arXiv subjects

Haibo Zhou

Publications and source records attributed to Haibo Zhou.

At least 37 records · Page 2Linked to original sources

arXiv2024

ST-Mamba: Spatial-Temporal Mamba for Traffic Flow Estimation Recovery using Limited Data

Traffic flow estimation (TFE) is crucial for urban intelligent traffic systems. While traditional on-road detectors are hindered by limited coverage and high costs, cloud computing and data mining of vehicular network data, such as driving speeds and GPS coordinates, present a promising and cost-effective alternative. Furthermore, minimizing data collection can significantly reduce overhead. However, limited data can lead to inaccuracies and instability in TFE. To address this, we introduce the spatial-temporal Mamba (ST-Mamba), a deep learning model combining a convolutional neural network (CNN) with a Mamba framework. ST-Mamba is designed to enhance TFE accuracy and stability by effectively capturing the spatial-temporal patterns within traffic flow. Our model aims to achieve results comparable to those from extensive data sets while only utilizing minimal data. Simulations using real-world datasets have validated our model's ability to deliver precise and stable TFE across an urban landscape based on limited data, establishing a cost-efficient solution for TFE.

cs.AI↗

arXiv2024

Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles

Traffic flow estimation (TFE) is crucial for intelligent transportation systems. Traditional TFE methods rely on extensive road sensor networks and typically incur significant costs. Sparse mobile crowdsensing enables a cost-effective alternative by utilizing sparsely distributed probe vehicle data (PVD) provided by connected vehicles. However, as pointed out by the central limit theorem, the sparsification of PVD leads to the degradation of TFE accuracy. In response, this paper introduces a novel and cost-effective TFE framework that leverages sparse PVD and improves accuracy by applying the spatial-temporal generative artificial intelligence (GAI) framework. Within this framework, the conditional encoder mines spatial-temporal correlations in the initial TFE results derived from averaging vehicle speeds of each region, and the generative decoder generates high-quality and accurate TFE outputs. Additionally, the design of the spatial-temporal neural network is discussed, which is the backbone of the conditional encoder for effectively capturing spatial-temporal correlations. The effectiveness of the proposed TFE approach is demonstrated through evaluations based on real-world connected vehicle data. The experimental results affirm the feasibility of our sparse PVD-based TFE framework and highlight the significant role of the spatial-temporal GAI framework in enhancing the accuracy of TFE.

cs.AI↗

arXiv2024

Semantics-Empowered Space-Air-Ground-Sea Integrated Network: New Paradigm, Frameworks, and Challenges

In the coming sixth generation (6G) communication era, to provide seamless and ubiquitous connections, the space-air-ground-sea integrated network (SAGSIN) is envisioned to address the challenges of communication coverage in areas with difficult conditions, such as the forest, desert, and sea. Considering the fundamental limitations of the SAGSIN including large-scale scenarios, highly dynamic channels, and limited device capabilities, traditional communications based on Shannon information theory cannot satisfy the communication demands. Moreover, bit-level reconstruction is usually redundant for many human-to-machine or machine-to-machine applications in the SAGSIN. Therefore, it is imperative to consider high-level communications towards semantics exchange, called semantic communications. In this survey, according to the interpretations of the term "semantics", including "significance", "meaning", and "effectiveness-related information", we review state-of-the-art works on semantic communications from three perspectives, which are 1) significance representation and protection, 2) meaning similarity measurement and meaning enhancement, and 3) ultimate effectiveness and effectiveness yielding. Sequentially, three types of semantic communication systems can be correspondingly introduced, namely the significance-oriented, meaning-oriented, and effectiveness/task-oriented semantic communication systems. Implementation of the above three types of systems in the SAGSIN necessitates a new perception-communication-computing-actuation-integrated paradigm (PCCAIP), where all the available perception, computing, and actuation techniques jointly facilitates significance-oriented sampling & transmission, semantic extraction & reconstruction, and task decision. Finally, we point out some future challenges on semantic communications in the SAGSIN. ...

cs.IT↗

arXiv2024

Optimizing 6G Integrated Sensing and Communications (ISAC) via Expert Networks

Integrated Sensing and Communications (ISAC) is one of the core technologies of 6G, which combines sensing and communication functions into a single system. However, limited computing and storage resources make it impractical to combine multiple sensing models into a single device, constraining the system's function and performance. Therefore, this article proposes enhancing ISAC with the mixture of experts (MoE) architecture. Rigorously, we first investigate ISAC and MoE, including their concepts, advantages, and applications. Then, we explore how MoE can enhance ISAC from the perspectives of signal processing and network optimization. Building on this, we propose an MoE based ISAC framework, which uses a gating network to selectively activate multiple experts in handling sensing tasks under given communication conditions, thereby improving the overall performance. The case study demonstrates that the proposed framework can effectively increase the accuracy and robustness in detecting targets by using wireless communication signal, providing strong support for the practical deployment and applications of the ISAC system.

