LB-SciFi: Online Learning-Based Channel Feedback for MU-MIMO in Wireless LANs
LB-SciFi: Online Learning-Based Channel Feedback for MU-MIMO in Wireless LANs
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DOI：
10.1109/icnp49622.2020.9259366
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发表时间：
2020-10
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影响因子：
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通讯作者：
Pedram Kheirkhah Sangdeh;Hossein Pirayesh;Aryan Mobiny;Huacheng Zeng
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文献类型：
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作者：
Pedram Kheirkhah Sangdeh;Hossein Pirayesh;Aryan Mobiny;Huacheng Zeng
多用户多输入多输出（MU - MIMO）是当前及下一代无线局域网（WLAN）的关键技术。尽管它已在无线局域网中广泛部署，但其潜力在实际系统中并未得到充分挖掘。这可归因于现有MU - MIMO协议中因信道获取而产生的大量空中时间开销，这严重削弱了MU - MIMO的吞吐量增益。在本文中，我们提出了LB - SciFi，这是一种用于无线局域网中MU - MIMO的基于学习的信道反馈框架。LB - SciFi利用深度神经网络自动编码器（DNN - AE）的最新进展，对802.11协议中的信道状态信息（CSI）进行压缩，从而节省空中时间并提高频谱效率。LB - SciFi的关键组成部分是一种在线DNN - AE训练方案，该方案使接入点（AP）能够借助现有802.11协议的边信息来训练DNN - AE。通过这种训练方案，DNN - AE能够在显著降低MU - MIMO空中时间开销的同时，保持与现有Wi - Fi客户端设备的向后兼容性。我们已在无线测试平台上实现了LB - SciFi，并在室内无线环境中评估了其性能。实验结果表明，与802.11反馈协议相比，LB - SciFi平均可减少73% 的空中时间开销，平均提高69% 的网络吞吐量。
Multi-user MIMO (MU-MIMO) is a key technology for current and next-generation wireless local area networks (WLANs). While it has widely been deployed in WLANs, its potential is not fully exploited in real-world systems. This can be attributed to the large airtime overhead induced by channel acquisition in existing MU-MIMO protocols, which significantly compromises the throughput gain of MU-MIMO. In this paper, we present LB-SciFi, a learning-based channel feedback framework for MU-MIMO in WLANs. LB-SciFi takes advantage of recent advances in deep neural network autoencoder (DNN-AE) to compress channel state information (CSI) in 802.11 protocols, thereby conserving airtime and improving spectral efficiency. The key component of LB-SciFi is an online DNN-AE training scheme, which allows an AP to train DNN-AEs by leveraging the side information of existing 802.11 protocols. With this training scheme, DNN-AEs are capable of significantly lowering the airtime overhead for MU-MIMO while preserving its backward compatibility with incumbent Wi-Fi client devices. We have implemented LB-SciFi on a wireless testbed and evaluated its performance in indoor wireless environments. Experimental results show that LB-SciFi offers an average of 73% airtime overhead reduction and increases network throughput by 69% on average when compared to 802.11 feedback protocols.