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The rapid increase in video content production has resulted in enormous data volumes, creating significant challenges for efficient analysis and resource management. To address this, robust video analysis tools are essential. This paper presents an innovative proof of concept using Generative Artificial Intelligence (GenAI) in the form of Vision Language Models to enhance the downstream video analysis process. Our tool generates customized textual summaries based on user-defined queries, providing focused insights within extensive video datasets. Unlike traditional methods that offer generic summaries or limited action recognition, our approach utilizes Vision Language Models to extract relevant information, improving analysis precision and efficiency. The proposed method produces textual summaries from extensive CCTV footage, which can then be stored for an indefinite time in a very small storage space compared to videos, allowing users to quickly navigate and verify significant events without exhaustive manual review. Qualitative evaluations result in 80% and 70% accuracy in temporal and spatial quality and consistency of the pipeline respectively., Comment: Accepted for TENCON 2024

Dual enrollment (DE) is one of the fastest growing programs that support the high school-to-college transition. Yet, there is limited empirical evidence about its impact on either students' college application choices or admission outcomes. Using a fuzzy regression discontinuity approach on data from two cohorts of ninth-grade students in one anonymous state, we found that taking DE credits increased the likelihood of applying to highly selective in-state four-year institutions. Attempting DE credits also increased the probability of gaining admission to a highly selective in-state four-year college. Heterogeneous analysis further indicates that the gains were extended across Black, Latinx, and white student populations.

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.

Uplink integrated sensing and communication (ISAC) systems have recently emerged as a promising research direction, enabling simultaneous uplink signal detection and target sensing. In this paper, we propose flexible projection (FP)-type receivers that unify the projection-type receivers and the successive interference cancellation (SIC)-type receivers by using a flexible tradeoff factor to adapt to dynamically changing uplink ISAC scenarios. The FP-type receivers address the joint signal detection and target response estimation problem through two coordinated phases: 1) Communication signal detection using a reconstructed signal whose composition is controlled by the tradeoff factor, followed by 2) Target response estimation performed through subtraction of the detected communication signal from the received signal. With adjustable tradeoff factors, the FP-type receivers can balance the enhancement of the signal-to-interference-plus-noise ratio (SINR) with the reduction of correlation in the reconstructed signal for communication signal detection. The pairwise error probabilities (PEPs) are analyzed for both maximum likelihood (ML) and zero-forcing (ZF) detectors, revealing that the optimal tradeoff factor should be determined based on the adopted detection algorithm and the relative power of the sensing and communication (S&C) signal. A homotopy optimization framework is first applied for the FP-type receivers with a fixed trade-off factor. This framework is then extended to develop dynamic FP (DFP)-type receivers, which iteratively adjust the trade-off factor for improved algorithm performance and environmental adaptability. Subsequently, two extensions are explored to further enhance the receivers' performance: parallel DFP (PDFP)-type receivers and a block-structured receiver design. Finally, the effectiveness of the proposed receiver designs is verified via simulations., Comment: 13 pages, 9 figures, submitted to an IEEE journal for possible publication

In this article, we present algorithms for computing parabolic inductions and Jacquet modules for the general linear group $G$ over a non-Archimedean local field. Given the Zelevinsky data or Langlands data of an irreducible smooth representation $\pi$ of $G$ and an essentially square-integrable representation $\sigma$, we explicitly determine the Jacquet module of $\pi$ with respect to $\sigma$ and the socle of the normalized parabolic induction $\pi \times \sigma$. Our result builds on and extends some previous work of M\oe glin-Waldspurger, Jantzen, M\'inguez, and Lapid-M\'inguez, and also uses other methods such as sequences of derivatives and an exotic duality. As an application, we give a simple algorithm for computing the highest derivative multisegment., Comment: v1: 46pages

Multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems rely on digital or hybrid digital and analog designs for beamforming against frequency-selective fading, which suffer from high hardware complexity and energy consumption. To address this, this work introduces a fully-analog stacked intelligent metasurfaces (SIM) architecture that directly performs wave-domain beamforming, enabling diagonalization of the end-to-end channel matrix and inherently eliminating inter-antenna interference (IAI) for MIMO OFDM transmission. By leveraging cascaded programmable metasurface layers, the proposed system establishes multiple parallel subchannels, significantly improving multi-carrier transmission efficiency while reducing hardware complexity. To optimize the SIM phase shift matrices, a block coordinate descent and penalty convex-concave procedure (BCD-PCCP) algorithm is developed to iteratively minimize the channel fitting error across subcarriers. Simulation results validate the proposed approach, determining the maximum effective bandwidth and demonstrating substantial performance improvements. Moreover, for a MIMO OFDM system operating at 28 GHz with 16 subcarriers, the proposed SIM configuration method achieves over 300\% enhancement in channel capacity compared to conventional SIM configuration that only accounts for the center frequency.

