Massive MIMO Beamforming in Monostatic Backscatter Multi-Tag Networks

This letter investigates the role of massive multiple-input multiple-output (MIMO) in improving the spectral and energy efficiency of monostatic backscatter communication systems, where a multiple-antenna reader aims to decode information backscattered from multiple tags. Specifically, we investigate the performance of the two most prominent precoders and combiners, namely, the matched filter and zero forcing. First, we derive capacity lower bounds for the four different underlying transceiver design configurations. Then, asymptotic analysis is conducted and it is shown that with perfect channel state information, the total transmit power can be scaled down by a factor of the square of the number of transmit antennas without loss in the performance. To further corroborate the practical utility of the considered massive MIMO multi-tag setting, the optimization of the backscatter coefficients for sum rate maximization and the effect of imperfect channel state information are also considered.

Automated summary

This study investigates the role of massive MIMO in improving the spectral and energy efficiency of monostatic backscatter communication systems, as well as the performance of precoders and combiners. The results show that with perfect channel state information, the total transmit power can be scaled down by a factor of the square of the number of transmit antennas without loss in performance.