scholarly journals Energy Efficiency Analysis of Cache-Enabled Cellular Networks with Limited Backhaul

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Congshan Fan ◽  
Tiankui Zhang ◽  
Zhimin Zeng ◽  
Yue Chen
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Network Performance ◽  
Content Delivery ◽  
Base Station ◽  
Library Size ◽  
Improve Energy Efficiency ◽  
Delivery Probability ◽  
Content Popularity ◽  
Geometry Method

Caching in the cellular networks has been proposed as a promising technology for reducing the content delivery latency and backhaul cost. Since the backhaul capacity is limited in the practical scenario, the network performance analysis of base station (BS) caching should address the effects of the limited backhaul. This paper investigates the energy efficiency of the cache-enabled cellular networks with the limited backhaul based on the stochastic geometry method. First, the successful content delivery probability (SCDP), which depends on the successful access delivery probability, successful backhaul delivery probability, and cache hit ratio, is analyzed under the limited backhaul. Based on the obtained SCDP results, we derive the analytical expressions of throughput, power consumption, and energy efficiency for various scenes including the general case, the interference-limited case, and the mean load approximation case. The accuracy of theoretical analysis is verified by the Monte Carlo simulation. The simulation results show that BS caching can dramatically improve energy efficiency when the content popularity is skewed, the content library size is small, and the backhaul capacity is relatively small. Furthermore, it is confirmed that there exists an optimal BS density which maximizes the energy efficiency of the cache-enabled cellular networks.

scholarly journals Cell Throughput based Sleep Control Scheme for Heterogeneous Cellular Networks

2018 ◽  
Vol 12 (1) ◽  
pp. 26-33
Author(s):  
Prapassorn Phaiwitthayaphorn ◽  
Kazuo Mori ◽  
Hideo Kobayashi ◽  
Pisit Boonsrimuang
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Cellular Networks ◽  
Operating Cost ◽  
Reducing Power ◽  
Network Capacity ◽  
Base Station ◽  
Base Stations ◽  
Improve Energy Efficiency ◽  
Sleep Control

The mobile traffic continuously grows at a rapid rate driven by the widespread use of wireless devices. Along with that, the demands for higher data rate and better coverage lead to increase in power consumption and operating cost of network infrastructure. The concept of heterogeneous networks (HetNets) has been proposed as a promising approach to provide higher coverage and capacity for cellular networks. HetNet is an advanced network consisting of multiple kinds of base stations, i.e., macro base station (MBS), and small base station (SBS). The overlay of many SBSs into the MBS coverage can provide higher network capacity and better coverage in cellular networks. However, the dense deployment of SBSs would cause an increase in the power consumption, leading to a decrease in the energy efficiency in downlink cellular networks. Another technique to improve energy efficiency while reducing power consumption in the network is to introduce sleep control for SBSs. This paper proposes cell throughput based sleep control which the cell capacity ratio for the SBSs is employed as decision criteria to put the SBSs into a sleep state. The simulation results for downlink communications demonstrate that the proposed scheme improves the energy efficiency, compared with the conventional scheme.

scholarly journals Survey of Green Radio Communications Networks: Techniques and Recent Advances

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mohammed H. Alsharif ◽  
Rosdiadee Nordin ◽  
Mahamod Ismail
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Multiple Input Multiple Output ◽  
Primary Energy ◽  
Base Station ◽  
Radio Communications ◽  
Improve Energy Efficiency ◽  
Advantages And Disadvantages ◽  
Green Radio

Energy efficiency in cellular networks has received significant attention from both academia and industry because of the importance of reducing the operational expenditures and maintaining the profitability of cellular networks, in addition to making these networks “greener.” Because the base station is the primary energy consumer in the network, efforts have been made to study base station energy consumption and to find ways to improve energy efficiency. In this paper, we present a brief review of the techniques that have been used recently to improve energy efficiency, such as energy-efficient power amplifier techniques, time-domain techniques, cell switching, management of the physical layer through multiple-input multiple-output (MIMO) management, heterogeneous network architectures based on Micro-Pico-Femtocells, cell zooming, and relay techniques. In addition, this paper discusses the advantages and disadvantages of each technique to contribute to a better understanding of each of the techniques and thereby offer clear insights to researchers about how to choose the best ways to reduce energy consumption in future green radio networks.

scholarly journals Reinforcement Learning Optimization for Energy-Efficient Cellular Networks with Coordinated Multipoint Communications

