• Near-Field Communications/Sensing (NFC/NISE)
  • Non-Orthogonal Multiple access (NOMA)/ Next Generation Multiple Access (NGMA)
  • Reconfigurable Intelligent Surface (RIS)/ Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS) Aided Communications
  • Integrated Sensing and Communication (ISAC)
  • Generative AI for Communications
  • Unmanned Aerial Vehicle (UAV) Communications

  • 1) Near-Field Communication/Sensing (NFC/NISE)

    As the fifth-generation (5G) wireless network continues to be commercialized, research and development efforts are already underway on a global scale to investigate the possibilities for the sixth-generation (6G) wireless network. Compared to the previous generations of wireless networks, 6G is expected to be data-driven, instantaneous, ubiquitous, and intelligent, thereby facilitating new applications and services, such as extended reality (XR), holographic communication, pervasive intelligence, digital twin, and Metaverse. Therefore, 6G has significantly higher performance targets compared to the past generations, such as a 100 times higher peak data rate of at least 1 terabit per second (Tb/s), an air interface latency of 0.01~0.1 milliseconds, and a 10 times larger connectivity density of up to ten million devices per square kilometer. To reach these stringent targets, 6G is expected to integrate the three technical trends: 1) extremely large-scale antenna arrays, 2) tremendously high frequencies, and 3) new types of antennas. The significant increase in the size of antenna arrays, extremely high frequencies, and the emerging new metamaterial-based antennas cause a paradigm shift for the EM characteristics in 6G. Generally, the EM field radiated from antennas can be divided into two regions: near-field region and far-field region. The EM waves in these regions exhibit different propagation properties. Specifically, the wavefront of EM waves in the far-field region can be reasonably well approximated as being planar. Conversely, more complex wave models, such as spherical waves, are required to accurately depict propagation in the near-field region. From the first-generation (1G) to 5G wireless networks, the near-field was generally limited to a few meters or even centimeters because of the low-dimensional antenna arrays and low frequencies. Therefore, communication systems could be efficiently designed based on far-field approximation. However, given the large aperture of ELAAs and tremendously high frequencies, 6G networks exhibit a large near-field region on the order of hundreds of meters. Furthermore, for the investigation of metamaterial-based continuous-aperture antennas, the conventional far-field plane wave assumption is also not suitable. Therefore, in 6G networks, the near-field region is not negligible, which motivates the investigation of the new near-field communication (NFC).

    NF beamfocusing
    Fig. 1: Beamforming in FFC and NFC.

    NF region
    Fig. 2: Illustration of field regions of (a) a continuous antenna element and (b) a spatially-discrete antenna array with respect to a point receiver.
    Some of our interesting recent Near-Field Communications/Sensing (NFC/NISE) works are below [Tutorial] [CL--Near-Field Integrated Sensing and Communications] [JSTSP--Near-Field Integrated Sensing and Communications] [TWC--Near-field Beamforming Design] [TCOM--Near-field Multi-user Beamfocusing Design] [Near-field Degrees-of-Freedom Analysis] [RIS-aided NFC]

    2) Next Generation Multiple Access (NGMA)

    Next generation wireless networks are expected to achieve significantly high capacity, extremely low latency, ultra high reliability, as well as massive and ubiquitous connectivity for supporting diverse disruptive applications. One of the most fundamental issues is the design of sophisticated multiple access (MA) techniques for the next generation wireless networks, namely next generation multiple access (NGMA). The key concept of NGMA is to enable a tremendous number of users/devices to be efficiently, flexibly, and intelligently connected with the network over the given wireless radio resources for not only satisfying stringent communication requirements but also realizing heterogenous functions. This project will explore the development of NGMA by exploiting NOMA and multiple antenna techniques with the aid of convex optimization and machine learning methods. In particular, a unified framework for NGMA is proposed based on multi-antenna transmission and NOMA, including both downlink and uplink multi-user communication scenarios. The aim of the proposed unified NGMA framework is to fully reap the benefits of multiple antenna and NOMA techniques and to enable highly flexible and customized communication services for future wireless networks. This opens up numerous research opportunities and directions.

    Downlink NGMA
    Fig. 1: Downlink NGMA model.

    UpLink NGMA
    Fig. 2: Uplink NGMA model.

    The details of the proposed unified NGMA framework can be founded in the survey/tutorial paper. [Paper]

    3) Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs)

    The novel concept of simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surfaces (RISs) is proposed in this project, where the incident wireless signal is divided into transmitted and reflected signals passing into both sides of the space surrounding the surface, thus facilitating a full-space manipulation of signal propagation. In this project, we will investigate (1) the fundamental electromagnetic and hardware models [Paper1][Paper2]; (2) the basic communication signal model and operating protocols [Paper1][Paper4]; (3) the corresponding fundamental performance analysis, beamforming design, and channel estimation methods [Paper4][Paper5][Paper6][Paper7]; (4) the interplay between STAR-RIS and other promising techniques and advanced tools [Paper7]. The full details of the proposed concept can be found in the presentation slides [Slides].

