arXiv subjects

Xiang Cheng

Publications and source records attributed to Xiang Cheng.

At least 127 records · Page 7Linked to original sources

arXiv2024

A LiDAR-Aided Channel Model for Vehicular Intelligent Sensing-Communication Integration

In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed LA-GBSM approach, a new millimeter wave (mmWave) channel model for sixth-generation (6G) vehicular intelligent sensing-communication integration is proposed, which can support the design of intelligent transportation systems (ITSs). The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time. Specifically, by detecting dynamic vehicles and static building/tress through LiDAR point clouds via machine learning, scatterers are divided into static and dynamic scatterers. Furthermore, statistical distributions of parameters, e.g., distance, angle, number, and power, related to static and dynamic scatterers are quantified under high, medium, and low VTD conditions. To mimic channel non-stationarity and consistency, based on the quantified statistical distributions, a new visibility region (VR)-based algorithm in consideration of newly generated static/dynamic scatterers is developed. Key channel statistics are derived and simulated. By comparing simulation results and ray-tracing (RT)-based results, the utility of the proposed LA-GBSM is verified.

eess.SP↗

arXiv2024

Superposed IM-OFDM (S-IM-OFDM): An Enhanced OFDM for Integrated Sensing and Communications

Integrated sensing and communications (ISAC) is a critical enabler for emerging 6G applications, and at its core lies in the dual-functional waveform design. While orthogonal frequency division multiplexing (OFDM) has been a popular basic waveform, its primitive version falls short in sensing due to the inherent unregulated auto-correlation properties. Furthermore, the sensitivity to Doppler shift hinders its broader applications in dynamic scenarios. To address these issues, we propose a superposed index-modulated OFDM (S-IM-OFDM). The proposed scheme improves the sensing performance without excess power consumption by translating the energy efficiency of IM-OFDM onto sensing-oriented signals over OFDM. Also, it maintains excellent communication performance in time-varying channels by leveraging the sensed parameters to compensate for Doppler. Compared to conventional OFDM, the proposed S-IM-OFDM waveform exhibits better sensing capabilities and wider applicability in dynamic scenarios. Both theoretical analyses and simulations corroborate its dual benefits.

eess.SP↗

arXiv2024

Beam Pattern Modulation Embedded mmWave Hybrid Transceiver Design Towards ISAC

Integrated Sensing and Communication (ISAC) emerges as a promising technology for B5G/6G, particularly in the millimeter-wave (mmWave) band. However, the widespread adoption of hybrid architecture in mmWave systems compromises multiplexing gain due to limited radio-frequency chains, resulting in mediocre performance when embedding sensing functionality. To avoid sacrificing the spectrum efficiency in hybrid structures while addressing performance bottlenecks in its extension to ISAC, we present an optimized beam pattern modulation-embedded ISAC (BPM-ISAC). BPM-ISAC applies index modulation over beamspace by selectively activating communication beams, aiming to minimize sensing beampattern mean squared error (MSE) under communication MSE constraints through dedicated hybrid transceiver design. Optimization involves the analog part through a min-MSE-based beam selection algorithm, followed by the digital part using an alternating optimization algorithm. Convergence and asymptotic pairwise error probability (APEP) analyses accompany numerical simulations, validating its overall enhanced ISAC performance over existing alternatives.

eess.SP↗

arXiv2024

A Survey on Federated Learning in Intelligent Transportation Systems

The development of Intelligent Transportation System (ITS) has brought about comprehensive urban traffic information that not only provides convenience to urban residents in their daily lives but also enhances the efficiency of urban road usage, leading to a more harmonious and sustainable urban life. Typical scenarios in ITS mainly include traffic flow prediction, traffic target recognition, and vehicular edge computing. However, most current ITS applications rely on a centralized training approach where users upload source data to a cloud server with high computing power for management and centralized training. This approach has limitations such as poor real-time performance, data silos, and difficulty in guaranteeing data privacy. To address these limitations, federated learning (FL) has been proposed as a promising solution. In this paper, we present a comprehensive review of the application of FL in ITS, with a particular focus on three key scenarios: traffic flow prediction, traffic target recognition, and vehicular edge computing. For each scenario, we provide an in-depth analysis of its key characteristics, current challenges, and specific manners in which FL is leveraged. Moreover, we discuss the benefits that FL can offer as a potential solution to the limitations of the centralized training approach currently used in ITS applications.

cs.NI↗

arXiv2024

Linear attention is (maybe) all you need (to understand transformer optimization)

