Neural Network Measurement Feedback Compression for Wireless UEs
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Solution Overview
Problem
Current wireless communication systems face inefficiencies in measurement feedback, as reporting raw measurements from user equipment (UE) to base stations results in high signaling overhead, which can hinder resource allocation and system optimization.
Innovation Solution
Implementing an encoder neural network (NN) at the UE to compress measurement data, reducing the number of bits required for reporting, and a decoder NN at the base station to decompress and retrieve the original measurements, thereby improving signaling efficiency and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw measurements are reported from UE to base station, then measurement accuracy is preserved, but signaling overhead increases
Solution Approach 1:
The patent extracts only the essential measurement information by training an autoencoder model where the encoder compresses raw measurements into a compact latent representation. This latent code captures the most significant measurement characteristics while discarding redundant data, thereby reducing signaling overhead while preserving critical measurement accuracy for network decisions.
Solution Approach 2:
The patent transforms measurement data from raw high-dimensional form to compressed low-dimensional latent space representation through the autoencoder's encoder network. This parameter transformation changes the data format from detailed raw measurements to compact encoded values that convey essential information with fewer bits, resolving the contradiction between accuracy and overhead.
2Quantity of substance
If measurement data is compressed using encoder NN, then signaling overhead is reduced, but processing complexity increases
Solution Approach 1:
The patent performs preliminary action by training the autoencoder model offline before deployment. The encoder and decoder networks are pre-trained to optimize compression and reconstruction performance, so that during actual operation, only the lightweight encoding process is needed at the UE. This shifts the complex processing to the training phase, reducing runtime complexity while maintaining compression effectiveness.
3Productivity
If autoencoder model is trained at UE, then compression performance is optimized, but training time and energy consumption increase
Solution Approach 1:
The patent uses copying by deploying the trained encoder model weights to the UE device. Instead of performing repeated training at the UE, the optimized encoder parameters are copied from the training environment to the target device. This allows the UE to use the pre-optimized compression model without incurring training time and energy costs during operation, while still benefiting from optimized compression performance.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may perform a measurement operation to attain multiple measurements to report to a base station. The measurements may correspond to a first number of bits if reported. The UE may compress the measurements using an encoder neural network (NN) to obtain an encoder output indicating the measurements. This encoder output may include a second number of bits that is less than the first number of bits. The UE may report the encoder output to the base station in this compressed form. At the base station, the encoder output may be decompressed according to a decoder NN. Once the base station decompresses the encoder output, the UE and base station may communicate according to the measurements determined from the decompression. In some cases, the base station may perform load redistribution based on the measurements.