Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels

Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often be...

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Bibliographic Details
Published in:IEEE open journal of the Communications Society Vol. 3; p. 1
Main Authors: Mostaani, Arsham, Vu, Thang X., Chatzinotas, Symeon, Ottersten, Bjorn
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Collaboration
Communication channels
communications for machine learning
Communications systems
Data compression
data quantization
machine learning for communications
Multi-agent systems
Multiagent systems
Quantization (signal)
semantic communications
Semantics
Task analysis
Task-oriented communications
semantic communications
data quantization
Task-oriented communications
machine learning for communications
communications for machine learning
ISSN:2644-125X, 2644-125X
Online Access:Get full text
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Summary:Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications.
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ISSN:2644-125X
2644-125X
DOI:10.1109/OJCOMS.2022.3213213