arXiv subjects

Changchuan Yin

Publications and source records attributed to Changchuan Yin.

At least 37 records · Page 2Linked to original sources

arXiv2023

Base Station Beamforming Design for Near-field XL-IRS Beam Training

Existing research on extremely large-scale intelligent reflecting surface (XL-IRS) beam training has assumed the far-field channel model for base station (BS)-IRS link. However, this approach may cause degraded beam training performance in practice due to the near-field channel model of the BS-IRS link. To address this issue, we propose two efficient schemes to optimize BS beamforming for improving the XL-IRS beam training performance. Specifically, the first scheme aims to maximize total received signal power on the XL-IRS, which generalizes the existing angle based BS beamforming design and can be resolved using the singular value decomposition (SVD) method. The second scheme aims to maximize the $\ell_1$-norm of incident signals on the XL-IRS, which is shown to achieve the maximum received power at the user. To solve the non-convex $\ell_1$-norm maximization problem, we propose an eficient algorithm by using the alternating optimization (AO) technique. Numerical results show that the proposed AO based BS beamforming design outperforms the SVD/angle based BS beamforming in terms of training accuracy and achievable received signal-to-noise ratio (SNR).

eess.SP↗

arXiv2023

Adaptive Bitrate Video Semantic Communication over Wireless Networks

This paper investigates the adaptive bitrate (ABR) video semantic communication over wireless networks. In the considered model, video sensing devices must transmit video semantic information to an edge server, to facilitate ubiquitous video sensing services such as road environment monitoring at the edge server in autonomous driving scenario. However, due to the varying wireless network conditions, it is challenging to guarantee both low transmission delay and high semantic accuracy at the same time if devices continuously transmit a fixed bitrate video semantic information. To address this challenge, we develop an adaptive bitrate video semantic communication (ABRVSC) system, in which devices adaptively adjust the bitrate of video semantic information according to network conditions. Specifically, we first define the quality of experience (QoE) for video semantic communication. Subsequently, a swin transformer-based semantic codec is proposed to extract semantic information with considering the influence of QoE. Then, we propose an Actor-Critic based ABR algorithm for the semantic codec to enhance the robustness of the proposed ABRVSC scheme against network variations. Simulation results demonstrate that at low bitrates, the mean intersection over union (MIoU) of the proposed ABRVSC scheme is nearly twice that of the traditional scheme. Moreover, the proposed ABRVSC scheme, which increases the QoE in video semantic communication by 36.57%, exhibits more robustness against network variations compared to both the fixed bitrate schemes and traditional ABR schemes.

cs.NI↗

arXiv2023

Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. The goal is to jointly determine the bitwidths employed for local FL model quantization and the set of devices participating in FL training at each iteration. We pose this as an optimization problem that aims to minimize the training loss of quantized FL under a per-iteration device sampling budget and delay requirement. However, the formulated problem is difficult to solve without (i) a concrete understanding of how quantization impacts global ML performance and (ii) the ability of the server to construct estimates of this process efficiently. To address the first challenge, we analytically characterize how limited wireless resources and induced quantization errors affect the performance of the proposed FL method. Our results quantify how the improvement of FL training loss between two consecutive iterations depends on the device selection and quantization scheme as well as on several parameters inherent to the model being learned. Then, we show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, this model-based RL approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Simulation results show that the proposed FL algorithm can reduce the convergence time.

cs.LG↗

arXiv2023

A Novel Two-Layer Codebook Based Near-Field Beam Training for Intelligent Reflecting Surface

In this paper, we study the codebook-based near-field beam training for intelligent reflecting surfaces (IRSs) aided wireless system. In the considered model, the near-field beam training is critical to focus signals at the location of user equipment (UE) to obtain prominent IRS array gain. However, existing codebook schemes cannot achieve low training overhead and high receiving power simultaneously. To tackle this issue, a novel two-layer codebook based beam training scheme is proposed. The layer-1 codebook is designed based on the omnidirectionality of a random-phase beam pattern, which estimates the UE distance with training overhead equivalent to that of one DFT codeword. Then, based on the estimated UE distance, the layer-2 codebook is generated to scan candidate UE locations and obtain the optimal codeword for IRS beamforming. Numerical results show that compared with benchmarks, the proposed two-layer beam training scheme achieves more accurate UE distance and angle estimation, higher data rate, and smaller training overhead.

