Massive multiple input multiple output (M-MIMO) technology plays a pivotal
role in fifth-generation (5G) and beyond communication systems, offering a wide
range of benefits, from increased spectral efficiency (SE) to enhanced energy
efficiency and higher reliability. However, these advantages are contingent
upon precise channel state information (CSI) availability at the base station
(BS). Ensuring precise CSI is challenging due to the constrained size of the
coherence interval and the resulting limitations on pilot sequence length.
Therefore, reusing pilot sequences in adjacent cells introduces pilot
contamination, hindering SE enhancement. This paper reviews recent advancements
and addresses research challenges in mitigating pilot contamination and
improving channel estimation, categorizing the existing research into three
broader categories: pilot assignment schemes, advanced signal processing
methods, and advanced channel estimation techniques. Salient representative
pilot mitigation/assignment techniques are analyzed and compared in each
category. Lastly, possible future research directions are discussed.

本文回顾了最近在减轻导频污染和改善信道估计方面取得的进展，并将现有研究分为导频分配方案、先进信号处理方法和高级信道估计技术三个更广泛的类别进行分类分析，分别比较了每个类别中的显著代表性导频减轻 / 分配技术，最后讨论了可能的未来研究方向。

大规模 MIMO 系统中的导频污染：挑战与未来前景

Pilot Contamination in Massive MIMO Systems: Challenges and Future  Prospects

In the realm of 5G communication systems, the accuracy of Channel State
Information (CSI) prediction is vital for optimizing performance. This letter
introduces a pioneering approach: the Spectral-Temporal Graph Neural Network
(STEM GNN), which fuses spatial relationships and temporal dynamics of the
wireless channel using the Graph Fourier Transform. We compare the STEM GNN
approach with conventional Recurrent Neural Network (RNN) and Long Short-Term
Memory (LSTM) models for CSI prediction. Our findings reveal a significant
enhancement in overall communication system performance through STEM GNNs. For
instance, in one scenario, STEM GNN achieves a sum rate of 5.009 bps/Hz which
is $11.9\%$ higher than that of LSTM and $35\%$ higher than that of RNN. The
spectral-temporal analysis capabilities of STEM GNNs capture intricate patterns
often overlooked by traditional models, offering improvements in beamforming,
interference mitigation, and ultra-reliable low-latency communication (URLLC).

在 5G 通信系统领域，准确预测信道状态信息 (CSI) 对优化性能至关重要。该论文介绍了一种开创性的方法：采用图傅里叶变换，将无线信道的空间关系和时间动态融合到谱时图神经网络 (STEM GNN) 中。我们将 STEM GNN 方法与传统的循环神经网络 (RNN) 和长短期记忆 (LSTM) 模型进行了比较，发现 STEM GNN 显著提高了整体通信系统性能。例如，在一个场景中，STEM GNN 的速率为 5.009 bps/Hz，比 LSTM 高出 11.9%，比 RNN 高出 35%。STEM GNN 的谱时分析能力捕捉到传统模型常常忽视的复杂模式，提供了波束成形、干扰抑制和超可靠低延迟通信 (URLLC) 方面的改进。

大规模 MIMO 信道状态信息预测的频谱时域图神经网络

Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction

Machine learning (ML) is a promising enabler for the fifth generation (5G)
communication systems and beyond. By imbuing intelligence into the network
edge, edge nodes can proactively carry out decision-making, and thereby react
to local environmental changes and disturbances while experiencing zero
communication latency. To achieve this goal, it is essential to cater for high
ML inference accuracy at scale under time-varying channel and network dynamics,
by continuously exchanging fresh data and ML model updates in a distributed
way. Taming this new kind of data traffic boils down to improving the
communication efficiency of distributed learning by optimizing communication
payload types, transmission techniques, and scheduling, as well as ML
architectures, algorithms, and data processing methods. To this end, this
article aims to provide a holistic overview of relevant communication and ML
principles, and thereby present communication-efficient and distributed
learning frameworks with selected use cases.

该研究论文总结了与机器学习相关的通信和 ML 原则，并提出了具有选定用例的通信高效和分布式学习框架。