scholarly journals Energy-aware caching and collaboration for green communication systems

2020 ◽  
pp. 47-59
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Energy Harvesting ◽  
Cellular Networks ◽  
Communication Systems ◽  
Green Energy ◽  
Base Stations ◽  
Next Generation ◽  
Content Caching ◽  
Self Powered

Social networks and mobile applications tend to enhance the need for high-quality content access. To meet the growing demand for data services in 5G cellular networks, it is important to develop effective content caching and distribution techniques, to reduce redundant data transmission and thereby improve network efficiency significantly. It is anticipated that energy harvesting and self-powered Small Base Stations' (SBS) are the rudimentary constituents of next-generation cellular networks. However, uncertainties in harvested energy are the primary reasons to opt for energy-efficient (EE) power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using edge collaborative caching, such EE design can also be achievable via the use of the content cache, decreasing the usage of capacity limited SBSs backhaul and reducing energy utilisation. Renewable energy (RE) harvesting technologies can be leveraged to manage the huge power demands of cellular networks. To reduce carbon footprint and improve energy efficiency, we tailored a more practical approach and propose green caching mechanisms for content distribution that utilise the content caching and renewable energy concept. Simulation results and analysis provide key insights that the proposed caching scheme brings a substantial improvement regarding content availability, cache storage capacity at the edge of cellular networks, enhances energy efficiency, and increases cache collaboration time up to 24%. Furthermore, self-powered base stations and energy harvesting are an ultimate part of next-generation wireless networks, particularly in terms of optimum economic sustainability and green energy in developing countries for the evolution of mobile networks.

scholarly journals Renewable Energy-Aware Sustainable Cellular Networks with Load Balancing and Energy-Sharing Technique

2020 ◽  
Vol 12 (22) ◽  
pp. 9340
Author(s):  
Md. Sanwar Hossain ◽  
Khondoker Ziaul Islam ◽  
Abu Jahid ◽  
Khondokar Mizanur Rahman ◽  
Sarwar Ahmed ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Load Balancing ◽  
Outage Probability ◽  
Cellular Networks ◽  
Renewable Energy Sources ◽  
Green Energy ◽  
Solar Pv ◽  
Wide Range ◽  
Energy Sharing

With the proliferation of cellular networks, the ubiquitous availability of new-generation multimedia devices, and their wide-ranging data applications, telecom network operators are increasingly deploying the number of cellular base stations (BSs) to deal with unprecedented service demand. The rapid and radical deployment of the cellular network significantly exerts energy consumption and carbon footprints to the atmosphere. The ultimate objective of this work is to develop a sustainable and environmentally-friendly cellular infrastructure through compelling utilization of the locally available renewable energy sources (RES) namely solar photovoltaic (PV), wind turbine (WT), and biomass generator (BG). This article addresses the key challenges of envisioning the hybrid solar PV/WT/BG powered macro BSs in Bangladesh considering the dynamic profile of the RES and traffic intensity in the tempo-spatial domain. The optimal system architecture and technical criteria of the proposed system are critically evaluated with the help of HOMER optimization software for both on-grid and off-grid conditions to downsize the electricity generation cost and waste outflows while ensuring the desired quality of experience (QoE) over 20 years duration. Besides, the green energy-sharing mechanism under the off-grid condition and the grid-tied condition has been critically analyzed for optimal use of green energy. Moreover, the heuristic algorithm of the load balancing technique among collocated BSs has been incorporated for elevating the throughput and energy efficiency (EE) as well. The spectral efficiency (SE), energy efficiency, and outage probability performance of the contemplated wireless network are substantially examined using Matlab based Monte–Carlo simulation under a wide range of network configurations. Simulation results reveal that the proper load balancing technique pledges zero outage probability with expected system performance whereas energy cooperation policy offers an attractive solution for developing green mobile communications employing better utilization of renewable energy under the proposed hybrid solar PV/WT/BG scheme.

scholarly journals A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6546
Author(s):  
Eva Masero ◽  
Luis A. Fletscher ◽  
José M. Maestre
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Cellular Networks ◽  
Predictive Control ◽  
Large Scale ◽  
Network Performance ◽  
Renewable Energy Sources ◽  
High Energy ◽  
Base Stations ◽  
Next Generation

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates).

scholarly journals Coverage and Energy Efficiency Analysis for Two-Tier Heterogeneous Cellular Networks Based on Matérn Hard-Core Process

2019 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Jie Yang ◽  
Ziyu Pan ◽  
Lihong Guo
Keyword(s):  
Energy Efficiency ◽  
Cellular Networks ◽  
Mutual Exclusion ◽  
Interference Management ◽  
Hard Core ◽  
Approximate Expression ◽  
Base Stations ◽  
Heterogeneous Cellular Networks ◽  
Coverage Analysis ◽  
Management Techniques

Due to the dense deployment of base stations (BSs) in heterogeneous cellular networks (HCNs), the energy efficiency (EE) of HCN has attracted the attention of academia and industry. Considering its mathematical tractability, the Poisson point process (PPP) has been employed to model HCNs and analyze their performance widely. The PPP falls short in modeling the effect of interference management techniques, which typically introduces some form of spatial mutual exclusion among BSs. In PPP, all the nodes are independent from each other. As such, PPP may not be suitable to model networks with interference management techniques, where there exists repulsion among the nodes. Considering this, we adopt the Matérn hard-core process (MHCP) instead of PPP, in which no two nodes can be closer than a repulsion radius from one another. In this paper, we study the coverage performance and EE of a two-tier HCN modelled by Matérn hard-core process (MHCP); we abbreviate this kind of two-tier HCN as MHCP-MHCP. We first derive the approximate expression of coverage probability of MHCP-MHCP by extending the approximate signal to interference ratio analysis based on the PPP (ASAPPP) method to multi-tier HCN. The concrete SIR gain of the MHCP model relative to the PPP model is derived through simulation and data fitting. On the basis of coverage analysis, we derive and formulate the EE of MHCP-MHCP network. Simulation results verify the correctness of our theoretical analysis and show the performance difference between the MHCP-MHCP and PPP modelled network.

