WLAN Channel Sounding Feedback With Autoencoder Pre-Processing
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently encoding and decoding channel information for beamforming feedback, particularly in high-throughput environments like EHT+, which affects communication performance.
Innovation Solution
Implementing pre-processing and post-processing operations using autoencoders to enhance the performance of encoders and decoders in wireless communication systems, specifically through the use of pre-processing functions with variable sections and inverse functions for encoding and decoding channel information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If channel information is encoded and compressed using an autoencoder for beamforming feedback, then communication performance is improved, but the complexity of the encoding and decoding process increases
Solution Approach 1:
The processing pipeline is segmented into distinct stages: pre-processing (normalization, masking), encoding, and post-processing (inverse masking, inverse normalization). This segmentation allows each stage to be optimized independently and simplifies the overall implementation by breaking down the complex transformation into manageable steps.
Solution Approach 2:
Pre-processing operations are performed on channel information before it is input to the autoencoder. This includes normalization to standard ranges and masking to remove irrelevant components, which prepares the data in an optimal format for encoding and improves the encoder's performance while reducing computational complexity during the encoding phase.
Solution Approach 3:
The post-processing stage applies inverse operations to the decoded channel information, including inverse masking and inverse normalization. This reverses the pre-processing transformations, restoring the channel information to its original scale and format, which is essential for accurate beamforming feedback while maintaining computational efficiency.
2Reliability
If pre-processing and post-processing functions with variable sections are applied to channel information, then the performance of the autoencoder is improved, but the computational requirements increase
Solution Approach 1:
The pre-processing and post-processing functions utilize variable sections that apply different transformations to different parts of the channel information based on their characteristics. This local quality approach allows the system to optimize performance for specific data regions while avoiding unnecessary computations across the entire dataset, thereby improving autoencoder performance with controlled computational overhead.
Solution Approach 2:
The processing functions dynamically adjust parameters such as normalization ranges and masking thresholds based on the characteristics of the channel information. This parameter change strategy optimizes the autoencoder's performance by adapting to different channel conditions while managing computational requirements through efficient parameter selection rather than exhaustive search.
Data Source
AI summary
Provided is a method of operating a first device that communicates with a second device in a wireless local area network (WLAN) system including the first device and the second device, the method including receiving a null data packet (NDP) from the second device, generating a plurality of channel information corresponding to a plurality of respective subcarriers by using the NDP, performing pre-processing on the plurality of channel information based on a pre-processing function designed to have a plurality of variable sections, performing encoding on the plurality of pre-processed channel information to result in a plurality of encoded channel information, and transmitting a beamforming feedback including the plurality of encoded channel information to the second device.