arXiv subjects

Chen Dong

Publications and source records attributed to Chen Dong.

At least 55 records · Page 3Linked to original sources

arXiv2023

MDVSC -- Wireless Model Division Video Semantic Communication

This paper introduces a novel method for transmitting video data over noisy wireless channels with high efficiency and controllability. The method derivates from model division multiple access (MDMA) to extract common semantic features from video frames. It also uses deep joint source-channel coding (JSCC) as the main framework to establish communication links and deal with channel noise. An entropy-based variable length coding scheme is developed to adjust the data amount accurately and explicitly. We name our method as model division video semantic communication (MDVSC). The main steps of our approach are as follows: first, video frames are transformed into a latent space to reduce computational complexity and redistribute data. Then, common features and individual features are extracted, and variable length coding is applied to further eliminate redundant semantic information under the communication bandwidth constraint. We evaluate our method on standard video test sequences and compare it with traditional wireless video coding methods. The results show that MDVSC generally surpasses the conventional methods in terms of quality metrics and has the capability to control code length precisely. Moreover, additional experiments and ablation studies are conducted to demonstrate its potential for various tasks.

cs.CE↗

arXiv2023

Semantic Communications System with Model Division Multiple Access and Controllable Coding Rate for Point Cloud

Point cloud, as a 3D representation, is widely used in autonomous driving, virtual reality (VR), and augmented reality (AR). However, traditional communication systems think that the point cloud's semantic information is irrelevant to communication, which hinders the efficient transmission of point clouds in the era of artificial intelligence (AI). This paper proposes a point cloud based semantic communication system (PCSC), which uses AI-based encoding techniques to extract the semantic information of the point cloud and joint source-channel coding (JSCC) technology to overcome the distortion caused by noise channels and solve the "cliff effect" in traditional communication. In addition, the system realizes the controllable coding rate without fine-tuning the network. The method analyzes the coded semantic vector's importance and discards semantically-unimportant information, thereby improving the transmission efficiency. Besides, PCSC and the recently proposed non-orthogonal model division multiple access (MDMA) technology are combined to design a point cloud MDMA transmission system (M-PCSC) for multi-user transmission. Relevant experimental results show that the proposed method outperforms the traditional method 10dB in the same channel bandwidth ratio under the PSNR D1 and PSNR D2 metrics. In terms of transmission, the proposed method can effectively solve the "cliff effect" in the traditional methods.

cs.MM↗

arXiv2023

Determination of the distribution of strong coupling constant with machine learning

In this work, we use the artificial neural network (ANN) method to study and predict the distribution of strong coupling constants by fitting the existing data. Our approach takes advantage of the ability of ANN to learn complex nonlinear relations and excellent generalization, and allows for a systematic treatment of the uncertainties associated with the data. To ensure the reliability of our results, we apply three evaluation indexes to evaluate the accuracy of model during training. Finally, we obtained the predicted values of the strong coupling constants at different energy scales, and compared and verified them with the existing experimental data. Our approach represents a promising way to improve the determination of the strong coupling constant at low energies, and could have important implications for future experimental and theoretical studies in quantum chromodynamics.

hep-ph↗

arXiv2023

MDVSC -- Wireless Model Division Video Semantic Communication

In this paper, we propose a new wireless video communication scheme to achieve high-efficiency video transmission over noisy channels. It exploits the idea of model division multiple access (MDMA) and extracts common semantic features across video frames. Besides, deep joint source-channel coding (JSCC) is applied to overcome the distortion caused by noisy channels. The proposed framework is collected under the name model division video semantic communication (MDVSC). In particular, temporal relative video frames are first transformed into a latent space for computing complexity reduction and data redistribution. Accordingly, a novel entropy-based variable length coding is developed further to compress semantic information under the communication bandwidth cost limitation. The whole MDVSC is an end-to-end learnable system. It can be formulated as an optimization problem whose goal is to minimize end-to-end transmission distortion under restricted communication resources. Across standard video source test sequences, test results show that the MDVSC outperforms traditional wireless video coding schemes generally under perceptual quality metrics and has the ability to control code length precisely.

