Neural Network Compression of UE Measurement Feedback
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Solution Overview
Problem
Current wireless communication systems face inefficiencies in measurement feedback due to the high signaling overhead associated with reporting raw measurements from user equipment (UE) to base stations, which can lead to suboptimal resource allocation and system performance.
Innovation Solution
The implementation of an encoder neural network (NN) at the UE to compress measurement data, reducing the number of bits required for reporting, and a corresponding decoder NN at the base station to decompress and accurately determine 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 maintained, 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 contains the most important measurement characteristics while discarding redundant information, thereby reducing signaling overhead while preserving critical measurement accuracy for network decisions.
Solution Approach 2:
The patent transforms measurement reporting from raw parameter transmission to transformed latent space representation. The autoencoder learns an optimal parameter transformation that maps high-dimensional raw measurements to a lower-dimensional latent space, maintaining the essential information needed for network operations while significantly reducing the number of bits required for reporting.
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 using measurement data collected during normal operation, allowing the system to learn optimal compression and reconstruction parameters in advance. This preliminary training phase separates the complex learning process from real-time operation, reducing online processing complexity.
Solution Approach 2:
The patent uses the trained autoencoder model as a reusable template that can be deployed across multiple UEs. Once the encoder and decoder are trained, they can be copied and instantiated on different devices without requiring retraining, significantly reducing the processing complexity burden on individual UEs while maintaining consistent compression performance across the network.
3Adaptability or versatility
If autoencoder model is trained at UE, then compression is adapted to UE measurements, but training time and energy consumption increase
Solution Approach 1:
The patent implements self-service by enabling the UE to autonomously collect its own measurement data and use it for training the autoencoder model. The UE leverages its own operational measurements (such as channel state information, signal quality metrics) as training data, eliminating the need for external training infrastructure or manual data collection. This self-service approach makes the training process adaptive to each UE's specific measurement characteristics while minimizing external dependencies.
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.