<br>基于 GAN-GRU 的 6G 无线通信时空预测信道模型,IEEE Transactions on Vehicular Technology

当前位置： X-MOL 学术 › IEEE Trans. Veh. Technol. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

基于 GAN-GRU 的 6G 无线通信时空预测信道模型
IEEE Transactions on Vehicular Technology ( IF 6.1 ) Pub Date : 2024-03-08 , DOI: 10.1109/tvt.2024.3367386
Zheao Li ₁ , Cheng-Xiang Wang ₁ , Chen Huang ₁ , Jie Huang ₁ , Junling Li ₁ , Wenqi Zhou ₁ , Yunfei Chen ₂

Affiliation

第六代 (6G) 无线通信的出现给信道建模带来了重大挑战。信道测量无法覆盖6G的所有场景和频段，传统模型缺乏准确的预测能力。针对这些问题，本文提出了一种新颖的6G时空联合预测信道模型来预测时空域中的信道，该模型可以重建丢失的测量数据并纠正异常数据。该模型设计了时空生成对抗网络（STGAN）框架，以信道大尺度和小尺度特征为条件，合成足够的时空信道数据集，有效克服数据短缺。结合路径识别和特征分类，耦合门控循环单元（GRU）框架对时空域中的未知通道进行精确预测。综合实验证明了该模型相对于其他方法的优越性，包括基于几何的随机通道模型（GBSM）、GRU、长短期记忆（LSTM）和径向基函数神经网络（RBF-NN）。该模型的有效性可归因于其架构能够捕获复杂的时空变化并根据连续测量准确预测非线性信道特征。对室内和室外信道测量的验证进一步证实了该模型的通用性和准确性。所提出的模型为高级无线通信的时空联合信道预测提供了稳健的解决方案。

"点击查看英文标题和摘要"

A GAN-GRU Based Space-Time Predictive Channel Model for 6G Wireless Communications

The advent of sixth-generation (6G) wireless communications has posed significant challenges to channel modeling. Channel measurements cannot cover all scenarios and frequency bands for 6G, and conventional models lack accurate predictive capabilities. To address these issues, this paper proposes a novel 6G space-time joint predictive channel model to predict channels in the space-time domains, which can rebuild lost measurement data and correct abnormal data. The proposed model designs a space-time generative adversarial network (STGAN) framework, conditioned on channel large-scale and small-scale characteristics, to synthesize sufficient space-time channel datasets, effectively overcoming data shortages. Accompanied by path identification and characteristic classification, the coupled gated recurrent unit (GRU) framework conducts precise predictions for unknown channels in the space-time domains. Comprehensive experiments demonstrate the proposed model's superiority over other methods, including the geometry-based stochastic channel model (GBSM), GRU, long short-term memory (LSTM), and radial basis function neural network (RBF-NN). The model's effectiveness can be attributed to its architecture to capture complex space-time variations and accurately predict non-linear channel characteristics based on continuous measurements. Validation on both indoor and outdoor channel measurements further confirms the model's generality and accuracy. The proposed model provides a robust solution in the space-time joint channel prediction for advanced wireless communications.

更新日期：2024-03-08

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南