scholarly journals Energy Efficient and Delay Aware 5G Multi-Tier Network

2019 ◽  
Vol 11 (9) ◽  
pp. 1019
Author(s):  
Nahina Islam ◽  
Ammar Alazab ◽  
Johnson Agbinya
Keyword(s):  
Energy Efficiency ◽  
Mobile Network ◽  
Base Station ◽  
Small Cells ◽  
Sleep Mode ◽  
Indoor Environments ◽  
Data Traffic ◽  
5G Networks ◽  
Energy Expenditures ◽  
Markov Decision

Multi-tier heterogeneous Networks (HetNets) with dense deployment of small cells in 5G networks are expected to effectively meet the ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major concerns to mobile network operators such as complex distributed control plane management, handover management issue, increases latency and increased energy expenditures. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for reducing energy expenditures. In this paper, a Markov Decision Process (MDP)-based algorithm is proposed to switch between three different power consumption modes of a base station (BS) for improving the energy efficiency and reducing latency in 5G networks. The MDP-based approach intelligently switches between the states of the BS based on the offered traffic while maintaining a prescribed minimum channel rate per user. Simulation results show that the proposed MDP algorithm together with the three-state BSs results in a significant gain in terms of energy efficiency and latency.

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 Interference Cancellation Based Spectrum Sharing for Massive MIMO Communication Systems

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3584
Author(s):  
Milembolo Miantezila Junior ◽  
Bin Guo ◽  
Chenjie Zhang ◽  
Xuemei Bai
Keyword(s):  
Interference Cancellation ◽  
Communication Systems ◽  
Spectrum Sharing ◽  
Null Space ◽  
Base Station ◽  
Base Stations ◽  
Small Cells ◽  
Data Traffic ◽  
New Approach ◽  
Lte Advanced

Cellular network operators are predicting an increase in space of more than 200 percent to carry the move and tremendous increase of total users in data traffic. The growing of investments in infrastructure such as a large number of small cells, particularly the technologies such as LTE-Advanced and 6G Technology, can assist in mitigating this challenge moderately. In this paper, we suggest a projection study in spectrum sharing of radar multi-input and multi-output, and mobile LTE multi-input multi-output communication systems near m base stations (BS). The radar multi-input multi-output and mobile LTE communication systems split different interference channels. The new approach based on radar projection signal detection has been proposed for free interference disturbance channel with radar multi-input multi-output and mobile LTE multi-input multi-output by using a new proposed interference cancellation algorithm. We chose the channel of interference with the best free channel, and the detected signal of radar was projected to null space. The goal is to remove all interferences from the radar multi-input multi-output and to cancel any disturbance sources from a chosen mobile Communication Base Station. The experimental results showed that the new approach performs very well and can optimize Spectrum Access.

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 Countrywide Mobile Spectrum Sharing with Small Indoor Cells for Massive Spectral and Energy Efficiencies in 5G and Beyond Mobile Networks

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3825 ◽  
Author(s):  
Rony Kumer Saha
Keyword(s):  
Energy Efficiency ◽  
Mobile Networks ◽  
Spectral Efficiency ◽  
Spectrum Sharing ◽  
Performance Metrics ◽  
Interference Management ◽  
Mobile Network ◽  
System Level ◽  
Small Cells ◽  
Generic Model

In this paper, we propose a technique to share the licensed spectrums of all mobile network operators (MNOs) of a country with in-building small cells per MNO by exploiting the external wall penetration loss of a building and introducing the time-domain eICIC technique. The proposed technique considers allocating the dedicated spectrum Bop per MNO only its to outdoor macro UEs, whereas the total spectrum of all MNOs of the country Bco to its small cells indoor per building such that technically any small indoor cell of an MNO can have access to Bco instead of merely Bop assigned only to the MNO itself. We develop an interference management strategy as well as an algorithm for the proposed technique. System-level capacity, spectral efficiency, and energy efficiency performance metrics are derived, and a generic model for energy efficiency is presented. An optimal amount of small indoor cell density in terms of the number of buildings L carrying these small cells per MNO to trade-off the spectral efficiency and the energy efficiency is derived. With the system-level numerical and simulation results, we define an optimal value of L for a dense deployment of small indoor cells of an MNO and show that the proposed spectrum sharing technique can achieve massive indoor capacity, spectral efficiency, and energy efficiency for the MNO. Finally, we demonstrate that the proposed spectrum sharing technique could meet both the spectral efficiency and the energy efficiency requirements for 5G mobile networks for numerous traffic arrival rates to small indoor cells per building of an MNO.