We introduce XIRVIO, a transformer-based Generative Adversarial Network (GAN) framework for monocular visual inertial odometry (VIO). By taking sequences of images and 6-DoF inertial measurements as inputs, XIRVIO's generator predicts pose trajectories through an iterative refinement process which are then evaluated by the critic to select the iteration with the optimised prediction. Additionally, the self-emergent adaptive sensor weighting reveals how XIRVIO attends to each sensory input based on contextual cues in the data, making it a promising approach for achieving explainability in safety-critical VIO applications. Evaluations on the KITTI dataset demonstrate that XIRVIO matches well-known state-of-the-art learning-based methods in terms of both translation and rotation errors., Comment: 7 pages, 6 figures

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems offer high spectral efficiency (SE) through multiple distributed access points (APs). However, the large number of antennas increases power consumption. We propose incorporating stacked intelligent metasurfaces (SIM) into CF mMIMO systems as a cost-effective, energy-efficient solution. This paper focuses on optimizing the joint power allocation of APs and the phase shift of SIMs to maximize the sum SE. To address this complex problem, we introduce a fully distributed multi-agent reinforcement learning (MARL) algorithm. Our novel algorithm, the noisy value method with a recurrent policy in multi-agent policy optimization (NVR-MAPPO), enhances performance by encouraging diverse exploration under centralized training and decentralized execution. Simulations demonstrate that NVR-MAPPO significantly improves sum SE and robustness across various scenarios.

Constant-envelope signals are widely employed in wireless communication systems due to their hardware-friendly design, energy efficiency, and enhanced reliability. However, detecting these signals reliably using simple, power-efficient receivers remains a critical challenge. While coherent detection methods generally offer superior performance, they require complex frequency synchronization, which increases receiver complexity and power consumption. In contrast, noncoherent detection is inherently simpler since it avoids frequency synchronization. However, traditional noncoherent detection approaches still rely on In-phase and Quadrature-phase (IQ) demodulators to extract the noisy received signal magnitudes, and assume the energy detector as the test statistic according to the IQ structure, without rigorous theoretical justification. Motivated by the practical need for robust and low-complexity detection, this paper proposes a comprehensive framework for optimal signal detection using a simple bandpass-filter envelope-detector (BFED) in conjunction with a Bayesian approach and the generalized likelihood ratio test (GLRT) under unknown amplitude conditions. By leveraging approximations of the modified Bessel functions, we derive two distinct regimes of the optimal detector. In the low SNR regime, we rigorously prove that the energy detector emerges as the Bayesian-optimal solution, thereby establishing its theoretical validity for the first time. In the high SNR regime, we derive a novel amplitude-based detector that directly compares the estimated amplitude against the noise standard deviation, leading to a simple yet optimal detection strategy. Numerical simulations validate the theoretical findings, confirming that both the energy and amplitude detectors achieve the minimum error probability in their respective SNR domains.

Flexible intelligent metasurfaces (FIMs) constitute a promising technology that could significantly boost the wireless network capacity. An FIM is essentially a soft array made up of many low-cost radiating elements that can independently emit electromagnetic signals. What's more, each element can flexibly adjust its position, even perpendicularly to the surface, to morph the overall 3D shape. In this paper, we study the potential of FIMs in point-to-point multiple-input multiple-output (MIMO) communications, where two FIMs are used as transceivers. In order to characterize the capacity limits of FIM-aided narrowband MIMO transmissions, we formulate an optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs, as well as the transmit covariance matrix, subject to a specific total transmit power constraint and to the maximum morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed. Numerical results verify that FIMs can achieve higher MIMO capacity than traditional rigid arrays. In some cases, the MIMO channel capacity can be doubled by employing FIMs., Comment: 6 pages, 4 figures, accepted by IEEE ICC 2025

A flexible intelligent metasurface (FIM) is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals and flexibly adjust its position through a 3D surface-morphing process. In our system, an FIM is deployed at a base station (BS) that transmits to multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS by jointly optimizing the transmit beamforming and the FIM's surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint for each user as well as to a constraint on the maximum morphing range of the FIM. To address this problem, an efficient alternating optimization method is proposed to iteratively update the FIM's surface shape and the transmit beamformer to gradually reduce the transmit power. Finally, our simulation results show that at a given data rate the FIM reduces the transmit power by about $3$ dB compared to conventional rigid 2D arrays., Comment: 6 pages, 4 figures, presented at IEEE GLOBECOM 2024

This paper proposes a multi-user Spatial Division Multiplexing (SDM) near-field access scheme, inspired by the orthogonal characteristics of multi-mode vortex waves. A Reconfigurable Meta-surface (RM) is ingeniously employed as the gateway for information transmission. This RM not only receives spatially overlapping multiplexed multi-mode vortex beams but also converts them into focused point beams in the near field. Specifically, a multi-port microstrip array method is utilized to generate multiple orthogonal vortex electromagnetic wave modes. Different ports serve as feeding points for baseband signals, allowing independent modulated data to be flexibly loaded onto different modes. After being adjusted by the RM, the vortex electromagnetic waves are converted into energy-focusing point beams, which can be directed to arbitrary 3D positions in the RM's near-field region and received by different users. Since the spatial positions of the point beams are non-overlapping, this approach not only ensures energy concentration but also significantly reduces inter-user interference. Near-field scanning results in a microwave anechoic chamber validate the effectiveness of this method, while real-time communication demonstrations confirm the system's capability for low-interference information multiplexing and transmission in practical scenarios.

