Area Spectral Efficiency Analysis and Energy Consumption Minimization in Multi-Antenna Poisson Distributed Networks

TL;DR

This paper analyzes how area spectral efficiency (ASE) varies with system parameters in multi-antenna Poisson networks and proposes joint optimization of network parameters to minimize energy consumption, revealing key relationships and optimal configurations.

Contribution

It provides a tight lower-bound for ASE considering MU-MIMO in Poisson networks and introduces algorithms for joint optimization of network parameters for energy efficiency.

Findings

ASE is a concave function of antennas and active users

Optimal active users are a fixed portion of antennas for maximum ASE

Joint optimization of BS density, antennas, and users minimizes energy consumption

Abstract

This paper aims at answering two fundamental questions: how area spectral efficiency (ASE) behaves with different system parameters; how to design an energy-efficient network. Based on stochastic geometry, we obtain the expression and a tight lower-bound for ASE of Poisson distributed networks considering multi-user MIMO (MU-MIMO) transmission. With the help of the lower-bound, some interesting results are observed. These results are validated via numerical results for the original expression. We find that ASE can be viewed as a concave function with respect to the number of antennas and active users. For the purpose of maximizing ASE, we demonstrate that the optimal number of active users is a fixed portion of the number of antennas. With optimal number of active users, we observe that ASE increases linearly with the number of antennas. Another work of this paper is joint optimization of the base station (BS) density, the number of antennas and active users to minimize the network energy consumption. It is discovered that the optimal combination of the number of antennas and active users is the solution that maximizes the energy-efficiency. Besides the optimal algorithm, we propose a suboptimal algorithm to reduce the computational complexity, which can achieve near optimal performance.