scholarly journals Optimizing Energy Efficiency for Supporting Near-Cloud Access Region of UAV-Based NOMA Networks in IoT Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huu Q. Tran ◽  
Ca V. Phan ◽  
Quoc-Tuan Vien
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Power Transmission ◽  
Access Network ◽  
Base Stations ◽  
Power Splitting ◽  
Power Domain ◽  
Communication Links ◽  
The Impact ◽  
Channel Environment

Nonorthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for the wireless fifth generation (5G) networks and beyond. On the one hand, UAVs can be deployed as flying base stations to build line-of-sight (LoS) communication links to two ground users (GUs) and to improve the performance of conventional terrestrial cellular networks. On the other hand, NOMA enables the share of an orthogonal resource to multiple users simultaneously, thus improving the spectral efficiency and supporting massive connectivities. This paper presents two protocols, namely, cloud-based central station- (CCS-) based power-splitting protocol (PSR) and time-switching protocol (TSR), for simultaneous wireless information and power transmission (SWIPT) at UAV employed in power domain NOMA-based multitier heterogeneous cloud radio access network (H-CRAN) of Internet of Things (IoT) system. The system model with k types of UAVs and two users in which the CCS manages the entire H-CRAN and operates as a central unit in the cloud is proposed in our work. Closed-form expressions of throughput and energy efficiency (EE) for UAVs are derived. In particular, the EE is determined for the impacts of power allocation at CCS, various UAV types, and channel environment. The simulation results show that the performance for CCS-based PSR outperforms that for CCS-based TSR for the impacts of power allocation at the CCS. On the contrary, the TSR protocol has a higher EE than the PSR in the cases of the impact of various UAV types and channel environment. The analytic results match Monte Carlo simulations.

scholarly journals How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 255
Author(s):  
Josip Lorincz ◽  
Zonimir Klarin
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Mobile Networks ◽  
Access Network ◽  
Mobile Network ◽  
Network Capacity ◽  
Base Stations ◽  
Density Class ◽  
Radio Access ◽  
The Impact

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

Network Throughput and Outage Analysis in a Poisson and Matérn Cluster based LTE-Advanced Small Cell Networks

2021 ◽  
Author(s):  
Joydev Ghosh
Keyword(s):  
Outage Probability ◽  
Cell Formation ◽  
Access Network ◽  
Small Cell ◽  
Network Throughput ◽  
Base Stations ◽  
Small Cells ◽  
Network Cell ◽  
Lte Advanced ◽  
The Impact

<div>In LTE-A (LTE-Advanced), the access network cell formation is an integrated form of outdoor unit and indoor unit. With the indoor unit extension the access network becomes heterogeneous (HetNet). HetNet is a straightforward way to provide quality of service (QoS) in terms better network coverage and high data rate. Although, due to uncoordinated, densely deployed small cells large interference may occur, particularly in case of operating small cells within the spectrum of macro base stations (MBS). This paper probes the impact of small cell on the outage probability and the average network throughput enhancement. The positions of the small cells are retained random and modelled with homogeneous Poisson Point Process (PPP) and Matérn Cluster process (MCP). The paper provides an analytic form which permits to compute the outage probability, including the mostly applied fast fading channel types. Furthermore, simulations are evaluated in order to calculate the average network throughput for both random processes. Simulation results highlights that the network throughput remarkably grows due to small cell deployment.</div>

scholarly journals Optimal and Robust Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Haijian Sun ◽  
Jie Yang ◽  
Guan Gui ◽  
Song Guo
Keyword(s):  
Resource Allocation ◽  
Closed Form ◽  
Power Allocation ◽  
Energy Region ◽  
High Energy ◽  
Cognitive Networks ◽  
Feasible Region ◽  
Power Splitting ◽  
Joint Power ◽  
The Impact

Simultaneous wireless information and power transfer (SWIPT)-enabled cognitive networks (CRNs) is recognized as one of the most promising techniques to improve spectrum efficiency and prolong operation lifetime in 5G and beyond. However, existing methods focus on the centralized algorithm and the power allocation under perfect channel state information (CSI). The analytical solution and the impact of power splitting (PS) on the optimal power allocation strategy are not addressed. In addition, the influence of the PS factor on the feasible region of transit power is rarely analyzed. In this paper, we propose a joint power allocation and PS algorithm under perfect CSI and imperfect CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power minimization of resource allocation problem is formulated as a multivariate nonconvex optimization which is hard to obtain the closed-form solution. Hence, we propose a suboptimal algorithm to alternatively optimize the power allocation and PS coefficient under the cases of the low-harvested energy region and the high-harvested energy region, respectively. Moreover, a closed-form distributed power allocation and PS expressions are derived by the Lagrangian approach. Simulation results confirm the proposed method with good robustness and high energy efficiency.