Transformer training is notoriously difficult, requiring a careful design of optimizers and use of various heuristics. We make progress towards understanding the subtleties of training Transformers by carefully studying a simple yet canonical linearized shallow Transformer model. Specifically, we train linear Transformers to solve regression tasks, inspired by J.~von Oswald et al.~(ICML 2023), and K.~Ahn et al.~(NeurIPS 2023). Most importantly, we observe that our proposed linearized models can reproduce several prominent aspects of Transformer training dynamics. Consequently, the results obtained in this paper suggest that a simple linearized Transformer model could actually be a valuable, realistic abstraction for understanding Transformer optimization.

cs.LG↗

arXiv2024

Efficient Sampling on Riemannian Manifolds via Langevin MCMC

We study the task of efficiently sampling from a Gibbs distribution $d π^* = e^{-h} d {vol}_g$ over a Riemannian manifold $M$ via (geometric) Langevin MCMC; this algorithm involves computing exponential maps in random Gaussian directions and is efficiently implementable in practice. The key to our analysis of Langevin MCMC is a bound on the discretization error of the geometric Euler-Murayama scheme, assuming $\nabla h$ is Lipschitz and $M$ has bounded sectional curvature. Our error bound matches the error of Euclidean Euler-Murayama in terms of its stepsize dependence. Combined with a contraction guarantee for the geometric Langevin Diffusion under Kendall-Cranston coupling, we prove that the Langevin MCMC iterates lie within $ε$-Wasserstein distance of $π^*$ after $\tilde{O}(ε^{-2})$ steps, which matches the iteration complexity for Euclidean Langevin MCMC. Our results apply in general settings where $h$ can be nonconvex and $M$ can have negative Ricci curvature. Under additional assumptions that the Riemannian curvature tensor has bounded derivatives, and that $π^*$ satisfies a $CD(\cdot,\infty)$ condition, we analyze the stochastic gradient version of Langevin MCMC, and bound its iteration complexity by $\tilde{O}(ε^{-2})$ as well.

math.ST↗

arXiv2024

Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions

MCMC algorithms offer empirically efficient tools for sampling from a target distribution $π(x) \propto \exp(-V(x))$. However, on the theory side, MCMC algorithms suffer from slow mixing rate when $π(x)$ is non-log-concave. Our work examines this gap and shows that when Poincaré-style inequality holds on a subset $\mathcal{X}$ of the state space, the conditional distribution of MCMC iterates over $\mathcal{X}$ mixes fast to the true conditional distribution. This fast mixing guarantee can hold in cases when global mixing is provably slow. We formalize the statement and quantify the conditional mixing rate. We further show that conditional mixing can have interesting implications for sampling from mixtures of Gaussians, parameter estimation for Gaussian mixture models and Gibbs-sampling with well-connected local minima.

cs.LG↗

arXiv2023

Single site-controlled inverted pyramidal InGaAs QD-nanocavity operating at the onset of the strong coupling regime

Precise positioning of single site-controlled inverted pyramidal InGaAs QD at the antinode of a GaAs photonic crystal cavity with nanometer-scale accuracy holds unique advantages compared to self-assembled QDs and offers great promise for practical on-chip photonic quantum information processing. However, the strong coupling regime in this geometry has not yet been achieved due to the low cavity Q-factor based on the (111)B-oriented membrane structures. Here, we reveal the onset of phonon-mediated coherent exciton-photon interaction on our tailored single site-controlled InGaAs QD - photonic crystal cavity. Our results present a Rabi-like oscillation of luminescence intensity between excitonic and photonic components correlated with their energy splitting pronounced at small detuning. Such Rabi-like oscillation is well reproduced by modeling the coherent exchange of the exciton-photon population. The modeling further reveals an oscillatory two-time covariance at QD-cavity resonance, which indicates that the system operates at the onset of the strong coupling regime. Moreover, by using the cavity mode as a probe of the virtual state of the QD induced by phonon scattering, it reveals an increase in phonon scattering rates near the QD-cavity resonance and asymmetric phonon emission and absorption rate even around 50 K.

cond-mat.mes-hall↗

arXiv2023

Novel 3D Geometry-Based Stochastic Models for Non-Isotropic MIMO Vehicle-to-Vehicle Channels

This paper proposes a novel three-dimensional (3D) theoretical regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels. The proposed RS-GBSM, combining line-of-sight (LoS) components, a two-sphere model, and an elliptic-cylinder model, has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics, and jointly considers the azimuth and elevation angles by using the von Mises Fisher distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding two-dimensional (2D) model. It is demonstrated that the 3D model is more accurate to characterize real V2V channels, in particular for pico cell scenarios. Finally, close agreement is achieved between the theoretical model, SoS simulation model, and simulation results, demonstrating the utility of the proposed models.

