Energy efficiency perspectives of femtocells in internet of things : recent advances and challenges

Energy efficiency is a growing concern in every aspect of the technology. Apart from maintaining profitability, energy efficiency means a decrease in the overall environmental effects, which is a serious concern in today's world. Using a femtocell in Internet of Things (IoT) can boost energy efficiency. To illustrate, femtocells can be used in smart homes, which is a subpart of the smart grid, as a communication mechanism in order to manage energy efficiency. Moreover, femtocells can be used in many IoT applications in order to provide communication. However, it is important to evaluate the energy efficiency of femtocells. This paper investigates recent advances and challenges in the energy efficiency of the femtocell in IoT. First, we introduce the idea of femtocells in the context of IoT and their role in IoT applications. Next, we describe prominent performance metrics in order to understand how the energy efficiency is evaluated. Then, we elucidate how energy can be modeled in terms of femtocell and provide some models from the literature. Since femtocells are used in heterogeneous networks to manage energy efficiency, we also express some energy efficiency schemes for deployment. The factors that affect the energy usage of a femtocell base station are discussed and then the power consumption of user equipment under femtocell coverage is mentioned. Finally, we highlight prominent open research issues and challenges. © 2013 IEEE

Leveraging intelligence from network CDR data for interference aware energy consumption minimization

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo

How much energy will your NGN consume? A model for energy consumption in next generation access networks: The case of Spain

The contribution to global energy consumption of the information and communications technology (ICT) sector has increased considerably in the last decade, along with its growing relevance to the overall economy. This trend will continue due to the seemingly ever greater use of these technologies, with broadband data traffic generated by the usage of telecommunication networks as a primary component. In fact, in response to user demand, the telecommunications industry is initiating the deployment of next generation networks (NGNs). However, energy consumption is mostly absent from the debate on these deployments, in spite of the potential impact on both expenses and sustainability. In addition, consumers are unaware of the energy impact of their choices in ultra-broadband services. This paper focuses on forecasting energy consumption in the access part of NGNs by modelling the combined effect of the deployment of two different ultra-broadband technologies (FTTH-GPON and LTE), the evolution of traffic per user, and the energy consumption in each of the networks and user devices. Conclusions are presented on the levels of energy consumption, their cost and the impact of different network design parameters. The effect of technological developments, techno-economic and policy decisions on energy consumption is highlighted. On the consumer side, practical figures and comparisons across technologies are provided. Although the paper focuses on Spain, the analysis can be extended to similar countries

A Survey of Cognitive Radio Access to TV White Spaces

Cognitive radio is being intensively researched as the enabling technology for license-exempt access to the so-called TV White Spaces (TVWS), large portions of spectrum in the UHF/VHF bands which become available on a geographical basis after digital switchover. Both in the US, and more recently, in the UK the regulators have given conditional endorsement to this new mode of access. This paper reviews the state-of-the-art in technology, regulation, and standardisation of cognitive access to TVWS. It examines the spectrum opportunity and commercial use cases associated with this form of secondary access