cs.IT↗

arXiv2023

Sum-Rate Maximization for Active RIS-Aided Downlink RSMA System

In this paper, the problem of sum-rate maximization for an active reconfigurable intelligent surface (RIS) assisted downlink rate-splitting multiple access (RSMA) transmission system is studied. In the considered model, the active RIS is deployed to overcome severe power attenuation, which is caused by the cumulative product of RIS incidence path loss and the reflection path loss. Since the active RIS can adjust both the phase and the amplitude of the incident signal simultaneously, the RIS control scheme requires delicate design to improve RSMA communication performance. To address this issue, a sum-rate maximization problem is formulated to jointly optimize the beamforming vectors, rate allocation vector, and RIS precoding matrix. To solve this non-convex sum-rate maximization problem, an iterative algorithm based on fractional programming (FP) and quadratic constraint quadratic programming (QCQP) is proposed. In particular, the proposed algorithm firstly decomposes the original problem into two subproblems, namely, 1) beamforming and rate allocation optimization and 2) active RIS precoding optimization. The corresponding variables of the two subproblems are optimized through sequential convex approximation (SCA) and block coordinate descent (BCD), respectively. Numerical results show that the proposed active RIS-aided RSMA system could increase the sum-rate by up to 45% over the conventional passive RIS-aided RSMA system with the same energy consumption.

cs.IT↗

arXiv2022

Image Segmentation Semantic Communication over Internet of Vehicles

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding method

cs.NI↗

arXiv2022

A Joint Learning and Communications Framework for Federated Learning over Wireless Networks

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.

cs.NI↗

arXiv2022

Evolutionary trend of SARS-CoV-2 inferred by the homopolymeric nucleotide repeats

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the current global COVID-19 pandemic, in which millions of lives have been lost. Understanding the zoonotic evolution of the coronavirus may provide insights for developing effective vaccines, monitoring the transmission trends, and preventing new zoonotic infections. Homopolymeric nucleotide repeats (HP), the most simple tandem repeats, are a ubiquitous feature of eukaryotic genomes. Yet the HP distributions and roles in coronavirus genome evolution are poorly investigated. In this study, we characterize the HP distributions and trends in the genomes of bat and human coronaviruses and SARS-CoV-2 variants. The results show that the SARS-CoV-2 genome is abundant in HPs, and has augmented HP contents during evolution. Especially, the disparity of HP poly-(A/T) and ploy-(C/G) of coronaviruses increases during the evolution in human hosts. The disparity of HP poly-(A/T) and ploy-(C/G) is correlated to host adaptation and the virulence level of the coronaviruses. Therefore, we propose that the HP disparity can be a quantitative measure for the zoonotic evolution levels of coronaviruses. Peculiarly, the HP disparity measure infers that SARS-CoV-2 Omicron variants have a high disparity of HP poly-(A/T) and ploy-(C/G), suggesting a high adaption to the human hosts.

q-bio.PE↗

arXiv2021

Multi-Factors Aware Dual-Attentional Knowledge Tracing

With the increasing demands of personalized learning, knowledge tracing has become important which traces students' knowledge states based on their historical practices. Factor analysis methods mainly use two kinds of factors which are separately related to students and questions to model students' knowledge states. These methods use the total number of attempts of students to model students' learning progress and hardly highlight the impact of the most recent relevant practices. Besides, current factor analysis methods ignore rich information contained in questions. In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism. More specifically, we propose a novel student-related factor which records the most recent attempts on relevant concepts of students to highlight the impact of recent exercises. To enrich questions representations, we use a pre-training method to incorporate two kinds of question information including questions' relation and difficulty level. We also add a regularization term about questions' difficulty level to restrict pre-trained question representations to fine-tuning during the process of predicting students' performance. Moreover, we apply a dual-attentional mechanism to differentiate contributions of factors and factor interactions to final prediction in different practice records. At last, we conduct experiments on several real-world datasets and results show that MF-DAKT can outperform existing knowledge tracing methods. We also conduct several studies to validate the effects of each component of MF-DAKT.