scholarly journals Clustering and Auction-Based Power Allocation Algorithm for Energy Efficiency Maximization in Multi-Cell Multi-Carrier NOMA Networks

2019 ◽  
Vol 9 (23) ◽  
pp. 5034 ◽  
Author(s):  
Abuzar B. M. Adam ◽  
Xiaoyu Wan ◽  
Zhengqiang Wang
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Cooperative Game ◽  
Multiple Access ◽  
Optimization Problem ◽  
Interference Mitigation ◽  
Base Stations ◽  
Frequency Division ◽  
Simulation Results ◽  
Inter Cell Interference

In this paper, we investigate the energy efficiency (EE) maximization in multi-cell multi-carrier non-orthogonal multiple access (MCMC-NOMA) networks. To achieve this goal, an optimization problem is formulated then the solution is divided into two parts. First, we investigate the inter-cell interference mitigation and then we propose an auction-based non-cooperative game for power allocation for base stations. Finally, to guarantee the rate requirements for users, power is allocated fairly to users. The simulation results show that the proposed scheme has the best performance compared with the existing NOMA-based fractional transmit power allocation (FTPA) and the conventional orthogonal frequency division multiple access (OFDMA).

scholarly journals Energy Efficiency Maximization of Dynamic CoMP-JT Algorithm in Dense Small Cell Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Xuefei Peng ◽  
Jiandong Li ◽  
Yifei Xu
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Optimal Solution ◽  
Small Cell ◽  
Base Stations ◽  
Maximization Problem ◽  
Second Phase ◽  
User Association ◽  
Coordinated Multipoint ◽  
Convex Problem

We firstly formulate the energy efficiency (EE) maximization problem of joint user association and power allocation considering minimum data rate requirement of small cell users (SUEs) and maximum transmit power constraint of small cell base stations (SBSs), which is NP-hard. Then, we propose a dynamic coordinated multipoint joint transmission (CoMP-JT) algorithm to improve EE. In the first phase, SUEs are associated with the SBSs close to them to reduce the loss of power by the proposed user association algorithm, where the associated SBSs of each small cell user (SUE) form a dynamic CoMP-JT set. In the second phase, through the methods of fractional programming and successive convex approximation, we transform the EE maximization subproblem of power allocation for SBSs into a convex problem that can be solved by proposed power allocation optimization algorithm. Moreover, we show that the proposed solution has a much lower computational complexity than that of the optimal solution obtained by exhaustive search. Simulation results demonstrate that the proposed solution has a better performance.

scholarly journals Beyond Cellular Green Generation: Potential and Challenges of the Network Separation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ilario Filippini ◽  
Alessandro Enrico Cesare Redondi ◽  
Antonio Capone
Keyword(s):  
Energy Efficiency ◽  
Data Transmission ◽  
Access Network ◽  
Base Stations ◽  
Analytical Models ◽  
Research Directions ◽  
Energy Efficiency Improvement ◽  
Wireless Access Network ◽  
Wide Adaptation ◽  
New Research

This article introduces the ideas investigated in the BCG2 project of the GreenTouch consortium. The basic concept is to separate signaling and data in the wireless access network. Transmitting the signaling information separately maintains coverage even when the whole data network is adapted to the current load situation. Such network-wide adaptation can power down base stations when no data transmission is needed and, thus, promises a tremendous increase in energy efficiency. We highlight the advantages of the separation approach and discuss technical challenges opening new research directions. Moreover, we propose two analytical models to assess the potential energy efficiency improvement of the BCG2 approach.

scholarly journals Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks With and Without Perfect CSI

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Haijian Sun ◽  
Jie Wang ◽  
Guan Gui ◽  
Song Guo
Keyword(s):  
Resource Allocation ◽  
Closed Form ◽  
Power Allocation ◽  
Energy Region ◽  
High Energy ◽  
Cognitive Networks ◽  
Feasible Region ◽  
Power Splitting ◽  
Joint Power ◽  
The Impact

Simultaneous wireless information and power transfer (SWIPT)-enabled cognitive networks (CRNs) is recognized as one of most promising techniques to improve spectrum efficiency and prolong operation lifetime in 5G and beyond. However, existing methods focus on the centralized algorithm and the power allocation under perfect channel state information (CSI). The analytical solution and the impact of the power splitting (PS) on the optimal power allocation strategy are not addressed. In addition, the influence of the PS factor on the feasible region of transit power is rarely analyzed. In this paper, we propose a joint power allocation and PS algorithm under perfect CSI and imperfect CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power minimization of resource allocation problem is formulated as a multivariate nonconvex optimization which is hard to obtain the closed-form solution. Hence, we propose a suboptimal algorithm to alternatively optimize the power allocation and PS coefficient under the cases of the low-harvested energy region and the high-harvested energy region, respectively. Moreover, a closed-form distributed power allocation and PS expressions are derived by the Lagrangian approach. Simulation results confirm the proposed method with good robustness and high energy efficiency.

scholarly journals Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks With and Without Perfect CSI

2020 ◽  
Author(s):  
Yongjun Xu ◽  
Haijian Sun ◽  
Jie Wang ◽  
Guan Gui ◽  
Song Guo
Keyword(s):  
Resource Allocation ◽  
Closed Form ◽  
Power Allocation ◽  
Energy Region ◽  
High Energy ◽  
Cognitive Networks ◽  
Feasible Region ◽  
Power Splitting ◽  
Joint Power ◽  
The Impact

Simultaneous wireless information and power transfer (SWIPT)-enabled cognitive networks (CRNs) is recognized as one of most promising techniques to improve spectrum efficiency and prolong operation lifetime in 5G and beyond. However, existing methods focus on the centralized algorithm and the power allocation under perfect channel state information (CSI). The analytical solution and the impact of the power splitting (PS) on the optimal power allocation strategy are not addressed. In addition, the influence of the PS factor on the feasible region of transit power is rarely analyzed. In this paper, we propose a joint power allocation and PS algorithm under perfect CSI and imperfect CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power minimization of resource allocation problem is formulated as a multivariate nonconvex optimization which is hard to obtain the closed-form solution. Hence, we propose a suboptimal algorithm to alternatively optimize the power allocation and PS coefficient under the cases of the low-harvested energy region and the high-harvested energy region, respectively. Moreover, a closed-form distributed power allocation and PS expressions are derived by the Lagrangian approach. Simulation results confirm the proposed method with good robustness and high energy efficiency.

scholarly journals On Maximizing Energy and Spectral Efficiencies Using Small Cells in 5G and Beyond Networks

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1676
Author(s):  
Rony Kumer Saha
Keyword(s):  
Mobile Networks ◽  
Urban Areas ◽  
Domain Switching ◽  
Base Stations ◽  
System Level ◽  
Small Cells ◽  
Indoor Environments ◽  
Time Frequency ◽  
Power Domain ◽  
The Impact

Addressing high capacity at low power as a key design goal envisages achieving high spectral efficiency (SE) and energy efficiency (EE) for the next-generation mobile networks. Because most data are generated in indoor environments, an ultra-dense deployment of small cells (SCs), particularly within multistory buildings in urban areas, is revealed as an effective technique to improve SE and EE by numerous studies. In this paper, we present a framework exploiting the four most interconnected-domain, including, power, time, frequency, and space, in the perspectives of SE and EE. Unlike existing literature, the framework takes advantage of higher degrees of freedom to maximize SE and EE using in-building SCs for 5G and beyond mobile networks. We derive average capacity, SE, and EE metrics, along with defining the condition for optimality of SE and EE and developing an algorithm for the framework. An extensive system-level evaluation is performed to show the impact of each domain on SE and EE. It is shown that employing multiband enabled SC base stations (SBSs) to increase operating spectrum in frequency-domain, reusing spectrum to SBSs more than once per building in spatial-domain, switching on and off each in-building SBS based on traffic availability to reduce SBS power consumption in power-domain, and using eICIC to avoid co-channel interference due to sharing spectrum with SBSs in time-domain can achieve massive SE and EE. Finally, we show that the proposed framework can satisfy SE, EE, as well as user experience data rate requirements for 5G and beyond mobile networks.

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