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.

使用预训练序列模型和上下文学习，在存在有限前传容量和导频污染的情况下，通过通道均衡技术实现对多用户无线系统的估计，比线性最小均方误差均衡器得到更低的均方误差。

基于上下文学习的无细胞多用户 MIMO 均衡

Cell-Free Multi-User MIMO Equalization via In-Context Learning

Large pre-trained sequence models, such as transformer-based architectures,
have been recently shown to have the capacity to carry out in-context learning
(ICL). In ICL, a decision on a new input is made via a direct mapping of the
input and of a few examples from the given task, serving as the task's context,
to the output variable. No explicit updates of model parameters are needed to
tailor the decision to a new task. Pre-training, which amounts to a form of
meta-learning, is based on the observation of examples from several related
tasks. Prior work has shown ICL capabilities for linear regression. In this
study, we leverage ICL to address the inverse problem of multiple-input and
multiple-output (MIMO) equalization based on a context given by pilot symbols.
A task is defined by the unknown fading channel and by the signal-to-noise
ratio (SNR) level, which may be known. To highlight the practical potential of
the approach, we allow for the presence of quantization of the received
signals. We demonstrate via numerical results that transformer-based ICL has a
threshold behavior, whereby, as the number of pre-training tasks grows, the
performance switches from that of a minimum mean squared error (MMSE) equalizer
with a prior determined by the pre-trained tasks to that of an MMSE equalizer
with the true data-generating prior.

利用 ICL 对多输入多输出均衡问题进行了处理，并通过数值结果展示了基于 Transformer 的 ICL 模型具有阈值行为的潜力。