Intelligent metasurfaces may be harnessed for realizing efficient holographic multiple-input and multiple-output (MIMO) systems, at a low hardware-cost and high energy-efficiency. As part of this family, we propose a hybrid beamforming design for stacked intelligent metasurfaces (SIM) aided wideband wireless systems relying on the near-field channel model. Specifically, the holographic beamformer is designed based on configuring the phase shifts in each layer of the SIM for maximizing the sum of the baseband eigen-channel gains of all users. To optimize the SIM phase shifts, we propose a layer-by-layer iterative algorithm for optimizing the phase shifts in each layer alternately. Then, the minimum mean square error (MMSE) transmit precoding method is employed for the digital beamformer to support multi-user access. Furthermore, the mitigation of the SIM phase tuning error is also taken into account in the digital beamformer by exploiting its statistics. The power sharing ratio of each user is designed based on the iterative waterfilling power allocation algorithm. Additionally, our analytical results indicate that the spectral efficiency attained saturates in the high signal-to-noise ratio (SNR) region due to the phase tuning error resulting from the imperfect SIM hardware quality. The simulation results show that the SIM-aided holographic MIMO outperforms the state-of-the-art (SoA) single-layer holographic MIMO in terms of its achievable rate. We further demonstrate that the near-field channel model allows the SIM-based transceiver design to support multiple users, since the spatial resources represented both by the angle domain and the distance domain can be exploited.

Intelligent surfaces represent a breakthrough technology capable of customizing the wireless channel cost-effectively. However, the existing works generally focus on planar wavefront, neglecting near-field spherical wavefront characteristics caused by large array aperture and high operation frequencies in the terahertz (THz). Additionally, the single-layer reconfigurable intelligent surface (RIS) lacks the signal processing ability to mitigate the computational complexity at the base station (BS). To address this issue, we introduce a novel stacked intelligent metasurfaces (SIM) comprised of an array of programmable metasurface layers. The SIM aims to substitute conventional digital baseband architecture to execute computing tasks with ultra-low processing delay, albeit with a reduced number of radio-frequency (RF) chains and low-resolution digital-to-analog converters. In this paper, we present a SIM-aided multiuser multiple-input single-output (MU-MISO) near-field system, where the SIM is integrated into the BS to perform beamfocusing in the wave domain and customize an end-to-end channel with minimized inter-user interference. Finally, the numerical results demonstrate that near-field communication achieves superior spatial gain over the far-field, and the SIM effectively suppresses inter-user interference as the wireless signals propagate through it., Comment: 12 pages, 5 figures, presented at VTC Spring 2024, Singapore

The introduction of intelligent interconnectivity between the physical and human worlds has attracted great attention for future sixth-generation (6G) networks, emphasizing massive capacity, ultra-low latency, and unparalleled reliability. Wireless distributed networks and multi-agent reinforcement learning (MARL), both of which have evolved from centralized paradigms, are two promising solutions for the great attention. Given their distinct capabilities, such as decentralization and collaborative mechanisms, integrating these two paradigms holds great promise for unleashing the full power of 6G, attracting significant research and development attention. This paper provides a comprehensive study on MARL-assisted wireless distributed networks for 6G. In particular, we introduce the basic mathematical background and evolution of wireless distributed networks and MARL, as well as demonstrate their interrelationships. Subsequently, we analyze different structures of wireless distributed networks from the perspectives of homogeneous and heterogeneous. Furthermore, we introduce the basic concepts of MARL and discuss two typical categories, including model-based and model-free. We then present critical challenges faced by MARL-assisted wireless distributed networks, providing important guidance and insights for actual implementation. We also explore an interplay between MARL-assisted wireless distributed networks and emerging techniques, such as information bottleneck and mirror learning, delivering in-depth analyses and application scenarios. Finally, we outline several compelling research directions for future MARL-assisted wireless distributed networks.

In this paper, we investigate a competitive market involving two agents who consider both their own wealth and the wealth gap with their opponent. Both agents can invest in a financial market consisting of a risk-free asset and a risky asset, under conditions where model parameters are partially or completely unknown. This setup gives rise to a non-zero-sum differential game within the framework of reinforcement learning (RL). Each agent aims to maximize his own Choquet-regularized, time-inconsistent mean-variance objective. Adopting the dynamic programming approach, we derive a time-consistent Nash equilibrium strategy in a general incomplete market setting. Under the additional assumption of a Gaussian mean return model, we obtain an explicit analytical solution, which facilitates the development of a practical RL algorithm. Notably, the proposed algorithm achieves uniform convergence, even though the conventional policy improvement theorem does not apply to the equilibrium policy. Numerical experiments demonstrate the robustness and effectiveness of the algorithm, underscoring its potential for practical implementation.

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green's functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

We investigate properties of the emission region as revealed by drifting subpulses of opposite drift directions at different parts of a pulse profile by using the rotating carousel model in an obliquely rotating pulsar magnetosphere of multiple emission states. Subpulse emission is assumed coming from m discrete emission areas that are distributed around the magnetic axis on a rotating carousel. The flow rate of the emission areas is determined by the E x B drift in an emission state, designated by the parameter y, in which E and the associated flow rate are dependent on y. In this model, subpulses appear to drift in an emission state if a relative speed exists between the plasma flow and corotation, and the diversity in the drift rates and directions corresponds to the relative speed being different in different parts of a profile. We apply the model to three pulsars that exhibit drifting subpulses of opposite drift directions to identify the emission states and the values of m. Our results show that different drifting subpulses correspond to particular values of m and y, and the latter implies that different emission states can coexist and operate concurrently in an emission region. We find that m does not show clear dependency on either the obliquity angle or emission state. We demonstrate that subpulse arrangement may vary across an emission region meaning that it is not always uniform on a carousel. We discuss drifting subpulses of opposite drift directions and subpulse drift-rate switching in terms of different emission states in our model, and speculate that they may be two manifestations of the same underlying mechanism., Comment: 13 pages, 4 figures

