Predictive ML Model for Regenerating Lost Time Series Frames
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Solution Overview
Problem
Existing methods for low-latency communication over unreliable networks fail to effectively detect and regenerate lost data during transmission, leading to unpredictable latency and high latency costs in interactive systems like autonomous vehicles and multiplayer gaming.
Innovation Solution
A predictive machine learning model is employed to represent and regenerate lost frames of time series data by training on output streams, detecting lost frames, and generating regenerated frames at the receiving device, thereby reducing latency and eliminating the need for data retransmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data retransmission is used to recover lost data, then reliability is improved, but latency increases significantly
Solution Approach 1:
The system performs preliminary actions by training machine learning models in advance to predict and generate potential future data frames. When packet loss occurs, these pre-trained models can immediately generate replacement data without waiting for retransmission, thereby maintaining reliability while avoiding the latency penalty of traditional retransmission protocols.
Solution Approach 2:
Instead of retransmitting the original lost data packets, the system creates copies of the lost data through machine learning prediction. The ML models generate predicted data frames that serve as functional copies of the lost information, allowing the receiver to reconstruct the data stream without requiring the original packets to be resent.
2Speed
If unreliable packet transport is used to reduce latency, then communication speed is improved, but packet loss increases
Solution Approach 1:
The system implements self-service by enabling the receiver to autonomously detect lost packets and generate appropriate replacement data using locally deployed machine learning models. This eliminates the need for complex acknowledgment and retransmission protocols, allowing the system to maintain high communication speed while independently handling packet loss without external intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where the receiver monitors incoming data streams for gaps and losses. This feedback triggers the machine learning models to generate replacement packets, creating a closed-loop system that continuously adapts to network conditions and maintains data integrity despite using unreliable transport protocols.
3Reliability
If traditional error correction and retransmission protocols are implemented, then data integrity is improved, but latency becomes unpredictable
Solution Approach 1:
The system fundamentally changes the parameter of how data recovery is achieved - shifting from deterministic retransmission (waiting for acknowledgments and resending) to probabilistic prediction (using ML models to generate likely data). This parameter change transforms latency from an unpredictable variable dependent on network round-trip times to a more predictable function of model inference speed.
4Measurement precision
If machine learning models are trained on output streams, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies partial action by training machine learning models on selected portions of the data stream (output streams with relevant patterns) rather than attempting to model entire data sets. This selective training approach achieves sufficient prediction accuracy for the specific application domain while keeping computational complexity and training time manageable.
Data Source
AI summary
A method for low-latency communication from a first device to a second device over an unreliable network using at least one predictive machine learning model includes representing at least one frame of time series data at the first device; recording at least one output stream, and a plurality of external inputs from the first device in an interaction recorder of the second device detecting, at the second device, at least one lost frame of time series data; training the at least one predictive machine learning model at the first device for predictive frame regeneration; regenerating the at least one lost frame of the time series data at the second device using the at least one predictive machine learning model; and combining an output stream from an application steam with the at least one regenerated frame of time series data to obtain a modified output stream.