cs.AI↗

arXiv2021

Emerging vaccine-breakthrough SARS-CoV-2 variants

The recent global surge in COVID-19 infections has been fueled by new SARS-CoV-2 variants, namely Alpha, Beta, Gamma, Delta, etc. The molecular mechanism underlying such surge is elusive due to 4,653 non-degenerate mutations on the spike protein, which is the target of most COVID-19 vaccines. The understanding of the molecular mechanism of transmission and evolution is a prerequisite to foresee the trend of emerging vaccine-breakthrough variants and the design of mutation-proof vaccines and monoclonal antibodies. We integrate the genotyping of 1,489,884 SARS-CoV-2 genomes isolates, 130 human antibodies, tens of thousands of mutational data points, topological data analysis, and deep learning to reveal SARS-CoV-2 evolution mechanism and forecast emerging vaccine-escape variants. We show that infectivity-strengthening and antibody-disruptive co-mutations on the S protein RBD can quantitatively explain the infectivity and virulence of all prevailing variants. We demonstrate that Lambda is as infectious as Delta but is more vaccine-resistant. We analyze emerging vaccine-breakthrough co-mutations in 20 countries, including the United Kingdom, the United States, Denmark, Brazil, and Germany, etc. We envision that natural selection through infectivity will continue to be the main mechanism for viral evolution among unvaccinated populations, while antibody disruptive co-mutations will fuel the future growth of vaccine-breakthrough variants among fully vaccinated populations. Finally, we have identified the co-mutations that have the great likelihood of becoming dominant: [A411S, L452R, T478K], [L452R, T478K, N501Y], [V401L, L452R, T478K], [K417N, L452R, T478K], [L452R, T478K, E484K, N501Y], and [P384L, K417N, E484K, N501Y]. We predict they, particularly the last four, will break through existing vaccines. We foresee an urgent need to develop new vaccines that target these co-mutations.

q-bio.BM↗

arXiv2021

Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems

In this paper, the problem of minimizing the weighted sum of age of information (AoI) and total energy consumption of Internet of Things (IoT) devices is studied. In the considered model, each IoT device monitors a physical process that follows nonlinear dynamics. As the dynamics of the physical process vary over time, each device must find an optimal sampling frequency to sample the real-time dynamics of the physical system and send sampled information to a base station (BS). Due to limited wireless resources, the BS can only select a subset of devices to transmit their sampled information. Thus, edge devices must cooperatively sample their monitored dynamics based on the local observations and the BS must collect the sampled information from the devices immediately, hence avoiding the additional time and energy used for sampling and information transmission. To this end, it is necessary to jointly optimize the sampling policy of each device and the device selection scheme of the BS so as to accurately monitor the dynamics of the physical process using minimum energy. This problem is formulated as an optimization problem whose goal is to minimize the weighted sum of AoI cost and energy consumption. To solve this problem, we propose a novel distributed reinforcement learning (RL) approach for the sampling policy optimization. The proposed algorithm enables edge devices to cooperatively find the global optimal sampling policy using their own local observations. Given the sampling policy, the device selection scheme can be optimized thus minimizing the weighted sum of AoI and energy consumption of all devices. Simulations with real data of PM 2.5 pollution show that the proposed algorithm can reduce the sum of AoI by up to 17.8% and 33.9% and the total energy consumption by up to 13.2% and 35.1%, compared to a conventional deep Q network method and a uniform sampling policy.