We detail the emission behaviors of three long-period pulsars detected using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during the CRAFTS survey. Their rotational periods range from 1.83 s to 4.75 s, and the null fractions measure between 28% and 53%. PSR J1945+1211 and PSR J2323+1214 exhibited quasi-periodic nulls, with duration of around 57 seconds. The longest null was observed in PSR J1945+1211, lasting 76 seconds. PSR J2323+1214 displayed varying null fractions between its leading and trailing components. For the first time in PSR J2323+1214, we detected five dwarf pulses, which are much weaker and narrower pulses than typical burst pulses. In addition, we investigate the microstructure of PSR J1900-0134 for the first time, revealing intricate pulses of up to 2.05 milliseconds and noting its complex emission characteristics. Bright pulses occur in all of these sources at different rates. These observations suggest complex magnetospheric processes, potentially related to magnetic reconnections, and provide insights into the origins of bright and microstructure pulses, as well as their distinctions from ordinary pulses., Comment: 36 pages, 23 figures

The collective interactions of nanoparticles arranged in periodic structures give rise to high-$Q$ in-plane diffractive modes known as surface lattice resonances (SLRs). While these resonances and their broader implications have been extensively studied within the framework of classical electrodynamics and linear response theory, a quantum optical theory capable of describing the dynamics of these structures, especially in the presence of material nonlinearities, beyond the use of \textit{ad hoc} few-mode approximations is largely missing. To this end, we consider here a lattice of metallic nanoparticles coupled to the background electromagnetic field and derive the quantum input-output relations within the electric dipole approximation. As a first application, we discuss coupling of the nanoparticle array to an external array of emitters and illustrate how the formalism can be extended to molecular optomechanics. Specifically, we show how the high $Q$-factors provided by SLRs can facilitate coupling between the nanoparticle array and collective vibrational modes. Finally, we examine a scenario in which the metallic nanoparticles themselves are replaced with saturable excitonic emitters, illustrating how the nonlinearity of the emitters can be used to switch the SLR condition between different electronic transitions. Our work provides a seamless framework to rigorously model SLR phenomena with quantum emitters without resorting to phenomenological treatments of either the electromagnetic environment or the material degrees of freedom., Comment: 9 pages main text (5 figures), 6 pages Supplement (1 figure)

Rydberg atomic quantum receivers exhibit great potential in assisting classical wireless communications due to their outstanding advantages in detecting radio frequency signals. To realize this potential, we integrate a Rydberg atomic quantum receiver into a classical multi-user multiple-input multiple-output (MIMO) scheme to form a multi-user Rydberg atomic quantum MIMO (RAQ-MIMO) system for the uplink. To study this system, we first construct an equivalent baseband signal model, which facilitates convenient system design, signal processing and optimizations. We then study the ergodic achievable rates under both the maximum ratio combining (MRC) and zero-forcing (ZF) schemes by deriving their tight lower bounds. We next compare the ergodic achievable rates of the RAQ-MIMO and the conventional massive MIMO schemes by offering a closed-form expression for the difference of their ergodic achievable rates, which allows us to directly compare the two systems. Our results show that RAQ-MIMO allows the average transmit power of users to be $> 25$ dBm lower than that of the conventional massive MIMO. Viewed from a different perspective, an extra $\sim 8.8$ bits/s/Hz/user rate becomes achievable by ZF RAQ-MIMO., Comment: 6 pages, 4 figures, accepted by 2025 IEEE International Conference on Communications (ICC 2025)

Terahertz (THz) communication combined with ultra-massive multiple-input multiple-output (UM-MIMO) technology is promising for 6G wireless systems, where fast and precise direction-of-arrival (DOA) estimation is crucial for effective beamforming. However, finding DOAs in THz UM-MIMO systems faces significant challenges: while reducing hardware complexity, the hybrid analog-digital (HAD) architecture introduces inherent difficulties in spatial information acquisition the large-scale antenna array causes significant deviations in eigenvalue decomposition results; and conventional two-dimensional DOA estimation methods incur prohibitively high computational overhead, hindering fast and accurate realization. To address these challenges, we propose a hybrid dynamic subarray (HDS) architecture that strategically divides antenna elements into subarrays, ensuring phase differences between subarrays correlate exclusively with single-dimensional DOAs. Leveraging this architectural innovation, we develop two efficient algorithms for DOA estimation: a reduced-dimension MUSIC (RD-MUSIC) algorithm that enables fast processing by correcting large-scale array estimation bias, and an improved version that further accelerates estimation by exploiting THz channel sparsity to obtain initial closed-form solutions through specialized two-RF-chain configuration. Furthermore, we develop a theoretical framework through Cram\'{e}r-Rao lower bound analysis, providing fundamental insights for different HDS configurations. Extensive simulations demonstrate that our solution achieves both superior estimation accuracy and computational efficiency, making it particularly suitable for practical THz UM-MIMO systems.

