Neural Channel Estimation Compression for 5G NR Memory Limits
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Solution Overview
Problem
In coherent detection systems, channel estimation requires significant memory resources, especially with increasing numbers of subcarriers, leading to memory space challenges in systems like 5G new radio (NR) baseband modems, necessitating effective data compression techniques to manage large bandwidth.
Innovation Solution
The implementation of a neural network-based autoencoder system for compressing and decompressing channel estimation data, allowing for interpolation and efficient storage of channel estimation values, utilizing techniques such as lossy compression and quantization to reduce data size while maintaining acceptable quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of subcarriers increases to support large bandwidth, then the data transmission capacity increases, but the memory space required for channel estimation data increases
Solution Approach 1:
The patent extracts only the essential channel estimation information from the full channel estimation data. By identifying and retaining only the most critical components needed for coherent detection, the system reduces memory requirements while maintaining detection performance. This is achieved through selective extraction of dominant channel characteristics rather than storing complete channel state information for all subcarriers.
Solution Approach 2:
The patent transforms channel estimation data from the time domain to the frequency domain, and further processes it to identify and retain only significant frequency components. By changing the representation parameters of channel data (from time-domain samples to frequency-domain spectral components), the system achieves compact representation that requires less memory while preserving essential channel characteristics for large bandwidths.
2Measurement precision
If channel estimation data is stored in full resolution, then the measurement precision is maintained, but the memory space requirement increases
Solution Approach 1:
The patent applies different processing quality levels to different portions of channel estimation data. Instead of uniformly reducing precision across all data, it identifies regions or frequency components that require high precision (those most critical for detection) and maintains full resolution for those, while applying compression or reduced precision to less critical portions. This local differentiation maintains overall measurement precision while reducing total memory requirements.
Solution Approach 2:
The patent creates a compressed representation or model of the channel estimation data that captures essential characteristics without storing the complete high-resolution dataset. By generating a simplified copy or approximation (such as dominant spectral components or interpolated values) that preserves measurement precision for critical parameters, the system reduces memory space while maintaining adequate channel estimation accuracy for coherent detection.
Data Source
AI summary
A method and system are provided. The method includes performing channel estimation on a reference signal (RS), compressing, with a neural network, the channel estimation of the RS, decompressing, with the neural network, the compressed channel estimation, and interpolating the decompressed channel estimation.