In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models

TL;DR

This paper investigates the use of state-space models for in-context learning-based equalization in cell-free massive MIMO, demonstrating comparable performance to transformers with significantly reduced computational complexity.

Contribution

It introduces the application of efficient state-space models for in-context learning in MIMO equalization, outperforming transformer-based models in parameter efficiency.

Findings

SSMs achieve similar performance to transformers.

SSMs require fewer parameters and computations.

Efficient in-context learning for MIMO systems.

Abstract

Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done without requiring any explicit optimization and by leveraging not only short pilot sequences but also contextual information such as long-term channel statistics. The operating principle underlying automatic adaptation is in-context learning (ICL), an emerging property of sequence models. Prior art adopted transformer-based sequence models, which, however, have a computational complexity scaling quadratically with the context length due to batch processing. Recently, state-space models (SSMs) have emerged as a more efficient alternative, affording a linear inference complexity in the context size. This work explores the potential of SSMs for ICL-based equalization in cell-free massive MIMO systems. Results show that selective SSMs achieve comparable performance to transformer-based models while requiring approximately eight times fewer parameters and five times fewer floating-point operations.