Renewable Energy-Enabled Cellular Networks

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.

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Renewable energy aware cluster formation for CoMP transmission in green cellular networks

2014 IEEE Global Communications Conference ◽

10.1109/glocom.2014.7037505 ◽

2014 ◽

Cited By ~ 2

Author(s):

Yi-Han Chiang ◽

Wanjiun Liao

Keyword(s):

Renewable Energy ◽

Cellular Networks ◽

Cluster Formation ◽

Energy Aware

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Base Station Sleeping and Resource Allocation in Renewable Energy Powered Cellular Networks

IEEE Transactions on Communications ◽

10.1109/tcomm.2014.2359883 ◽

2014 ◽

Vol 62 (11) ◽

pp. 3801-3813 ◽

Cited By ~ 94

Author(s):

Jie Gong ◽

John S. Thompson ◽

Sheng Zhou ◽

Zhisheng Niu

Keyword(s):

Resource Allocation ◽

Renewable Energy ◽

Cellular Networks ◽

Base Station

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Renewable Energy-Enabled Cellular Networks

10.36227/techrxiv.16550607 ◽

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>

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

Sensors ◽

10.3390/s22010270 ◽

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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