Nan Li’s scientific contributions

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Publications (4)


Fig. 1: Metaverse Construction Task
Fig. 2: Goal-oriented Semantic Communication Framework for the Metaverse Construction
Fig. 3: Knowledge Base Extraction
Fig. 4: Semantic Encoder
Fig. 5: Semantic Decoder for Metaverse Construction

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Goal-oriented Semantic Communications for Metaverse Construction via Generative AI and Optimal Transport
  • Preprint
  • File available

November 2024

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17 Reads

Zhe Wang

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Nan Li

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Yansha Deng

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The emergence of the metaverse has boosted productivity and creativity, driving real-time updates and personalized content, which will substantially increase data traffic. However, current bit-oriented communication networks struggle to manage this high volume of dynamic information, restricting metaverse applications interactivity. To address this research gap, we propose a goal-oriented semantic communication (GSC) framework for metaverse. Building on an existing metaverse wireless construction task, our proposed GSC framework includes an hourglass network-based (HgNet) encoder to extract semantic information of objects in the metaverse; and a semantic decoder that uses this extracted information to reconstruct the metaverse content after wireless transmission, enabling efficient communication and real-time object behaviour updates to the scenery for metaverse construction task. To overcome the wireless channel noise at the receiver, we design an optimal transport (OT)-enabled semantic denoiser, which enhances the accuracy of metaverse scenery through wireless communication. Experimental results show that compared to the conventional metaverse construction, our proposed GSC framework significantly reduces wireless metaverse construction latency by 92.6\%, while improving metaverse object status accuracy and viewing experience by 45.6\% and 44.7\%, respectively.

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Goal-Oriented Semantic Communication for Wireless Visual Question Answering with Scene Graphs

November 2024

As demands for communication and computational capabilities escalate, traditional bit-oriented communication falls short of these stringent requirements, especially for mission-critical and computation-intensive applications. Visual Question Answering (VQA), a representative application, has adopted edge computing to mitigate local computational constraints and accelerate visual perception with natural language. However, it encounters significant communication challenges such as limited bandwidth, reduced transmission power, and increased noise levels, leading to considerable latency and reduced efficiency in image and question transmission. we propose a goal-oriented semantic communication (GSC) framework that focuses on effectively extracting and transmitting semantic information most relevant to the VQA goals, improving the answering accuracy and enhancing the effectiveness and efficiency. The objective is to maximize the answering accuracy, and we propose a scene graphs (SG)-based image semantic extraction and ranking approach to prioritize the semantic information based on the goal of questions. Experimental results demonstrate that our GSC framework improves answering accuracy by up to 59% under Rayleigh channels while reducing total latency by up to 65% compared to traditional bit-oriented transmission.


Fig. 1. Image communication framework and goal-oriented semantic communication framework for metaverse application
Fig. 2. Nerf-based metaverse construction
Goal-oriented Semantic Communication for the Metaverse Application

August 2024

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26 Reads

With the emergence of the metaverse and its role in enabling real-time simulation and analysis of real-world counterparts, an increasing number of personalized metaverse scenarios are being created to influence entertainment experiences and social behaviors. However, compared to traditional image and video entertainment applications, the exact transmission of the vast amount of metaverse-associated information significantly challenges the capacity of existing bit-oriented communication networks. Moreover, the current metaverse also witnesses a growing goal shift for transmitting the meaning behind custom-designed content, such as user-designed buildings and avatars, rather than exact copies of physical objects. To meet this growing goal shift and bandwidth challenge, this paper proposes a goal-oriented semantic communication framework for metaverse application (GSCM) to explore and define semantic information through the goal levels. Specifically, we first analyze the traditional image communication framework in metaverse construction and then detail our proposed semantic information along with the end-to-end wireless communication. We then describe the designed modules of the GSCM framework, including goal-oriented semantic information extraction, base knowledge definition, and neural radiance field (NeRF) based metaverse construction. Finally, numerous experiments have been conducted to demonstrate that, compared to image communication, our proposed GSCM framework decreases transmission latency by up to 92.6% and enhances the virtual object operation accuracy and metaverse construction clearance by up to 45.6% and 44.7%, respectively.


Goal-Oriented Semantic Communication for Wireless Image Transmission via Stable Diffusion

August 2024

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2 Reads

Efficient image transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality image reconstruction over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we design a semantic autoencoder that effectively extracts semantic information from images to reduce the transmission data size while ensuring high-quality reconstruction. Recognizing the impact of wireless channel noise on semantic information transmission, we propose an SD-based denoiser for GSC (SD-GSC) conditional on instantaneous channel gain to remove the channel noise from the received noisy semantic information under known channel. For scenarios with unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received semantic information. Experimental results show that SD-GSC outperforms state-of-the-art ADJSCC and Latent-Diff DNSC, with the Peak Signal-to-Noise Ratio (PSNR) improvement by 7 dB and 5 dB, and the Fr\'echet Inception Distance (FID) reduction by 16 and 20, respectively. Additionally, PSD-GSC archives PSNR improvement of 2 dB and FID reduction of 6 compared to MMSE equalizer-enhanced SD-GSC.