scholarly journals Area Spectral Efficiency and Energy Efficiency Tradeoff in Ultradense Heterogeneous Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Lanhua Xiang ◽  
Hongbin Chen ◽  
Feng Zhao
Keyword(s):  
Energy Efficiency ◽  
Heterogeneous Networks ◽  
Spectral Efficiency ◽  
Point Processes ◽  
Optimization Problem ◽  
Focal Point ◽  
Base Station ◽  
Data Traffic ◽  
Total Energy Consumption ◽  
Area Spectral Efficiency

In order to meet the demand of explosive data traffic, ultradense base station (BS) deployment in heterogeneous networks (HetNets) as a key technique in 5G has been proposed. However, with the increment of BSs, the total energy consumption will also increase. So, the energy efficiency (EE) has become a focal point in ultradense HetNets. In this paper, we take the area spectral efficiency (ASE) into consideration and focus on the tradeoff between the ASE and EE in an ultradense HetNet. The distributions of BSs in the two-tier ultradense HetNet are modeled by two independent Poisson point processes (PPPs) and the expressions of ASE and EE are derived by using the stochastic geometry tool. The tradeoff between the ASE and EE is formulated as a constrained optimization problem in which the EE is maximized under the ASE constraint, through optimizing the BS densities. It is difficult to solve the optimization problem analytically, because the closed-form expressions of ASE and EE are not easily obtained. Therefore, simulations are conducted to find optimal BS densities.

scholarly journals A Novel Interference Avoidance Based on a Distributed Deep Learning Model for 5G-enabled IoT

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6555
Author(s):  
Radwa Ahmed Osman ◽  
Sherine Nagy Saleh ◽  
Yasmine N. M. Saleh
Keyword(s):  
Energy Efficiency ◽  
Deep Learning ◽  
Optimization Technique ◽  
Data Communication ◽  
Learning Model ◽  
Base Station ◽  
System Throughput ◽  
Interference Avoidance ◽  
5G Networks ◽  
Deep Learning Model

The co-existence of fifth-generation (5G) and Internet-of-Things (IoT) has become inevitable in many applications since 5G networks have created steadier connections and operate more reliably, which is extremely important for IoT communication. During transmission, IoT devices (IoTDs) communicate with IoT Gateway (IoTG), whereas in 5G networks, cellular users equipment (CUE) may communicate with any destination (D) whether it is a base station (BS) or other CUE, which is known as device-to-device (D2D) communication. One of the challenges that face 5G and IoT is interference. Interference may exist at BSs, CUE receivers, and IoTGs due to the sharing of the same spectrum. This paper proposes an interference avoidance distributed deep learning model for IoT and device to any destination communication by learning from data generated by the Lagrange optimization technique to predict the optimum IoTD-D, CUE-IoTG, BS-IoTD and IoTG-CUE distances for uplink and downlink data communication, thus achieving higher overall system throughput and energy efficiency. The proposed model was compared to state-of-the-art regression benchmarks, which provided a huge improvement in terms of mean absolute error and root mean squared error. Both analytical and deep learning models reached the optimal throughput and energy efficiency while suppressing interference to any destination and IoTG.

An Alternating Optimization Algorithm for Energy Efficiency in Heterogeneous Networks

2018 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Kha Ha ◽  
Tien Ha
Keyword(s):  
Energy Efficiency ◽  
Heterogeneous Networks ◽  
Optimization Problems ◽  
Minimum Mean Square Error ◽  
Minimum Energy ◽  
Iterative Algorithms ◽  
Base Station ◽  
Small Cells ◽  
Total System ◽  
Design Problems

