to shed light on the potential future research directions of grant-free massive access., Comment: Accepted by IEEE IoT Journal

Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. By taking into account age-of-information (AoI), we investigate the trajectory planning problem of an unmanned aerial vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network. Since the total AoI of the IoT network depends on the flight time of the UAV and the data collection time at hovering points, we jointly optimize the selection of hovering points and the visiting order to these points. We exploit the state-of-the-art transformer and the weighted A*, which is a path search algorithm, to design a machine learning algorithm to solve the formulated problem. The whole UAV-IoT system is fed into the encoder network of the proposed algorithm, and the algorithm's decoder network outputs the visiting order to ground clusters. Then, the weighted A* is used to find the hovering point for each cluster in the ground IoT network. Simulation results show that the trained model by the proposed algorithm has a good generalization ability to generate solutions for IoT networks with different numbers of ground clusters, without the need to retrain the model. Furthermore, results show that our proposed algorithm can find better UAV trajectories with the minimum total AoI when compared to other algorithms.

Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. While accurate channel estimation is essential for beamforming and data detection, the unique characteristics of near-field channels pose additional challenges to the effective acquisition of channel state information. In this paper, we propose a novel codebook design, which allows efficient near-field channel estimation with significantly reduced codebook size. Specifically, we consider the eigen-problem based on the near-field electromagnetic wave transmission model. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Based on the proposed near-field codebook design, we further introduce a two-step channel estimation scheme. Simulation results demonstrate that the proposed codebook design not only achieves superior sparsification performance of near-field channels with a lower leakage effect, but also significantly improves the accuracy in compressive sensing channel estimation., Comment: 6 pages, 5 figures

Multi-modal Large Language Models (MLLMs) have made significant strides in expanding the capabilities of Large Language Models (LLMs) through the incorporation of visual perception interfaces. Despite the emergence of exciting applications and the availability of diverse instruction tuning data, existing approaches often rely on CLIP or its variants as the visual branch, and merely extract features from the deep layers. However, these methods lack a comprehensive analysis of the visual encoders in MLLMs. In this paper, we conduct an extensive investigation into the effectiveness of different vision encoders within MLLMs. Our findings reveal that the shallow layer features of CLIP offer particular advantages for fine-grained tasks such as grounding and region understanding. Surprisingly, the vision-only model DINO, which is not pretrained with text-image alignment, demonstrates promising performance as a visual branch within MLLMs. By simply equipping it with an MLP layer for alignment, DINO surpasses CLIP in fine-grained related perception tasks. Building upon these observations, we propose a simple yet effective feature merging strategy, named COMM, that integrates CLIP and DINO with Multi-level features Merging, to enhance the visual capabilities of MLLMs. We evaluate COMM through comprehensive experiments on a wide range of benchmarks, including image captioning, visual question answering, visual grounding, and object hallucination. Experimental results demonstrate the superior performance of COMM compared to existing methods, showcasing its enhanced visual capabilities within MLLMs.

Bright galaxies at sub-millimetre wavelengths from Herschel are now well known to be predominantly strongly gravitationally lensed. The same models that successfully predicted this strongly lensed population also predict about one percent of faint $450{\mu}$m-selected galaxies from deep James Clerk Maxwell Telescope (JCMT) surveys will also be strongly lensed. Follow-up ALMA campaigns have so far found one potential lens candidate, but without clear compelling evidence e.g. from lensing arcs. Here we report the discovery of a compelling gravitational lens system confirming the lensing population predictions, with a $z_{s} = 3.4 {\pm} 0.4$ submm source lensed by a $z_{spec} = 0.360$ foreground galaxy within the COSMOS field, identified through public JWST imaging of a $450{\mu}$m source in the SCUBA-2 Ultra Deep Imaging EAO Survey (STUDIES) catalogue. These systems will typically be well within the detectable range of future wide-field surveys such as Euclid and Roman, and since sub-millimetre galaxies are predominantly very red at optical/near-infrared wavelengths, they will tend to appear in near-infrared channels only. Extrapolating to the Euclid-Wide survey, we predict tens of thousands of strongly lensed near-infrared galaxies. This will be transformative for the study of dusty star-forming galaxies at cosmic noon, but will be a contaminant population in searches for strongly lensed ultra-high-redshift galaxies in Euclid and Roman., Comment: 9 pages, 5 figures, 4 tables; added figure, updated Discussion section, and minor revisions; accepted for publication in MNRAS

The Space-Air-Ground-Sea integrated network calls for more robust and secure transmission techniques against jamming. In this paper, we propose a textual semantic transmission framework for robust transmission, which utilizes the advanced natural language processing techniques to model and encode sentences. Specifically, the textual sentences are firstly split into tokens using wordpiece algorithm, and are embedded to token vectors for semantic extraction by Transformer-based encoder. The encoded data are quantized to a fixed length binary sequence for transmission, where binary erasure, symmetric, and deletion channels are considered for transmission. The received binary sequences are further decoded by the transformer decoders into tokens used for sentence reconstruction. Our proposed approach leverages the power of neural networks and attention mechanism to provide reliable and efficient communication of textual data in challenging wireless environments, and simulation results on semantic similarity and bilingual evaluation understudy prove the superiority of the proposed model in semantic transmission., Comment: 6 pages, 5 figures. Accepted by IEEE/CIC ICCC 2023

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy., Comment: To appear in IEEE Transactions on Communications. Codes will be open to all on https://gaozhen16.github.io/ soon

This paper focuses on advancing outdoor wireless systems to better support ubiquitous extended reality (XR) applications, and close the gap with current indoor wireless transmission capabilities. We propose a hybrid knowledge-data driven method for channel semantic acquisition and multi-user beamforming in cell-free massive multiple-input multiple-output (MIMO) systems. Specifically, we firstly propose a data-driven multiple layer perceptron (MLP)-Mixer-based auto-encoder for channel semantic acquisition, where the pilot signals, CSI quantizer for channel semantic embedding, and CSI reconstruction for channel semantic extraction are jointly optimized in an end-to-end manner. Moreover, based on the acquired channel semantic, we further propose a knowledge-driven deep-unfolding multi-user beamformer, which is capable of achieving good spectral efficiency with robustness to imperfect CSI in outdoor XR scenarios. By unfolding conventional successive over-relaxation (SOR)-based linear beamforming scheme with deep learning, the proposed beamforming scheme is capable of adaptively learning the optimal parameters to accelerate convergence and improve the robustness to imperfect CSI. The proposed deep unfolding beamforming scheme can be used for access points (APs) with fully-digital array and APs with hybrid analog-digital array. Simulation results demonstrate the effectiveness of our proposed scheme in improving the accuracy of channel acquisition, as well as reducing complexity in both CSI acquisition and beamformer design. The proposed beamforming method achieves approximately 96% of the converged spectrum efficiency performance after only three iterations in downlink transmission, demonstrating its efficacy and potential to improve outdoor XR applications., Comment: 15 pages, 15 figures

This paper proposes a novel media access control (MAC) mechanism, called the robust preamble-based MAC mechanism (R-PMAC), which can be applied to power line communication (PLC) networks in the context of the Industrial Internet of Things (IIoT). Compared with other MAC mechanisms such as P-MAC and the MAC layer of IEEE1901.1, R-PMAC has higher networking speed. Besides, it supports whitelist authentication and functions properly in the presence of data frame loss. Firstly, we outline three basic mechanisms of R-PMAC, containing precise time difference calculation, preambles generation and short ID allocation. Secondly, we elaborate its networking process of single layer and multiple layers. Thirdly, we illustrate its robust mechanisms, including collision handling and data retransmission. Moreover, a low-cost hardware platform is established to measure the time of connecting hundreds of PLC nodes for the R-PMAC, P-MAC, and IEEE1901.1 mechanisms in a real power line environment. The experiment results show that R-PMAC outperforms the other mechanisms by achieving a 50% reduction in networking time. These findings indicate that the R-PMAC mechanism holds great potential for quickly and effectively building a PLC network in actual industrial scenarios., Comment: This paper has been accepted by IEEE Internet of Things Journal

