Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks
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arXiv
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| Format: | Preprint |
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2024
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| author | Wang, Jiaxiang Yang, Yinchao Yang, Zhaohui Huang, Chongwen Chen, Mingzhe Zhang, Zhaoyang Shikh-Bahaei, Mohammad |
| author_facet | Wang, Jiaxiang Yang, Yinchao Yang, Zhaohui Huang, Chongwen Chen, Mingzhe Zhang, Zhaoyang Shikh-Bahaei, Mohammad |
| contents | 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 single 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. Since the standard signal-to-interference-plus-noise ratio (SINR) equation does not accurately capture the performance of our semantic communication system, we introduce a weight parameter into the SINR equation to better represent the system's performance. Due to its 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 each user's performance. 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), respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_20937 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks Wang, Jiaxiang Yang, Yinchao Yang, Zhaohui Huang, Chongwen Chen, Mingzhe Zhang, Zhaoyang Shikh-Bahaei, Mohammad Signal Processing 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 single 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. Since the standard signal-to-interference-plus-noise ratio (SINR) equation does not accurately capture the performance of our semantic communication system, we introduce a weight parameter into the SINR equation to better represent the system's performance. Due to its 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 each user's performance. 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), respectively. |
| title | Generative AI Empowered Semantic Feature Multiple Access (SFMA) Over Wireless Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.20937 |