Decentralized Beam Control via Adaptive Importance Encoding
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Solution Overview
Problem
Current beam generating devices operate inefficiently due to duplication of work and resource wastage when multiple devices attempt to achieve the same objectives in close proximity, as they lack a coordinated method to allocate resources and direct their efforts effectively.
Innovation Solution
Implementing a decentralized control system using machine learning (ML) and multi-agent reinforcement learning (MARL) to enable beam generating devices to communicate and coordinate their actions based on observations and messages, allowing them to optimize resource allocation and objective fulfillment by determining the best actions to take, such as scanning directions or waveform properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple beam generating devices operate independently using heuristics or pre-programmed operations, then each device can function autonomously, but resource wastage and duplication of work increase
Solution Approach 1:
The patent implements a feedback mechanism where beam generating devices share observations and messages about target detections, environmental conditions, and operational status. This feedback enables devices to coordinate their actions, avoid duplicating work, and allocate resources more efficiently while maintaining autonomous operation through decentralized decision-making.
Solution Approach 2:
The patent introduces a communication protocol as an intermediary that facilitates information exchange between beam generating devices. This intermediary enables coordinated action by allowing devices to share relevant data about their operations and environmental observations, thereby reducing resource wastage without requiring centralized control.
2Ease of manufacture
If beam generating devices use heuristics or pre-programmed operations, then implementation is simple, but efficiency and adaptability to changing environments deteriorate
Solution Approach 1:
The patent transitions from static heuristics and pre-programmed operations to dynamic, adaptive decision-making. Beam generating devices use machine learning models that continuously learn from observations and communicated information, allowing them to adapt to changing environments and optimize their operations in real-time, thereby improving productivity while maintaining implementation feasibility.
Solution Approach 2:
The patent employs parameter changes by using machine learning models that adjust their internal parameters (weights, biases) based on observed data and communicated information. This allows the system to adapt to varying environmental conditions and improve operational efficiency without requiring complex reprogramming, balancing simplicity with adaptability.
3Device complexity
If beam generating devices operate without coordination, then device complexity is reduced, but duplication of work and time wastage increase
Solution Approach 1:
The patent implements a feedback-based coordination mechanism where devices share observations and operational status through a communication protocol. This feedback enables devices to make informed decisions about their actions, avoid duplicating work, and reduce time wastage while maintaining relatively simple device architecture through decentralized control.
4Productivity
If centralized control is used to coordinate beam generating devices, then resource allocation improves, but system complexity and communication requirements increase
Solution Approach 1:
The patent segments the control function across multiple independent beam generating devices, with each device making its own decisions based on local observations and communicated information. This segmentation eliminates the need for a centralized controller, reducing system complexity while maintaining efficient resource allocation through decentralized coordination enabled by the communication protocol.
Data Source
AI summary
Discussed herein are devices, systems, and methods for decentralized device management. A method can include receiving a first message from a second device, implementing a first machine learning (ML) model that operates on the received first message and an observation to determine a next objective to be completed by the first device, and training a simulator to produce the first message based on the observation.