MIMO Ambient Backscatter Communications: Capacity Maximization and Beamforming Optimization

Multiple-input multiple-output (MIMO) ambient backscatter communication (AmBC) systems are investigated in order to develop approaches to achieve power, multiplexing, and diversity gains. These results can be utilized to motivate the development of MIMO AmBC by providing performance bounds on the MIMO AmBC gains. Our approach to the investigation is to reformulate the MIMO AmBC channel model as an accurate linear MIMO channel model. Using this model, we show that the MIMO AmBC received signal is real-valued, hence the dimension of the received signal is halved. In addition, we show that MIMO AmBC has a per antenna constant modulus transmit signal constraint. Therefore, increases in antennas at the Tag provide a power gain in contrast to conventional MIMO systems. Under these constraints, when channel state information at the Reader (CSIR) is available, we provide estimates of channel capacity. Assuming channel state information at the Tag (CSIT) is also available, we use a fixed-point iteration algorithm to maximize channel capacity. With CSIT, beamforming design and the corresponding majorization-minimization (MM) algorithm are proposed to find the optimal transmit and receive beamformers so that diversity gain can be leveraged. It is also shown that in the low signal-to-noise ratio (SNR) operating region, beamforming design maximizes channel capacity. In addition, we provide numerical results for the diversity-multiplexing tradeoff (DMT). Utilizing comparisons between AmBC and conventional MIMO, we highlight the unique characteristics of MIMO AmBC. These approaches to maximize power, capacity, and diversity gains demonstrate the potential of MIMO AmBC.