scholarly journals Optimizing Power by Combining Small Cells with a Massive MIMO for Maximum Energy Efficiency under Two Path Loss Models

2019 ◽  
Author(s):  
Ch. Aravind Kumar
Keyword(s):  
Energy Efficiency ◽  
Massive Mimo ◽  
Path Loss ◽  
Base Station ◽  
Design Criterion ◽  
Small Cell ◽  
Small Cells ◽  
Total Power ◽  
Efficient System ◽  
Loss Models

Currently, we are proceeding towards the 5th generationof wireless networks. The 5G is expected to utilize verywide bandwidth and would have the greater capacity comparedto 4G. Therefore, the energy efficiency is one of the prominentroles in the design criterion. To achieve the maximum energyefficiency, the typical approaches are to increase the throughputor to decrease the power consumption. Recently, the approachto combine the small cell access points to Massive MIMO hasbeen proved as one efficient way to achieve the maximum energyefficiency. In this work, the approach to combine two typeof network, namely Small Cell Access Point (SCA) and BaseStation (BS) with Massive MIMO, is considered for designing anenergy efficient system. We investigate further this approach byextending the simulation to cover both 3GPP LTE as well as IMT2020 environment with two different carrier frequencies underthree scenarios. The performance is measured and comparedwith the existing work in terms of total power consumptionper subcarrier of both the Base Station (BS) and Small CellAccess Points (SCAs). It is apparent that our proposed methodprovides the higher information rate at the same total powerper sub carrier than the existing work. Additionally, generalspeaking, the 3GPP path loss model provides the lower totalpower consumption per sub carrier at BS than IMT-2020, whileboth path loss models almost has no impact on total powerconsumption at SCAs, regardless of carrier frequency and thenumber of antennas adopted in Massive MIMO at BS.

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.

Distributed Resource Allocation for Green HetNets with Renewable Energy Resources

2021 ◽  
pp. 2159029
Author(s):  
Bin Zhang ◽  
Yi Liu
Keyword(s):  
Resource Allocation ◽  
Energy Efficiency ◽  
Renewable Energy ◽  
Base Station ◽  
Small Cell ◽  
Small Cells ◽  
Renewable Energy Resources ◽  
Distributed Resource Allocation ◽  
Resource Allocation Algorithm ◽  
Power Resources

In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm.

Toward Green Evolution of Cellular Networks by High Order Sectorisation and Small Cell Densification

2017 ◽  
pp. 1-28
Author(s):  
Abdelrahman Arbi ◽  
Timothy O'Farrell ◽  
Fu-Chun Zheng ◽  
Simon C. Fletcher
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Network Capacity ◽  
Base Station ◽  
Small Cell ◽  
Small Cells ◽  
Deployment Strategy ◽  
Energy Efficiency Improvement ◽  
The Impact

Network densification by adding either more sectors per site or by deploying an overlay of small cells is always considered to be a key method for enhancing the RAN coverage and capacity. The impact of these two techniques on cellular network energy consumption is investigated in this chapter. The aim is to find an energy efficient deployment strategy when trading-off the order of sectorisation with the intensity of small cell densification. A new enhanced base station power consumption model is presented, followed by a novel metric framework for the evaluation of the RAN energy efficiency. The use of the power model and the proposed metrics is demonstrated by applying them to a RAN case study when the two techniques are used to improve the network capacity. In addition, the chapter evaluates the amount of network energy efficiency improvement when various adaptive sectorisation schemes are implemented. The results show that the strategy of adding more sectors is less energy efficient than directly deploying an overlay of small cells, even when adaptive sectorisation is implemented.

scholarly journals Energy Efficiency in 5G Communications – Conventional to Machine Learning Approaches

2020 ◽  
Vol 4 ◽  
pp. 1-9
Author(s):  
Muhammad Khalil Shahid ◽  
Filmon Debretsion ◽  
Aman Eyob ◽  
Irfan Ahmed ◽  
Tarig Faisal
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Data Exchange ◽  
Mixed Reality ◽  
Mimo Systems ◽  
Base Station ◽  
Small Cells ◽  
Total Power ◽  
5G Networks ◽  
Total Power Consumption

