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Research Interests


My general research interests are to develop sustainable, ubiquitous, and green B5G/6G networks with advanced physical layer technologies and methods, with particular focus on the below topics.

Brief introduces of each topic above and selected research contributions can be found below.

Simultaneously Transmitting and Reflecting Surfaces (STARS)


        
              Fig. 1: Conventional reflective RIS.                                     Fig. 2: Concept of STARS.

     
              Fig. 3: STARS operating protocols.                                         Fig. 4: ETSI activties.

Over the past few years, reconfigurable intelligent surface (RIS) technology has been a heavily researched topic. Nevertheless, most of conventional RISs can only reflect the incident wireless signals, which leads to limited flexibility due to the half-space smart radio environment (see Fig. 1). To address this issue, we proposed the concept of simultaneously transmitting and reflecting surfaces (STARS), where the incident wireless signals can be not only reflected into one side of the surface but also transmitted (refracted) into the other side, i.e., realizing a full-space smart radio environment (see Fig. 2). The integration of transmission and reflection makes STAR-RIS fundamentally different from existing RISs. To address these challenges and reap its full benefits, our research contributions include but not limit to:

Near-field Communications and Sensing (NFC/NISE)


      
                                                      Fig. 1: Near-field Communications and Sensing.

Multiple-input multiple-out (MIMO) is one key enabling technology for 5G/6G and beyond. To satisfy the new stringent requirements in next-generation wireless networks, (extremely) large-scale MIMO and ultra-high operating frequencies have to be employed. This, however, leads to significantly large spherical-wave-based near-field regions with respect to transceivers, which provide new opportunities for advanced communication/sensing designs. For example, the 'spotlight' beam focusing can be achieved in the spherical-wave-based near-field region, which is fundamentally different from the 'flashlight' beam steering in existing far-field region (see Fig. 1). This enables more precise and energy-efficient near-field communication (NFC) design with less inter-user interference. Moreover, for near-field sensing (NISE), it enables distance-domain sensing only using the narrow bandwidth, which is more spectrum-efficient, and advanced velocity estimation. The potentials of NFC/NISE have not been fully characterized and unlocked, which need further research efforts. Our research contributions include but not limit to:

Next Generation Multiple Access (NGMA)

Multiple access (MA) is one of the fundamental technologies in wireless networks. Future wireless networks not only have to satisfy stringent communication requirements but also have to support diverse functionalities and connect heterogeneous types of devices. In line with this, existing communication-oriented MA schemes are expected to be replaced by next generation multiple access (NGMA). In the recently published International Mobile Telecommunications (IMT)-2030 Framework, it has been highlighted that ``for multiple access, technologies including NOMA and grant free multiple access are expected to be considered to meet future requirements''. Our research exploits the “non-orthogonality” principle and push the evolution from non-orthogonal multiple access (NOMA) to NGMA with the following conducted research topics.

1. NOMA-empowered Integrated Sensing and Communications (ISAC)

                  
                                                    Fig. 1: Promising Applications of ISAC.

        
                         Fig. 2: Inter-functionality Interference Management in Downlink and Uplink ISAC.

Despite being promising for 6G (Fig. 1), the strike of a good performance trade-off between the two functionalities is a challenging task when designing ISAC. The intrinsic reason is that ISAC may suffer from severe inter-functionality interference due to the hardware platform and radio resource sharing. This calls for the development of efficient interference mitigation and resource management approaches. The prominent features of NOMA in efficient interference management and flexible resource allocation match well with the requirements encountered in ISAC. Our main research contributions are listed below.

2. Interplay between NOMA and Semantic Communications

        
                                Fig. 1: Illustration of Semantic Communications for Image Recognition.

Current wireless communication designs still focus on the technical-level problem using the Shannon classical information theory, i.e., How accurately can the symbols of communication be transmitted? There is a paucity of investigations on the other two semantic-level and effectiveness-level, i.e., How precisely do the transmitted symbols convey the desired meaning? and How effectively does the received meaning affect conduct in the desired way? which are more related to the ultimate goal of communications problems.

In response to the semantic- and effectiveness-level communication problems, semantic communications have recently attracted significant research attentions from both industry and academia. In semantic communications, only the key information that contains the specific meaning/actions/goals relevant to the destinations needs to be transmitted, as shwon in Fig. 1.

          
                     Fig. 2: Semi-NOMA for Heterogeneous Semantic and Bit Downlink Communications.

       
                                    Fig. 3: Opportunistic Semantic and Bit Communications in Uplink NOMA.

Against this background, our research focuses on the interplay between NOMA and semantic communications:

Mobile Edge Generation (MEG)

Artificial intelligent generated content (AIGC) has shown remarkable success since 2022. However, conventional centralized AIGC service suffers from several issues, e.g., high communication overhead and latency, the lack of diversity and personalization. To overcome these obstacles, we proposed a novel concept of mobile edge generation (MEG), where generative artificial intelligence (GAI) models are distributed at edge servers (ESs) and user equipment (UE) to enable joint execution of generation tasks. As the investigation of MEG is in a very early stage, there are numerous challenges to be addressed. Our research contributions are listed as below: