Multi-Agent Data Transmission with Joint QoS and Encoding Control
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Solution Overview
Problem
Existing multi-agent systems face challenges in ensuring efficient and reliable data transmission with minimal end-to-end latency and maximizing performance, particularly in dynamic environments, due to suboptimal selection of encoding and communication parameters, inefficient resource utilization, interference among agents, and lack of adaptability to varying network conditions.
Innovation Solution
A method for optimizing data transmission that involves classifying raw data streams based on predetermined characteristics, obtaining interdependent QoS requests, and computing a figure of merit to jointly optimize encoding and communication parameters using bi-optimization or weighted sum methods, ensuring minimal latency and optimal quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If encoding parameters are chosen based on agent capabilities and desired data quality, then data quality is improved, but transmission latency increases due to higher bandwidth requirements
Solution Approach 1:
The patent dynamically adjusts encoding parameters (bitrate, resolution, compression level) based on current network conditions, agent capabilities, and QoS requirements. This allows the system to optimize the trade-off between data quality and transmission latency by changing parameters in real-time rather than using fixed settings
Solution Approach 2:
The system implements dynamic resource allocation where encoding and communication parameters are continuously optimized based on varying network conditions, traffic patterns, and QoS requirements. This dynamic approach allows the system to adapt to changing conditions and maintain optimal performance
2Productivity
If communication parameters are selected based on available network resources and transmission range, then transmission efficiency is improved, but data quality deteriorates due to compression
Solution Approach 1:
The patent jointly optimizes encoding parameters and communication parameters by considering their interdependence. The system changes parameters dynamically based on network conditions, ensuring that compression levels and transmission settings work together to maintain data quality while maximizing transmission efficiency
Solution Approach 2:
The patent merges the optimization of encoding parameters and communication parameters into a unified framework. By considering the interdependence between these parameters, the system achieves joint optimization that prevents suboptimal performance from independent optimization
3Productivity
If multiple agents transmit data simultaneously over shared network resources, then system throughput is improved, but interference increases causing latency and performance degradation
Solution Approach 1:
The patent implements QoS differentiation where different data streams receive different levels of service based on their specific requirements, agent priorities, and network conditions. This allows critical data to receive higher priority transmission while less critical data uses available capacity, reducing interference and latency for important streams
Solution Approach 2:
The system dynamically allocates network resources among multiple agents based on current traffic patterns, QoS requirements, and network conditions. This dynamic resource allocation optimizes system throughput while managing interference and latency through adaptive control
4Device complexity
If static resource allocation is used among agents, then system complexity is reduced, but adaptability to dynamic environments and varying network conditions deteriorates
Solution Approach 1:
The patent implements self-organizing mechanisms where agents and network elements autonomously adjust their behavior based on local conditions and feedback. This allows the system to adapt to dynamic environments through distributed decision-making rather than centralized control, maintaining low complexity while achieving high adaptability
Data Source
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AI summary
The invention relates to a method for optimizing data transmission in a multi-agent system, comprising: - obtaining raw data streams respectively from at least two agents; - classifying the received raw data streams based on predetermined characteristics; - obtaining requirements regarding end-to-end (E2E) latency and performance metrics from at least one node of the multi-agent system; - obtaining a set of interdependent QoS requests for the raw data streams suitable for data transmission via a a communication system; - computing a figure of merit for all the raw data streams based on the classified raw data streams, the node requirements, and the set of interdependent QoS requests; and - encoding the raw data streams and transmitting through the communication system the encoded data streams to the node according to the computed figure of merit. The invention ensures efficient and reliable transmission in the multi-agent communication system.