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Huibin Lu ◽  
Baozhu Hu ◽  
Zhiyuan Ma ◽  
Shuhuan Wen
Keyword(s):  
Energy Efficiency ◽  
Reinforcement Learning ◽  
Cellular Networks ◽  
Theoretical Approach ◽  
Energy Efficient ◽  
Local Maximum ◽  
Base Station ◽  
Coordinated Multipoint ◽  
Improve Energy Efficiency ◽  
Simulation Results

Recently, there is an emerging trend of addressing “energy efficiency” aspect of wireless communications. And coordinated multipoint (CoMP) communication is a promising method to improve energy efficiency. However, since the downlink performance is also important for users, we should improve the energy efficiency as well as keeping a perfect downlink performance. This paper presents a control theoretical approach to study the energy efficiency and downlink performance issues in cooperative wireless cellular networks with CoMP communications. Specifically, to make the decisions for optimal base station grouping in energy-efficient transmissions in CoMP, we develop a Reinforcement Learning (RL) Algorithm. We apply theQ-learning of the RL Algorithm to get the optimal policy for base station grouping with introduction of variations at the beginning of theQ-learning to preventQfrom falling into local maximum points. Simulation results are provided to show the process and effectiveness of the proposed scheme.

scholarly journals Energy-Efficient and Reliable Internet of Things for 5G: A Framework for Interference Control

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2165
Author(s):  
Radwa Ahmed Osman ◽  
Amira I. Zaki
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Cellular Networks ◽  
Communication Networks ◽  
Network Performance ◽  
Optimization Technique ◽  
Base Station ◽  
Optimization Strategy ◽  
Interference Control ◽  
Proposed Model

The Internet of Things (IoT) is one of the promising paradigms that enable massive machines and devices to communicate with each other in future communication networks to promote a high level of awareness about our world and improve our daily life. IoT devices (IoTDs) communicate with an IoT base station (IoTBS) or IoT gateway (IoTG) by sharing the resources of other cellular users (CUEs). Due to the leakage of the spectral efficiency, interference exists among IoTG and base station (BS) due to CUEs and IoTDs. In this paper, a new framework is proposed called the interference control model. This proposed model aims to control the interference among IoTG and BS and is based on using the Lagrange optimization technique to reduce interference and maximize the energy efficiency and reliability of the IoT and cellular networks in fifth-generation (5G) systems. First, we formulate the multi-objective optimization problem to achieve the objective of the proposed model. Then, based on the optimization strategy, we derive the closed-form expressions of key quality-of-service (QoS) performance such as system reliability, throughput, and energy efficiency. Finally, the proposed algorithm has been evaluated and examined through different assumptions and several simulation scenarios. The obtained results validate the effectiveness and the accuracy of our proposed idea and also indicate significant improvement in the network performance of IoT and cellular networks.

scholarly journals Massive MIMO: Achievable Energy Efficiency for 5G systems with Multi-user Environment

2019 ◽  
Vol 8 (2) ◽  
pp. 6527-6534
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Path Loss ◽  
Antenna System ◽  
Base Station ◽  
Small Cells ◽  
Limiting Factors ◽  
Improve Energy Efficiency ◽  
User Terminal ◽  
Simulation Results

Massive Multi-Input and Multi-Output (MIMO) antenna system potentially provides a promising solution to improve energy efficiency (EE) for 5G wireless systems. The aim of this paper is to enhance EE and its limiting factors are explored. The maximum EE of 48 Mbit/Joule was achieved with 15 user terminal (UT)s. This problem is related to the uplink spectral efficiency with upper bound for future wireless networks. The maximal EE is obtained by optimizing a number of base station (BS) antennas, pilot reuse factor, and BSs density. We presented a power consumption model by deriving Shannon capacity calculations with closed-form expressions. The simulation result highlights the EE maximization with optimizing variables of circuit power consumption, hardware impairments, and path-loss exponent. Small cells achieve high EE and saturate to a constant value with BSs density. The MRC scheme achieves maximum EE of 36 Mbit/Joule with 12 UTs. The simulation results show that peak EE is obtained by deploying massive BS antennas, where the interference and pilot contamination are mitigated by coherent processing. The simulation results were implemented by using MATLAB 2018b.

scholarly journals Energy Efficient Caching in Backhaul-Aware Cellular Networks with Dynamic Content Popularity

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jiequ Ji ◽  
Kun Zhu ◽  
Ran Wang ◽  
Bing Chen ◽  
Chen Dai
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Energy Efficient ◽  
Base Stations ◽  
Noise Model ◽  
Traffic Demand ◽  
Hit Rate ◽  
Dynamic Content ◽  
Content Popularity ◽  
Cache Capacity