cs.MM↗

arXiv2023

Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems

Compared with the current Shannon's Classical Information Theory (CIT) paradigm, semantic communication (SemCom) has recently attracted more attention, since it aims to transmit the meaning of information rather than bit-by-bit transmission, thus enhancing data transmission efficiency and supporting future human-centric, data-, and resource-intensive intelligent services in 6G systems. Nevertheless, channel noises are common and even serious in 6G-empowered scenarios, limiting the communication performance of SemCom, especially when Signal-to-Noise (SNR) levels during training and deployment stages are different, but training multi-networks to cover the scenario with a broad range of SNRs is computationally inefficient. Hence, we develop a novel De-Noising SemCom (DNSC) framework, where the designed de-noiser module can eliminate noise interference from semantic vectors. Upon the designed DNSC architecture, we further combine adversarial learning, variational autoencoder, and diffusion model to propose the Latent Diffusion DNSC (Latent-Diff DNSC) scheme to realize intelligent online de-noising. During the offline training phase, noises are added to latent semantic vectors in a forward Markov diffusion manner and then are eliminated in a reverse diffusion manner through the posterior distribution approximated by the U-shaped Network (U-Net), where the semantic de-noiser is optimized by maximizing evidence lower bound (ELBO). Such design can model real noisy channel environments with various SNRs and enable to adaptively remove noises from noisy semantic vectors during the online transmission phase. The simulations on open-source image datasets demonstrate the superiority of the proposed Latent-Diff DNSC scheme in PSNR and SSIM over different SNRs than the state-of-the-art schemes, including JPEG, Deep JSCC, and ADJSCC.

eess.IV↗

arXiv2023

Opportunistic Routing aided Cooperative Communication MRC Network with Energy-Harvesting Nodes

In this paper, we consider a multi-hop cooperative network founded on two energy-harvesting (EH) decode-and-forward (DF) relays which are provided with harvest-store-use (HSU) architecture to harvest energy from the ambience using the energy buffers. For the sake of boosting the data delivery in this network, maximal ratio combining (MRC) at destination to combine the signals received from source and relays, as well as an opportunistic routing (OR) algorithm which considers channel status information, location and energy buffer status of relays is proposed. With applying discrete-time continuous-state space Markov chain model (DCSMC), the algorithm-based theoretical expression for limiting distribution of stored energy in infinite-size buffer is derived. Further more, with using both the limiting distributions of energy buffers and the probability of transmitter candidates set, the algorithm-based theoretical expressions for outage probability, throughput and timesolt cost for each data of the network are obtained. The simulation results are presented to validate the derived algorithm-based theoretical expressions.

cs.IT↗

arXiv2023

Modeling and Performance Analysis of Single-Server Database Over Quasi-static Rayleigh Fading Channel

Cloud database is the key technology in cloud computing. The effective and efficient service quality of the cloud database is inseparable from communication technology, just as improving communication quality will reduce the concurrency phenomenon in the ticketing system. In order to visually observe the impact of communication on the cloud database, we propose a Communication-Database (C-D) Model with a single-server database over the quasi-static Rayleigh fading channel, which consists of three parts: CLIENTS SOURCE, COMMUNICATION SYSTEM and DATABASE SYSTEM. This paper uses the queuing model, M/G/1//K, to model the whole system. The C-D Model is analyzed in two cases: nonlinearity and linearity, which correspond to some instances of SISO and MIMO. The simulation results of average staying time, average number of transactions and other performance characteristics are basically consistent with the theoretical results, which verifies the validity of the C-D Model. The comparison of these experimental results also proves that poor communication quality does lead to the reduction in the quality of service.

cs.DB↗

arXiv2022

Efficient Sensor Scheduling Strategy Based on Spatio-temporal Scope Information Model

In this paper, based on the spatio-temporal correlation of sensor nodes in the Internet of Things (IoT), a Spatio-temporal Scope information model (SSIM) is proposed to quantify the scope valuable information of sensor data, which decays with space and time, to guide the system for efficient decision making in the sensed region. A simple sensor monitoring system containing three sensor nodes is considered, and two optimal scheduling decision mechanisms, single-step optimal and long-term optimal decision mechanisms, are proposed for the optimization problem. For the single-step mechanism, the scheduling results are analyzed theoretically, and approximate numerical bounds on the node layout between some of the scheduling results are obtained, consistent with the simulation results. For the long-term mechanism, the scheduling results with different node layouts are obtained using the Q-learning algorithm. The performance of the two mechanisms is verified by conducting experiments using the relative humidity dataset, and the differences in performance of the two mechanisms are discussed; in addition, the limitations of the model are summarized.

