Browsing Computing by Journal
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Energy efficiency led reduced CO2 emission in green LTE networksThe technological advancements in smart phones and their applications have rapidly raised the number of users and their data demands. To fulfil enlarged user's data requirements, Basestation (BS) engages their resources over prolong time intervals at the cost of increased power consumption. In parallel, operators are expanding network infrastructure by employing additional BSs which also adds in power consumption. This directly increases carbon emission (CO2) thus results in to more global warming. Therefore, Information and Communication Technology (ICT) has become major contributor in global warming while mobile communication is one of the key contributors within ICT. This paper investigates reduced CO2 emission through decreased power consumption in LTE networks. Proposed energy saving scheme is validated through the analysis of various performance related parameters in MATLAB. Results have proven that proposed scheme reduces CO2 emission by 2.10 tonnes per BS.
Green communications: techniques and challengesGreen technology has drawn a huge amount of attention with the development of the modern world. Similarly with the development in communication technology the industries and researchers are focusing to make this communication as green as possible. In cellular technology the evolution of 5G is the next step to fulfil the user demands and it will be available to the users in 2020. This will increase the energy consumption by which will result in excess emission of co2. In this paper different techniques for the green communication technology and some challenges are discussed. These techniques include device-to-device communication (D2D), massive Multiple-Input Multiple-Output (MIMO) systems, heterogeneous networks (HetNets) and Green Internet of Things (IoT).
A real-time monthly DR price system for the smart energy gridThe smart grid is the next generation bidirectional modern grid. Energy users' are keen on reducing their bill and energy suppliers are also keen on reducing their industrial cost. Our demand response model would benefit them both. We have tested our model with the UK based traditional price value using a real-time basis. Energy users significantly reduced their bill and energy suppliers reduced their industrial cost due to load shifting. The Price Control Unit (PCU) and Price Suggestions Unit (PSU) utilise and embedded algorithms to vary price based upon demand. Our model makes suggestions based on energy threshold and makes use of stochastic approximation methods to produce prices. Our results shows that bill and peak load reductions benefit both the energy provider and users. This model also addresses users' preferences, if users are non-responsive, they can still reduce their bills.