Neural Network Vector Compression Orthogonality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining orthogonality of compressed vectors during vector compression, leading to degraded channel tuning performance on the decode side.
Innovation Solution
The proposed solution involves using neural network layers on both the encode and decode sides to generate orthogonal compressed or decompressed vectors. An indication of the dependency order is communicated between the encode and decode sides to ensure that encoder or decoder layers produce vectors that satisfy an orthogonality condition, eliminating the need for additional orthogonalization procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vector compression is performed without maintaining orthogonality, then compression efficiency is improved, but channel tuning performance deteriorates
Solution Approach 1:
The patent changes the parameter of vector orthogonality maintenance during compression. Instead of maintaining strict orthogonality (which reduces compression efficiency), the system allows controlled deviation from orthogonality while using neural network layers to learn and compensate for this deviation, thereby achieving both compression efficiency and acceptable channel tuning performance.
Solution Approach 2:
The patent introduces neural network layers as an intermediary between the compression process and the channel tuning process. These neural networks learn the relationship between compressed vectors and channel characteristics, compensating for the loss of orthogonality and enabling effective channel tuning without requiring strictly orthogonal compressed vectors.
2Reliability
If additional orthogonalization procedures are applied after compression, then orthogonality is restored, but processing latency and resource consumption increase
Solution Approach 1:
The patent applies preliminary action by training neural network layers during the compression phase to inherently preserve or restore orthogonality properties. Instead of applying orthogonalization procedures after compression (which would increase latency), the system pre-trains the neural networks to produce compressed vectors that maintain the necessary orthogonality properties, thereby avoiding additional processing steps.
Solution Approach 2:
The patent replaces traditional mechanical orthogonalization procedures (such as Gram-Schmidt processes) with a learned neural network-based approach. The neural networks learn to maintain orthogonality through their weight parameters and activation functions, substituting the need for explicit orthogonalization algorithms and reducing processing complexity and latency.
3Reliability
If neural network layers are trained to generate orthogonal vectors, then orthogonality is maintained, but training complexity and computational resources increase
Solution Approach 1:
The patent applies partial action by training neural network layers to satisfy orthogonality conditions only to the extent necessary for effective channel tuning. Rather than enforcing strict mathematical orthogonality, the system trains the networks to achieve sufficient orthogonality that improves channel tuning performance without requiring excessive computational resources or complex training procedures.
Data Source
AI summary
Some examples of the techniques described herein may provide encoding or decoding approaches to generate vectors that meet an orthogonality condition in vector compression procedures (e.g., vector compression or decompression). Encoder layers (e.g., neural network layers) on the encode side may generate orthogonal compressed vectors or decoder layers (e.g., neural network layers) on the decode side may generate orthogonal decompressed vectors. For instance, a neural network layer may be trained to generate a compressed or decompressed vector that satisfies an orthogonality condition relative to one or more previously compressed or decompressed vectors in accordance with a dependency order or a compression sequence. An indication of the dependency order may be communicated between the encode side and the decode side to order encoder or decoder layers (e.g., to execute encoder or decoder layers in the compression sequence) to produce compressed or decompressed vectors that satisfy an orthogonality condition.