Scalable Multi-Modal Learning for Cross-Link Channel Prediction in Massive IoT Networks
Scalable Multi-Modal Learning for Cross-Link Channel Prediction in Massive IoT Networks
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DOI：
10.1145/3565287.3610280
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发表时间：
2023-10
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影响因子：
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通讯作者：
Kun Woo Cho;Marco Cominelli;Francesco Gringoli;Joerg Widmer;Kyle Jamieson
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作者：
Kun Woo Cho;Marco Cominelli;Francesco Gringoli;Joerg Widmer;Kyle Jamieson
明天的大规模IoT传感器网络被中毒以推动上行链路交通需求，尤其是在密集的部署领域，以满足这一需求，但是，网络设计师利用了经常需要准确的渠道状态信息（CSI）的工具开销，从而减少网络吞吐量，而间接费用通常与客户端的数量相比，因此在这种庞大的IoT传感器网络中特别关注。虽然先前的工作已在一个频带上使用传输来预测同一链接上另一个频段的通道，但本文付出了下一步，以减少CSI开销：预测我们提出的交叉的CSI -link Channel预测（CLCP），该技术利用多视图表示学习来预测大量用户的渠道响应，从而使频道估计的开销远远超过了开销以前可能。网络。我们在两个大规模的室内场景中评估CLCP从20个MHz到160 MHz，最多可达144个不同的802.11AX用户和四个不同的频道带宽。
Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.