Blind Equalization Network

Unlike traditional receivers that compute an explicit channel matrix using known pilot symbols, a blind equalization network learns to invert the channel implicitly within its weight parameters. The network ingests raw received I/Q samples and outputs symbol decisions or soft bit estimates directly.

• Eliminates pilot overhead entirely, increasing net spectral efficiency
• Learns a direct mapping from received signal to transmitted symbols
• Adapts to channel conditions through the non-linear transformations in its hidden layers
• Particularly effective when channel statistics are stationary but instantaneous realizations vary

The network collapses two traditionally separate signal processing blocks into a single learned function. Classical receivers cascade a linear equalizer (e.g., MMSE) with a demapper; a blind equalization network learns a joint non-linear decision boundary that simultaneously compensates for inter-symbol interference and performs symbol detection.

• Replaces the entire synchronization, equalization, and demapping chain
• Learns non-linear decision regions that outperform linear equalizers on severely distorted channels
• Handles complex impairments like amplifier non-linearity and phase noise without explicit models
• Example: DeepRx architectures demonstrate this joint optimization for 5G NR waveforms

Blind equalization networks are trained using self-supervised objectives that do not require ground-truth transmitted symbols. The network learns by exploiting known properties of the communication signal, such as constant modulus, finite alphabet constraints, or statistical independence.

• Constant Modulus Algorithm (CMA) loss penalizes deviations from a fixed envelope magnitude
• Multi-modulus loss extends CMA to higher-order QAM constellations
• Information-theoretic objectives like mutual information maximization guide learning
• Adversarial training can enforce the output distribution to match known constellation statistics

These networks operate effectively in non-coherent scenarios where the receiver has no knowledge of the carrier phase or channel amplitude. The architecture learns phase-invariant representations that map directly to transmitted symbols regardless of arbitrary rotations introduced by the propagation channel.

• Eliminates the need for carrier phase recovery loops
• Robust to rapid phase variations caused by oscillator drift or Doppler shift
• Learns differential encoding strategies implicitly within the network
• Enables reliable communication in high-mobility environments where pilot-based tracking fails

Deployed blind equalization networks can continue to adapt through online fine-tuning using decision-directed or blind cost functions. As the channel environment evolves, the network updates its weights based on its own symbol decisions, maintaining performance without re-transmitting pilots.

• Decision-directed mode: uses detected symbols as pseudo-labels for gradient updates
• Sliding window of recent received samples enables tracking of time-varying channels
• Low-rank adaptation (LoRA) techniques allow efficient weight updates on edge hardware
• Critical for underwater acoustic and HF communications where channels change rapidly

Modern blind equalization networks incorporate algorithmic priors from classical signal processing to improve sample efficiency and generalization. Rather than learning everything from scratch, the architecture embeds known structures like matched filtering or the Viterbi algorithm as differentiable layers.

• ViterbiNet integrates a learned branch metric into the Viterbi decoding trellis
• Input projections onto Fourier or wavelet bases reduce the learning burden
• Unfolded gradient descent layers mimic iterative optimization with learned step sizes
• These hybrid architectures require fewer training samples than purely black-box networks