Cryptocurrencies are widely used, yet current methods for analyzing transactions heavily rely on opaque, black-box models. These lack interpretability and adaptability, failing to effectively capture behavioral patterns. Many researchers, including us, believe that Large Language Models (LLMs) could bridge this gap due to their robust reasoning abilities for complex tasks. In this paper, we test this hypothesis by applying LLMs to real-world cryptocurrency transaction graphs, specifically within the Bitcoin network. We introduce a three-tiered framework to assess LLM capabilities: foundational metrics, characteristic overview, and contextual interpretation. This includes a new, human-readable graph representation format, LLM4TG, and a connectivity-enhanced sampling algorithm, CETraS, which simplifies larger transaction graphs. Experimental results show that LLMs excel at foundational metrics and offer detailed characteristic overviews. Their effectiveness in contextual interpretation suggests they can provide useful explanations of transaction behaviors, even with limited labeled data.

Advanced Air Mobility (AAM) is transforming transportation systems by extending them into near-ground airspace, offering innovative solutions to mobility challenges. In this space, electric vertical take-off and landing vehicles (eVTOLs) perform a variety of tasks to improve aviation safety and efficiency, such as collaborative computing and perception. However, eVTOLs face constraints such as compacted shape and restricted onboard computing resources. These limitations necessitate task offloading to nearby high-performance base stations (BSs) for timely processing. Unfortunately, the high mobility of eVTOLs, coupled with their restricted flight airlines and heterogeneous resource management creates significant challenges in dynamic task offloading. To address these issues, this paper introduces a novel network slice-based Low-Altitude Intelligent Network (LAIN) framework for eVTOL tasks. By leveraging advanced network slicing technologies from 5G/6G, the proposed framework dynamically adjusts communication bandwidth, beam alignment, and computing resources to meet fluctuating task demands. Specifically, the framework includes an access pairing method to pre-schedule optimal eVTOL-BS-slice assignments, a pre-assessment algorithm to avoid resource waste, and a deep reinforcement learning-based slice orchestration mechanism to optimize resource allocation and lifecycle management. Simulation results demonstrate that the proposed framework outperforms existing benchmarks in terms of resource allocation efficiency and operational/violation costs across varying eVTOL velocities. This work provides valuable insights into intelligent network slicing for future AAM transportation systems.

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is essential in multipath wireless communication scenarios, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method attained improved estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical multipath environments.

Accurate indoor pathloss prediction is crucial for optimizing wireless communication in indoor settings, where diverse materials and complex electromagnetic interactions pose significant modeling challenges. This paper introduces TransPathNet, a novel two-stage deep learning framework that leverages transformer-based feature extraction and multiscale convolutional attention decoding to generate high-precision indoor radio pathloss maps. TransPathNet demonstrates state-of-the-art performance in the ICASSP 2025 Indoor Pathloss Radio Map Prediction Challenge, achieving an overall Root Mean Squared Error (RMSE) of 10.397 dB on the challenge full test set and 9.73 dB on the challenge Kaggle test set, showing excellent generalization capabilities across different indoor geometries, frequencies, and antenna patterns. Our project page, including the associated code, is available at https://lixin.ai/TransPathNet/., Comment: Accepted to ICASSP 2025

As wireless communication advances toward the 6G era, the demand for ultra-reliable, high-speed, and ubiquitous connectivity is driving the exploration of new degrees-of-freedom (DoFs) in communication systems. Among the key enabling technologies, Movable Antennas (MAs) integrated into Flexible Cylindrical Arrays (FCLA) have shown great potential in optimizing wireless communication by providing spatial flexibility. This paper proposes an innovative optimization framework that leverages the dynamic mobility of FCLAs to improve communication rates and overall system performance. By employing Fractional Programming (FP) for alternating optimization of beamforming and antenna positions, the system enhances throughput and resource utilization. Additionally, a novel Constrained Grid Search-Based Adaptive Moment Estimation Algorithm (CGS-Adam) is introduced to optimize antenna positions while adhering to antenna spacing constraints. Extensive simulations validate that the proposed system, utilizing movable antennas, significantly outperforms traditional fixed antenna optimization, achieving up to a 31\% performance gain in general scenarios. The integration of FCLAs in wireless networks represents a promising solution for future 6G systems, offering improved coverage, energy efficiency, and flexibility.

Orbital angular momentum (OAM) can enhance the spectral efficiency by multiplying a set of orthogonal modes on the same frequency channel. To maintain the orthogonal among different OAM modes, perfect alignments between transmitters and receivers are strictly required. However, in multi-user OAM communications, the perfect alignments between the transmitter and all the receivers are impossible. The phase turbulence, caused by misaligned transmitters and receivers, leads to serious inter-mode interference, which greatly degrades the capacity of OAM transmissions. To eliminate the negative effects of phase turbulence and further enhance the transmission capacity, we introduce RIS into the system, and propose a joint precoder and reflector design for reconfigurable intelligent surface (RIS)-assisted multi-user OAM communication systems. Specifically, we propose a three-layer design at the transmitter side, which includes inter-user OAM mode interference cancellation, inter-mode self-interference elimination and the power allocation among different users. By analyzing the characteristics of the overall channels, we are able to give the specific expressions of the precoder designs, which significantly reduce the optimization complexity. We further leverage RIS to guarantee the line-ofsight (LoS) transmissions between the transmitter and users for better sum rate performance. To verify the superiority of the proposed multi-user OAM transmission system, we compare it with traditional MIMO transmission schemes, numerical results have shown that our proposed design can achieve better sum rate performance due to the well-designed orthogonality among different users and OAM modes.

