Power consumption trade-off in channel estimation with hybrid transceiver
1University of Oulu, Faculty of Information Technology and Electrical Engineering, Communications Engineering
|Online Access:||PDF Full Text (PDF, 7.9 MB)|
|Persistent link:|| http://urn.fi/URN:NBN:fi:oulu-201606092486
|Publish Date:|| 2016-06-15
|Thesis type:||Master's thesis (tech)
The usage of massive antenna arrays coupled with millimeter-wave (mmW) transmissions has emerged as enabling technology of the fifth generation mobile communication standard, the 5G. This solution has great potentials to provide Gb/s data-rate and high cell capacity by leveraging the synergy amongst high resolution spatial filtering, adaptive beamforming and channel sparsity. One of the main challenges, however, is related to the implementation and digital processing as with a conventional transceiver architecture, an increase of the number of antennas implies more analog-to-digital (or digital-to-analog) converters, more power amplifiers and baseband units. Subsequently, the energy, factor-size and computational power requirements become impractical.
To counter these effects a hybrid transceiver design has been proposed, in which multiple analog front-ends are combined into a single (or multiple) baseband processing unit allowing the transceiver to reduce the complexity of the digital signal processing as well as the power consumption. In this Thesis we investigate different architecture models and evaluate the trade-off between energy consumption and performance in channel estimation. More specifically, we study a hybrid receiver model with 64 antenna elements, parallel digital paths and, for the channel estimation, we consider the adaptive-least absolute shrinkage and selection operator (A-LASSO) algorithm that leverages channel sparsity into the estimation.
Simulation results have shown that a transceiver architecture with only four base-bands performed best over the different cell sizes. Compared to the fully digital receiver this results in tenfold power consumption reduction according to analysis.
© Tobias Ziegler, 2016. This publication is copyrighted. You may download, display and print it for your own personal use. Commercial use is prohibited.