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Presenter: Farnoosh Talaei
Supervisor:

Date: Thu, December 14, 2017
Time: 13:30:00 - 00:00:00
Place: Engineering Office Wing Room 430

ABSTRACT

The potential availability of large bandwidth channels at mm-wave frequencies and the small wavelength of the mm-waves, offer the mm-wave massive MIMO communication as a promising technology for 5G cellular networks which enables Giga-bit-per second data rates. Due to the high fabrication cost and power consumption of the RF units at mm-wave frequencies, hybrid analog/digital architectures are proposed for efficient implementation of mm-wave massive MIMO systems. The hardware constraint of the hybrid architecture makes the mm-wave channel estimation a challenge. Exploiting the sparse nature of the mm-wave channel in angular domain we have proposed a new channel estimator which leverages the compressed sensing (CS) tools for recovering the angular support of the MIMO-OFDM mm-wave channel. The angular channel is treated in a continuous framework which resolves the limited angular resolution of the discrete sparse channel models used in the previous CS based channel estimators. The power leakage problem is also addressed by modelling the continuous angular channel as a multi-band signal with the bandwidth of each sub-band being proportional to the amount of power leakage. The RF combiner is designed to be implemented using a network of low-power switches for antenna subset selection based on a multi-coset sampling pattern. Simulation results validate the effectiveness of the proposed hybrid channel estimator both in terms of the estimation accuracy and the RF power consumption.