    STAR principle
    Fig. 1: The concept of STAR. [Slides][Paper1][Paper2]

    STAR protocol
    Fig. 2: Operating protocols for STAR.
    Some of our interesting recent Simultaneously Transmitting And Reflecting Surface (STARS) works are below [Presentation slides] [WCM-Protocol Design] [TWC--Beamforming Design] [CL--Modelling and Performance Analysis] [CL--Coverage Characterization] [Hardware Modelling and Implementation of STARS] [STARS for Intergrated Sensing and Communications]

    4) Integrated Sensing and Communication (ISAC)

    Integrated Sensing and Communication (ISAC) is a promising technique for the next generation wireless network. Unlike the past five generations of wireless network where there is only one envisaged function, i.e., wireless communication, the next generation wireless network expects a paradigm shift to support multiple functions including the communication, sensing, control, and computation to meet the demand of the fusion techniques such as unmanned aerial vehicles (UAVs), autonomous driving, virtual and augmented reality, Internet of vehicles, and smart cities. In ISAC, the sensing and communication are not regarded as independent functions but are jointly designed toward the goals and mutual benefits of both, which introduce two gains to the wireless network, namely integration gain and coordination gain. Integration gain is from the dual usage of the signal, spectrum, and device in the ISAC for both functions, thereby reducing the cost. Coordination gain is from the deeper level integration in ISAC to achieve mutual assistance between the two functions, such as the communication-assisted sensing and the sensing-assisted communication. This project will explore the interplay between ISAC and non-orthogonal multiple access (NOMA) in the following aspect: 1) use NOMA to boost the load capability of ISAC [Paper]; 2) follow the concept of NOMA to manage or exploit the interference in ISAC; 3) investigate the potential of NOMA as the enabler of more sophisticated ISAC frameworks [Paper].

    ISAC model

    1)Non-Orthogonal Multiple access (NOMA) for Beyond 5G and IoT networks

    Non-orthogonal multiple access (NOMA), which has been recently proposed for the 3rd generation partnership projects long-term evolution advanced (3GPP-LTE-A), constitutes a promising technology of addressing the above-mentioned challenges in 5G networks by accommodating several users within the same orthogonal resource block. By doing so, significant bandwidth efficiency enhancement can be attained over conventional orthogonal multiple access (OMA) techniques. This motivated numerous researchers to dedicate substantial research contributions to this field. We advocate on power-domain multiplexing aided NOMA, with a focus on the theoretical NOMA principles, multiple antenna aided NOMA design, on the interplay between NOMA and cooperative transmission, on the resource control of NOMA, semi-grant-free NOMA [Paper], on the co-existence of NOMA with other emerging potential 5G techniques and on the comparison with other NOMA variants. We highlight the main advantages of power-domain multiplexing NOMA compared to other existing NOMA techniques. We investigate the application of machine learning into NOMA networks [Paper] [Paper].


    This project is jointly worked with [Princeton University], [Southampton University], [Lancaster University] and [China Mobile]. Details for this project can be found in [Paper] and [Magazine]. An interesting demonstration for this project is available on [Video] [Researchgate] and [Slides]. A full tutorial version is available here [Tutorial]. The below video presents our recent research results on the application of NOMA towards Next Generation Multiple Access in 6G.

    2) Reconfigurable Intelligent Surfaces (RIS) Aided Multi-user Communications

    This project is to introduce the multi-user RIS systems, with the particular focus on the interplay between non-orthogonal multiple access (NOMA) and RIS. More particularly, in this project, I will 1) describe the basic principles and modelling of RISs both from physics and communications perspectives [Paper] [Paper]; 2) present performance evaluation of multi-antenna assisted multi-user orthogonal multiple access (OMA)/NOMA RIS systems [Paper] [Paper] [Paper] [Paper] , with considering the spatial effect [Paper] [Paper] [Paper]; 3) introduce beamforming design in NOMA-RIS system [Paper] [Paper]; 4) exploit the effective resource management in NOMA-RIS single cell [Paper] [Paper] [Paper] and multiple cell networks; 5) investigate the deployment and multiple access in multi-user RIS systems [Paper]; 6) exploit the applications of machine learning to RIS-NOMA networks [Paper]. 7) summary the recent results [Paper] and discuss possible future research opportunities.


    A survey/tutorial paper for this project is available on [Paper]. More related works can be found in my publications.

    3) AI enabled UAV Communications based OMA/NOMA

    This project proposes a novel framework for unmaned aerial vehicle (UAV) networks with massive access capability supported by non-orthogonal multiple access (NOMA). In order to better understand NOMA enabled UAV networks, three case studies are carried out. We first provide performance evaluation of NOMA enabled UAV networks by adopting stochastic geometry to model the positions of UAVs and ground users [Paper] [Paper] [Paper] [Paper] [Paper] [Paper]. Then we investigate the joint trajectory design and power allocation for static NOMA users based on a simplified two-dimensional (2D) model that UAV is flying around at fixed height [Paper] [Paper] [Paper]. As a further advance, we demonstrate the UAV placement issue with the aid of machine learning techniques when the ground users are roaming and the UAVs are capable of adjusting their positions in three-dimensions (3D) accordingly [Paper] [Paper] [Paper] [Paper] [Paper]. With these case studies, we can comprehensively understand the UAV systems from fundamental theory to practical implementation.


