The Physical Layer Challenge

Signal degradation, interference, and nonlinearities limit wireless performance. Traditional algorithms (Viterbi decoders, equalization) are reaching their limits. AI/ML offers a fundamentally new approach: learning the optimal signal processing strategy from data.

QSL's Physical Layer AI Solutions

1. Neural Receivers & Deep Learning Detection

  • **End-to-End Learning:** Deep neural networks trained to map received signals to transmitted bits, learning the optimal receiver in an adversarial environment.
  • **Channel Adaptation:** Real-time learning adapts to changing channel conditions, interference, and nonlinearities.
  • **MIMO & Antenna Arrays:** AI-optimized multi-antenna reception with adaptive beamforming and precoding.

2. Digital Pre-Distortion (DPD) with ML

  • **Nonlinearity Correction:** ML models learn power amplifier characteristics and apply real-time corrections to linearize transmission.
  • **Spectral Efficiency:** DPD reduces out-of-band emissions, enabling higher power efficiency and tighter spectrum packing.
  • **Adaptive Learning:** The model continuously adapts to device aging, temperature variations, and load changes.

3. Spectrum & Interference Management

  • **Dynamic Spectrum Access:** AI learns spectrum occupancy patterns and optimizes frequency allocation in real-time.
  • **Cognitive Radio:** Intelligent interference detection and avoidance for shared spectrum environments.
  • **Network Optimization:** Distributed ML for coordinated multi-cell optimization and handover prediction.

Business Impact

Our clients see increased network capacity, reduced latency, improved power efficiency, and significant spectral gains. One wireless OEM saw 25% throughput improvement and 15% power reduction within 6 months of deployment.