Decentralized Beam Control Using ML Coordination in Overlap Zones
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Solution Overview
Problem
Current beam generating devices operate inefficiently due to heuristics and pre-programmed operations, leading to resource wastage and duplication of work when multiple devices target the same objectives in overlapping geographical regions.
Innovation Solution
Implementing machine learning models for communication and decision-making among beam generating devices to optimize resource allocation and objective fulfillment through multi-agent reinforcement learning, using curricula learning to train devices to cooperate effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If beam generating devices operate based on heuristics and pre-programmed operations, then device operation is simplified, but system efficiency deteriorates and resource wastage occurs
Solution Approach 1:
The patent replaces traditional heuristic-based control mechanisms with machine learning models that process observations and messages to determine objectives and generate communications. This substitution enables adaptive decision-making that improves system efficiency while maintaining operational simplicity through automated learning.
Solution Approach 2:
Beam generating devices autonomously determine their own objectives and generate communications based on observations and received messages from other devices. This self-service capability eliminates the need for complex centralized control while improving overall system productivity through distributed intelligent decision-making.
2Area of stationary object
If multiple beam generating devices operate in overlapping geographical regions, then coverage area is increased, but duplication of work occurs and resources are wasted
Solution Approach 1:
The patent implements feedback mechanisms where beam generating devices exchange messages about their observations and states. This feedback enables devices to coordinate their operations, avoid duplicating work on the same targets, and optimize resource allocation across overlapping coverage regions while maintaining expanded geographic coverage.
Solution Approach 2:
The patent merges the decision-making processes of multiple beam generating devices through shared observations and coordinated objectives. By combining their operational plans based on mutual information exchange, the system achieves efficient resource utilization across the combined coverage area without redundant operations.
3Productivity
If machine learning models are implemented for decentralized control, then system performance and coordination are improved, but device complexity increases
Solution Approach 1:
The patent segments the overall control function into separate machine learning models: one for determining objectives based on observations and messages, and another for generating communications. This segmentation manages device complexity by dividing complex decision-making into modular, specialized components while maintaining high system performance.
Data Source
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AI summary
Discussed herein are devices, systems, and methods for beam generating device management. A method can include producing a directed electromagnetic beam, receiving a first message from a second beam generating device, and implementing a first machine learning (ML) model that operates on the received first message to determine a next objective to be completed by the beam generating circuitry.