This paper studies the problems of precoding designs to achieve the energy efficiency (EE) in the uplink heterogeneous networks in which the multiple small cells are deployed in a macro-cell.  We consider two design problems which maximize either the total system energy efficiency (SEE) or the minimum energy efficiency (MinEE) among users subject to the transmit power constraints at each user and interference constraints caused to the macro base station. Since the optimization problems are non-convex fractional programming in matrix variables, it cannot be straightforward to obtain the optimal solutions. To tackle with the non-convexity challenges of the design problems, we adopt the relationships between the minimum mean square error (MMSE) and achievable data rate to recast the EE problems into ones more amenable. Then, we employ the block coordinate ascent (BCA) and the Dinkelbach methods to develop efficient iterative algorithms in which the closed form solutions are obtained or the semi-definite programming (SDP) problems are solved at each iteration. Simulation results are provided to investigate the EE performance of the EE optimization as compared to those of the spectral efficiency (SE) optimization.

scholarly journals Base Station Energy Efficiency Improvement for Next Generation Mobile Networks

2017 ◽  
Vol 63 (2) ◽  
pp. 187-194 ◽  
Author(s):  
Weston Mwashita ◽  
Marcel Ohanga Odhiambo
Keyword(s):  
Mobile Networks ◽  
Energy Efficient ◽  
Carbon Dioxide Emissions ◽  
Mobile Network ◽  
Base Station ◽  
Base Stations ◽  
Data Traffic ◽  
Energy Efficiency Improvement ◽  
Next Generation Mobile Networks ◽  
Operational Expenditure

Abstract As more and more Base Stations (BSs) are being deployed by mobile operators to meet the ever increasing data traffic, solutions have to be found to try and reduce BS energy consumption to make the BSs more energy efficient and to reduce the mobile networks’ operational expenditure (OPEX) and carbon dioxide emissions. In this paper, a BS sleeping technology deployable in heterogeneous networks (HetNets) is proposed. The proposed scheme is validated by using extensive OMNeT++/SimuLTE simulations. From the simulations, it is shown that some lightly loaded micro BSs can be put to sleep in a HetNet when the network traffic is very low without compromising the QoS of the mobile network.

scholarly journals Spectrum Allocation and Reuse in 5G New Radio on Licensed and Unlicensed Millimeter-Wave Bands in Indoor Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Rony Kumer Saha
Keyword(s):  
Millimeter Wave ◽  
Interference Management ◽  
Mobile Network ◽  
Spectrum Allocation ◽  
Mobile Systems ◽  
Small Cells ◽  
Indoor Environments ◽  
60 Ghz ◽  
Management Scheme ◽  
New Radio

In this paper, by exploiting the frequency-domain, we propose a countrywide millimeter-wave (mmWave) spectrum allocation and reuse technique to allocate and reuse spatially the countrywide 28 GHz licensed spectrum and 60 GHz unlicensed spectrum to small cells (SCs) on each floor of a building of each Fifth-Generation (5G) New Radio (NR) Mobile Network Operator (MNO) of an arbitrary country. We develop an interference management scheme, model user statistics per SC, and interferer statistics per apartment and formulate the amount of the 28 GHz and 60 GHz spectra per MNO. We derive average capacity, spectral efficiency (SE), energy efficiency (EE), and cost efficiency (CE) when employing the proposed technique, as well as the traditional static licensed spectrum allocation technique. We discuss the implementation of the proposed technique and evaluate the performance under two scenarios, namely, SCs operate only in the 28 GHz in scenario 1, and both 28 GHz and 60 GHz in scenario 2. Extensive results and analyses are carried out for four MNOs, i.e., MNOs 1, 2, 3, and 4, in scenario 1. However, in scenario 2, in addition to MNOs 1, 2, 3, and 4, an incumbent Wireless Gigabit (WiGig) operator is considered. It is shown that the proposed technique with no co-channel interference can improve average capacity, SE, EE, and CE of MNO 1 by 3 times, 1.65 times, 75%, and 60%, respectively, in scenario 1, whereas 6.12 times, 5.104 times, 85.8%, and 83.15%, respectively, in scenario 2. Moreover, with an increase in reuse factors, SE increases linearly and EE increases negative exponentially. Further, we show that the proposed technique can satisfy SE and EE requirements for sixth-generation (6G) mobile systems. Finally, we discuss offered benefits and point out key issues of the proposed technique for further studies.

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.

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