cs.IT↗

arXiv2021

Mobility-Aware Seamless Handover with MPTCP in Software-Defined HetNets

In this paper, the problem of vertical handover in software-defined network (SDN) based heterogeneous networks (HetNets) is studied. In the studied model, HetNets are required to offer diverse services for mobile users. Using an SDN controller, HetNets have the capability of managing users' access and mobility issues but still have the problems of ping-pong effect and service interruption during vertical handover. To solve these problems, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed. The proposed handover method is executed in the controller of the software-defined HetNets (SDHetNets) and consists of three steps: location prediction, network selection, and handover execution. In particular, the method first predicts the user's location in the next moment with an echo state network (ESN). Given the predicted location, the SDHetNet controller can determine the candidate network set for the handover to pre-allocate network wireless resources. Second, the target network is selected through fuzzy analytic hierarchical process (FAHP) algorithm, jointly considering user preferences, service requirements, network attributes, and user mobility patterns. Then, seamless handover is realized through the proposed MPTCP-based handover mechanism. Simulations using real-world user trajectory data from Korea Advanced Institute of Science & Technology show that the proposed method can reduce the handover times by 10.85% to 29.12% compared with traditional methods. The proposed method also maintains at least one MPTCP subflow connected during the handover process and achieves a seamless handover.

cs.NI↗

arXiv2020

UMAP-assisted $K$-means clustering of large-scale SARS-CoV-2 mutation datasets

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a worldwide devastating effect. The understanding of evolution and transmission of SARS-CoV-2 is of paramount importance for the COVID-19 control, combating, and prevention. Due to the rapid growth of both the number of SARS-CoV-2 genome sequences and the number of unique mutations, the phylogenetic analysis of SARS-CoV-2 genome isolates faces an emergent large-data challenge. We introduce a dimension-reduced $k$-means clustering strategy to tackle this challenge. We examine the performance and effectiveness of three dimension-reduction algorithms: principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP). By using four benchmark datasets, we found that UMAP is the best-suited technique due to its stable, reliable, and efficient performance, its ability to improve clustering accuracy, especially for large Jaccard distanced-based datasets, and its superior clustering visualization. The UMAP-assisted $k$-means clustering enables us to shed light on increasingly large datasets from SARS-CoV-2 genome isolates.

q-bio.QM↗

arXiv2020

Inverted repeats in coronavirus SARS-CoV-2 genome and implications in evolution

The coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, has caused 60 millions of infections and 1.38 millions of fatalities. Genomic analysis of SARS-CoV-2 can provide insights on drug design and vaccine development for controlling the pandemic. Inverted repeats in a genome greatly impact the stability of the genome structure and regulate gene expression. Inverted repeats involve cellular evolution and genetic diversity, genome arrangements, and diseases. Here, we investigate the inverted repeats in the coronavirus SARS-CoV-2 genome. We found that SARS-CoV-2 genome has an abundance of inverted repeats. The inverted repeats are mainly located in the gene of the Spike protein. This result suggests the Spike protein gene undergoes recombination events, therefore, is essential for fast evolution. Comparison of the inverted repeat signatures in human and bat coronaviruses suggest that SARS-CoV-2 is mostly related SARS-related coronavirus, SARSr-CoV/RaTG13. The study also reveals that the recent SARS-related coronavirus, SARSr-CoV/RmYN02, has a high amount of inverted repeats in the spike protein gene. Besides, this study demonstrates that the inverted repeat distribution in a genome can be considered as the genomic signature. This study highlights the significance of inverted repeats in the evolution of SARS-CoV-2 and presents the inverted repeats as the genomic signature in genome analysis.

q-bio.OT↗

arXiv2020

UAV Trajectory and Communication Co-design: Flexible Path Discretization and Path Compression

