Energy Efficient Operation of Adaptive Massive MIMO 5G HetNets

TL;DR

This paper investigates energy-efficient operation strategies for massive MIMO 5G HetNets, focusing on joint optimization of resource allocation and spatial layering to minimize power consumption across varying load scenarios.

Contribution

It introduces a comprehensive power consumption model and develops novel algorithms for joint resource allocation, including user-to-BS assignment, PRB, and power control, tailored for practical constraints.

Findings

Single spatial layer minimizes energy in low load scenarios.

Spatial layering becomes necessary in high load scenarios.

Using higher frequency bands with maximum PRBs improves user support.

Abstract

For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support users with large rate requirements via spatial multiplexing and assigning each user maximum available PRBs.