Engineering · 8 min read · April 20, 2026
ML predicts nonlinear distortion in massive MIMO arrays
Machine learning models forecast signal degradation from power amplifier nonlinearity in 5G/6G systems, enabling 12% throughput gains via adaptive power allocation.
ML-based prediction of nonlinear power amplifier distortion in massive MIMO improves user throughput by 12% over fixed operating schemes.
- — Massive MIMO pushed to energy limits exhibits nonlinear power amplifier behavior overlooked in most prior work.
- — High Peak-to-Average Power Ratio in OFDM signals (4G, 5G, 6G) triggers distortion that existing models fail to capture accurately.
- — 3D ray-tracing simulation reveals standard Rayleigh and line-of-sight channel models underestimate real-world nonlinear effects.
- — Statistical model using Generalized Extreme Value distribution characterizes signal-to-distortion ratio for interfered users.
- — ML model predicts distortion for scheduled users by learning spatial channel characteristics and per-antenna amplifier operating points.
- — Predicted distortion enables per-user power allocation that adapts to actual hardware constraints rather than assuming linearity.
- — Median 12% throughput gain demonstrated over baseline fixed operating point power allocation schemes.
Frequently asked
- As networks push hardware to energy limits, power amplifiers operate in nonlinear regimes, distorting signals and reducing throughput. Most designs assume linearity, leading to suboptimal resource allocation. Accounting for this nonlinearity can recover 12% or more in user throughput, making it critical for efficient 5G/6G deployment.