 Research
 Open Access
 Published:
Fuzzylogic framework for future dynamic cellular systems
EURASIP Journal on Wireless Communications and Networking volume 2015, Article number: 247 (2015)
Abstract
There is a growing need to develop more robust and energyefficient network architectures to cope with ever increasing traffic and energy demands. The aim is also to achieve energyefficient 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 lowtraffic periods where most mobile systems are over engineered. Energy savings are also achieved in hightraffic periods by capitalising on traffic variations in the spatial domain. We adopt a fuzzylogic algorithm for the multiobjective decisions we face in the system, where it provides stability and the ability to handle imprecise data.
Introduction
Increasing concern regarding the energy consumption of cellular networks is driving operators to optimise energy utilisation without detracting from the user experience. This has motivated researchers to investigate solutions to reduce energy consumption. Indeed, energy consumption within the information and communication technology (ICT) sector has become an important subject for both economic and environmental consideration. ICT alone accounts for 2– 10 % of global greenhouse gas emissions, a figure expected to increase annually [1, 2]. The volume of transmitted data is predicted to increase by a factor of approximately ten every 5 years, which equates to an increase in ICTrelated energy consumption of approximately 16– 20 % over that same period [3]. Thus, energy consumption is an everincreasing problem that becomes more pressing the longer it remains unaddressed. This work is motivated by the reality that mobile communication systems are designed to support the maximum demanded throughput needed during peak traffic periods. As the traffic demand varies with time and space, this results in some areas that are overoptimised, hence, in excessive energy consumption. Thus, it is essential that the system be capable of scaling its energy consumption with traffic, without sacrificing quality of service (QoS).
In current systems, major energy is consumed in the radio access portion of the network, making it the ideal target for optimisation. There is much room for improvement in the energy efficiency of cellular systems, since a basestation consumes more than 90 % of its peak energy even if it is experiencing little or no activity [4]. Even if some of the radio transceivers in a basestation are switched off, which provides some savings, this is still not sufficient [4]. To make significant energy savings, a dynamic deployment approach is required that allow the system to operate in an energyefficient mode.
Cellular architecture is generally categorised as macro, micro, pico, and femtocells, according to their cell sizes. To maintain communication coverage, a small cellbased topology requires many basestations with a low level of transmitting power to provide users with high data rates. On the other hand, a large cellbased topology requires only a few basestations, each with a high level of transmitting power. Each type of basestation has its own characteristics in terms of coverage, data rate, and power consumption. We aim to adopt a heterogeneous network comprising different basestation types so that for different periods of the day, the most suitable architecture is deployed.
Although different papers have evaluated the idea of dynamically changing the architecture of the network in the aim of saving energy [5, 6], they lacked in developing a framework in which the decision can be made. As switching a basestation affects the network performance by changing its coverage, throughput and other elements, it is considered as a dynamic deployment architecture. The decision in which network elements are switched off and on is a multipleobjective decisionmaking (MODM) problem comprising different criteria and requirements, such as link quality, quality of service, network availability and network reliability.
Traffic load varies from time to time and location to location, and current mobile networks are not designed to benefit from such variation in traffic. Therefore, a large amount of energy is wasted on basestations that are not active with users in lowtraffic periods. This has sparked the idea of dynamically switching off basestations, thereby achieving a dynamic deployment architecture. When a basestation is switched off, radio coverage and QoS must still be guaranteed by neighbouring basestations or other means [5, 6]. A common approach in reconfiguring the network is in deriving necessary thresholds to be satisfied that might be in terms of service outage probability [7], traffic in terms of Erlangs [8], percent of the peak traffic [4] and minimum signaltointerferenceplusnoise ratio (SINR) [9], but it fails to provide a full picture on how the decision can be made.
As discussed above, there has been a significant amount of work showing the benefits of dynamic deployment; however, there is a lack of work tackling the problem of how a dynamic deployment can be achieved without human intervention by identifying the specific nodes that can be switched off or on. This paper proposes an energyefficient dynamicarchitecture technique based on fuzzy logic. In particular, we propose a novel scheme, the fuzzylogic architecture selection. We show how using multiple network parameters in architecture decision reduces energy consumption.