We compare three filtrations of the tope space of an oriented matroid. The first is the dual Varchenko-Gelfand degree filtration, the second filtration is from Kalinin's spectral sequence, and the last one derives from Quillen's augmentation filtration. We show that all three filtrations and the respective maps coincide over $\mathbb{Z}/ 2\mathbb{Z}$. We also show that the dual Varchenko-Gelfand degree filtration can be made into a filtration of the $\mathbb{Z}$-sign cosheaf on the fan of the underlying matroid. This was previously carried out with $\mathbb{Z}/ 2\mathbb{Z}$-coefficients by the first author and Renaudineau using the Quillen filtration and has applications to real algebraic geometry via patchworking.

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver's responses to the radio frequency (RF) signal, on the potential noise sources, and on the system performance. Next, we investigate the association distortion effects that might occur, specifically demonstrating the boundaries of linear dynamic regions, which provides critical insights into its practical implementations in wireless systems. Extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that they deliver complementary benefits: LO-free systems excel in proximity operations, while LO-dressed systems are eminently suitable for long-distance sensing at extremely low power levels. More specifically, LO-dressed systems achieve a significant signal-to-noise ratio (SNR) gain of approximately 44 dB over conventional RF receivers, exhibiting an effective coverage range extension over conventional RF receivers by a factor of 150. Furthermore, LO-dressed systems support higher-order quadrature amplitude modulation (QAM) at reduced symbol error rates (SER) compared to conventional RF receivers, hence significantly enhancing wireless communication performance., Comment: This manuscript has been submitted to IEEE journal, with 13 pages of body and 2 pages of supplementary material

Current Multilingual ASR models only support a fraction of the world's languages. Continual Learning (CL) aims to tackle this problem by adding new languages to pre-trained models while avoiding the loss of performance on existing languages, also known as Catastrophic Forgetting (CF). However, existing CL methods overlook the adaptation of the token embedding lookup table at the decoder, despite its significant contribution to CF. We propose Embedding Layer Surgery where separate copies of the token embeddings are created for each new languages, and one of the copies is selected to replace the old languages embeddings when transcribing the corresponding new language. Unfortunately, this approach means LID errors also cause incorrect ASR embedding selection. Our Task-wise Beam Search allows self-correction for such mistakes. By adapting Whisper to 10 hours of data for each of 10 unseen languages from Common Voice, results show that our method reduces the Average WER (AWER) of pre-trained languages from 14.2% to 11.9% compared with Experience Replay, without compromising the AWER of the unseen languages., Comment: Published in 2024 IEEE Spoken Language Technology Workshop

We demonstrate the merits of using a neuromorphic, or event-based camera (EBC), for tracking of both passive and active matter. For passive matter, we tracked the Brownian motion of different micro-particles and estimated their diffusion coefficient. For active matter, we explored the case of tracking murine spermatozoa and extracted motility parameters from the motion of cells. This has applications in enhancing outcomes for clinical fertility treatments. Using the EBC, we obtain results equivalent to those from an sCMOS camera, yet achieve a reduction in file size of up to two orders of magnitude. This is important in the modern computer era, as it reduces data throughput, and is well-aligned with edge-computing applications. We believe the EBC is an excellent choice, particularly for long-term studies of active matter.

Reconfigurable arrays of trapped single atoms are an excellent platform for the simulation of many-body physics and the realisation of high-fidelity quantum gates. The confinement of atoms is often achieved with focussed laser beams acting as optical dipole-force traps that allow for both static and dynamic positioning of atoms. In these traps, light-assisted collisions -- enhancing the two-atom loss rate -- ensure that single atom occupation of traps can be realised. However, the time-averaged probability of trapping a single atom is limited to $0.5$ when loading directly from a surrounding cloud of laser-cooled atoms, preventing deterministic filling of large arrays. In this work, we demonstrate that increasing the depth of a static, optical dipole trap enables the transition from fast loading on a timescale of $2.1\,$s to an extended trap lifetime of $7.9\,$s. This method demonstrates an achievable filling ratio of $(79\pm2)\,\%$ without the need of rearranging atoms to fill vacant traps., Comment: 7 pages, 6 figures

This study develops a question-answering system based on Retrieval-Augmented Generation (RAG) using Chinese Wikipedia and Lawbank as retrieval sources. Using TTQA and TMMLU+ as evaluation datasets, the system employs BGE-M3 for dense vector retrieval to obtain highly relevant search results and BGE-reranker to reorder these results based on query relevance. The most pertinent retrieval outcomes serve as reference knowledge for a Large Language Model (LLM), enhancing its ability to answer questions and establishing a knowledge retrieval system grounded in generative AI. The system's effectiveness is assessed through a two-stage evaluation: automatic and assisted performance evaluations. The automatic evaluation calculates accuracy by comparing the model's auto-generated labels with ground truth answers, measuring performance under standardized conditions without human intervention. The assisted performance evaluation involves 20 finance-related multiple-choice questions answered by 20 participants without financial backgrounds. Initially, participants answer independently. Later, they receive system-generated reference information to assist in answering, examining whether the system improves accuracy when assistance is provided. The main contributions of this research are: (1) Enhanced LLM Capability: By integrating BGE-M3 and BGE-reranker, the system retrieves and reorders highly relevant results, reduces hallucinations, and dynamically accesses authorized or public knowledge sources. (2) Improved Data Privacy: A customized RAG architecture enables local operation of the LLM, eliminating the need to send private data to external servers. This approach enhances data security, reduces reliance on commercial services, lowers operational costs, and mitigates privacy risks., Comment: 8 pages, 13 figures, 1 table

