Li Qiao

Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao et al.

Generative foundation AI models have recently shown great success in synthesizing natural signals with high perceptual quality using only textual prompts and conditioning signals to guide the generation process. This enables semantic communications at extremely low data rates in future wireless networks. In this paper, we develop a latency-aware semantic communications framework with pre-trained generative models. The transmitter performs multi-modal semantic decomposition on the input signal and transmits each semantic stream with the appropriate coding and communication schemes based on the intent. For the prompt, we adopt a re-transmission-based scheme to ensure reliable transmission, and for the other semantic modalities we use an adaptive modulation/coding scheme to achieve robustness to the changing wireless channel. Furthermore, we design a semantic and latency-aware scheme to allocate transmission power to different semantic modalities based on their importance subjected to semantic quality constraints. At the receiver, a pre-trained generative model synthesizes a high fidelity signal using the received multi-stream semantics. Simulation results demonstrate ultra-low-rate, low-latency, and channel-adaptive semantic communications.

Hang Yin, Li Qiao, Yu Ma et al.

Despite significant advancements in traditional syntactic communications based on Shannon's theory, these methods struggle to meet the requirements of 6G immersive communications, especially under challenging transmission conditions. With the development of generative artificial intelligence (GenAI), progress has been made in reconstructing videos using high-level semantic information. In this paper, we propose a scalable generative video semantic communication framework that extracts and transmits semantic information to achieve high-quality video reconstruction. Specifically, at the transmitter, description and other condition signals (e.g., first frame, sketches, etc.) are extracted from the source video, functioning as text and structural semantics, respectively. At the receiver, the diffusion-based GenAI large models are utilized to fuse the semantics of the multiple modalities for reconstructing the video. Simulation results demonstrate that, at an ultra-low channel bandwidth ratio (CBR), our scheme effectively captures semantic information to reconstruct videos aligned with human perception under different signal-to-noise ratios. Notably, the proposed ``First Frame+Desc." scheme consistently achieves CLIP score exceeding 0.92 at CBR = 0.0057 for SNR > 0 dB. This demonstrates its robust performance even under low SNR conditions.

Xinkai Liu, Mahdi Boloursaz Mashhadi, Li Qiao et al.

Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality, enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framework, the transmitter decomposes the source signal into multiple semantic classes based on the multi-user intent, i.e. each user is assumed to be interested in details of only a subset of the semantic classes. To better utilize the wireless resources, the transmitter sends to each user only its intended classes, and multicasts a highly compressed semantic map to all users over shared wireless resources that allows them to locally synthesize the other classes, namely non-intended classes, utilizing pre-trained diffusion models. The signal retrieved at each user is thereby partially reconstructed and partially synthesized utilizing the received semantic map. We design a communication/computation-aware scheme for per-class adaptation of the communication parameters, such as the transmission power and compression rate, to minimize the total latency of retrieving signals at multiple receivers, tailored to the prevailing channel conditions as well as the users' reconstruction/synthesis distortion/perception requirements. The simulation results demonstrate significantly reduced per-user latency compared with non-generative and intent-unaware multicasting benchmarks while maintaining high perceptual quality of the signals retrieved at the users.