Hybrid beamforming and DFT-based channel estimation for millimeter wave MIMO systems

Millimeter wave (mmWave) communication systems are considered to be a promising technology enabling gigabit-per-second data rates, as the mmWave band offers large transmission bandwidth. However, precoding in mmWave systems cannot be performed in digital domain, due to the large number of radio freq...

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Vydáno v:2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) s. 1 - 7
Hlavní autoři: Soleimani, Maliheh, Elliott, Robert C., Krzymien, Witold A., Melzer, Jordan, Mousavi, Pedram
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2017
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ISBN:1538635291, 9781538635292
ISSN:2166-9589
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Shrnutí:Millimeter wave (mmWave) communication systems are considered to be a promising technology enabling gigabit-per-second data rates, as the mmWave band offers large transmission bandwidth. However, precoding in mmWave systems cannot be performed in digital domain, due to the large number of radio frequency chains required. Deploying a hybrid precoding transceiver architecture, where mmWave precoding is divided among digital and analog processing domains, is an attractive and cost-efficient alternative. Yet, the design of near-optimal hybrid precoders is a non-trivial optimization problem. In this paper, we consider transmit precoding and receive combining in mmWave systems with large antenna arrays in a partially-connected structure, then we make use of projection algorithms to greatly simplify the design problem of digital baseband and analog radio frequency precoders into two sub-optimization problems whose optimal solutions can be found. We also develop a channel estimation algorithm to estimate mmWave channel parameters via a codebook of beamforming vectors obtained through the discrete Fourier transform design. The results illustrate that the system using the proposed projection hybrid precoding and channel estimation algorithms approaches the spectral efficiency achievable when perfect channel knowledge is available.
ISBN:1538635291
9781538635292
ISSN:2166-9589
DOI:10.1109/PIMRC.2017.8292276