Integrated sensing and communication (ISAC) has become an attractive technology for future wireless networks. In this paper, we propose a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided dynamic scatterers tracking scheme for ISAC in high mobility millimeter wave communication systems, where the STAR-RIS is employed to provide communication service for indoor user with the base station (BS) and simultaneously sense and track the interested outdoor dynamic scatterers. Specifically, we resort to an active STAR-RIS to respectively receive and further deal with the impinging signal from its double sides at the same time. Then, we develop a transmission strategy with the activation scheme of the STAR-RIS elements, and construct the signal models within the system. After acquiring the channel parameters related to the BS-RIS channel, the dynamic paths can be identified from all the scattering paths, and the dynamic targets can be classified with respect to their radar cross sections. We further track the outdoor scatterers at STAR-RIS by resorting to the Gaussian mixture-probability hypothesis density filter. With the tracked locations of the outdoor scatterers, a beam prediction strategy for both the precoder of BS and the refraction phase shift vector of STAR-RIS is developed to enhance the communication performance of the indoor user. Besides, a target mismatch detection and path collision prediction mechanism is proposed to reduce the training overhead and improve the transmission performance. Finally, the feasibility and effectiveness of our proposed STAR-RIS aided dynamic scatterers tracking scheme for ISAC are demonstrated and verified via simulation results., Comment: 14 pages, 14 figures

Quantum sensing technologies have experienced rapid progresses since entering the `second quantum revolution'. Among various candidates, schemes relying on Rydberg atoms exhibit compelling advantages for detecting radio frequency signals. Based on this, Rydberg atomic quantum receivers (RAQRs) have emerged as a promising solution to classical wireless communication and sensing. To harness the advantages and exploit the potential of RAQRs in wireless sensing, we investigate the realization of the direction of arrival (DOA) estimation by RAQRs. Specifically, we first conceive a Rydberg atomic quantum uniform linear array (RAQ-ULA) aided receiver for multi-target detection and propose the corresponding signal model of this sensing system. Furthermore, we propose the Rydberg atomic quantum estimation of signal parameters by designing a rotational invariance based technique termed as RAQ-ESPRIT relying on our model. The proposed algorithm solves the sensor gain mismatch problem, which is due to the presence of the RF local oscillator in the RAQ-ULA and cannot be well addressed by using the conventional ESPRIT. Lastly, we characterize our scheme through numerical simulations., Comment: 5 pages, 2 figures, submitted for possible publication on IEEE journal

Electric Vertical Take-off and Landing vehicles (eVTOLs) are driving Advanced Air Mobility (AAM) toward transforming urban transportation by extending travel from congested ground networks to low-altitude airspace. This transition promises to reduce traffic congestion and significantly shorten commute times. To ensure aviation safety, eVTOLs must fly within prescribed flight corridors. These corridors are managed by ground-based Air Traffic Control (ATCo) stations, which oversee air-ground communication and flight scheduling. However, one critical challenge remains: the lack of high rate air-ground communication and safe flight planning within these corridors. The introduction of 6G-oriented Stacked Intelligent Metasurface (SIM) technology presents a high rate communication solution. With advanced phase-shifting capabilities, SIM enables precise wireless signal control and supports beam-tracking communication with eVTOLs. Leveraging this technology, we propose a Composite Potential Field (CPF) approach. This method dynamically integrates target, separation, and communication fields to optimize both SIM communication efficiency and flight safety. Simulation results validate the effectiveness of this DT-based approach. Compared to the potential field flight control benchmark, it improves the transmission rate by 8.3\%. Additionally, it reduces flight distance deviation from the prescribed corridor by 10\% compared to predetermined optimization methods., Comment: 15 pages, 11 figures

In the field of multi-access edge computing (MEC), efficient computation offloading is crucial for improving resource utilization and reducing latency in dynamically changing environments. This paper introduces a new approach, termed as Multi-Task Domain Adaptation (MTDA), aiming to enhance the ability of computational offloading models to generalize in the presence of domain shifts, i.e., when new data in the target environment significantly differs from the data in the source domain. The proposed MTDA model incorporates a teacher-student architecture that allows continuous adaptation without necessitating access to the source domain data during inference, thereby maintaining privacy and reducing computational overhead. Utilizing a multi-task learning framework that simultaneously manages offloading decisions and resource allocation, the proposed MTDA approach outperforms benchmark methods regarding mean squared error and accuracy, particularly in environments with increasing numbers of users. It is observed by means of computer simulation that the proposed MTDA model maintains high performance across various scenarios, demonstrating its potential for practical deployment in emerging MEC applications.