eess.SP↗

arXiv2024

Coverage Analysis of Downlink Transmission in Multi-Connectivity Cellular V2X Networks

With the increasing of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliable and high-speed cellular vehicle-to-everything (C-V2X) communication has posed significant challenges due to the high mobility of vehicles. For improving the network performance and reliability, multi-connectivity technology has emerged as a crucial transmission mode for C-V2X in the 5G era. To this end, this paper proposes a framework for analyzing the performance of multi-connectivity in C-V2X downlink transmission, with a focus on the performance indicators of joint distance distribution and coverage probability. Specifically, we first derive the joint distance distribution of multi-connectivity. By leveraging the tools of stochastic geometry, we then obtain the analytical expressions of coverage probability based on the previous results for general multi-connectivity cases in C-V2X. Subsequently, we evaluate the effect of path loss exponent and downlink base station density on coverage probability based on the proposed analytical framework. Finally, extensive Monte Carlo simulations are conducted to validate the effectiveness of the proposed analytical framework and the simulation results reveal that multi-connectivity technology can significantly enhance the coverage probability in C-V2X.

eess.SP↗

arXiv2024

Leveraging Self-Supervised Learning for MIMO-OFDM Channel Representation and Generation

In communications theory, the capacity of multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems is fundamentally determined by wireless channels, which exhibit both diversity and correlation in spatial, frequency and temporal domains. It is further envisioned to exploit the inherent nature of channels, namely representation, to achieve geolocation-based MIMO transmission for 6G, exemplified by the fully-decoupled radio access network (FD-RAN). Accordingly, this paper first employs self-supervised learning to obtain channel representation from unlabeled channel, then proposes a channel generation assisted approach for determining MIMO precoding matrix solely based on geolocation. Specifically, we exploit the small-scale temporal domain variations of channels at a fixed geolocation, and design an ingenious pretext task tailored for contrastive learning. Then, a Transformer-based encoder is trained to output channel representations. We further develop a conditional diffusion generator to generate channel representations from geolocation. Finally, a Transformer-encoder-based decoder is utilized to reconstruct channels from generated representations, where the optimal channel is selected for calculating the precoding matrix for both single and dual BS transmission. We conduct experiments on a public ray-tracing channel dataset, and the extensive simulation results demonstrate the effectiveness of our channel representation method, and also showcase the performance improvement in geolocation-based MIMO transmission.

cs.IT↗

arXiv2024

Performance Analysis of Uplink/Downlink Decoupled Access in Cellular-V2X Networks

This paper firstly develops an analytical framework to investigate the performance of uplink (UL) / downlink (DL) decoupled access in cellular vehicle-to-everything (C-V2X) networks, in which a vehicle's UL/DL can be connected to different macro/small base stations (MBSs/SBSs) separately. Using the stochastic geometry analytical tool, the UL/DL decoupled access C-V2X is modeled as a Cox process, and we obtain the following theoretical results, i.e., 1) the probability of different UL/DL joint association cases i.e., both the UL and DL are associated with the different MBSs or SBSs, or they are associated with different types of BSs; 2) the distance distribution of a vehicle to its serving BSs in each case; 3) the spectral efficiency of UL/DL in each case; and 4) the UL/DL coverage probability of MBS/SBS. The analyses reveal the insights and performance gain of UL/DL decoupled access. Through extensive simulations, \textcolor{black}{the accuracy of the proposed analytical framework is validated.} Both the analytical and simulation results show that UL/DL decoupled access can improve spectral efficiency. The theoretical results can be directly used for estimating the statistical performance of a UL/DL decoupled access C-V2X network.

eess.SP↗

arXiv2024

Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization

Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization.

