Energy efficiency of mmWave massive MIMO precoding with low-resolution DACs

With the congestion of the sub-6 GHz spectrum, the interest in massive multiple-input multiple-output (MIMO) systems operating on millimeter wave spectrum grows. In order to reduce the power consumption of such massive MIMO systems, hybrid analog/digital transceivers and application of low-resolution digital-to-analog/analog-to-digital converters have been recently proposed. In this work, we investigate the energy efficiency of quantized hybrid transmitters equipped with a fully/partially-connected phase-shifting network composed of active/passive phase-shifters and compare it to that of quantized digital precoders. We introduce a quantized single-user MIMO system model based on an additive quantization noise approximation considering realistic power consumption and loss models to evaluate the spectral and energy efficiencies of the transmit precoding methods. Simulation results show that partially-connected hybrid precoders can be more energy-efficient compared to digital precoders, while fully-connected hybrid precoders exhibit poor energy efficiency in general. Also, the topology of phase-shifting components offers an energy-spectral efficiency trade-off: active phase-shifters provide higher data rates, while passive phase-shifters maintain better energy efficiency.Comment: Published in IEEE Journal of Selected Topics in Signal Processin

Calibrated Learning for Online Distributed Power Allocation in Small-Cell Networks

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori , and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies