Universal ML-Based CSI Compressor for Massive MIMO

The patent implements a universal encoder architecture that can process multiple input sizes and produce various latent vector dimensions through a single model. The encoder uses embedding layers with learnable parameters that adapt to different input dimensions, and employs ReLU activation functions with configurable output sizes. This allows one encoder to replace multiple specialized encoders, reducing hardware complexity while maintaining the ability to handle different antenna configurations and compression rates in massive MIMO systems.

The patent replaces traditional codebook-based and compressed sensing methods with a machine learning-based encoder architecture. Instead of relying on predefined codebooks or sparsity exploitation algorithms that become computationally intensive with large antenna arrays, the system uses a neural network encoder with embedding layers and ReLU activations. This ML-based approach automatically learns optimal representations of CSI without requiring explicit sparsity exploitation or large codebook searches, thereby reducing design complexity while maintaining low feedback rates in massive MIMO systems.

The patent extracts the essential information from high-dimensional CSI matrices in massive MIMO systems by using an encoder that processes the full CSI input but outputs a compressed latent vector representation. The embedding layers and ReLU activation functions extract only the most critical channel information, discarding redundant data. This allows the system to maintain high capacity with many antennas while transmitting only the essential compressed representation, thereby reducing feedback overhead proportionally to the compression ratio achieved by the encoder.