The remainder of this paper is organised as follows: Section 2 describes the dynamic network management framework, Section 3 explains the fuzzylogic algorithm proposed and Section 4 presents the simulation and results. Finally, Section 5 concludes the work.
Dynamic networkmanagement framework
A flexible framework that covers different aspects of the network and has the ability to make a multipleobjective decision for energy utilisation is required. The designed framework should have the ability to cope with high traffic demand and provide an energyefficient operation and flexibility. Figure 1 presents the dynamic networkmanagement concept, which has the following three main phases:

1.
Network informationgathering: In this phase, all the information that is needed to identify the requirement of architectureswitching is gathered, with the ability to initiate an architecture switch.

2.
Architecture multipleobjective decisionmaking: Here, it is determined whether a network architecture change can or needs to be adopted by selecting the most suitable deployment architecture, based on a given criteria, such as link quality and network reliability. Also, it gives instructions to the execution phase.

3.
Architecture execution: This phase manages the change of network architecture based on the users affected during the network architecture change.
Architecture handover mechanism
Based on the average daily data traffic profile in Europe, we decided to switch off on an hourly basis. The decision is driven by the fact that the traffic is almost constant within a single hour. On this basis, at the beginning of each time slot, the system parameters would be gathered and provided to the MODM unit, and if the switchoff decision was taken, then this would signal the basestations in the area to initiate for activation. At this point, one of three choices is implemented [10], which are explained as follows.

Soft switchoff: In this scenario, when the switchoff decision is taken, the network waits until no user is accessing the cell, then switchedoff only when idle. This can be considered as the least invasive approach for users.

Semihard switchoff: Here, as soon as the switchoff decision is taken, no new service requests are accepted by the cell, which can be switchedoff as soon as all services in progress at the time of the switchoff decision terminate. This implies that some service requests will be blocked.

Hard switchoff: With this option, immediately after the switchoff decision is taken, users are forced to implement a handover from the macrocell that is going to be switched off to microcells in the area. This is the most invasive approach for users, but forced handover is foreseen by longterm evolution (LTE) standards, and thus, the algorithm is well within the possibilities of present LTE equipment.
We assume that the basestation can switch off completely as there are active microcells that can serve the traffic (and vice versa). This would result in large savings as a basestation consumes power even if the RF part is switched off. At the next time slot, the process is repeated as each micro and macrocell measures the traffic demand that is served. The same process occurs when the traffic exceeds a certain level; the macrocell is initiated and microcells are switched off.
The computational cost of using Fuzzy logic is minimal as a new entity can be installed that computes the decisions and executes them. Each basestation (i.e. macrostation) is responsible for forming a decision on which of the micronodes are active, and if its services to users are required or not. On the other hand, micronodes are responsible for serving the users in their small coverage area if they are activated. Therefore, from the perspective of micronodes, it is considered to be a centralised approach. However, from the perspective of the network as a whole, it is a decentralised approach as the decision is carried out on a cell level. The centralised approach benefits from an overall picture of the cell status, and thus, the macrostation can manage the performance level with the knowledge of the impact activating each node would have. On the other hand, the decentralisation benefits the network in terms of the simplicity it provides and its suitability for cellular deployments in a wide scenario.
Decisionmaking
In this section, we evaluate some of the popular decisionmaking approaches and discuss their suitability to be adopted for dynamic network architecture decisionmaking.
Functionbased decision approach
The functionbased decision approach combines different metrics in a costfunction manner. Therefore, it is the sum of several weighted functions. The general form of a cost function F _{ n } for the network n is [11, 12]
where \(l_{s,i}^{n}\) represents the cost in the ith architecture to carry out the sth service on network n, and w _{ s,i } is the weight (importance) assigned to the ith architecture to perform services, where \(\sum _{i} w_{s,i} =1\).
The use of cost function has been widely adopted in different handover mechanisms [13]. Although it has been successful for the use of handover decisions, this approach may not be suitable for network architecture decision. As from the perspective of the network, different criteria changes at different periods of the day that may cause the decision to fluctuate between two outcomes, causing instability in the overall network. Therefore, functionbased decisionmaking may be more suitable for usercentric decisionmaking problems.
