Abstract
This paper investigates a novel generative artificial intelligence (GAI) empowered multi-user semantic communication system called semantic feature multiple access (SFMA) for video transmission, which comprises a base station (BS) and paired users. The BS generates and combines semantic information of several frames simultaneously requested by paired users into a signal. Users recover their frames from this combined signal and input the recovered frames into a GAI-based video frame interpolation model to generate the intermediate frame. To optimize transmission rates and temporal gaps between simultaneously transmitted frames, we formulate an optimization problem to maximize the system sum rate while minimizing temporal gaps. We observe that the standard signal-to-interference-plus-noise ratio (SINR) equation does not accurately capture the performance of our semantic communication system. Therefore, we introduce a weight parameter into the SINR equation to better represent the system's performance. Due to the complexity introduced by the weight parameter's dependence on transmit power, we propose a three-step solution. First, we develop a user pairing algorithm that pairs two users with the highest preference value, a weighted combination of semantic transmission rate and temporal gap. Second, we optimize inter-group power allocation by formulating an optimization problem that allocates proper transmit power across all user groups to maximize system sum rates while satisfying each user's minimum rate requirement. Third, we address intra-group power allocation to enhance the performance of each user. This research is supported in part by National Key RD Program of China (Grant No. 2023YFB2904804), in part by Young Elite Scientists Sponsorship Program by CAST 2023QNRC001, in part by the National Natural Science Foundation of China under Grants 62331023, 62394292 and U20A20158, in part by Zhejiang Provincial Science and Technology Plan Project under Grant 2024C01033, in part by Zhejiang University Global Partnership Fund, in part by Ministry of Industry and Information Technology under Grant TC220H07E, in part by Zhejiang Provincial Key R&D Program under Grant 2023C01021 and the Fundamental Research Funds for the Central Universities under Grant 226-2024-00069, and in part by the U.S. National Science Foundation under Grants ECCS-2434054 and CNS-2332834. Simulation results demonstrate that our method improves transmission rates by up to 24.8%, 45.8%, and 66.1% compared to fixed-power non-orthogonal multiple access (F-NOMA), orthogonal joint source-channel Coding (O-JSCC), and orthogonal frequency division multiple access (OFDMA) schemes, respectively.