Integrated Sensing, Communication, and Computing: An Information-oriented Resource Transaction Mechanism
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909078908829696 |
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| author | Chen, Ning Cheng, Zhipeng Fan, Xuwei Liu, Zhang Huang, Bangzhen Yang, Jie Zhao, Yifeng Huang, Lianfen |
| author_facet | Chen, Ning Cheng, Zhipeng Fan, Xuwei Liu, Zhang Huang, Bangzhen Yang, Jie Zhao, Yifeng Huang, Lianfen |
| contents | Information acquisition from target perception represents the key enabling technology of the Internet of Automatic Vehicles (IoAV), which is essential for the decision-making and control operation of connected automatic vehicles (CAVs). Exploring target information involves multiple operations on data, e.g., wireless sensing (for data acquisition), communication (for data transmission), and computing (for data analysis), which all rely on the consumption of time-space-frequency-computing (TSFC) multi-domain resources. Due to the coupled resource sharing of sensing, communication, and computing procedures, the resource management of information-oriented IoAV is commonly formulated as a non-convex NP-hard problem. In this article, further combining the integrated sensing and communication (ISAC) and computing, we introduce the integrated sensing, communication, and computing (ISCC), wherein the TSFC resources are decoupled from the specific processes and shared universally among sensing, communication, and computing processes. Furthermore, the information-oriented resource trading platform (IRTP) is established, which transforms the problem of ISCC resource management into a resource-information substitution model. Finally, we embed the employment topology structure in IoAV into neural network architecture, taking advantage of the graph neural network (GNN) and multi-worker reinforcement learning, and propose the dynamic resource management strategy based on the asynchronous advantage GNN (A2GNN) algorithm, which can achieve the convergence both of information gain maximization and resource consumption minimization, realizing efficient information-oriented resource management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_11759 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Integrated Sensing, Communication, and Computing: An Information-oriented Resource Transaction Mechanism Chen, Ning Cheng, Zhipeng Fan, Xuwei Liu, Zhang Huang, Bangzhen Yang, Jie Zhao, Yifeng Huang, Lianfen Distributed, Parallel, and Cluster Computing Information acquisition from target perception represents the key enabling technology of the Internet of Automatic Vehicles (IoAV), which is essential for the decision-making and control operation of connected automatic vehicles (CAVs). Exploring target information involves multiple operations on data, e.g., wireless sensing (for data acquisition), communication (for data transmission), and computing (for data analysis), which all rely on the consumption of time-space-frequency-computing (TSFC) multi-domain resources. Due to the coupled resource sharing of sensing, communication, and computing procedures, the resource management of information-oriented IoAV is commonly formulated as a non-convex NP-hard problem. In this article, further combining the integrated sensing and communication (ISAC) and computing, we introduce the integrated sensing, communication, and computing (ISCC), wherein the TSFC resources are decoupled from the specific processes and shared universally among sensing, communication, and computing processes. Furthermore, the information-oriented resource trading platform (IRTP) is established, which transforms the problem of ISCC resource management into a resource-information substitution model. Finally, we embed the employment topology structure in IoAV into neural network architecture, taking advantage of the graph neural network (GNN) and multi-worker reinforcement learning, and propose the dynamic resource management strategy based on the asynchronous advantage GNN (A2GNN) algorithm, which can achieve the convergence both of information gain maximization and resource consumption minimization, realizing efficient information-oriented resource management. |
| title | Integrated Sensing, Communication, and Computing: An Information-oriented Resource Transaction Mechanism |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2401.11759 |