Multipleattribute decision strategies
The dynamic deployment architecture deals with the problem of choosing architectures to adopt from a set of possible architectures. This is considered to be a multipleattribute decisionmaking (MADM) problem, which deals with choosing a decision from a set of alternatives that are specified by their attributes [12]. There are a number of different MADM methods adopted throughout the literature, such as simple additive weighting—the weighted sum of all the attribute values determines a given network score level—and analytic hierarchy process—in this approach, the problem (main objective) is decomposed into its constituent parts (criteria, subcriteria, alternatives etc.) [14].
Contextaware strategies
A contextaware approach relies on the knowledge of the context information from the network as well as the mobile terminals to form a decision. In this premise, this approach evaluates the context information and tracks changes of the network and can then provide a contextaware decision on whether a network architecture change is required. The contextaware decision approach can be applied with an analytic hierarchy process method such as the work given in [15, 16].
Fuzzy logic (FL)
The conventional MADM methods lack the ability to make an efficient decision when imprecision or ambiguity is introduced to the data. Therefore, the use of fuzzy logic provides the ability to deal with imprecise data, and also to evaluate multiple criteria simultaneously to provide a robust mathematical framework for decisionmaking [12, 17–19]. Fuzzy logic has been used in a variety of fields, for example, handoverdecision protocols [17, 19]. It has also been used in wireless sensor networks for cluster formation and energy efficiency of cluster formation [20]. Others have used fuzzy logic for clusterhead selection in wireless sensor networks [21, 22]. Moreover, fuzzy logic provides the ability for human experts’ qualitative thinking to be a part of the algorithm, which provides a higher level of efficiency [23]. This makes the fuzzylogic approach the most suitable to adopt for dynamic network architecture decisionmaking.
Applying fuzzy logic in decisionmaking
In this paper, we consider a heterogeneous network comprising different basestation types. To optimise energy consumption, some of the network basestations are switched off and others are switched on. The decision of which network elements are active and which are not is an MODM problem, comprising different network criteria and requirements. We adopt a fuzzylogic approach for decisionmaking due to its inherent strength in solving problems where imprecision and statistical uncertainty is introduced. Conventional decisionmaking algorithms lack the ability to efficiently solve a decision problem where imprecise data are imposed; thereby the use of fuzzy logic provides the ability to handle imprecise data and to combine and evaluate multiple criteria simultaneously.
The algorithm has three different stages as shown in Fig. 2. In the first stage, the system parameters are fed into a fuzzifier, in which they are transformed from a crisp set of parameters into fuzzy sets. A fuzzy set comprises of elements with varying degrees of membership in sets ranging from zero to one depending on the membership function [19] as seen in Fig. 3. On the other hand, in a crisp set, a value is considered a member of a class only if it has full membership in the class. Therefore, in a fuzzy set, an element can be a member of more than one class. The membership values are generated from the mapping of a value (crisp value) onto a membership function. Three trapezoidal membership functions are used for representing all the subsets of the inputs and outcomes. A trapezoidal membership function is specified by four parameters {A _{1},A _{2},A _{3},A _{4}} [24]:
In the second stage, the fuzzy sets are fed into an inference engine where a set of fuzzy rules is applied. These fuzzy rules can be defined as a set of possible outcome scenarios involving ifthen rules [25]:
where \(\mathcal {R}_{i}\) is the ith fuzzy rule, x are the variables of the premise that appear also in the part of the consequence, x= [x _{1},x _{2},...,x _{ n }], \({\eta _{n}^{i}}\) are the antecedent fuzzy sets or the premise variables for the inputs, y _{ i } is the output of the ith fuzzy rule and g _{ n } is the function that implies the value of y _{ n } when x _{1}−x _{ n } satisfies the premise.
The third stage involves converting the fuzzy output to a crisp value in which the decision can be made (defuzzification). It is the transformation of a fuzzy quantity into a crisp (precise) quantity. We adopt the centroid method for converting the fuzzy outcome to a crisp value in which the decision would be made.
