Task-Oriented Communication Design at Scale

With countless promising applications in various domains such as IoT and Industry 4.0, task-oriented communication design (TOCD) is getting accelerated attention from the research community. This paper presents a novel approach for designing scalable task-oriented quantization and communications in...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 73; no. 1; pp. 378 - 393
Main Authors: Mostaani, Arsham, Vu, Thang X., Habibi, Hamed, Chatzinotas, Symeon, Ottersten, Bjorn
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Communication
communication for machine learning
Complexity
Control systems
Costs
Industrial applications
Industry 4.0
joint communication and control
Multi-agent systems
Multiagent systems
Policies
Quantization (signal)
Reinforcement learning
Task analysis
Task-oriented data compression
Time complexity
Training
Training
joint communication and control
Quantization (signal)
Costs
Reinforcement learning
communication for machine learning
Task-oriented data compression
Task analysis
Time complexity
Multi-agent systems
ISSN:0090-6778, 1558-0857, 1558-0857
Online Access:Get full text
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Summary:With countless promising applications in various domains such as IoT and Industry 4.0, task-oriented communication design (TOCD) is getting accelerated attention from the research community. This paper presents a novel approach for designing scalable task-oriented quantization and communications in cooperative multi-agent systems (MAS). The proposed approach utilizes the TOCD framework and the value of information (VoI) concept to enable efficient communication of quantized observations among agents while maximizing the average return performance of the MAS, a parameter that quantifies the MAS's task effectiveness. The computational complexity of learning the VoI, however, grows exponentially with the number of agents. Thus, we propose a three-step framework: (i) learning the VoI (using reinforcement learning (RL)) for a two-agent system, (ii) designing the quantization policy for an N-agent MAS using the learned VoI for a range of bit-budgets and, (iii) learning the agents' control policies using RL while following the designed quantization policies in the earlier step. Our analytical results show the applicability of the proposed framework under a wide range of problems. Numerical results show striking improvements in reducing the computational complexity of obtaining VoI needed for the TOCD in a MAS problem without compromising the average return performance of the MAS.
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ISSN:0090-6778
1558-0857
1558-0857
DOI:10.1109/TCOMM.2024.3416898