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2023 | OriginalPaper | Buchkapitel

3. Millimeter-Wave Vehicular Channel Estimation

verfasst von : Xiang Cheng, Shijian Gao, Liuqing Yang

Erschienen in: mmWave Massive MIMO Vehicular Communications

Verlag: Springer International Publishing

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Abstract

This chapter works on designing an efficient channel estimator for hybrid mmWave massive multiple-input multiple-output (mMIMO) systems. The proposed doubly-sparse approach relies on a judiciously designed training pattern to decouple the convoluted channel. By doing so, it becomes convenient to exploit the under-investigated channel sparsity in the delay domain together with the well-known beamspace sparsity. Furthermore, dedicated probing strategies are accordingly developed to ensure compatibility with the hybrid structure while utilizing double sparsity. Compared with existing alternatives, the proposed mmWave channel estimator works exceptionally in doubly-selective (frequency-time) channels and can hugely reduce the training overhead, storage demand, and computational complexity thanks to the exploitation of double (delay-beamspace) sparsity.
Fußnoten
1
Without loss of generality, L is assumed to be an integer here. If N fN c is not an integer, one can simply use L = ⌊N fN c⌋.
 
2
Evidently, the energy detector is somewhat heuristic. Recall that the energy detector actually plays the role of a binary classifier, a promising direction is to seek the power of deep neural networks. Specifically, given the channel model, a bunch of synthesized data can be generated to train the network for classification (tap detection) in a supervised manner. The offline trained network could then be used for online prediction.
 
3
Similar to the random-probing stage, we introduce the steered-probing state based on one frame consisting of L subframes. In practice or numerical comparisons, one can simply replace L with the actual number of subframes, i.e., cal(I)R.
 
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Metadaten
Titel
Millimeter-Wave Vehicular Channel Estimation
verfasst von
Xiang Cheng
Shijian Gao
Liuqing Yang
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-030-97508-1_3