ML-Based Communication Link Prediction for Dynamic Antenna Systems
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Solution Overview
Problem
Existing communication systems, particularly those using common data link (CDL) protocols with directional antennas, face challenges in maintaining connectivity as entities move in space due to antenna orientation and interference issues, leading to link failures and high error rates.
Innovation Solution
A communication system that employs machine learning to predict communication link performance based on state data, adjusts transmission parameters, or reroutes data through alternative platforms to ensure reliable data transmission by evaluating performance conditions and using a network interface and processing circuit to manage antenna orientation and link status dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If directional antennas are used for point-to-point communications, then communication directionality and signal strength are improved, but connectivity is lost when entities move in space due to antenna orientation issues
Solution Approach 1:
The patent applies dynamics by making the antenna system adaptable to changing spatial conditions. The machine learning model dynamically adjusts antenna orientation and selects alternative communication paths based on real-time position data and predicted link quality, transforming a static directional antenna system into a dynamic one that maintains connectivity during entity movement.
Solution Approach 2:
The patent introduces intermediary elements including the machine learning model that mediates between the transmitter and receiver, and alternative relay platforms that serve as intermediaries when direct links fail. The ML model processes state data and predicts performance, acting as an intelligent intermediary that decides whether to use direct transmission or alternative paths.
2Reliability
If machine learning models are used to predict communication link performance, then transmission reliability is improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by training the machine learning model offline beforehand using historical communication data. The model learns patterns of link failure and performance degradation in advance, so during actual operation it can make rapid predictions without requiring complex real-time computations, thus reducing online computational complexity while maintaining high reliability.
3Reliability
If communication links are maintained in dynamic environments, then connectivity is improved, but error rates increase due to shadowed and disconnected links
Solution Approach 1:
The patent implements feedback by using the machine learning model to continuously monitor predicted link performance based on state data, and adjusting transmission decisions accordingly. When the model predicts poor link quality or shadowing conditions, the system feedback loops to select alternative relay platforms or adjust transmission parameters, thereby maintaining connectivity while minimizing error rates through informed decision-making.
Data Source
AI summary
A communication system including a transmit antenna that transmits an output signal including output information, a network interface that receives state data regarding a first remote platform, and a processing circuit. The processing circuit predicts a performance of a communication link that the transmit antenna uses to transmit the output signal based on the state data, evaluates a performance condition based on the estimated performance and a threshold performance, responsive to the performance condition being satisfied, uses the transmit antenna to transmit the output signal, and responsive to the performance condition not being satisfied, at least one of (i) adjusts at least one of a time parameter or a frequency parameter that the transmit antenna uses to transmit the output signal, or (ii) provide an instruction to a second remote platform to cause the second remote platform to transmit the output information to the first remote platform.