The complexity of Machine Learning (ML) systems increases each year, with current implementations of large language models or text-to-image generators having billions of parameters and requiring billions of arithmetic operations. As these systems are widely utilized, ensuring their reliable operation is becoming a design requirement. Traditional error detection mechanisms introduce circuit or time redundancy that significantly impacts system performance. An alternative is the use of Concurrent Error Detection (CED) schemes that operate in parallel with the system and exploit their properties to detect errors. CED is attractive for large ML systems because it can potentially reduce the cost of error detection. In this paper, we introduce Concurrent Classifier Error Detection (CCED), a scheme to implement CED in ML systems using a concurrent ML classifier to detect errors. CCED identifies a set of check signals in the main ML system and feeds them to the concurrent ML classifier that is trained to detect errors. The proposed CCED scheme has been implemented and evaluated on two widely used large-scale ML models: Contrastive Language Image Pretraining (CLIP) used for image classification and Bidirectional Encoder Representations from Transformers (BERT) used for natural language applications. The results show that more than 95 percent of the errors are detected when using a simple Random Forest classifier that is order of magnitude simpler than CLIP or BERT. These results illustrate the potential of CCED to implement error detection in large-scale ML models.

Quantum Hall systems host chiral edge states extending along the one-dimensional boundary of any two-dimensional sample. In solid state materials, the edge states serve as perfectly robust transport channels that produce a quantised Hall conductance; due to their chirality, and the topological protection by the Chern number of the bulk bandstructure, they cannot be spatially localized by defects or disorder. Here, we show experimentally that the chiral edge states of a lossy quantum Hall system can be localized. In a gyromagnetic photonic crystal exhibiting the quantum Hall topological phase, an appropriately structured loss configuration imparts the edge states' complex energy spectrum with a feature known as point-gap winding. This intrinsically non-Hermitian topological invariant is distinct from the Chern number invariant of the bulk (which remains intact) and induces mode localization via the "non-Hermitian skin effect". The interplay of the two topological phenomena - the Chern number and point-gap winding - gives rise to a non-Hermitian generalisation of the paradigmatic Chern-type bulk-boundary correspondence principle. Compared to previous realisations of the non-Hermitian skin effect, the skin modes in this system have superior robustness against local defects and disorders.

Over-the-air computation (OAC) is a promising technique to achieve fast model aggregation across multiple devices in federated edge learning (FEEL). In addition to the analog schemes, one-bit digital aggregation (OBDA) scheme was proposed to adapt OAC to modern digital wireless systems. However, one-bit quantization in OBDA can result in a serious information loss and slower convergence of FEEL. To overcome this limitation, this paper proposes an unsourced massive access (UMA)-based generalized digital OAC (GD-OAC) scheme. Specifically, at the transmitter, all the devices share the same non-orthogonal UMA codebook for uplink transmission. The local model update of each device is quantized based on the same quantization codebook. Then, each device transmits a sequence selected from the UMA codebook based on the quantized elements of its model update. At the receiver, we propose an approximate message passing-based algorithm for efficient UMA detection and model aggregation. Simulation results show that the proposed GD-OAC scheme significantly accelerates the FEEL convergences compared with the state-of-the-art OBDA scheme while using the same uplink communication resources., Comment: 2023 IEEE International Symposium on Information Theory (ISIT)

The Bloch band theory and Brillouin zone (BZ) that characterize wave behaviors in periodic mediums are two cornerstones of contemporary physics ranging from condensed matter to topological physics. Recent theoretical breakthrough revealed that, under the projective symmetry algebra enforced by artificial gauge fields, the usual two-dimensional (2D) BZ (orientable Brillouin two-torus) can be fundamentally modified to a non-orientable Brillouin Klein bottle with radically distinct topology and novel topological phases. However, the physical consequence of artificial gauge fields on the more general three-dimensional (3D) BZ (orientable Brillouin three-torus) was so far missing. Here, we report the first theoretical discovery and experimental observation of non-orientable Brillouin Klein space and orientable Brillouin half-turn space in a 3D acoustic crystal with artificial gauge fields. We experimentally identify peculiar 3D momentum-space non-symmorphic screw rotation and glide reflection symmetries in the measured band structures. Moreover, we demonstrate a novel 3D Klein bottle insulator featuring a nonzero Z_2 topological invariant and self-collimated topological surface states at two opposite surfaces related by a nonlocal twist, radically distinct from all previous topological insulators. Our discovery not only fundamentally modifies the 3D Bloch band theory and 3D BZ, but also opens the door towards a wealth of previously overlooked momentum-space topologies and unexplored topological physics with gauge symmetry beyond the existing paradigms.