PUSCH#
JAX implementations of Physical Uplink Shared Channel algorithms.
Overview#
The PUSCH JAX module provides differentiable implementations of the inner receiver signal processing chain. These implementations can be lowered to TensorRT for high-performance execution and are organized into signal processing stages:
Inner Receiver (Signal Processing)
Channel Estimation - Traditional and neural network-based channel estimators that can be trained end-to-end
AI Tukey Filter - ML-based channel estimation filter with pretrained models for improved performance in challenging channel conditions
Noise Estimation - Noise covariance estimation for MMSE equalization
Delay Compensation - Time-domain delay correction
Equalization - MMSE channel equalization
Free Energy Filter - Advanced filtering for channel estimation refinement
Soft Demapping - LLR generation from equalized symbols
Signal Quality Metrics - Noise variance, RSRP, and SINR computation
End-to-End Processing
Complete Inner Receiver - Full signal processing pipeline that can be lowered to TensorRT for real-time execution