The performance optimization of UAV communication systems requires the joint design of UAV trajectory and communication efficiently. To tackle the challenge of infinite design variables arising from the continuous-time UAV trajectory optimization, a commonly adopted approach is by approximating the UAV trajectory with piecewise-linear path segments in three-dimensional (3D) space. However, this approach may still incur prohibitive computational complexity in practice when the UAV flight period/distance becomes long, as the distance between consecutive waypoints needs to be kept sufficiently small to retain high approximation accuracy. To resolve this fundamental issue, we propose in this paper a new and general framework for UAV trajectory and communication co-design. First, we propose a flexible path discretization scheme that optimizes only a number of selected waypoints (designable waypoints) along the UAV path for complexity reduction, while all the designable and non-designable waypoints are used in calculating the approximated communication utility along the UAV trajectory for ensuring high trajectory discretization accuracy. Next, given any number of designable waypoints, we propose a novel path compression scheme where the UAV 3D path is first decomposed into three one-dimensional (1D) sub-paths and each sub-path is then approximated by superimposing a number of selected basis paths weighted by their corresponding path coefficients, thus further reducing the path design complexity. Finally, we provide a case study on UAV trajectory design for aerial data harvesting from distributed ground sensors, and numerically show that the proposed schemes can significantly reduce the UAV trajectory design complexity yet achieve favorable rate performance as compared to conventional path/time discretization schemes.

cs.IT↗

arXiv2020

Host immune response driving SARS-CoV-2 evolution

The transmission and evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are of paramount importance to the controlling and combating of coronavirus disease 2019 (COVID-19) pandemic. Currently, near 15,000 SARS-CoV-2 single mutations have been recorded, having a great ramification to the development of diagnostics, vaccines, antibody therapies, and drugs. However, little is known about SARS-CoV-2 evolutionary characteristics and general trend. In this work, we present a comprehensive genotyping analysis of existing SARS-CoV-2 mutations. We reveal that host immune response via APOBEC and ADAR gene editing gives rise to near 65\% of recorded mutations. Additionally, we show that children under age five and the elderly may be at high risk from COVID-19 because of their overreacting to the viral infection. Moreover, we uncover that populations of Oceania and Africa react significantly more intensively to SARS-CoV-2 infection than those of Europe and Asia, which may explain why African Americans were shown to be at increased risk of dying from COVID-19, in addition to their high risk of getting sick from COVID-19 caused by systemic health and social inequities. Finally, our study indicates that for two viral genome sequences of the same origin, their evolution order may be determined from the ratio of mutation type C$>$T over T$>$C.

q-bio.GN↗

arXiv2020

Characterizing SARS-CoV-2 mutations in the United States

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been mutating since it was first sequenced in early January 2020. The genetic variants have developed into a few distinct clusters with different properties. Since the United States (US) has the highest number of viral infected patients globally, it is essential to understand the US SARS-CoV-2. Using genotyping, sequence-alignment, time-evolution, $k$-means clustering, protein-folding stability, algebraic topology, and network theory, we reveal that the US SARS-CoV-2 has four substrains and five top US SARS-CoV-2 mutations were first detected in China (2 cases), Singapore (2 cases), and the United Kingdom (1 case). The next three top US SARS-CoV-2 mutations were first detected in the US. These eight top mutations belong to two disconnected groups. The first group consisting of 5 concurrent mutations is prevailing, while the other group with three concurrent mutations gradually fades out. Our analysis suggests that female immune systems are more active than those of males in responding to SARS-CoV-2 infections. We identify that one of the top mutations, 27964C$>$T-(S24L) on ORF8, has an unusually strong gender dependence. Based on the analysis of all mutations on the spike protein, we further uncover that three of four US SASR-CoV-2 substrains become more infectious. Our study calls for effective viral control and containing strategies in the US.

q-bio.GN↗

arXiv2020

A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks

In this paper, a joint task, spectrum, and transmit power allocation problem is investigated for a wireless network in which the base stations (BSs) are equipped with mobile edge computing (MEC) servers to jointly provide computational and communication services to users. Each user can request one computational task from three types of computational tasks. Since the data size of each computational task is different, as the requested computational task varies, the BSs must adjust their resource (subcarrier and transmit power) and task allocation schemes to effectively serve the users. This problem is formulated as an optimization problem whose goal is to minimize the maximal computational and transmission delay among all users. A multi-stack reinforcement learning (RL) algorithm is developed to solve this problem. Using the proposed algorithm, each BS can record the historical resource allocation schemes and users' information in its multiple stacks to avoid learning the same resource allocation scheme and users' states, thus improving the convergence speed and learning efficiency. Simulation results illustrate that the proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm.

eess.SP↗