    Details for this project are available on [Paper]. More related works can be found in my publications.

    4) When Big Data Meets Machine Learning in Wireless Networks

    Recent several decades have witnessed the exponential growth in commercial data services, which lead to step in the so-called big data era. The pervasive increasing data traffic present both the imminent challenges and new opportunities to all aspects of wireless system design, such as efficient wireless caching, base station deployment and adaptive multiple access design. Machine learning, as one of the most promising artificial intelligence tools, has been invoked in many areas both in the academia and industry. Nevertheless, the application of machine learning in wireless communication scenarios is still in its infancy, which motivates to develop this project. The aim of this project is to use social media data to predict the requirements of mobile users for improving the performance of wireless networks. More particularly, a unified machine learning framework with the aid of the social media data is proposed in this project. Four stages are included in the proposed framework, which consists syntax processing, semantics analysis, data modelling and online prediction/refinement. The main benefits of the proposed framework is by utilizing the social media data which reflect the real requirements of users, to assist refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of wireless networks. The proposed framework is as follows.


    Details for this project are available on [Paper]

    5) Integrated Intelligent Sensing and Computer Vision

    Human Activity Recognition (HAR) has recently received remarkable attentions in numerous applications such as assisted living and remote monitoring. Existing solutions based on sensors and vision technologies have obtained achievements but still suffering from considerable limitations in the environmental requirement. Wireless signals like WiFi-based sensing has emerged as a new paradigm for HAR since it is convenient and not restricted in the environment. In this paper, a new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition where the synchronized video serves as the supplement for the wireless data. Moreover, a wireless-vision benchmark (WiVi) is collected for 9 class actions recognition in three different visual conditions, including the scenes without occlusion, with partial occlusion, and with full occlusion. Both machine learning method - support vector machine (SVM) as well as deep learning methods are used for the accuracy verification of the data set. We demonstrate that WiVi data set satisfies the primary demand and all three branches in proposed pipeline keep more than 80% of activity recognition accuracy over multiple action segmentation from 1s to 3s. In particular, WiNN is the most robust method in terms of all the actions on three action segmentation compared to the others.


    Details for this project are available on [Paper] [Youtube] [Youtube Full Presentation].

    6) Matching Theory Based Resource Allocation in 5G Networks and Beyond

    Matching theory is a powerful tool to study the formation of dynamic and mutually beneficial relations among different types of rational and selfish agents. It has been widely used to develop high performance, low complexity, and decentralized protocols. Recently, there has been significant progress in intensive research work that uses matching theory to handle resource allocation problems in wireless networks. We are working on invoke matching theory to solve the resource allocation problems in 5G networks and beyond, such as in NOMA, D2D communications, heterogeneous cellular networks, millimeter wave communications, etc. A simple matching algorithm is listed as follows.


    Details for this project are available on [Paper] and [Slides]. More related works can be found in my publications.

    7) Stochastic Geometry on Wireless Communications

    Stochastic geometry is a powerful mathematical and statistical tool for modeling and analyzing wireless networks. Unlike the traditional topology approaches which always ignore the density and mobility of nodes, stochastic geometry is capable of capturing the topological randomness of the networks and hence can provide tractable analytical results for the average network behaviors according to some distributions. This is particularly essential in large-scale networks which consist of a large number of randomly deployed nodes (e.g., BSs, mobile users, etc.) whose channels and locations are with high uncertainty. We are invoking stochastic geometry to model different wireless network scenarios, such as physical layer security, UAV communications, millimeter wave communications, NOMA, IoT networks, etc. Below figure is an example of stochastic geometry model, which shows the spatial distribution of a wireless powered secure D2D communication scenario.


    Details for this project are available on [Paper] and [Slides]. More detials for stochastic geometry can be found in my [Thesis].

    8) Exploiting QoE Awareness in Wireless Networks: A Cross-Layer Perspective

    Wireless networks are conventionally evaluated using PHY connection-centric metrics such as throughput or sum-rate capacity. However, conventional connection-centric designs have become a barrier to meeting the diverse application requirements and the quality expectation of the end users, especially for the rapid expanding visual-experience-oriented services, such as VR, AR, and video streaming. Even though the data rate and throughput are increasing, current mobile networks are still facing poor user experience and low service quality. The gap between the PHY system performance and higher-level user’s perceived experience drives a paradigm shift from connection-centric designs to experience-centric designs. QoE is the perceptual quality of service (QoS) from the user’s perspective. Given the context that the growth of future traffic demands is largely driven by the visual-experience-oriented services, such as VR, AR, and video streaming, there is broad consensus among leading industry and academic initiatives that improving user’s QoE is a key pillar to sustaining the revenue of service providers in future networks.


    Details for this project are available on [Paper] and [Poster].