Fuzzy-logic framework for future dynamic cellular systems

There is a growing need to develop more robust and energy-efficient network architectures to cope with ever increasing traffic and energy demands. The aim is also to achieve energy-efficient adaptive cellular system architecture capable of delivering a high quality of service (QoS) whilst optimising energy consumption. To gain significant energy savings, new dynamic architectures will allow future systems to achieve energy saving whilst maintaining QoS at different levels of traffic demand. We consider a heterogeneous cellular system where the elements of it can adapt and change their architecture depending on the network demand. We demonstrate substantial savings of energy, especially in low-traffic periods where most mobile systems are over engineered. Energy savings are also achieved in high-traffic periods by capitalising on traffic variations in the spatial domain. We adopt a fuzzy-logic algorithm for the multi-objective decisions we face in the system, where it provides stability and the ability to handle imprecise data

Self organising cloud cells: a resource efficient network densification strategy

Network densification is envisioned as the key enabler for 2020 vision that requires cellular systems to grow in capacity by hundreds of times to cope with unprecedented traffic growth trends being witnessed since advent of broadband on the move. However, increased energy consumption and complex mobility management associated with network densifications remain as the two main challenges to be addressed before further network densification can be exploited on a wide scale. In the wake of these challenges, this paper proposes and evaluates a novel dense network deployment strategy for increasing the capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance. Our deployment architecture consists of smart small cells, called cloud nodes, which provide data coverage to individual users on a demand bases while taking into account the spatial and temporal dynamics of user mobility and traffic. The decision to activate the cloud nodes, such that certain performance objectives at system level are targeted, is carried out by the overlaying macrocell based on a fuzzy-logic framework. We also compare the proposed architecture with conventional macrocell only deployment and pure microcell-based dense deployment in terms of blocking probability, handover probability and energy efficiency and discuss and quantify the trade-offs therein

Self-Organizing Architecture: A Resource Efficient Deployment Strategy.

In recent years, there has been a tremendous increase in the number of handsets, in particular smartphones, supporting a wide range of applications. Furthermore, the amount of mobile data traffic is expected to increase dramatically in the coming years. If the current traffic demand growth rate were maintained, current cellular system capacity would not be able to cope with it. Therefore future cellular systems have to be designed to contain the expected traffic growth. On the other hand, energy efficiency of cellular systems has emanated as one of the important performance indicators coupled with the current international focus on climate-change issues and increasing energy prices. There is always a trade-off between the coverage, quality of service, power consumption and capacity issues when considering the recent forecasts of expected traffic growth. The capability of optimizing the energy consumption of a cellular system without reducing from the user experience (i. e. compromising operational parameters) by means of exploiting the variation in traffic in both time and space domains is investigated. A bottom-up approach is used to model the power consumption of several types of LTE base stations. An energy-efficient algorithm based on a dynamic multi architecture deployment strategy is proposed demonstrating the ability to consume less energy by capitalizing on traffic diversity in the spatial and time domains. There is a need to design an energy-efficient framework for decision-making for future cellular systems. In particular, a novel scheme is proposed, the fuzzy-logic architecture selection (FLAS). We show how using multiple network parameters in architecture decision reduces energy consumption. Network densification is envisioned as a key enabler for 2020 vision that requires cellular systems to grow in capacity by hundreds of times. However, increased energy consumption and complex mobility management associated with network densification remains as the two main challenges to be addressed before further network densification can be exploited on a wide scale. In the wake of these challenges, a novel dense network deployment strategy for increasing the capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance is proposed and evaluated. The proposed deployment architecture consists of smart small cells, called cloud nodes, which provide data coverage to individual users on a demand basis while taking into account the spatiotemporal dynamics of user mobility and traffic. Furthermore, the comparison of the performance of the proposed architectures with both conventional macro deployment as well as pure micro cell based dense deployment in terms of number of KPIs is conducted and discuss and quantify the trade-off therein

Self-Organizing Architecture: A Resource Efficient Deployment Strategy.

In recent years, there has been a tremendous increase in the number of handsets, in particular smartphones, supporting a wide range of applications. Furthermore, the amount of mobile data traffic is expected to increase dramatically in the coming years. If the current traffic demand growth rate were maintained, current cellular system capacity would not be able to cope with it. Therefore future cellular systems have to be designed to contain the expected traffic growth. On the other hand, energy efficiency of cellular systems has emanated as one of the important performance indicators coupled with the current international focus on climate-change issues and increasing energy prices. There is always a trade-off between the coverage, quality of service, power consumption and capacity issues when considering the recent forecasts of expected traffic growth. The capability of optimizing the energy consumption of a cellular system without reducing from the user experience (i. e. compromising operational parameters) by means of exploiting the variation in traffic in both time and space domains is investigated. A bottom-up approach is used to model the power consumption of several types of LTE base stations. An energy-efficient algorithm based on a dynamic multi architecture deployment strategy is proposed demonstrating the ability to consume less energy by capitalizing on traffic diversity in the spatial and time domains. There is a need to design an energy-efficient framework for decision-making for future cellular systems. In particular, a novel scheme is proposed, the fuzzy-logic architecture selection (FLAS). We show how using multiple network parameters in architecture decision reduces energy consumption. Network densification is envisioned as a key enabler for 2020 vision that requires cellular systems to grow in capacity by hundreds of times. However, increased energy consumption and complex mobility management associated with network densification remains as the two main challenges to be addressed before further network densification can be exploited on a wide scale. In the wake of these challenges, a novel dense network deployment strategy for increasing the capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance is proposed and evaluated. The proposed deployment architecture consists of smart small cells, called cloud nodes, which provide data coverage to individual users on a demand basis while taking into account the spatiotemporal dynamics of user mobility and traffic. Furthermore, the comparison of the performance of the proposed architectures with both conventional macro deployment as well as pure micro cell based dense deployment in terms of number of KPIs is conducted and discuss and quantify the trade-off therein

Energy and Spectrum Efficient Systems with Adaptive Modulation and Spectrum Sharing for Cellular Systems

Increasing concern about the energy consumption of cellular networks is driving operators to optimise energy utilisation without sacrificing user experience. In this paper, we consider the capability of spectrum sharing between two or more operators with the objective of achieving energy-efficient operation. Since each operator has a fixed amount of spectrum, they are required to increase the modulation index to achieve a higher data rate for a targeted QoS, which increases the demand on energy. We study the effect of using energy-efficient multilevel quadrature amplitude modulation (MQAM) with the capability of spectrum sharing between operators. Simulations show that using adaptive modulation with spectrum sharing between base stations of different operators can give a more flexible region for the system to operate with a reasonable trade-off

Energy-Effcient Dynamic Deployment Architecture for Future Cellular Systems

There is a need to develop energy-efficient adaptive systems for future telecommunication networks. While traffic varies at different times, the power consumption of the radio access network does not scale with it effectively. To make significant energy savings, a dynamic deployment approach is required to allow the system to operate in an energy-efficient mode with respect to traffic load. By deploying small base stations within the area of a conventional macro station, we are able to reduce energy consumption while maintaining QoS. This paper proposes an energy-efficient dynamic deployment architecture based on fuzzy-logic. The algorithm aids in the decision of the architecture layout deployment. Moreover, by implementing the proposed adaptive energy-efficient algorithm, the network gains flexibility that can increase coverage or throughput throughout the same network by adapting its operation to source its requirements better and change them when new requirements arise