Neural Receiver / AI-PHY Suite

This experimental suite covers AI-native physical-layer receiver methods: learned demappers, receiver-only training, and BER/BLER link benchmarks. It is the first non-semantic expansion suite because it reuses Noema’s existing symbol boundaries, channel blocks, dataset capture runner, differentiable export, and bit-accounting checks.

Runnable Proof Benchmark

The first benchmark pack is:

benchmarks/neural_receiver_ai_phy/qpsk_awgn_receiver_v1.yaml

It runs a defensible small link-level chain:

seeded random bits
  -> canonical payload/tx bit boundaries
  -> QPSK mapper
  -> AWGN channel
  -> classical demapper baseline or neural receiver adapter
  -> BER / BLER

The built-in neural receiver adapter currently has a deterministic reference_qpsk mode and a tiny linear_npz checkpoint mode. The reference mode is not claimed to be a trained neural receiver; it is the stable adapter slot that lets benchmark packs, graph rendering, capture taps, manifests, and result plots exercise the same path a researcher would replace with a trained receiver.

The reusable QPSK receiver calibration under I/Q imbalance template adds a stable receiver front-end distortion after AWGN. Its post-training campaign compares ordinary uncompensated QPSK, a simulation-only calibrated oracle, and a learned artifact on paired bits and noise. Unlike the ideal-AWGN smoke pack, this scenario has a meaningful learned objective: infer the shifted and rotated decision boundaries without receiving the hidden calibration parameters.

Capture Shape

Receiver-only training data can be captured from taps such as:

rx_symbols + noise variance + target_bits -> offline receiver training dataset

The benchmark recipes keep tx_bit_boundary and rx_bit_boundary in the standard Noema transport spine so bit accounting, BER, BLER, and channel-use accounting stay comparable to semantic communication recipes.

Current Boundary

The current suite does not include Sionna LDPC/coded receiver baselines, soft-output neural receivers with LLR losses, channel-mismatch generalization grids, or OFDM/MIMO receiver tasks. These capabilities require separate benchmark protocols rather than implicit extensions of the QPSK/AWGN workflow.