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Source Value-Based Resource Allocation in Task-Oriented Communications
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AI
DOI：10.1109/JIOT.2024.3430905.png)
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Abstract
En 中文
With the explosive growth of communication requirements for real-time intelligent tasks, mobile communication is shifting from the traditional communication to task-oriented communication, where the transmitted data is shifting from undifferentiated transmission to value-oriented transmission. To maximize the value of transmitted data, it is urgent to match the source decisions with the task demands and wireless channel state. In this article, we focus on the joint source-channel optimization problem in task-oriented communication, and we design the timeliness-accuracy degradation (TAD) metric to measure the value of transmitted source. Moreover, we design a source value-based resource allocation scheme to minimize the TAD through joint optimization of task data generation and compression strategies, bandwidth allocation, and transmit power selection. Furthermore, to avoid the curse of dimensionality, we propose dimension-refined reinforcement learning (DRRL) algorithm to obtain the optimal solution of the problem in a stable and low-complexity manner. Numerical results demonstrate that the designed scheme can effectively improve the task performance and verify the low complexity and stability of the algorithm.
Keywords:
Task analysis
Measurement
Optimization
Resource management
Communication systems
Delays
Performance evaluation
Edge intelligence (EI)
resource allocation
task-oriented communication
value of information
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W