Energy Efficiency In Mobile Networks Research Articles

An improved approach for managing energy efficiency in mobile networks

It is highly expected that soon there will be environmental and economic negative implications from the amount of energy consumed by wireless network devices. Therefore, many researchers have paid attention toward addressing these challenges to investigate the impact of these wireless networks on both environment and the economy. This paper proposes an approach for alternating work among the fifth generation (5G) with Long-Term Evolution (LTE) wireless networks. The idea of the proposed approach relies on turning off specific base stations (BSs) and antennas for the users based on the required quality of service (QoS). Some BSs like 5G networks aim to provide high-speed communications with significant savings in energy consumption during high traffic periods. On the other hand, there is a slow speed with the high consumption of energy in other BSs like LTE networks. Our proposed solution employs the idea of activating some of the BSs networks and changing the number of active antennas that achieves optimal results for the entire area. Doing so lead to a significant reduction in energy consumption when the traffic load is low. The experimental results illustrate that our proposed solution outperforms the most recent approaches by saving a significant amount in power consumption while maintaining a stable service awareness during switching situations.

Towards a Sustainable Green Design for Next-Generation Networks

The evolution in the Information and Communications Technologies industry results in excessive energy consumption and carbon dioxide emission in the wireless networks. In this context, energy efficiency in mobile networks has been attracting considerable attention as green communications and operational expenditures reduction depend on it. Although the Internet of Things is to be supported by devices that are low-energy consuming, the power consumption of the huge number to be connected for several applications and services demand significant attention. To offer insights into green communications, this paper reviews various energy efficiency improvement techniques. Also, we consider a hybrid model in which the main grid power and dynamically harvested green energy from renewable energy sources can be leveraged to support the energy demand of the radio access network. In this regard, we reformulate the energy consumption model and consider an energy-efficient power allocation algorithm for green energy optimization. Numerical results show that with resource allocation algorithm exploitation, the energy efficiency can be enhanced. Besides, the amount of the grid energy consumption can be considerably minimized, resulting in the greenhouse gas emissions reduction in the wireless networks.

Energy Efficiency in Cognitive Radio Network: Green Technology Towards Next Generation Networks

Energy efficiency of mobile network is always a challenging task. From the past decade, it is observable that the users who are using multimedia services are increasing in rapid way. These multimedia applications require higher data rates. High data rates will consume more energy of mobile network, which results poor energy efficiency. To meet higher data rates and to achieve energy efficiency, Cognitive Mobile Network with small cell model was explained in this paper. Dynamics of the power grid also have significant impact on mobile networks, hence smart grid implementation was proposed instead of traditional power grid. Most of the existed studies on cognitive mobile network focussed on spectrum sensing only. This paper focussed on cognitive radio network implementation by considering spectrum sensing and smart grid environment. An iterative algorithm was proposed to attain equillibrium condition to the problem. Interference management and energy efficient power allocation were achieved with the introduction of smart grid. Simulation results proved that optimum power allocation and energy efficiency are possible with the introduction of smart grid in cognitive network.

Offloading Mobile Traffic via Green Content Broker

A continuous surge of mobile data traffic not only congests mobile networks but also results in a dramatic increase in the energy consumption of mobile networks. Device-to-device (D2D) communications is a promising technique for offloading mobile data traffic and enhancing energy efficiency of mobile networks. By enabling D2D communications, the base stations (BSs) may reduce their energy consumption through traffic offloading, and mobile users may increase their quality of services by retrieving contents from their neighboring peers instead of the remote BSs. By leveraging D2D communications, we propose a novel mobile traffic offloading scheme—the content brokerage. In the content brokerage scheme, a new network node called the green content broker (GCB) is introduced to arrange the content delivery between the content requester and the content owner. The GCB is powered by green energy, e.g., solar energy, to reduce the ${\bf CO}_{\bf 2}$ footprints of mobile networks. In the scheme, maximizing traffic offloading with the constraints of the amount of green energy and bandwidth is an nondeterministic polynomial time (NP) problem. We propose a heuristic traffic offloading (HTO) algorithm to approximate the optimal solution with low computational complexity. Our simulation results valid the performance of the content brokerage scheme and the HTO algorithm.

Impact of picocells on the capacity and energy efficiency of mobile networks

The deployment of small cells in mobile networks has aroused a large interest in the last few years. This paper investigates the impact of picocell deployment on the performance and power consumption of mobile networks. Since different network upgrades introduce different performance gains, comparing different configurations exclusively on their overall power consumption can be rather biased. For this reason, a new key performance indicator, termed “energy efficiency”, is introduced and used throughout this study, bringing together network performance and its overall power consumption. In the first section of the study, a theoretical analysis for the Erlang-like capacity of a network, considering a uniform topology and traffic, is performed, using queuing theory analysis, namely processor-sharing queues. Results show that in all cases the deployment of picocells improve the performance of the network, however the energy efficiency is noted to be dependent on the deployment scenario considered. In the second part of the study, a more realistic scenario with non-uniform topology and traffic is considered, which is carried out through a large-scale system level simulator. Results show that by deploying picocells in areas experiencing high levels of traffic, the energy efficiency of the network can be considerably improved.