# Energy Efficient Power Control for the Two-tier Networks with Small Cells and Massive MIMO

## TL;DR

This paper introduces a distributed energy-efficient power control algorithm for two-tier networks with macrocell massive MIMO and small cells, improving fairness and reducing complexity.

## Contribution

It proposes a novel distributed power control method using evolutionary game theory to optimize energy efficiency in two-tier networks.

## Key findings

- Significant fairness improvements demonstrated in simulations
- Linear complexity of the algorithm compared to brute force methods
- Effective energy efficiency optimization in multi-user, multi-cell scenarios

## Abstract

In this paper, energy efficient power control for the uplink two-tier networks where a macrocell tier with a massive multiple-input multiple-output (MIMO) base station is overlaid with a small cell tier is investigated. We propose a distributed energy efficient power control algorithm which allows each user in the two-tier network taking individual decisions to optimize its own energy efficiency (EE) for the multi-user and multi-cell scenario. The distributed power control algorithm is implemented by decoupling the EE optimization problem into two steps. In the first step, we propose to assign the users on the same resource into the same group and each group can optimize its own EE, respectively. In the second step, multiple power control games based on evolutionary game theory (EGT) are formulated for each group, which allows each user optimizing its own EE. In the EGT-based power control games, each player selects a strategy giving a higher payoff than the average payoff, which can improve the fairness among the users. The proposed algorithm has a linear complexity with respect to the number of subcarriers and the number of cells in comparison with the brute force approach which has an exponential complexity. Simulation results show the remarkable improvements in terms of fairness by using the proposed algorithm.