Extracting and predicting multipath profiles under high mobility
Extracting and predicting multipath profiles under high mobility
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DOI：
10.1145/3492866.3549710
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Ghufran Baig;Changhan Ge;L. Qiu;Yuanjie Li;Wangyang Li;Wei Sun;Jian He;Zhehui Zhang;Songwu Lu
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文献类型：
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作者：
Ghufran Baig;Changhan Ge;L. Qiu;Yuanjie Li;Wangyang Li;Wei Sun;Jian He;Zhehui Zhang;Songwu Lu
无线信号经由由发射器与接收器之间的不同反射和穿透产生的多径传播。提取多径简档（例如，沿着每条路径的延迟和多普勒）使得能够实现许多重要的应用，例如信道预测和交叉带信道估计（即，估计不同频率上的信道）。多径估计的益处进一步随着移动性而增加，因为在这种情况下信道不太稳定并且跟踪更重要。然而，高速移动性对多径估计提出了重大挑战。在本文中，而不是使用时频域信道表示，我们利用延迟多普勒域表示准确地提取和预测多径特性。具体来说，我们使用脉冲在延迟多普勒域作为导频估计的多径参数和应用的多径信息预测无线信道作为一个示例应用。我们的设计原理是，移动性比无线信道更可预测，因为移动性具有惯性，而无线信道是移动性，多径和噪声之间复杂相互作用的结果。我们通过声学和RF实验，包括使用USRP的车辆实验来评估我们的方法。我们的结果表明，估计的多径匹配地面真相，和由此产生的信道预测是更准确的比传统的信道预测方案。
The wireless signal propagates via multipath arising from different reflections and penetration between a transmitter and receiver. Extracting multipath profiles (e.g., delay and Doppler along each path) from received signals enables many important applications, such as channel prediction and crossband channel estimation (i.e., estimating the channel on a different frequency). The benefit of multipath estimation further increases with mobility since the channel in that case is less stable and more important to track. Yet high-speed mobility poses significant challenges to multipath estimation. In this paper, instead of using time-frequency domain channel representation, we leverage the delay-Doppler domain representation to accurately extract and predict multipath properties. Specifically, we use impulses in the delay-Doppler domain as pilots to estimate the multipath parameters and apply the multipath information to predicting wireless channels as an example application. Our design rationale is that mobility is more predictable than the wireless channel since mobility has inertial while the wireless channel is the outcome of a complicated interaction between mobility, multipath, and noise. We evaluate our approach via both acoustic and RF experiments, including vehicular experiments using USRP. Our results show that the estimated multipath matches the ground truth, and the resulting channel prediction is more accurate than the traditional channel prediction schemes.