Using Machine Learning based Adaptive Hybrid Beamforming for Energy Efficient Massive MIMO

As an extension work of the previous project "Energy Efficient Massive MIMO with QoS-Aware Hybrid Beamforming", here, of our interests is to shrink the power consumption of a hybrid beamforming MIMO BS in the multi-user downlink communication, via regulating the budgets of the transmission power and the RF chains circuit power in a mixed-timescale fashion. Given the instantaneous channel state information (CSI) and the user data rate requirements, we applied the proposed QoS-aware eigenmode selection hybrid beamforming to reduce transmission power. Considering the statistical CSI in a large timescale, we adapt the number of active RF chains to minimize the total average BS power, with the outage probability constraint satisfied. A partition-based machine learning framework, with the extracted features, called power coefficients, is designed to track the optimal RF chain configuration. Simulations demonstrate that substantial transmission power can be reduced with the EiS-HBF scheme, while the adaptive RF chain switching technique is also effective to track the optimal RF configuration to significantly cut down the total average BS power.
Note that we are currently finalizing a journal paper based upon the results of this project.