cs.IT↗

arXiv2022

TIDAL: Topology-Inferred Drug Addiction Learning

Drug addiction or drug overdose is a global public health crisis, and the design of anti-addiction drugs remains a major challenge due to intricate mechanisms. Since experimental drug screening and optimization are too time-consuming and expensive, there is urgent need to develop innovative artificial intelligence (AI) methods for addressing the challenge. We tackle this challenge by topology-inferred drug addiction learning (TIDAL) built from integrating topological Laplacian, deep bidirectional transformer, and ensemble-assisted neural networks (EANNs). The topological Laplacian is a novel algebraic topology tool that embeds molecular topological invariants and algebraic invariants into its harmonic spectra and non-harmonic spectra, respectively. These invariants complement sequence information extracted from a bidirectional transformer. We validate the proposed TIDAL framework on 22 drug addiction related, 4 hERG, and 12 DAT datasets, showing that TIDAL is a state-of-the-art framework for the modeling and analysis of drug addiction data. We carry out cross-target analysis of the current drug addiction candidates to alert their side effects and identify their repurposing potentials, revealing drugmediated linear and bilinear target correlations. Finally, TIDAL is applied to shed light on relative efficacy, repurposing potential, and potential side effects of 12 existing anti-addiction medications. Our results suggest that TIDAL provides a new computational strategy for pressingly-needed anti-substance addiction drug development.

q-bio.BM↗

arXiv2022

Analysis of the interaction between $ϕ$ meson and nucleus

In this work, we systematically study the $ϕ$ meson and nucleus interaction by analyzing and fitting the cross sections of $γN$$\rightarrow ϕ$$N$ ($N$ represent the nucleus) reactions near the threshold. With the help of vector meson dominant model, the distribution of $ϕ$-$N$ scattering length as a function of energy is presented, and the results show that there is a slight increase in scattering length with increasing energy. Based on this, the average scattering length of $ϕ$-proton is obtained as $0.10\pm0.01$ fm by combining experimental data and theoretical models. Moreover, the average scattering length of $ϕ$-deuteron interaction is derived to be $0.014\pm0.002$ fm for the first time. Further, the effect of the momentum transfer $|t_{min}|$ on the $ϕ$-$N$ scattering length at the threshold is discussed. The obtained results not only provide important theoretical information for a more comprehensive and accurate study of the $ϕ$-$N$ scattering length, but also provide a basis for future experimental measurements of $ϕ$ meson production.

nucl-th↗

arXiv2022

Exploiting potentialities for space-based quantum communication network: downlink quantum key distribution modelling and scheduling analysis

With the goal of a space-based quantum network is to have satellites distribute keys between any nodes on the ground, we consider an evolved quantum network from a near-term form, in which a space-based relay, satellite executes a sequence of satellite-based quantum key distribution (SatQKD) missions, allowing any two ground nodes to have a shared secure key. Accordingly, we develop a comprehensive framework for the dynamic simulation of SatQKD and consider scheduling QKD downlink in future space-based quantum communication network. The embedding of precise orbital model including beam diffractions and atmosphere effects makes our framework more realistic. Incorporated with the local meteorological data to channel loss model, the cloud cover contribution to transmission disturbance is quantified to realize a quasi-experimental scenario. We provide a trade-off consideration between the duration available for communications and its corresponding average link loss during one orbit, which could be used to support decisions involving the locations of ground stations and the selection of the orbital parameters for a quantum satellite. Our work also shows that satellite downlink schedule could allow for the possibility to consider strategies for different SatQKD missions such as extending connection for distant ground nodes, prioritized delivery, or promoting keys utilization, which can be used as a guideline to support decisions for future satellite application.

quant-ph↗

arXiv2022

Exploration of trace anomaly contribution to proton mass based on light vector meson photoproduction

In the quantum chromodynamics, the mass source of the proton is decomposed into four parts by the energy momentum tensor : quark energy term, gluon energy term, quark mass term and trace anomaly term. And the trace anomaly term is the most crucial contribution for studying the internal structure of the proton. In this work, under the definition of the vector meson dominant model, the trace anomaly contribution of the proton is extracted from the experimental datas of light vector mesons $ρ$, $ω$ and $ϕ$ photoproduction at the near-threshold, which are ($1.15\pm0.08$)$\%$, ($2.70\pm0.04$)$\%$ and ($5.58\pm0.25$)$\%$, respectively. Eventually, the average trace anomaly contribution of the proton is ($4.36\pm0.40$) $\%$, which only account for a small fraction of the total proton mass. The result of this work will provide theoretical information support for further study of proton internal structure.

hep-ph↗

arXiv2022

Mass radius and mechanical properties of the proton via strange $ϕ$ meson photoproduction