The defuzzifier combines the output sets corresponding to all the fired rules in some way to obtain a single output set and then finds a crisp number that is representative of this combined output set, e.g. the centroid defuzzifier finds the union of all the output sets and uses the centroid of the union as the crisp output. For example, the centroid of set A, whose domain is discretized into points N, is given as
where the membership grade of x∈X in A is μ _{ A }(x), which is a fuzzy set in [0,1]. We consider optimising the energy consumption of the network using a fuzzylogicbased approach while satisfying two criteria in the decisionmaking, the probability of handover and the blocking probability for simplification, although this algorithm can handle many different aspects of the network with the same premise. The probability of handover would reflect the impact of the increasing traffic and the ability to service the incoming traffic. On the other hand, blocking probability would reflect the ability of the system to serve the incoming traffic.
In the algorithm, at each time step, the basestation would measure the incoming traffic and evaluate the handover and blocking probability (other criteria can be added) for each possible architecture (micro or macro). Hence, the decision is based on the current requirement of the basestation and its ability to save power consumption. The handover and blockingprobability results are divided into three levels: high, moderate and low to represent the minimal required values. A total of nine fuzzy rules are formulated to cover all possible combinations (three subsets for each input). Table 1 summarises the rules within the inference engine.
At this point, each architecture would have an output score value (a crisp value) and the architecture with the highest value would be adopted.
Basestation types and power models
The power consumption of a basestation depends on the cell size (covered area), as well as the degree of coverage required. Conventional macrocells are designed to provide large area coverage, thereby featuring large power consumption figures. On the other hand, microcells cover a much smaller area and feature much lower power consumption figures. The relation between the average power consumption (P _{ in }) and the average radiated power per site is given in [26–28]:
where P _{0} is the power consumption at the minimum nonzero output power, P _{ out } is the RF output power, P _{ max } is the maximum RF output power at maximum load, Δ _{ P } is the slope of the loaddependent power consumption and N _{ TRX } is the number of transceiver chains. The parameters of the linear power model for the considered basestation types are listed in Table 2. Summing up the power consumption, figures of all elements in a network would then yield the total power consumption of the network:
where the individual power figures P _{ in } for ith basestation correspond to the power consumption of each individual basestation type. The total power consumption is then scaled by the network area.
Probability of handover
In this section, we aim to find the probability of a mobile terminal handing off to a new basestation. In Fig. 4, we consider the scenario of a mobile terminal located at point X handing off from an old basestation to a future basestation. We assume that cells are in a hexagonal shape, where the borders of the basestations are defined by the threshold value of the received signal strength (RSS) that would initiate the handover process. Initially, the mobile terminal would be served by the old basestation and is moving with a velocity of v, which is uniformly distributed in [v _{ min };v _{ max }]. We assume that a mobile terminal can move in any direction with equal probability; hence, the PDF of the mobile terminal direction of motion θ is [29]:
We also assume that the speed and direction of motion of a mobile terminal from point X until it goes out of coverage remains constant. Since the distance from point X to the cell boundary is not great, this assumption is valid [29]. At this point, the mobile terminal would handover when the direction of motion is between θ∈(−𝜗,𝜗), from Fig. 4:
where p is the distance between point X and the cell boundary and a is the hexagon side length. From [29, 30], the probability of a mobile terminal handing off in a time less than τ is:
Blocking probability
In order to provide a realistic analysis for energy efficiency, it is important to apply a realistic traffic model as the basis of testing. Thus, we adopt the traffic model given in [27] which defines the average daily data traffic profile in Europe. We assume that user arrival process is a continuoustime Markov process with rate λ _{ u }, and each user can generate multiple data connections, arriving according to the Poisson process with rate λ _{ d }. Therefore, for u users, the data connection arrival rate is s λ _{ d }. This traffic model is applied using a MMPP/M/l/DPS queue (a singleserver processorsharing queue, with MMPP (Markovmodulated Poisson process) arrival process and Markovian service time) [31]. The user service rate is exponentially distributed with a mean value of μ _{ u }=1/Γ, where Γ is the mean value of the service time. The amount of information transferred in each data connection is exponentially distributed with a mean value R. Therefore, the data connection service time is exponentially distributed with a mean value μ _{ d }=T _{ h }/R, where T _{ h } is the throughput. The steadystate probability is defined as w(u,d), where u and d are the number of users and data connections, respectively, with a maximum of U users that can be admitted and a maximum of D data connections. The blocking probability is the probability of having a new user or a data connection unable to be admitted for service.