eess.SP↗

arXiv2023

Neuromorphic Incremental on-chip Learning with Hebbian Weight Consolidation

As next-generation implantable brain-machine interfaces become pervasive on edge device, incrementally learning new tasks in bio-plasticity ways is urgently demanded for Neuromorphic chips. Due to the inherent characteristics of its structure, spiking neural networks are naturally well-suited for BMI-chips. Here we propose Hebbian Weight Consolidation, as well as an on-chip learning framework. HWC selectively masks synapse modifications for previous tasks, retaining them to store new knowledge from subsequent tasks while preserving the old knowledge. Leveraging the bio-plasticity of dendritic spines, the intrinsic self-organizing nature of Hebbian Weight Consolidation aligns naturally with the incremental learning paradigm, facilitating robust learning outcomes. By reading out spikes layer by layer and performing back-propagation on the external micro-controller unit, MLoC can efficiently accomplish on-chip learning. Experiments show that our HWC algorithm up to 23.19% outperforms lower bound that without incremental learning algorithm, particularly in more challenging monkey behavior decoding scenarios. Taking into account on-chip computing on Synsense Speck 2e chip, our proposed algorithm exhibits an improvement of 11.06%. This study demonstrates the feasibility of employing incremental learning for high-performance neural signal decoding in next-generation brain-machine interfaces.

cs.ET↗

arXiv2023

Theory and Algorithms for Diffusion Processes on Riemannian Manifolds

We study geometric stochastic differential equations (SDEs) and their approximations on Riemannian manifolds. In particular, we introduce a simple new construction of geometric SDEs, using which with bounded curvature. In particular, we provide the first (to our knowledge) non-asymptotic bound on the error of the geometric Euler-Murayama discretization. We then bound the distance between the exact SDE and a discrete geometric random walk, where the noise can be non-Gaussian; this analysis is useful for using geometric SDEs to model naturally occurring discrete non-Gaussian stochastic processes. Our results provide convenient tools for studying MCMC algorithms that adopt non-standard noise distributions.

math.PR↗

arXiv2023

Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines

In the era of sixth-generation (6G) wireless communications, integrated sensing and communications (ISAC) is recognized as a promising solution to upgrade the physical system by endowing wireless communications with sensing capability. Existing ISAC is mainly oriented to static scenarios with radio-frequency (RF) sensors being the primary participants, thus lacking a comprehensive environment feature characterization and facing a severe performance bottleneck in dynamic environments. To date, extensive surveys on ISAC have been conducted but are limited to summarizing RF-based radar sensing. Currently, some research efforts have been devoted to exploring multi-modal sensing-communication integration but still lack a comprehensive review. Therefore, we generalize the concept of ISAC inspired by human synesthesia to establish a unified framework of intelligent multi-modal sensing-communication integration and provide a comprehensive review under such a framework in this paper. The so-termed Synesthesia of Machines (SoM) gives the clearest cognition of such intelligent integration and details its paradigm for the first time. We commence by justifying the necessity of the new paradigm. Subsequently, we offer a definition of SoM and zoom into the detailed paradigm, which is summarized as three operation modes. To facilitate SoM research, we overview the prerequisite of SoM research, i.e., mixed multi-modal (MMM) datasets. Then, we introduce the mapping relationships between multi-modal sensing and communications. Afterward, we cover the technological review on SoM-enhance-based and SoM-concert-based applications. To corroborate the superiority of SoM, we also present simulation results related to dual-function waveform and predictive beamforming design. Finally, we propose some potential directions to inspire future research efforts.

eess.SP↗

arXiv2023

Transformers learn to implement preconditioned gradient descent for in-context learning

Several recent works demonstrate that transformers can implement algorithms like gradient descent. By a careful construction of weights, these works show that multiple layers of transformers are expressive enough to simulate iterations of gradient descent. Going beyond the question of expressivity, we ask: Can transformers learn to implement such algorithms by training over random problem instances? To our knowledge, we make the first theoretical progress on this question via an analysis of the loss landscape for linear transformers trained over random instances of linear regression. For a single attention layer, we prove the global minimum of the training objective implements a single iteration of preconditioned gradient descent. Notably, the preconditioning matrix not only adapts to the input distribution but also to the variance induced by data inadequacy. For a transformer with $L$ attention layers, we prove certain critical points of the training objective implement $L$ iterations of preconditioned gradient descent. Our results call for future theoretical studies on learning algorithms by training transformers.

cs.LG↗

arXiv2023

Restart Sampling for Improving Generative Processes

Generative processes that involve solving differential equations, such as diffusion models, frequently necessitate balancing speed and quality. ODE-based samplers are fast but plateau in performance while SDE-based samplers deliver higher sample quality at the cost of increased sampling time. We attribute this difference to sampling errors: ODE-samplers involve smaller discretization errors while stochasticity in SDE contracts accumulated errors. Based on these findings, we propose a novel sampling algorithm called Restart in order to better balance discretization errors and contraction. The sampling method alternates between adding substantial noise in additional forward steps and strictly following a backward ODE. Empirically, Restart sampler surpasses previous SDE and ODE samplers in both speed and accuracy. Restart not only outperforms the previous best SDE results, but also accelerates the sampling speed by 10-fold / 2-fold on CIFAR-10 / ImageNet $64 \times 64$. In addition, it attains significantly better sample quality than ODE samplers within comparable sampling times. Moreover, Restart better balances text-image alignment/visual quality versus diversity than previous samplers in the large-scale text-to-image Stable Diffusion model pre-trained on LAION $512 \times 512$. Code is available at https://github.com/Newbeeer/diffusion_restart_sampling

cs.LG↗

arXiv2023

Multiflagellarity leads to the size-independent swimming speed of peritrichous bacteria

