Improved Energy Efficiency of Massive MIMO-OFDM in Battery-Limited IoT Networks

We investigate the feasibility of improving the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems applied to a battery-limited Internet of Things (IoT) networks. Improving EE is especially important for battery limited IoT devices. We observe the uplink and downlink aspects of massive MIMO-OFDM-based IoT networks and categorize some of the effective methods to consider. As uplink aspect, we consider the uplink reference signal (RS) power control. Reducing uplink RS power could induce the battery saving of IoT devices but could cause an increase in channel estimation error. As downlink aspect, we consider the peak-to-average power ratio reduction of the OFDM signal and downlink transmitter power control. These techniques are well-known as effective EE improvement methods, but there is little work showing the actual EE gain in system perspective. In addition, we also consider the utilization of radio frequency energy transfer using unmanned aerial vehicles to extend the operating time of battery-limited IoT devices. We derive the theoretical closed-form approximations of spectral efficiency and EE when applying these methods and provide EE gains for various scenarios. Numerical results show that the theoretical analysis is in good agreement with the simulation results and thus can be used as useful tools to improve EE.