Fluid antenna systems (FAS) enable dynamic antenna positioning, offering new opportunities to enhance integrated sensing and communication (ISAC) performance. However, existing studies primarily focus on communication enhancement or single-target sensing, leaving multi-target scenarios underexplored. Additionally, the joint optimization of beamforming and antenna positions poses a highly non-convex problem, with traditional methods becoming impractical as the number of fluid antennas increases. To address these challenges, this letter proposes a block coordinate descent (BCD) framework integrated with a deep reinforcement learning (DRL)-based approach for intelligent antenna positioning. By leveraging the deep deterministic policy gradient (DDPG) algorithm, the proposed framework efficiently balances sensing and communication performance. Simulation results demonstrate the scalability and effectiveness of the proposed approach.

Movable antennas (MAs) enhance flexibility in beamforming gain and interference suppression by adjusting position within certain areas of the transceivers. In this paper, we propose an MA-assisted integrated sensing and communication framework, wherein MAs are deployed for reconfiguring the channel array responses at both the receiver and transmitter of a base station. Then, we develop an optimization framework aimed at maximizing the sensing signal-to-interference-plus-noise-ratio (SINR) by jointly optimizing the receive beamforming vector, the transmit beamforming matrix, and the positions of MAs while meeting the minimum SINR requirement for each user. To address this nonconvex problem involving complex coupled variables, we devise an alternating optimization-based algorithm that incorporates techniques including the Charnes-Cooper transform, second-order Taylor expansion, and successive convex approximation (SCA). Specifically, the closed form of the received vector and the optimal transmit matrix can be first obtained in each iteration. Subsequently, the solutions for the positions of the transmit and receive MAs are obtained using the SCA method based on the second-order Taylor expansion. The simulation results show that the proposed scheme has significant advantages over the other baseline schemes. In particular, the proposed scheme has the ability to match the performance of the fixed position antenna scheme while utilizing fewer resources.

Fairly allocating indivisible goods is a frequently occurring task in everyday life. Given an initial allocation of the goods, we consider the problem of reforming it via a sequence of exchanges to attain fairness in the form of envy-freeness up to one good (EF1). We present a vast array of results on the complexity of determining whether it is possible to reach an EF1 allocation from the initial allocation and, if so, the minimum number of exchanges required. In particular, we uncover several distinctions based on the number of agents involved and their utility functions. Furthermore, we derive essentially tight bounds on the worst-case number of exchanges needed to achieve EF1.

This research proposes a novel approach utilizing Orbital Angular Momentum (OAM) beams to enhance Radar Cross Section (RCS) diversity for target detection in future transportation systems. Unlike conventional OAM beams with hollow-shaped divergence patterns, the new proposed OAM beams provide uniform illumination across the target without a central energy void, but keep the inherent phase gradient of vortex property. We utilize waveguide slot antennas to generate four different modes of these novel OAM beams at X-band frequency. Furthermore, these different mode OAM beams are used to illuminate metal models, and the resulting RCS is compared with that obtained using plane waves. The findings reveal that the novel OAM beams produce significant azimuthal RCS diversity, providing a new approach for the detection of weak and small targets.This study not only reveals the RCS diversity phenomenon based on novel OAM beams of different modes but also addresses the issue of energy divergence that hinders traditional OAM beams in long-range detection applications.

In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communication rate among all users by jointly optimizing the BS's transmit beamforming, the positions of its transmit fluid antennas, and the positions of the CUs' receive fluid antennas. To address this non-convex problem, we propose a block coordinate descent (BCD) algorithm integrating semidefinite relaxation (SDR), rank-one constraint relaxation (SRCR), successive convex approximation (SCA), and majorization-minimization (MM). Simulation results demonstrate that FAS significantly enhances system performance and robustness, with notable gains when both the BS and CUs are equipped with fluid antennas. Even under low transmit power conditions, deploying FAS at the BS alone yields substantial performance gains. However, the effectiveness of FAS depends on the availability of sufficient movement space, as space constraints may limit its benefits compared to fixed antenna strategies. Our findings highlight the potential of FAS to mitigate HIs and enhance multi-user system performance, while emphasizing the need for practical deployment considerations.

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for Network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, Network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. In the final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Allocating indivisible goods is a ubiquitous task in fair division. We study additive welfarist rules, an important class of rules which choose an allocation that maximizes the sum of some function of the agents' utilities. Prior work has shown that the maximum Nash welfare (MNW) rule is the unique additive welfarist rule that guarantees envy-freeness up to one good (EF1). We strengthen this result by showing that MNW remains the only additive welfarist rule that ensures EF1 for identical-good instances, two-value instances, as well as normalized instances with three or more agents. On the other hand, if the agents' utilities are integers, we demonstrate that several other rules offer the EF1 guarantee, and provide characterizations of these rules for various classes of instances., Comment: Appears in the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2025

Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across a wide range of multimodal tasks. However, fine-tuning these models for domain-specific applications remains a computationally intensive challenge. This paper introduces State Space Memory Integration (SSMI), a novel approach for efficient fine-tuning of LVLMs. By integrating lightweight Mamba-based state space modules into the LVLM architecture, SSMI captures long-range dependencies and injects task-specific visual and sequential patterns effectively. Unlike traditional fine-tuning methods, SSMI requires only a fraction of the model's parameters to be updated, making it computationally efficient and scalable. Experiments on benchmark datasets, including COCO Captioning, VQA, and Flickr30k, demonstrate that SSMI achieves state-of-the-art performance while maintaining robustness and generalization capabilities. Comprehensive analysis further validates the advantages of SSMI in terms of efficiency, adaptability, and interpretability, positioning it as a compelling solution for fine-tuning large-scale vision-language models.