Chuanting Zhang

• Doctor of Philosophy
• PostDoc Position at University of Bristol

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-bas...

Wireless traffic prediction plays an indispensable role in cellular networks to achieve proactive adaptation for communication systems. Along this line, Federated Learning (FL)-based wireless traffic prediction at the edge attracts enormous attention because of the exemption from raw data transmission and enhanced privacy protection. However FL-bas...

Wireless traffic prediction is critical to the intelligent operation of cellular networks, such as load balancing, congestion control, value-added service promotion, etc. However, the BTS data in each region has certain differences and privacy, and centralized prediction needs to transmit a large amount of traffic data, which will not only cause ba...

The Radio frequency (RF) fingerprinting technique makes highly secure device authentication possible for future networks by exploiting hardware imperfections introduced during manufacturing. Although this technique has received considerable attention over the past few years, RF fingerprinting still faces great challenges of channel-variation-induce...

Hybrid beamforming (HBF) is a promising approach for balancing the hardware cost, training overhead and system performance in massive MIMO systems. Optimizing the HBF through deep learning (DL) has gained considerable attention in recent years due to its potential in dealing with the nonconvex problems. However, existing DL-based HBF methods requir...

This paper studies the secure transmission challenge confronted with future communication systems. In our considered model, the confidential communication between legitimate users is strengthened by an aerial intelligent reflecting surface (AIRS) deployed on aerial platforms such as an unmanned aerial vehicle (UAV). The average secrecy rate for all...

This paper focuses on securing confidential communication in multiple intelligent reflecting surfaces (IRS) assisted terahertz (THz) systems, where a potential eavesdropper can intercept either the base station (BS)-IRS link or the IRS-user link. Notably, the secure transmission may be intercepted and blocked by the eavesdropper due to the blockage...

Hybrid beamforming (HBF) is a promising approach to obtain a better balance between hardware complexity and system performance in massive MIMO communication systems. However, the HBF optimization problem is a challenging task due to its nonconvex property in terms of design complexity and spectral efficiency (SE) performance. In this work, a low-co...

Wireless traffic prediction plays a vital role in managing high dynamic and low latency communication networks, especially in 6G wireless networks. Regarding data and computing resources constraints in edge devices, federated wireless traffic prediction has attracted considerable interest. However, federated learning is limited to dealing with hete...

Currently, the sixth-generation (6G) communication research roadmap is being frequently discussed and designed, in which, undoubtedly, aerial telecommunication infrastructures play crucial roles for boosting transmission capacity, enlarging coverage, and democratizing the benefits of information and communications technology (ICT) over the globe. H...

In this article, we present the analysis of the digital divide to illustrate the unfair access to the benefits brought by information and communications technology (ICT) over the globe and provide our solution termed big communications (BigCom) to close the digital divide and democratize the benefits of ICT. To facilitate the implementation of BigC...

Distributed Artificial Intelligence (DAI) is one of the most promising techniques to provide intelligent services under strict privacy protection regulations for multiple clients. By applying DAI, training on raw data is carried out locally. At the same time, the trained outputs, e.g., model parameters from multiple local clients, are sent back to...

Due to the limited energy, communication bandwidth and computing ability of edge devices in industrial IoT (IIoT) networks, it is incredibly challenging to compress and transmit those massive manufacturing data collected at the edge, thus greatly degrading the transmission and computing efficiency and finally results in long latency. To address thi...

Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to so...

Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Though edge features-based or node similarity-based methods have been proposed to s...

Wireless traffic prediction is essential for cellular networks to realize intelligent network operations, such as load-aware resource management and predictive control. Existing prediction approaches usually adopt centralized training architectures and require the transferring of huge amounts of traffic data, which may raise delay and privacy conce...

Distributed Artificial Intelligence (DAI) is regarded as one of the most promising techniques to provide intelligent services under strict privacy protection regulations for multiple clients. By applying DAI, training on raw data is carried out locally, while the trained outputs, e.g., model parameters, from multiple local clients, are sent back to...

The digital divide restricting the access of people living in developing areas to the benefits of modern information and communications technologies has become a major challenge and research focus. To well understand and finally bridge the digital divide, we first need to discover a proper measure to characterize and quantify the telecommunication...

In this paper, we study MIMO transmission schemes based on deep learning (DL). We propose a novel DL-based MIMO communication structure by combing a beamforming network at the transmitter side and a radio transformer network (RTN) at the receiver side. Compared with the classical DL-based MIMO communication systems, the interference is potentially...

Recurrent neural network (RNN) based models are widely adopted to capture temporal dependencies in the state-of-the-art approaches for cellular traffic prediction. However, RNN is inefficient and incapable of capturing long-range temporal dependencies of traffic data. Besides, its inherent sequential nature makes it time consuming in capture the te...

In this article, we present the analysis of the digital divide to illustrate the unfair access to the benefits brought by information and communications technology (ICT) over the globe and provide our solution termed big communications (BigCom) to close the digital divide and democratize the benefits of ICT. To facilitate the implementation of BigC...

The digital divide restricting the access of people living in developing areas to the benefits of modern information and communications technologies has become a major challenge and research focus. To well understand and finally bridge the digital divide, we first need to discover a proper measure to characterize and quantify the telecommunication...

Machine (deep) learning enabled accurate traffic modeling and prediction is an indispensable part for future big data-driven intelligent cellular networks, since it can help autonomic network control and management as well as service provisioning. Along this line, this paper proposes a novel deep learning architecture, namely Spatial-Temporal Cross...

With accurate traffic prediction, future cellular networks can make self-management and embrace intelligent and efficient automation. This letter devotes itself to citywide cellular traffic prediction and propose a deep learning approach to model the nonlinear dynamics of wireless traffic. By treating traffic data as images, both the spatial and te...

Collaborative Filtering (CF) is one of the most popular frameworks for recommender systems. However, sparsity of user-item interactions degrades the performance of CF significantly. Using auxiliary information is a common way to solve this sparsity problem. Heterogeneous information networks (HINs), which contains a plurality of types of nodes or r...

As particulate materials in the air can cause several kinds of respiratory and cardiovascular diseases, the air quality information predicting attracts more and more attention. Knowing these information in advance is very important to protect human from health problems. With the development of computer technology, the data we can collect is increas...

In order to effectively retain details and suppress noise, a multi-focus image fusion method based on Surfacelet transform and compound PCNN is proposed. Surfacelet transform is a powerful multi-resolution analysis tool which is able to decompose the original image into a number of different frequency band sub-images, compound PCNN model is a combi...

A medical image fusion method based on bi-dimensional empirical mode decomposition (BEMD) and dual-channel PCNN is proposed in this paper. The multi-modality medical images are decomposed into intrinsic mode function (IMF) components and a residue component. IMF components are divided into high-frequency and low-frequency components based on the co...