In a wireless network, gathering information at the base station about mobile
users based only on uplink channel measurements is an interesting challenge.
Indeed, accessing the users locations and predicting their downlink channels
would be particularly useful in order to optimize the network efficiency. In
this paper, a supervised machine learning approach addressing these tasks in an
unified way is proposed. It relies on a labeled database that can be acquired
in a simple way by the base station while operating. The proposed regression
method can be seen as a computationally efficient two layers neural network
initialized with a non-parametric estimator. It is illustrated on realistic
channel data, both for the positioning and channel mapping tasks, achieving
better results than previously proposed approaches, at a lower cost.

通过使用无线网络中用户上传链路信号的信息，提出了一种基于有标注数据库的监督机器学习方法，可以同时预测用户位置和信道质量，具有比之前提出的方法更优的结果，成本更低。

基于相似性的信道映射和用户定位预测

Similarity-based prediction for channel mapping and user positioning

Can we map the channels at one set of antennas and one frequency band to the
channels at another set of antennas---possibly at a different location and a
different frequency band? If this channel-to-channel mapping is possible, we
can expect dramatic gains for massive MIMO systems. For example, in FDD massive
MIMO, the uplink channels can be mapped to the downlink channels or the
downlink channels at one subset of antennas can be mapped to the downlink
channels at all the other antennas. This can significantly reduce (or even
eliminate) the downlink training/feedback overhead. In the context of
cell-free/distributed massive MIMO systems, this channel mapping can be
leveraged to reduce the fronthaul signaling overhead as only the channels at a
subset of the distributed terminals need to be fed to the central unit which
can map them to the channels at all the other terminals. This mapping can also
find interesting applications in mmWave beam prediction, MIMO radar, and
massive MIMO based positioning.
In this paper, we introduce the new concept of channel mapping in space and
frequency, where the channels at one set of antennas and one frequency band are
mapped to the channels at another set of antennas and frequency band. First, we
prove that this channel-to-channel mapping function exists under the condition
that the mapping from the candidate user positions to the channels at the first
set of antennas is bijective; a condition that can be achieved with high
probability in several practical MIMO communication scenarios. Then, we note
that the channel-to-channel mapping function, even if it exists, is typically
unknown and very hard to characterize analytically as it heavily depends on the
various elements of the surrounding environment. With this motivation, we
propose to leverage the powerful learning capabilities of deep neural networks
....

本文提出一种将频率和空间的信道图映射至其他频率和空间信道的新概念 —— 信道映射，并证明该映射函数在多种实际 MIMO 通信场景下存在，可由深度神经网络进行学习建模，可用于减少传输和反馈开销，适用于大规模 MIMO，分布式终端，毫米波束预测，MIMO 雷达和基于 MASSIVE MIMO 的定位。