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 cautionary approach in transitioning to ‘green’ energy technologies and practices is required

2016 ◽  
Vol 31 (2) ◽  
Author(s):  
Puleng Matatiele ◽  
Mary Gulumian
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Technological Development ◽  
Essential Elements ◽  
Green Energy ◽  
Potential Contribution ◽  
Green Technologies ◽  
Green Jobs ◽  
Renewable Energy Technologies ◽  
Energy Technologies

AbstractRenewable energy technologies (wind turbines, solar cells, biofuels, etc.) are often referred to as ‘clean’ or ‘green’ energy sources, while jobs linked to the field of environmental protection and energy efficiency are referred to as ‘green’ jobs. The energy efficiency of clean technologies, which is likely to reduce and/or eliminate reliance on fossil fuels, is acknowledged. However, the potential contribution of green technologies and associated practices to ill health and environmental pollution resulting from consumption of energy and raw materials, generation of waste, and the negative impacts related to some life cycle phases of these technologies are discussed. Similarly, a point is made that the green jobs theme is mistakenly oversold because the employment opportunities generated by transitioning to green technologies are not necessarily safe and healthy jobs. Emphasis is put on identifying the hazards associated with these green designs, assessing the risks to the environment and worker health and safety, and either eliminating the hazards or minimizing the risks as essential elements to the design, construction, operation, and maintenance of green technologies. The perception that it is not always economically possible to consider all risk factors associated with renewable energy technologies at the beginning without hampering their implementation, especially in the poor developing countries, is dismissed. Instead, poor countries are encouraged to start implementing environmentally sound practices while transitioning to green technologies in line with their technological development and overall economic growth.

scholarly journals Renewable Energy-Enabled Cellular Networks

2021 ◽  
Author(s):  
Young-Chai Ko ◽  
Kug-Jin Jung ◽  
Ki Hong Park ◽  
Mohamed-Slim Alouini
Keyword(s):  
Renewable Energy ◽  
Cellular Networks ◽  
Energy Demand ◽  
Feasibility Studies ◽  
Power Grids ◽  
Base Stations ◽  
Future Research ◽  
Survey Paper ◽  
Aerial Platforms ◽  
Carbon Dioxide Co2

<div> <div> <div> <p>Renewable energy (RE)-powered base stations (BSs) have been considered as an attractive solution to address the exponential increasing energy demand in cellular networks while decreasing carbon dioxide (CO2) emissions. For the regions where reliable power grids are insufficient and infeasible to deploy, such as aerial platforms and harsh environments, RE has been an alternative power source for BSs. In this survey paper, we provide an overview of RE-enabled cellular networks, detailing their analysis, classification, and related works. First, we introduce the key components of RE-powered BSs along with their frequently adopted models. Second, we analyze the proposed strategies and design issues for RE-powered BSs that can be incorporated into cellular networks and categorize them into several groups to provide a good grasp. Third, we introduce feasibility studies on RE-powered BSs based on the recent literature. Fourth, we investigate RE-powered network components other than terrestrial BSs to address potential issues regarding RE-enabled networks. Finally, we suggest future research directions and conclusions. </p><p><br></p> </div> </div> </div>

scholarly journals Power Allocation and Energy Cooperation for UAV-Enabled MmWave Networks: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 270
Author(s):  
Mari Carmen Domingo
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Harvesting ◽  
Power Allocation ◽  
Cellular Networks ◽  
High Energy ◽  
Propagation Loss ◽  
Throughput Maximization ◽  
Energy Cooperation ◽  
Multi Agent

Unmanned Aerial Vehicle (UAV)-assisted cellular networks over the millimeter-wave (mmWave) frequency band can meet the requirements of a high data rate and flexible coverage in next-generation communication networks. However, higher propagation loss and the use of a large number of antennas in mmWave networks give rise to high energy consumption and UAVs are constrained by their low-capacity onboard battery. Energy harvesting (EH) is a viable solution to reduce the energy cost of UAV-enabled mmWave networks. However, the random nature of renewable energy makes it challenging to maintain robust connectivity in UAV-assisted terrestrial cellular networks. Energy cooperation allows UAVs to send their excessive energy to other UAVs with reduced energy. In this paper, we propose a power allocation algorithm based on energy harvesting and energy cooperation to maximize the throughput of a UAV-assisted mmWave cellular network. Since there is channel-state uncertainty and the amount of harvested energy can be treated as a stochastic process, we propose an optimal multi-agent deep reinforcement learning algorithm (DRL) named Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to solve the renewable energy resource allocation problem for throughput maximization. The simulation results show that the proposed algorithm outperforms the Random Power (RP), Maximal Power (MP) and value-based Deep Q-Learning (DQL) algorithms in terms of network throughput.

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