The steadystate probability is defined as the stationary vector π=(π _{0},π _{1},π _{2},…,π _{ D+1}), where π _{ d }=(π _{ d,0},π _{ d,1},π _{ d,2},…,π _{ d,U)} and π _{ d,u }=w(u,d) satisfies the following:
where Q is the infinitesimal generator matrix. From the steadystate probability we can calculate the blocking probability as follows [31]:
Results and discussion
Models and assumptions are basically aligned with 3rd Generation Partnership Project (3GPP) simulation case 1 [32]. An orthogonal frequencydivision multiple access (OFDMA) system employing a frequency reuse of one: that is, the same time and frequency resources are allocated for transmission in each cell is considered. The traffic model given in [27] is adopt, which defines the average daily data traffic profile in Europe. As the optimization considered in this paper is a longterm optimization, and shadowing and fast fading are averaged over space and time, respectively, their effects will be neglected here, thereby focusing on the distantdependent path loss effect, and the link gain between the basestation and a mobile will be defined by the path loss effect, with the assumption that the given in Table 2 is in line with [33]. In this section, we compare the performance of a conventional network architecture with the proposed energyefficient adaptive architecture. We first concentrate on the area power consumption of a pure macrocell scenario and extend the investigation to the hybrid case with a certain number of microcells per sector. We consider a hexagonal grid of macrosites where each basestation would cover an area with R= 1 km. On the other hand, microdevices feature a single omnidirectional antenna and cover a much smaller area; there are five microcells in each cell to ensure coverage when the macrocell is switched off. We assume that the energyefficient algorithm operates on an hourly basis. This decision is driven by the fact that traffic load is almost constant within the duration of an hour. We assume that a basestation sector is either transmitting at full power or fully switched off.
As in the scenario given in Fig. 4, a comparison between a macro with coverage of R=1 km and a micro with coverage of R=300 m for different τ is shown in Figs. 5 and 6, respectively. As can be seen at τ=10 sec, the percentage of the cell area with a probability not equal to zero is 11 % for the macrostation and 36.7 % for the microstation, which is expected due to the difference in coverage requirement.
Moreover, if we assume that traffic is uniformly distributed in the cell area, then we can calculate the percentage of traffic load in the area where the probability of handover at a given time duration τ is not equal to zero from Figs. 5 and 6. As seen from Fig. 7, the algorithm would not allow the handover probability to exceed a certain value. When microcells are active (a total of five microcells were used to ensure coverage), they consume a maximum total of 0.2471 kW/km ^{2} and a minimum of 0.2155 kW/km ^{2} varying with traffic load and the number of microcells used, and for four microcells, the power consumption ranges between 0.1977 and 0.1724 kW/km ^{2}. On the other hand, macros consume a maximum total of 0.3364 kW/km ^{2} and a minimum of 0.3002 kW/km ^{2}.
In Figs. 7 and 8, we can see the comparison between the conventional cellular system and the proposed energyefficient hybrid cellular system. As can be observed, as the traffic decreases, the algorithm tends to switch off more macrocells and activate the microcells in the targeted area. This results in the desired scaling of power consumption, providing large savings in lowtraffic periods. The system as designed is able to minimise energy consumption throughout different periods of the day, as it automatically adjusts to different traffic demand levels. The algorithm demonstrates the ability to consume less energy during periods of high traffic demand by capitalising on traffic diversity in the spatial domain. In the proposed algorithm, the microcells are activated dynamically during different periods while avoiding interference with the macro basestation and providing coverage and service to the weaker areas of the network.
