Abstract

Heterogeneous networks (HetNets), consisting of macro-cells and overlaying pico-cells, have been recognized as a promising paradigm to support the exponential growth of data traffic demands and high network energy efficiency (EE). However, for two-tier heterogeneous architecture deployment of HetNets, the inter-tier interference will be challenging. Time domain further-enhanced inter-cell interference coordination (FeICIC) proposed in 3GPP Release-11 becomes necessary to mitigate the inter-tier interference by applying low power almost blank subframe (ABS) scheme. Therefore, for HetNets deployment in reality, the pico-cell range expansion (CRE) bias, the power of ABS and the density of pico base stations (PBSs) are three important factors for the network EE improvement. Aiming to improve the network EE, the above three factors are jointly considered in this paper. In particular, we first derive the closed-form expression of the network EE as a function of pico CRE bias, power reduction factor of low power ABS and PBS density based on stochastic geometry model. Then, the approximate relationship between pico CRE bias and power reduction factor is deduced, followed by a linear search algorithm to get the near-optimal pico CRE bias and power reduction factor together at a given PBS density. Next, a linear search algorithm is further proposed to optimize PBS density based on fixed pico CRE bias and power reduction factor. Due to the fact that the above pico CRE bias and power reduction factor optimization and PBS density optimization are optimized separately, a heuristic algorithm is further proposed to optimize pico CRE bias, power reduction factor and PBS density jointly to achieve global network EE maximization. Numerical simulation results show that our proposed heuristic algorithm can significantly enhance the network EE while incurring low computational complexity.

Highlights

  • The exponential growth of data traffic demand, huge energy consumption and large amounts of global carbon dioxide emissions severely restrict the sustainable development of wireless cellular networks

  • Due to the fact that these variables are affected by each other, we further propose a heuristic pico cell range expansion (CRE) bias, power reduction factor and pico base stations (PBSs) density joint optimization algorithm to globally optimize network EE based on the joint pico CRE bias and power reduction factor optimization (JBPO) algorithm and PDO algorithm

  • The deployments of PBS and macro base stations (MBSs) follow the Poisson point process (PPP) model and the typical user equipments (UEs) is deployed in the origin

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Summary

Introduction

The exponential growth of data traffic demand, huge energy consumption and large amounts of global carbon dioxide emissions severely restrict the sustainable development of wireless cellular networks. The data traffic volume demand in the fifth-generation (5G). The limited spectrum resources constrain the network capacity improvement [1]. Heterogeneous networks (HetNets) consisting of macro base stations (MBSs) and low-power pico base stations (PBSs) can improve SE by reusing the spectrum geographically [3] and enhance the wireless link quality by shortening the distance between the transmitter and the receiver [4]. HetNets are deemed as a promising technique to support the deluge of data traffic with high network EE. Due to the complex two-tier heterogeneous architecture deployment of HetNets, the challenging inter-tier interference and PBS deployment density will deteriorate the network EE if they are not treated carefully, which are the concerns of this paper for aiming to improve the network EE

Motivation
Contributions
Organization
Related Works
System Model
User Type Probability
Distribution of Serving BS Distance
The Ratio of PSF
Average Achievable Downlink Rate
Network Power Consumption
Network Energy Efficiency
Joint Optimization of FeICIC Parameters and Base-Station Density
Joint Optimization of Pico CRE Bias and Power Reduction Factor
1: Initialization
Optimization of PBS Density
Computational Complexity
Numerical Results and Analysis
Conclusions
Full Text
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