cs.NI↗

arXiv2024

Locally Differentially Private In-Context Learning

Large pretrained language models (LLMs) have shown surprising In-Context Learning (ICL) ability. An important application in deploying large language models is to augment LLMs with a private database for some specific task. The main problem with this promising commercial use is that LLMs have been shown to memorize their training data and their prompt data are vulnerable to membership inference attacks (MIA) and prompt leaking attacks. In order to deal with this problem, we treat LLMs as untrusted in privacy and propose a locally differentially private framework of in-context learning(LDP-ICL) in the settings where labels are sensitive. Considering the mechanisms of in-context learning in Transformers by gradient descent, we provide an analysis of the trade-off between privacy and utility in such LDP-ICL for classification. Moreover, we apply LDP-ICL to the discrete distribution estimation problem. In the end, we perform several experiments to demonstrate our analysis results.

cs.CR↗

arXiv2024

Empowering Wireless Networks with Artificial Intelligence Generated Graph

In wireless communications, transforming network into graphs and processing them using deep learning models, such as Graph Neural Networks (GNNs), is one of the mainstream network optimization approaches. While effective, the generative AI (GAI) shows stronger capabilities in graph analysis, processing, and generation, than conventional methods such as GNN, offering a broader exploration space for graph-based network optimization. Therefore, this article proposes to use GAI-based graph generation to support wireless networks. Specifically, we first explore applications of graphs in wireless networks. Then, we introduce and analyze common GAI models from the perspective of graph generation. On this basis, we propose a framework that incorporates the conditional diffusion model and an evaluation network, which can be trained with reward functions and conditions customized by network designers and users. Once trained, the proposed framework can create graphs based on new conditions, helping to tackle problems specified by the user in wireless networks. Finally, using the link selection in integrated sensing and communication (ISAC) as an example, the effectiveness of the proposed framework is validated.

cs.NI↗

arXiv2024

Performance Analysis for Downlink Transmission in Multi-Connectivity Cellular V2X Networks

With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge.Recently, multi-connectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era. To this end, this paper proposes an analytical framework for the performance of downlink in multi-connectivity C-V2X networks. Specifically, by modeling the vehicles and base stations as one-dimensional Poisson point processes, we first derive and analyze the joint distance distribution of multi-connectivity. Then through leveraging the tools of stochastic geometry, the coverage probability and spectral efficiency are obtained based on the previous results for general multi-connectivity cases in C-V2X. Additionally, we evaluate the effect of path loss exponent and the density of downlink base station on system performance indicators. We demonstrate through extensive Monte Carlo simulations that multi-connectivity technology can effectively enhance network performance in C-V2X. Our findings have important implications for the research and application of multi-connectivity C-V2X in the 5G and B5G era.

eess.SP↗

arXiv2024

Channel-Feedback-Free Transmission for Downlink FD-RAN: A Radio Map based Complex-valued Precoding Network Approach

As the demand for high-quality services proliferates, an innovative network architecture, the fully-decoupled RAN (FD-RAN), has emerged for more flexible spectrum resource utilization and lower network costs. However, with the decoupling of uplink base stations and downlink base stations in FD-RAN, the traditional transmission mechanism, which relies on real-time channel feedback, is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter. This paper proposes a novel transmission scheme without relying on physical layer channel feedback. Specifically, we design a radio map based complex-valued precoding network~(RMCPNet) model, which outputs the base station precoding based on user location. RMCPNet comprises multiple subnets, with each subnet responsible for extracting unique modal features from diverse input modalities. Furthermore, the multi-modal embeddings derived from these distinct subnets are integrated within the information fusion layer, culminating in a unified representation. We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function. We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16\% and 76\% performance improvements over the conventional real-valued neural network and statistical codebook approach, respectively.

cs.IT↗

arXiv2024

Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse

Driven by the great advances in metaverse and edge computing technologies, vehicular edge metaverses are expected to disrupt the current paradigm of intelligent transportation systems. As highly computerized avatars of Vehicular Metaverse Users (VMUs), the Vehicle Twins (VTs) deployed in edge servers can provide valuable metaverse services to improve driving safety and on-board satisfaction for their VMUs throughout journeys. To maintain uninterrupted metaverse experiences, VTs must be migrated among edge servers following the movements of vehicles. This can raise concerns about privacy breaches during the dynamic communications among vehicular edge metaverses. To address these concerns and safeguard location privacy, pseudonyms as temporary identifiers can be leveraged by both VMUs and VTs to realize anonymous communications in the physical space and virtual spaces. However, existing pseudonym management methods fall short in meeting the extensive pseudonym demands in vehicular edge metaverses, thus dramatically diminishing the performance of privacy preservation. To this end, we present a cross-metaverse empowered dual pseudonym management framework. We utilize cross-chain technology to enhance management efficiency and data security for pseudonyms. Furthermore, we propose a metric to assess the privacy level and employ a Multi-Agent Deep Reinforcement Learning (MADRL) approach to obtain an optimal pseudonym generating strategy. Numerical results demonstrate that our proposed schemes are high-efficiency and cost-effective, showcasing their promising applications in vehicular edge metaverses.

