  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.
