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arXiv 2408.09381eess.SP
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双弥散信道中的信道估计、插值与外推

Channel Estimation, Interpolation and Extrapolation in Doubly-dispersive Channels

Zijun Gong, Fan Jiang, Yuhui Song, Cheng Li, Xiaofeng Tao

AI总结:

本文针对双弥散信道,提出基于FFT的低复杂度信道估计与插值算法,结合延迟-多普勒域数据辅助信道外推技术,在考虑混叠与ISCI影响下有效降低OTFS系统的信道训练开销。

摘要:

OTFS(正交时频空间)技术凭借其在时变信道中对抗多普勒扩展的能力得到广泛认可。本文将论证OTFS相较于OFDM(正交频分复用)的另一项优势:信道训练开销大幅降低。具体而言,信道在延迟-多普勒(D-D)域的稀疏性意味着其时频(T-F)域的信道增益存在强相关性,可利用这一特性通过插值减少信道训练开销。一个直接的问题是:双弥散信道中需要多少训练开销?传统观点认为开销仅取决于延迟扩展与多普勒扩展的乘积,但本文将证明其还与时频窗口尺寸相关。有限的时频窗口会导致延迟-多普勒域出现无限扩展,且时频域采样后混叠将不可避免。混叠的两个直接后果是信道训练开销增加与干扰产生。导致信道估计误差的另一因素是由不确定性原理引发的符号间载波间干扰(ISCI)。本文在信道建模中同时考虑了混叠与ISCI,提出了一种基于FFT的低复杂度信道估计与插值算法。较大的时频窗口对于降低信道训练开销和减少混叠是必要的,但会增加处理时延。所幸本文证明所提算法可通过流水线方式实现。此外,本文还证明在延迟-多普勒域中可实现数据辅助的信道跟踪,以进一步降低信道估计频率,即信道外推。本文还分析了混叠与ISCI对信道插值误差的影响。

英文摘要:

The OTFS (Orthogonal Time Frequency Space) is widely acknowledged for its ability to combat Doppler spread in time-varying channels. In this paper, another advantage of OTFS over OFDM (Orthogonal Frequency Division Multiplexing) will be demonstrated: much reduced channel training overhead. Specifically, the sparsity of the channel in delay-Doppler (D-D) domain implies strong correlation of channel gains in time-frequency (T-F) domain, which can be harnessed to reduce channel training overhead through interpolation. An immediate question is how much training overhead is needed in doubly-dispersive channels? A conventional belief is that the overhead is only dependent on the product of delay and Doppler spreads, but we will show that it's also dependent on the T-F window size. The finite T-F window leads to infinite spreading in D-D domain, and aliasing will be inevitable after sampling in T-F domain. Two direct consequences of the aliasing are increased channel training overhead and interference. Another factor contributing to channel estimation error is the inter-symbol-carrier-interference (ISCI), resulting from the uncertainty principle. Both aliasing and ISCI are considered in channel modelling, a low-complexity algorithm is proposed for channel estimation and interpolation through FFT. A large T-F window is necessary for reduced channel training overhead and aliasing, but increases processing delay. Fortunately, we show that the proposed algorithm can be implemented in a pipeline fashion. Further more, we showed that data-aided channel tracking is possible in D-D domain to further reduce the channel estimation frequency, i.e., channel extrapolation. The impacts of aliasing and ISCI on channel interpolation error are analyzed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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