Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917809064247296 |
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| author | Wu, Yinyu Zhang, Xuhui Ren, Jinke Xing, Huijun Shen, Yanyan Cui, Shuguang |
| author_facet | Wu, Yinyu Zhang, Xuhui Ren, Jinke Xing, Huijun Shen, Yanyan Cui, Shuguang |
| contents | Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently. Numerical results demonstrate that the proposed algorithm can achieve lower latency than two baseline algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02047 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning Wu, Yinyu Zhang, Xuhui Ren, Jinke Xing, Huijun Shen, Yanyan Cui, Shuguang Systems and Control Artificial Intelligence Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently. Numerical results demonstrate that the proposed algorithm can achieve lower latency than two baseline algorithms. |
| title | Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2408.02047 |