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We provide electrical projects based on power electronics, MATLAB Simulink and SIM Power
For Electronics Engineering Students we support technologies like ARM, GSM, GPS, RFID, Robotics, VLSI, NSL, NS3, OMNet++, OPNet, QUALNET, PeerSim
Energy-Efficient Base-Stations Sleep-Mode Techniques in Green Cellular Networks: A Survey Due to global climate change as well as economic concern of network operators, energy consumption of the infrastructure of cellular networks, or “Green Cellular Networking,” has become a popular research topic. While energy saving can be achieved by adopting renewable energy resources or improving design of certain hardware (e.g., power amplifier) to make it more energy-efficient, the cost of purchasing, replacing, and installing new equipment (including manpower, transportation, disruption to normal operation, as well as associated energy and direct cost) is often prohibitive. By comparison, approaches that work on the operating protocols of the system do not require changes to current network architecture, making them far less costly and easier for testing and implementation. In this survey, we first present facts and figures that highlight the importance of green mobile networking and then review existing green cellular networking research with particular focus on techniques that incorporate the concept of the “sleep mode” in base stations. It takes advantage of changing traffic patterns on daily or weekly basis and selectively switches some lightly loaded base stations to low energy consumption modes. As base stations are responsible for the large amount of energy consumed in cellular networks, these approaches have the potential to save a significant amount of energy, as shown in various studies. However, it is noticed that certain simplifying assumptions made in the published papers introduce inaccuracies. This review will discuss these assumptions, particularly, an assumption that ignores the effect of traffic-load-dependent factors on energy consumption. We show here that considering this effect may lead to noticeably lower benefit than in models that ignore this effect. Finally, potential future research directions are discussed.