cs.NI↗

arXiv2023

Capacity and Delay of Unmanned Aerial Vehicle Networks with Mobility

Unmanned aerial vehicles (UAVs) are widely exploited in environment monitoring, search-and-rescue, etc. However, the mobility and short flight duration of UAVs bring challenges for UAV networking. In this paper, we study the UAV networks with n UAVs acting as aerial sensors. UAVs generally have short flight duration and need to frequently get energy replenishment from the control station. Hence the returning UAVs bring the data of the UAVs along the returning paths to the control station with a store-carry-and-forward (SCF) mode. A critical range for the distance between the UAV and the control station is discovered. Within the critical range, the per-node capacity of the SCF mode is O(n/log n) times higher than that of the multi-hop mode. However, the per-node capacity of the SCF mode outside the critical range decreases with the distance between the UAV and the control station. To eliminate the critical range, a mobility control scheme is proposed such that the capacity scaling laws of the SCF mode are the same for all UAVs, which improves the capacity performance of UAV networks. Moreover, the delay of the SCF mode is derived. The impact of the size of the entire region, the velocity of UAVs, the number of UAVs and the flight duration of UAVs on the delay of SCF mode is analyzed. This paper reveals that the mobility and short flight duration of UAVs have beneficial effects on the performance of UAV networks, which may motivate the study of SCF schemes for UAV networks.

cs.RO↗

arXiv2023

Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-driven Approach

To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes integer interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel; 2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations; and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G.

cs.NI↗

arXiv2023

Towards Scalable Wireless Federated Learning: Challenges and Solutions

The explosive growth of smart devices (e.g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of data. The generated massive data together with the rapid advancement of machine learning (ML) techniques spark a variety of intelligent applications. To distill intelligence for supporting these applications, federated learning (FL) emerges as an effective distributed ML framework, given its potential to enable privacy-preserving model training at the network edge. In this article, we discuss the challenges and solutions of achieving scalable wireless FL from the perspectives of both network design and resource orchestration. For network design, we discuss how task-oriented model aggregation affects the performance of wireless FL, followed by proposing effective wireless techniques to enhance the communication scalability via reducing the model aggregation distortion and improving the device participation. For resource orchestration, we identify the limitations of the existing optimization-based algorithms and propose three task-oriented learning algorithms to enhance the algorithmic scalability via achieving computation-efficient resource allocation for wireless FL. We highlight several potential research issues that deserve further study.

cs.NI↗

arXiv2023

Mobile Metaverse: A Road Map from Metaverse to Metavehicles

With the rapid development of communication technologies and extended reality (XR), the services and applications of the Metaverse are gradually entering our lives. However, the current development of the Metaverse provides users with services that are homogeneous with the user experience that the Internet has brought in the past, making them more like an extension of the Internet. In addition, as a mobile application carrier for the Metaverse, it is also worth considering how vehicles with diverse onboard components can develop in synergy with the Metaverse. In this article, we focus on the core of the Metaverse, namely user experience, and provide a road map from Metaverse to Metaverse vehicles (Metavehicles). Specifically, we first elaborate on six features of the Metaverse from the perspective of user experience and propose a hierarchical framework for the Metaverse based on the evolutionary logic of the features. Under the guidance of this framework, we discuss the empowerment of onboard components of Metavehicles on the development of the Metaverse, and analyze the service experience that Metavehicles can bring to two types of users, namely drivers and passengers. Finally, considering the differentiated development levels of Metaverse and autonomous driving, we further establish a hierarchical framework for Metavehicles from three aspects (i.e., enhance Metaverse, enhance driving experience, and enhance entertainment experience), providing an evolutionary path for the development of Metavehicles.

cs.NI↗

arXiv2023

Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the high complexity raises the research of neural network (NN) based approaches to trade-off the performance and complexity. To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques. This approach aims to alleviate common issues encountered in each of these training paradigms, including the infeasibility of obtaining optimal solutions as labels and overfitting in supervised learning, ensuring higher convergence performance in unsupervised learning, and improving training efficiency in reinforcement learning. Simulation results demonstrate the enhanced performance of the proposed AD-based methods compared to traditional learning methods. Remarkably, this research paves the way for the integration of traditional optimization insights and emerging NN techniques in wireless communication system optimization.

cs.LG↗