Caching popular contents at base stations (BSs) has been regarded as an effective approach to alleviate the backhaul load and to improve the quality of service. To meet the explosive data traffic demand and to save energy consumption, energy efficiency (EE) has become an extremely important performance index for the 5th generation (5G) cellular networks. In general, there are two ways for improving the EE for caching, that is, improving the cache-hit rate and optimizing the cache size. In this work, we investigate the energy efficient caching problem in backhaul-aware cellular networks jointly considering these two approaches. Note that most existing works are based on the assumption that the content catalog and popularity are static. However, in practice, content popularity is dynamic. To timely estimate the dynamic content popularity, we propose a method based on shot noise model (SNM). Then we propose a distributed caching policy to improve the cache-hit rate in such a dynamic environment. Furthermore, we analyze the tradeoff between energy efficiency and cache capacity for which an optimization is formulated. We prove its convexity and derive a closed-form optimal cache capacity for maximizing the EE. Simulation results validate the proposed scheme and show that EE can be improved with appropriate choice of cache capacity.

A Super Base Station Architecture for Future Ultra-Dense Cellular Networks: Toward Low Latency and High Energy Efficiency

2018 ◽  
Vol 56 (6) ◽  
pp. 35-41 ◽  
Author(s):  
Mingjin Gao ◽  
Jun Li ◽  
Dushantha Nalin K. Jayakody ◽  
He Chen ◽  
Yonghui Li ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
High Energy ◽  
Base Station ◽  
Low Latency ◽  
High Energy Efficiency

scholarly journals Stochastic Geometry Analysis of Downlink Spectral and Energy Efficiency in Ultradense Heterogeneous Cellular Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jiaqi Lei ◽  
Hongbin Chen ◽  
Feng Zhao
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Stochastic Geometry ◽  
Density Ratio ◽  
Base Station ◽  
Small Cell ◽  
Base Stations ◽  
Heterogeneous Cellular Networks ◽  
Downlink Throughput ◽  
The Impact

The energy efficiency (EE) is a key metric of ultradense heterogeneous cellular networks (HCNs). Earlier works on the EE analysis of ultradense HCNs by using the stochastic geometry tool only focused on the impact of the base station density ratio and ignored the function of different tiers. In this paper, a two-tier ultradense HCN with small-cell base stations (SBSs) and user equipments (UEs) densely deployed in a traditional macrocell network is considered. Firstly, the performance of the ultradense HCN in terms of the association probability, average link spectral efficiency (SE), average downlink throughput, and average EE is theoretically analyzed by using the stochastic geometry tool. Then, the problem of maximizing the average EE while meeting minimum requirements of the average link SE and average downlink throughput experienced by UEs in macrocell and small-cell tiers is formulated. As it is difficult to obtain the explicit expression of average EE, impacts of the SBS density ratio and signal-to-interference-plus-noise ratio (SINR) threshold on the network performance are investigated through numerical simulations. Simulation results validate the accuracy of theoretical results and demonstrate that the maximum value of average EE can be achieved by optimizing the SBS density ratio and the SINR threshold.

scholarly journals Context-Aware Mode Selection for 5G Multi-Hop Cellular Networks

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 840
Author(s):  
Lucas-Estañ ◽  
Gozalvez ◽  
Sepulcre
Keyword(s):  
Cellular Networks ◽  
Network Performance ◽  
Mode Selection ◽  
Base Station ◽  
Operating Conditions ◽  
Context Aware ◽  
5G Networks ◽  
D2d Communications ◽  
Selection Scheme ◽  
Wide Range

In the present day, 5G and beyond networks are being designed to support the future increase of data traffic and service demands. To support such increase, 5G networks will incorporate device-centric technologies with adequate mechanisms to scale and handle the growing and very large number of connected devices and traffic demands. Device-centric technologies include Device-to-Device (D2D) communications and Multi-hop Cellular Networks (MCNs). In device-centric wireless networks, devices will be able to connect to the network using two different connection modes: through a traditional cellular connection, or through a multi-hop cellular connection based on D2D communications with intermediate mobile devices. Device-centric technologies will therefore provide new connectivity options and significant opportunities to enhance the capacity and efficiency of 5G networks. However, new challenges will need to be addressed. One of them is the selection of the most adequate connection mode for each mobile device, because it will be key to improve the network performance and efficiency. This work proposes a context-aware mode selection scheme capable of identifying and selecting the most adequate connection mode for each device under a wide range of deployment and operating conditions. The proposed scheme estimates the benefits and risks of each connection mode based on context information available at the base station guaranteeing low signaling overhead. The obtained results show that the proposed mode selection scheme helps achieving throughput gains higher than 200% compared to traditional single-hop cellular communications for devices at the cell edge, and significant gains are also achieved compared to other mode selection schemes implemented and evaluated.

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