AI-based resource management in future mobile networks

Η υποστίριξη και ενίσχυση των δίκτυων 5ης γενιάς και πέρα από αλγόριθμους Τεχνητής Νοημοσύνης για την επίλυση προβλημάτων βελτιστοποίησης δικτύου, μελετάται πρόσφατα προκειμένου η νέα γενιά των δικτύων να ανταποκριθεί στις απαιτήσεις ποιότητας υπηρεσίας σχετικά με την κάλυψη, τη χωρητικότητα των χρηστών και το κόστος εγκατάστασης. Μία από τις βασικές ανάγκες είναι η βελτιστοποίηση στην διαδικασία της εγκατάστασης σταθμών βάσης δικτύου. Σε αυτή την εργασία προτείνεται μια μετα-ευριστική μέθοδος, με όνομα «Γενετικός Αλγόριθμός» (Genetic Algorithm) για την επίλυση προβλημάτων βελτιστοποίησης λαμβάνοντας υπόψη τους περιορισμούς ζήτησης. Ο κύριος στόχος είναι η παρουσίαση της εναλλακτικής αυτής λύσης, η οποία είναι η χρήση του Γενετικού Αλγόριθμου, για τη βελτιστοποίηση της διαδικασίας εγκατάστασης των σταθμών βάσης του δικτύου. Με την χρήση του αλγορίθμου για την εγκατάσταση σταθμών βάσης παρέχονται οι ίδιες υπηρεσίες με πριν και ελαχιστοποιείται την κατανάλωση ενέργειας της υποδομής του δικτύου, λαμβάνοντας υπόψιν ομοιογενή και ετερογενή σενάρια σταθμών βάσης. Οι προσομοιώσεις πραγματοποιήθηκαν σε γλώσσα προγραμματισμού Python και τα καλύτερα αποτελέσματα εγκατάστασης παρουσιάστηκαν και αποθηκεύτηκαν. Έγινε σύγκριση της εγκατάστασης αποκλειστικά μακρο-σταθμών βάσης με μικρότερου μεγέθους (σε κάλυψη) σταθμών βάσης πάνω από την υπάρχουσα. Με την χρήση των μικρότερων σταθμών βάσης, η εγκατάσταση του δικτύου θα επιτρέψει βελτιώσεις στην κάλυψη των χρηστών και θα μειώσει το κόστος, την κατανάλωση ενέργειας και τις παρεμβολές μεταξύ των κυψελών. Όλα τα σενάρια μελετήθηκαν σε 3 περιοχές με διαφορετική πυκνότητα χρηστών (A, B και C). Ως προς την ικανοποίηση των απαιτήσεων αναφορικά με την ποιότητα υπηρεσιών και των κινητών συσκευών, η ανάπτυξη μικρών σταθμών βάσης είναι επωφελής, συγκεκριμένα σε περιοχές hotspot.The 5G and beyond networks supported by Artificial Intelligence algorithms in solving network optimization problems are recently studied to meet the quality-of-service requirements regarding coverage, capacity, and cost. One of the essential necessities is the optimized deployment of network base stations. This work proposes the meta-heuristic algorithm Genetic Algorithm to solve optimization problems considering the demand constraints. The main goal is present the alternative solution, which is using the Genetic Algorithm to optimize BSs network deployment. This deployment provides the same services as existing deployments and minimizes the network infrastructure's energy consumption, including using homogenous and heterogenous scenarios of base stations. The simulations were performed in Python programming language, and the results as the best plans for each generation were presented and saved. A comparison of the macro base station deployment and small base station deployment was made on top of the existing one. By applying the small base stations, the network deployment will enable user coverage enhancements and reduce the deployment cost, energy consumption, and inter-cell interference. All the scenarios were assembled in user density area A, user density area B, and user density area C areas of interest. In meeting the requirements for QoS and UE, the small base station deployment is beneficial, namely in hotspot areas

Demand Response Management in Smart Grid Networks: a Two-Stage Game-Theoretic Learning-Based Approach

In this diploma thesis, the combined problem of power company selection and Demand Response Management in a Smart Grid Network consisting of multiple power companies and multiple customers is studied via adopting a distributed learning and game-theoretic technique. Each power company is characterized by its reputation and competitiveness. The customers who act as learning automata select the most appropriate power company to be served, in terms of price and electricity needs’ fulfillment, via a distributed learning based mechanism. Given customers\u27 power company selection, the Demand Response Management problem is formulated as a two-stage game theoretic optimization framework, where at the first stage the optimal customers\u27 electricity consumption is determined and at the second stage the optimal power companies’ pricing is calculated. The output of the Demand Response Management problem feeds the learning system in order to build knowledge and conclude to the optimal power company selection. A two-stage Power Company learning selection and Demand Response Management (PC-DRM) iterative algorithm is proposed in order to realize the distributed learning power company selection and the two-stage distributed Demand Response Management framework. The performance of the proposed approach is evaluated via modeling and simulation and its superiority against other state of the art approaches is illustrated