In this work, the cross sections of $ϕ$ photoproduction are systematically investigated with the two gluon exchange model and the pomeron model. The results obtained show that the theoretical values of the two models agree well with the experimental data. Since the mass radius and mechanical properties of the proton are encoded in the scalar gravitational form factor of the momentum energy tensor, based on the differential cross sections of $ϕ$ photoproduction at near-threshold predicted by the two gluon exchange model, the average mass radius of the proton is derived as $\sqrt{\left\langle r^{2}\right\rangle_\mathrm{m} }=0.78 \pm 0.06$ fm. As a comparison, we directly extract the proton mass radius from the experimental data of $ϕ$ photoproduction to obtain an average value of $0.80\pm0.05$ fm, which is very close to the result given by the two gluon exchange model. Taking a similar approach, we extracted the average value of $m_{D}$ to obtain the distribution of the pressure and shear force contributed within the proton, and compared the results with other groups. The results of this paper may provide necessary theoretical information for the subsequent in-depth study of the internal structure and properties of proton.

nucl-th↗

arXiv2022

Opportunistic Routing Aided Cooperative Communication Network with Energy-Harvesting

In this paper, a cooperative communication network based on energy-harvesting (EH) decode-and-forward (DF) relays that harvest energy from the ambience using buffers with harvest-store-use (HSU) architecture is considered. An opportunistic routing (OR) protocol, which selects the transmission path of packet based on the node transmission priority, is proposed to improve data delivery in this network. Additionally, an algorithm based on state transition matrix (STM) is proposed to obtain the probability distribution of the candidate broadcast node set. Based on the probability distribution, the existence conditions and the theoretical expressions for the limiting distribution of energy in energy buffers using discrete-time continuous-state space Markov chain (DCSMC) model are derived. Furthermore, the closed-form expressions for network outage probability and throughput are obtained with the help of the limiting distributions of energy stored in buffers. Numerous experiments have been performed to validate the derived theoretical expressions.

cs.IT↗

arXiv2022

Innovative semantic communication system

Traditional communication systems focus on the transmission process, and the context-dependent meaning has been ignored. The fact that 5G system has approached Shannon limit and the increasing amount of data will cause communication bottleneck, such as the increased delay problems. Inspired by the ability of artificial intelligence to understand semantics, we propose a new communication paradigm, which integrates artificial intelligence and communication, the semantic communication system. Semantic communication is at the second level of communication based on Shannon and Weaver\cite{6197583}, which retains the semantic features of the transmitted information and recovers the signal at the receiver, thus compressing the communication traffic without losing important information. Different from other semantic communication systems, the proposed system not only transmits semantic information but also transmits semantic decoder. In addition, a general semantic metrics is proposed to measure the quality of semantic communication system. In particular, the semantic communication system for image, namely AESC-I, is designed to verify the feasibility of the new paradigm. Simulations are conducted on our system with the additive white Gaussian noise (AWGN) and the multipath fading channel using MNIST and Cifar10 datasets. The experimental results show that DeepSC-I can effectively extract semantic information and reconstruct images at a relatively low SNR.

eess.SP↗

arXiv2021

A compressive sensing based parameter estimation for free space continuous variable quantum key distribution

In satellite-based free-space continuous-variable QKD (CV-QKD), the parameter estimation for the atmospheric channel fluctuations due to the turbulence effects and attenuation is crucial for analyzing and improving the protocol performance. In this paper, compressive sensing (CS) theory is applied to free-space CV-QKD to achieve the channel parameter estimation with low computational complexity and small amount of data. According to CS theory, the possibility of the sparse representation for free-space channel is analyzed and the two types of sparse reconstruction models for the channel parameters are constructed combining with the stability of the sub-channels. The most part of variable for parameter estimation is saved by using the model relying on the variables in the quantum signals, while all the variables can be used to generate the secret key by using the model relying on the second-order statistics of the variables. The methods are well adapted for the cases with the limited communication time since a little or no variable is sacrificed for parameter estimation. Finally, simulation results are given to verify the effectiveness of the proposed methods.

quant-ph↗

arXiv2021

A novel method based on node correlation to evaluate the important nodes in complex networks

Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability. Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation, showing that the interaction between nodes has an influence on the importance of nodes. In this paper, a novel method based on node distribution and global influence in complex networks is proposed. Our main idea is that the importance of nodes being linked not only to the relative position in the network but also to the correlations with each other. The nodes in the complex networks are classified according to the distance matrix, then the correlation coefficient between pairs of nodes is calculated. From the whole perspective in the network, the global similarity centrality (GSC) is proposed based on the relevance and shortest distance between any two nodes. The efficiency, accuracy and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes. Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.

cs.SI↗

arXiv2021

Sparse Reconstruction for Radar Imaging based on Quantum Algorithms

The sparse-driven radar imaging can obtain the high-resolution images about target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this paper, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. Firstly, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method.

quant-ph↗