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.
Millimeter-Wave Vehicular Channel Estimation
Wireless Networks
2022-05-03
23 pages
Article/Chapter (Book)
Electronic Resource
English
mmWave , Hybrid structure , Massive multiple-input multiple-output , Channel estimation , Double selectivity , Double sparsity , Frequency-time , Delay-beamspace Engineering , Communications Engineering, Networks , Computer Communication Networks , Transportation Technology and Traffic Engineering , Automotive Engineering
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