Conclusions
In this paper, we have proposed an energyefficient framework for cellular systems that operates whilst conserving energy. We have shown that deploying a fuzzylogic architecture selection algorithm that is able to respond to different traffic demands, whilst maintaining systemwide QoS can minimise energy consumption without human intervention. The aim was not to have a system that conserves energy by compromising operational parameters, but a system that consumes less energy whilst maintaining coverage, handover probability and QoS. The system as designed is able to minimise energy consumption throughout different periods of the day, as it adjusts to different traffic demand levels. The fuzzylogic algorithm prevents the system from lowering system performance, but chooses the best outcome while avoiding continuous switching from on state to off state or vice versa, which can affect the system performance substantially. The proposed algorithm has the advantage of being scalable to accept other variables as decision criteria, thereby providing more accurate decisionmaking. Furthermore, the algorithm can be tuned to be more relaxed in terms of the criteria to provide either more flexibility in allowing more energy saving or more strict in terms of the minimal accepted system performance.
References
Global Action Plan, “An inefficient truth”, Report, Dec. 2007.
A Gladisch, C Lange, R Leppla, in European Conference on Optical Communication. Power efficiency of optical versus electronic access networks (IEEE,Brussels, Belgium, 2008), pp. 1–4.
B Badic, T O’Farrrell, P Loskot, J He, in IEEE Vehicular Technology Conference. Energy efficient radio access architectures for green radio: Large versus small cell size deployment (IEEE,Anchorage, AK, 2009), pp. 1–5.
E Oh, B Krishnamachari, X Liu, Z Niu, Toward dynamic energyefficient operation of cellular network infrastructure. IEEE, Commun. Mag.49(6), 56–61 (2011).
E Oh, B Krishnamachari, in IEEE Global Telecommunications Conference. Energy savings through dynamic base station switching in cellular wireless access networks (IEEE,Miami, FL, 2010), pp. 1–5.
MA Marsan, L Chiaraviglio, D Ciullo, M Meo, in International Conference on Communications and Electronics. Multiple daily base station switchoffs in cellular networks (IEEE,Hue, 2012), pp. 245–250.
D Cao, S Zhou, Z Niu, in IEEE International Conference on Communications. Optimal base station density for energyefficient heterogeneous cellular networks (IEEE,Ottawa, ON, 2012), pp. 4379–4383.
MF Hossain, KS Munasinghe, A Jamalipour, in IEEE Wireless Communications and Networking Conference. Two level cooperation for energy efficiency in multiRAN cellular network environment (IEEE,Shanghai, 2012), pp. 2493–2497.
P Ghosh, SS Das, S Naravaram, P Chandhar, in National Conference on Communications. Energy saving in OFDMA cellular systems using basestation sleep mode: 3GPPLTE a case study (IEEE,Kharagpur, 2012), pp. 1–5.
L Chiaraviglio, D Ciullo, M Meo, MA Marsan, in International Teletraffic Congress. Energyefficient management of UMTS access networks (IEEE,Paris, 2009), pp. 1–8.
J McNair, F Zhu, Vertical handoffs in fourthgeneration multinetwork environments. IEEE Wireless Communications. 11(3), 8–15 (2004).
M Kassar, B Kervella, G Pujolle, An overview of vertical handover decision strategies in heterogeneous wireless networks. Comput. Commun.31(10), 2607–2620 (2008).
HJ Wang, RH Katz, J Giese, in IEEE Workshop on Mobile Computing Systems and Applications. Policyenabled handoffs across heterogeneous wireless networks (IEEE,New Orleans, LA, 1999), pp. 51–60.
PML Chan, YF Hu, RE Sheriff, in IEEE Wireless Communications and Networking Conference. Implementation of fuzzy multiple objective decision making algorithm in a heterogeneous mobile environment (IEEE,FL, USA, 2002), pp. 332–336.
S Balasubramaniam, J Indulska, Vertical handover supporting pervasive computing in future wireless networks. Comput. Commun.27(8), 708–719 (2004).
L Mokhesi, A Bagula, in Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists. Contextaware handoff decision for wireless access networks using bayesian networks (ACM,Vanderbijlpark, South Africa, 2009), pp. 104–111.