Demand for wireless and mobile data is increasing along with development of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (ER) applications. In order to handle ultra-high data exchange rates while offering low latency levels, fifth generation (5G) networks have been proposed. Energy efficiency is one of the key objectives of 5G networks. The notion is defined as the ratio of throughput and total power consumption, and is measured using the number of transmission bits per Joule. In this paper, we review state-of-the-art techniques ensuring good energy efficiency in 5G wireless networks. We cover the base-station on/off technique, simultaneous wireless information and power transfer, small cells, coexistence of long term evolution (LTE) and 5G, signal processing algorithms, and the latest machine learning techniques. Finally, a comparison of a few recent research papers focusing on energy-efficient hybrid beamforming designs in massive multiple-input multiple-output (MIMO) systems is presented. Results show that machine learningbased designs may replace best performing conventional techniques thanks to a reduced complexity machine learning encoder

scholarly journals Energy Efficiency Augmentation in Massive MIMO Systems through Linear Precoding Schemes and Power Consumption Modeling

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rao Muhammad Asif ◽  
Jehangir Arshad ◽  
Mustafa Shakir ◽  
Sohail M. Noman ◽  
Ateeq Ur Rehman
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Antenna Arrays ◽  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Mimo System ◽  
Base Station ◽  
Total Power ◽  
Minimum Mean Squared Error

Massive multiple-input multiple-output or massive MIMO system has great potential for 5th generation (5G) wireless communication systems as it is capable of providing game-changing enhancements in area throughput and energy efficiency (EE). This work proposes a realistic and practically implementable EE model for massive MIMO systems while a general and canonical system model is used for single-cell scenario. Linear processing schemes are used for detection and precoding, i.e., minimum mean squared error (MMSE), zero-forcing (ZF), and maximum ratio transmission (MRT/MRC). Moreover, a power dissipation model is proposed that considers overall power consumption in uplink and downlink communications. The proposed model includes the total power consumed by power amplifier and circuit components at the base station (BS) and single antenna user equipment (UE). An optimal number of BS antennas to serve total UEs and the overall transmitted power are also computed. The simulation results confirm considerable improvements in the gain of area throughput and EE, and it also shows that the optimum area throughput and EE can be realized wherein a larger number of antenna arrays at BS are installed for serving a greater number of UEs.

scholarly journals User Association and Small-Cell Base Station On/Off Strategies for Energy Efficiency of Ultradense Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Gao ◽  
Qing Ren ◽  
Pei Shang Gu ◽  
Xin Song
Keyword(s):  
Energy Efficiency ◽  
Optimization Method ◽  
Base Station ◽  
Small Cell ◽  
Base Stations ◽  
User Association ◽  
User Mobility ◽  
Efficient Design ◽  
Widespread Application ◽  
Cell Base

The widespread application of wireless mobile services and requirements of ubiquitous access have resulted in drastic growth of the mobile traffic and huge energy consumption in ultradense networks (UDNs). Therefore, energy-efficient design is very important and is becoming an inevitable trend. To improve the energy efficiency (EE) of UDNs, we present a joint optimization method considering user association and small-cell base station (SBS) on/off strategies in UDNs. The problem is formulated as a nonconvex nonlinear programming problem and is then decomposed into two subproblems: user association and SBS on/off strategies. In the user association strategy, users associate with base stations (BSs) according to their movement speeds and utility function values, under the constraints of the signal-to-interference ratio (SINR) and load balancing. In particular, taking care of user mobility, users are associated if their speed exceeds a certain threshold. The macrocell base station (MBS) considers user mobility, which prevents frequent switching between users and SBSs. In the SBS on/off strategy, SBSs are turned off according to their loads and the amount of time required for mobile users to arrive at a given SBS to further improve network energy efficiency. By turning off SBSs, negative impacts on user associations can be reduced. The simulation results show that relative to conventional algorithms, the proposed scheme achieves energy efficiency performance enhancements.

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