Cell-Free Multi-User MIMO Equalization via In-Context Learning
This work addresses signal processing challenges in wireless communications for improved network efficiency, but it is incremental as it extends existing in-context learning methods to a new scenario.
The paper tackled multi-user equalization in cell-free MIMO systems with limited fronthaul capacity by applying in-context learning, resulting in lower mean squared error estimates compared to linear minimum mean squared error equalizers, particularly under conditions of limited fronthaul capacity and pilot contamination.
Large pre-trained sequence models, such as transformers, excel as few-shot learners capable of in-context learning (ICL). In ICL, a model is trained to adapt its operation to a new task based on limited contextual information, typically in the form of a few training examples for the given task. Previous work has explored the use of ICL for channel equalization in single-user multi-input and multiple-output (MIMO) systems. In this work, we demonstrate that ICL can be also used to tackle the problem of multi-user equalization in cell-free MIMO systems with limited fronthaul capacity. In this scenario, a task is defined by channel statistics, signal-to-noise ratio, and modulation schemes. The context encompasses the users' pilot sequences, the corresponding quantized received signals, and the current received data signal. Different prompt design strategies are proposed and evaluated that encompass also large-scale fading and modulation information. Experiments demonstrate that ICL-based equalization provides estimates with lower mean squared error as compared to the linear minimum mean squared error equalizer, especially in the presence of limited fronthaul capacity and pilot contamination.