Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining
This proposes a paradigm shift for beyond massive MIMO systems to serve as OTA neural networks, potentially benefiting ultra low power wireless devices for inference tasks.
The paper tackles the problem of enabling efficient Over-The-Air (OTA) edge inference by showing that an eXtremely Large (XL) MIMO system with analog combining can act as a universal function approximator, achieving performance comparable to deep learning with orders of magnitude lower complexity.
In this paper, we show that an eXtremely Large (XL) Multiple-Input Multiple-Output (MIMO) wireless system with appropriate analog combining components exhibits the properties of a universal function approximator, similar to a feedforward neural network. By treating the channel coefficients as the random nodes of a hidden layer and the receiver's analog combiner as a trainable output layer, we cast the XL MIMO system to the Extreme Learning Machine (ELM) framework, leading to a novel formulation for Over-The-Air (OTA) edge inference without requiring traditional digital processing nor pre-processing at the transmitter. Through theoretical analysis and numerical evaluation, we showcase that XL-MIMO-ELM enables near-instantaneous training and efficient classification, even in varying fading conditions, suggesting the paradigm shift of beyond massive MIMO systems as OTA artificial neural networks alongside their profound communications role. Compared to deep learning approaches and conventional ELMs, the proposed framework achieves on par performance with orders of magnitude lower complexity, making it highly attractive for inference tasks with ultra low power wireless devices.