L Xia, Lg Jiang, C He, in IEEE International Conference on Communications. A novel fuzzy logic vertical handoff algorithm with aid of differential prediction and predecision method (IEEE,Glasgow, 2007), pp. 5665–5670.
S Ghosh, Q Razouqi, HJ Schumacher, A Celmins, A survey of recent advances in fuzzy logic in telecommunications networks and new challenges. IEEE Trans. Fuzzy Syst.6(3), 443–447 (1998).
PML Chan, RE Sheriff, YF Hu, P Conforto, C Tocci, Mobility management incorporating fuzzy logic for heterogeneous a IP environment. IEEE Commun. Mag.39(12), 42–51 (2001).
R Mathur, MK Sharma, A Misra, D Baveja, in International Conference on Advances in Computing, Control, Telecommunication Technologies. Energyefficient deployment of distributed mobile sensor networks using fuzzy logic systems (IEEE,Trivandrum, Kerala, 2009), pp. 121–125.
G Ran, H Zhang, S Gong, Improving on LEACH protocol of wireless sensor networks using fuzzy logic. Int. J. Inf. Comput. Sci.7(3), 767–775 (2010).
JM Kim, SH Park, YJ Han, TM Chung, in International Conference on Advanced Communication Technology. CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks (IEEE,GangwonDo, 2008), pp. 654–659.
X Yan, YA Şekercioğlu, S Narayanan, A survey of vertical handover decision algorithms in fourth generation heterogeneous wireless networks. Comput. Netw.54(11), 1848–1863 (2010).
W ElHajj, D Kountanis, A AlFuqaha, M Guizani, in IEEE International Conference on Communications, 8. A fuzzybased hierarchical energy efficient routing protocol for large scale mobile Ad Hoc networks (FEER) (IEEE,Istanbul, 2006), pp. 3585–3590.
T Takagi, M Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern.SMC15(1), 116–132 (1985).
F Richter, AJ Fehske, GP Fettweis, in IEEE Vehicular Technology Conference. Energy efficiency aspects of base station deployment strategies for cellular networks (IEEE,Anchorage, AK, 2009), pp. 1–5.
G Auer, V Giannini, I Godor, P Skillermark, M Olsson, MA Imran, D Sabella, MJ Gonzalez, C Desset, O Blume, in IEEE Vehicular Technology Conference. Cellular energy efficiency evaluation framework (IEEE,Yokohama, 2011), pp. 1–6.
G Auer, V Giannini, C Desset, I Godor, P Skillermark, M Olsson, MA Imran, D Sabella, MJ Gonzalez, O Blume, A Fehske, How much energy is needed to run a wireless network?IEEE Wirel. Commun.18(5), 40–49 (2011).
S Mohanty, IF Akyildiz, A crosslayer (layer 2 + 3) handoff management protocol for nextgeneration wireless systems. IEEE Trans. Mob. Comput.5(10), 1347–1360 (2006).
A Papoulis, SU Pillai, Probability, Random Variables, and Stochastic Processes (McGrawHill Companies, New York, 2002).
Y Qi, MA Imran, R Tafazolli, in IEEE International Symposium on Personal Indoor and Mobile Radio Communications. Energyaware adaptive sectorisation in LTE systems (IEEE,Toronto, ON, 2011), pp. 2402–2406.
3GPP TR 36.814, “Further advancements for EUTRA physical layer aspects”, Tech. Rep., Release 9, (Mar. 2010). Available at “http://www.3gpp.org/dynareport/36814.htm.
3GPP TR 25.814, “Physical layer aspects for evolved Universal Terrestrial Radio Access (UTRA)”, Tech. Rep., Release 7, Sep. 2007. Available at “http://www.3gpp.org/DynaReport/25814.htm.
Author information
Authors and Affiliations
Corresponding author
Additional information
Competing interests
The authors declare that they have no competing interests.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Alsedairy, T., AlImari, M. & Imran, M. Fuzzylogic framework for future dynamic cellular systems. J Wireless Com Network 2015, 247 (2015). https://doi.org/10.1186/s1363801504734
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1363801504734
Keywords
 Energy efficiency
 Adaptive network architecture
 Multiobjective decisionmaking