To swim through a viscous fluid, a flagellated bacterium must overcome the fluid drag on its body by rotating a flagellum or a bundle of multiple flagella. Because the drag increases with the size of bacteria, it is expected theoretically that the swimming speed of a bacterium inversely correlates with its body length. Nevertheless, despite extensive research, the fundamental size-speed relation of flagellated bacteria remains unclear with different experiments reporting conflicting results. Here, by critically reviewing the existing evidence and synergizing our own experiments of large sample sizes, hydrodynamic modeling and simulations, we demonstrate that the average swimming speed of \textit{Escherichia coli}, a premier model of peritrichous bacteria, is independent of their body length. Our quantitative analysis shows that such a counterintuitive relation is the consequence of the collective flagellar dynamics dictated by the linear correlation between the body length and the number of flagella of bacteria. Notably, our study reveals how bacteria utilize the increasing number of flagella to regulate the flagellar motor load. The collective load sharing among multiple flagella results in a lower load on each flagellar motor and therefore faster flagellar rotation, which compensates for the higher fluid drag on the longer bodies of bacteria. Without this balancing mechanism, the swimming speed of monotrichous bacteria generically decreases with increasing body length, a feature limiting the size variation of the bacteria. Altogether, our study resolves a long-standing controversy over the size-speed relation of flagellated bacteria and provides new insights into the functional benefit of multiflagellarity in bacteria.

physics.flu-dyn↗

arXiv2023

High-dimensional time-frequency entanglement in a singly-filtered biphoton frequency comb

High-dimensional quantum entanglement is a cornerstone for advanced technology enabling large-scale noise-tolerant quantum systems, fault-tolerant quantum computing, and distributed quantum networks. The recently developed biphoton frequency comb (BFC) provides a powerful platform for high-dimensional quantum information processing in its spectral and temporal quantum modes. Here we propose and generate a singly-filtered high-dimensional BFC via spontaneous parametric down-conversion by spectrally shaping only the signal photons with a Fabry-Perot cavity. High-dimensional energy-time entanglement is verified through Franson-interference recurrences and temporal correlation with low-jitter detectors. Frequency- and temporal- entanglement of our singly-filtered BFC is then quantified by Schmidt mode decomposition. Subsequently, we distribute the high-dimensional singly-filtered BFC state over a 10 km fiber link with a post-distribution time-bin dimension lower bounded to be at least 168. Our demonstrations of high-dimensional entanglement and entanglement distribution show the capability of the singly-filtered quantum frequency comb for high-efficiency quantum information processing and high-capacity quantum networks.

quant-ph↗

arXiv2023

M$^3$SC: A Generic Dataset for Mixed Multi-Modal (MMM) Sensing and Communication Integration

The sixth generation (6G) of mobile communication system is witnessing a new paradigm shift, i.e., integrated sensing-communication system. A comprehensive dataset is a prerequisite for 6G integrated sensing-communication research. This paper develops a novel simulation dataset, named M3SC, for mixed multi-modal (MMM) sensing-communication integration, and the generation framework of the M3SC dataset is further given. To obtain multi-modal sensory data in physical space and communication data in electromagnetic space, we utilize AirSim and WaveFarer to collect multi-modal sensory data and exploit Wireless InSite to collect communication data. Furthermore, the in-depth integration and precise alignment of AirSim, WaveFarer, and Wireless InSite are achieved. The M3SC dataset covers various weather conditions, various frequency bands, and different times of the day. Currently, the M3SC dataset contains 1500 snapshots, including 80 RGB images, 160 depth maps, 80 LiDAR point clouds, 256 sets of mmWave waveforms with 8 radar point clouds, and 72 channel impulse response (CIR) matrices per snapshot, thus totaling 120,000 RGB images, 240,000 depth maps, 120,000 LiDAR point clouds, 384,000 sets of mmWave waveforms with 12,000 radar point clouds, and 108,000 CIR matrices. The data processing result presents the multi-modal sensory information and communication channel statistical properties. Finally, the MMM sensing-communication application, which can be supported by the M3SC dataset, is discussed.

eess.SP↗

arXiv2023

Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over the existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIMOTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI).

cs.DS↗