Adaptive Neural Encoder-Decoder Latent Sizing for Network Congestion
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Solution Overview
Problem
Existing communication networks face challenges in optimizing resources for network and application functions due to high demand or maintenance, leading to inefficient use of resources and potential hindrances in sending timely and reliable information.
Innovation Solution
A neural network (NN) encoder-decoder system is deployed in the network to adjust the size of latent representations based on performance metrics, ensuring optimal data transfer and decoding for network or application functions, using machine learning to process and compress data according to network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the network transfers data for network and application functions, then the functions can be performed, but the resource consumption increases and network performance may be hindered during high demand or maintenance
Solution Approach 1:
The system dynamically changes the parameter of data size by adjusting the latent representation dimensionality based on network conditions. During normal operation, the encoder outputs a standard latent representation size. When network congestion or maintenance is detected through performance metrics, the system adapts the latent representation size to reduce data transfer volume, thereby resolving the contradiction between maintaining function performance and reducing resource consumption.
Solution Approach 2:
The encoder-decoder system operates dynamically by continuously monitoring network performance metrics and adjusting the latent representation size in real-time. The system transitions between different operational states (normal vs. congested) and adapts the data transfer parameters accordingly, enabling the network to maintain functionality while reducing resource consumption during high demand periods.
2Measurement precision
If the network transfers larger data for better decoding performance, then the decoding accuracy improves, but the signaling overhead increases and network resources are consumed
Solution Approach 1:
The system changes the parameter of latent representation size dynamically. During normal network conditions, the encoder outputs a larger latent representation to ensure high decoding accuracy. When network congestion or resource constraints are detected, the system reduces the latent representation size, accepting slightly lower decoding accuracy in exchange for reduced signaling overhead and network resource consumption.
Solution Approach 2:
The system applies partial action by transferring only the necessary portion of data (latent representation) based on network conditions. Instead of always transferring the full data representation, the system adjusts the amount of data transferred to match actual network capacity and requirements, reducing unnecessary signaling overhead while maintaining adequate decoding performance.
Data Source
AI summary
A communication network includes a first network element that has a neural network (NN) encoder with multiple NN encoder layers. The NN encoder is configured to generate a latent representation of input data observed at the first network element. The communication network also includes a second network element that has a NN decoder configured to decode the latent representation. The NN decoder has multiple NN decoder layers. The first network element obtains performance metric values of the NN decoder and communication network and determines, in accordance with the performance metric values, which of the NN encoder layers is to output the latent representation and which of the NN decoder layer is to receive the latent representation. Performance metric values indicative of un-desired (e.g., less than optimal) performance and network conditions may cause the first network element to output the latent representation from a different-size NN encoder layer and, correspondingly, to select a NN decoder layer that has the same size.