Cell-free massive multiple-input-multiple-output is promising to meet the
stringent quality-of-experience (QoE) requirements of railway wireless
communications by coordinating many successional access points (APs) to serve
the onboard users coherently. A key challenge is how to deliver the desired
contents timely due to the radical changing propagation environment caused by
the growing train speed. In this paper, we propose to proactively cache the
likely-requesting contents at the upcoming APs which perform the coherent
transmission to reduce end-to-end delay. A long-term QoE-maximization problem
is formulated and two cache placement algorithms are proposed. One is based on
heuristic convex optimization (HCO) and the other exploits deep reinforcement
learning (DRL) with soft actor-critic (SAC). Compared to the conventional
benchmark, numerical results show the advantage of our proposed algorithms on
QoE and hit probability. With the advanced DRL model, SAC outperforms HCO on
QoE by predicting the user requests accurately.

本文提出使用缓存算法和深度强化学习等技术，来解决铁路无线通信中，列车高速对信号传输产生的影响，从而提高服务体验质量和用户满意度。

利用深度强化学习在无小区大规模 MIMO 系统中进行边缘缓存

Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems

A key challenge of massive MTC (mMTC), is the joint detection of device
activity and decoding of data. The sparse characteristics of mMTC makes
compressed sensing (CS) approaches a promising solution to the device detection
problem. However, utilizing CS-based approaches for device detection along with
channel estimation, and using the acquired estimates for coherent data
transmission is suboptimal, especially when the goal is to convey only a few
bits of data.
First, we focus on the coherent transmission and demonstrate that it is
possible to obtain more accurate channel state information by combining
conventional estimators with CS-based techniques. Moreover, we illustrate that
even simple power control techniques can enhance the device detection
performance in mMTC setups.
Second, we devise a new non-coherent transmission scheme for mMTC and
specifically for grant-free random access. We design an algorithm that jointly
detects device activity along with embedded information bits. The approach
leverages elements from the approximate message passing (AMP) algorithm, and
exploits the structured sparsity introduced by the non-coherent transmission
scheme. Our analysis reveals that the proposed approach has superior
performance compared to application of the original AMP approach.

该论文研究了在大规模机器类型通信中，如何通过压缩感知等技术实现设备检测和数据解码，特别是提出了一种新的非相干传输方案，与现有的消息传递算法相比，该方案表现更优。