ENERGY EFFICIENT SMALL CELL NETWORKS USING DYNAMIC CLUSTERING by IJARIDEA Journal

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

ENERGY EFFICIENT SMALL CELL NETWORKS USING DYNAMICCLUSTERING S.Praveen Kumar1, K.Narendra2, V.Krishna Vamsi3 1 Assistant Professor, SRM University, India 2,3 ECE, SRM University, India praveenkumar.se@ktr.srmuniv.ac.in1, narenraj619@gmail.com2, kvamsi920@gmail.com3 Abstract— Clustering approach that fuses, past established area based measurements, the impacts of the time-differing BS stack. Grouping empowers intra-bunch coordination among the base stations with the end goal of streamlining the downlink execution by means of load adjusting. Because of between group impedance, the bunches need to rival each other to settle on choices on enhancing the vitality proficiency by means of a dynamic decision of rest and dynamic states.

Keywords— Energy Efficiency, Small Cell Networks. I. INTRODUCTION

While being a powerful technique in expanding the remote vitality proficiency of the system, the BS rest mode likewise has the negative potential to build benefit delays and compound the nature of administration for the clients. Amid the rest mode, the BS and its radio transmissions are turned off at whatever point conceivable, typically under low movement stack conditions. On the off chance that for instance the system's recurrence transporter is not required for the objective nature of administration, it can be killed so as to limit the system wide vitality utilization. Small cells: A cell phone system is worked by, bury alia, utilizing and joining various cell phone benefit scope territories. By far most of the present portable systems comprise of the scope ranges served by the system's cells known as full scale cells. In spite of being the stand the most intense cells, even large scale cells can't really serve the majority of the clients in their general vicinity.

Explanations behind the absence of satisfactory administration can be: • An excessive number of clients in a given region (ordinarily in urban region). • Excessively poor flag quality in structures, potentially because of too thick dividers, and so forth. • A lot of obstruction from neighboring cells. • Excessively incredible separation between the MT and the BS. The motivation behind covering the administration holes in the full scale cell systems, littler system cells have been created. The littler cells help not just in expanding the ability to serve the MTs inside the system, additionally in the power funds in both the MTs and the BSs too There are various distinctive channel task techniques for accomplishing the ideal radio asset usage. The most well-known calculation, in which each cell allocates its own particular RF channels to the distinctive MTs in the cell. All calls inside the phone are served by the phone's unused channels – if no channels stay accessible, no calls can be made. Getting channel task under typical recurrence task conditions, the acquiring channel task utilizes the settled channel task - calculation, yet in the event that no radio channels stay accessible, it will obtain channels from its neighboring cells. © 2017, IJARIDEA All Rights Reserved

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

Coercive obtaining channel task complies with the run of recurrence reuse separation, and it diminishes the requirement for co-direct task in reusing cell patterns. Dynamic channel task: Little cells offload remote activity from BSs by overlaying a layer of little cell APs, which really diminishes the normal separation between the transmitters and clients, hence bringing about lower engendering misfortunes and higher information rates and vitality proficiency. The fraction of time BS b needs to serve the traffic ηb(x) from BS b to location x is defined as,

(1)

. Consequently, the fractional transfer time of BS b is given by [22]: .

(2)

Here, the fractional transfer time ρb represents the fractional time required for a BS to deliver its requested traffic. Moreover, the average number of flows at BS b is given by and it is proportional to the expected delay at BS b [23, pp. 169], [22]. Thus, the parameter ρb which indicates the flow level behaviour in terms of fractional time can be referred to as time load or simply load, hereinafter [4], [11], [22]. For any BS, a successful transmission implies that the BS delivers the traffic to its respective UEs, i.e. ρb ∈ (0,1) for anyWork b ∈ B. Therefore, the effective transmission power Pb of BS b, when it uses a transmission power of . (3) A switched ON BS b consumes PbBase power to operate its radio frequency components and baseband unit in addition to the effective transmission power PbWork [24]. From an energy efficiency perspective, some BSs might have an incentive to switch OFF. This allows to reduce the power consumption of the baseband units by a fraction qb <1 by turning OFF the radio frequency components [25], [26]. However, during the OFF state, BSs need to sense the UEs in their vicinity and thus, have non-zero energy consumption ensured by qb >0 Let Ib be the transmission indicator of BS b specified Ib =oneindicates the ON state whereas Ib =zero reflects the OFF state. Thus, the power consumption of BS b can be given by:

(4) At time instant t, every BS b advertises its calculable load ρˆb(t) via a broadcast management message. Considering each, the received signal strength and the load at time t, UE at location x selects BS b(x,t), where x ∈ Lb(x,t), as follows: b(x,t) = argmax . Here, PbRx (t) = Pb(t)Ib(t)hb(x,t) is the received signal power at UE in location x from BS b at time t. Distance from nodes to base stations is found using d=sqrt((x2-x1)^2+(y2-y1)^2);

(5)

Problem Statement: Little cell arrangements and various leveled organizations with overlay full scale cells can possibly manual for a state where a considerable measure of cells are scarcely stacked. This is pertinent altogether to things inside which the movement stack shifts over the time.

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

Underneath high load circumstances, the chief determination could likewise be to give scope utilizing a few little cells, though in low load circumstances, the system administration may tidy up the cells with a couple of clients in them. A typical storywithin the remote building group is that rain and climate make millimeter-wave range pointless and wasteful for versatile interchanges. In any case, on the off chance that one takes into instructed the very truth that thought the way that today's cell sizes in urban situations are in the request of 200m, it turns out to be very clear that mm-wave cell innovation will so defeat these issues. II. WIRELESS SENSOR NETWORKS

A remote sensor system is a mix of hubs sorted out into a helpful system. Such frameworks will reform the approachthat we tend to live and work. As of now, remote sensor systems are starting to be conveyed at a quickened pace. A. Micro rest modes:

The BS suspends its transmission for the request of milliseconds. While in miniaturized scale rest mode the BSs are required to wake up very quickly. B. Profound rest modes:

The BS transmitters are tidy up for augmented times of your time while in profound sleepmode, some transmit circuits are totally turned off in the BS. This suggests wake up times are subsequently significantly more. To be absolutely ready to use the capability of the BS rest modes, good conventions should be created that may empower suspending the reference image transmission at low loads. Contingent upon the little cell method of operation, diverse criteria besides, similar to MT's area information, MT grouping, and so on., likewise are coordinated inside the get to (and awakening) choice calculations (Imran, Boccardi, and Ho 2011). C. Prepared state:

In which the greater part of the components of the little cell are absolutely working and the pilot channel transmissions are communicate in order to accomplish an unequivocal radio scope region. All permitted clients inside the remote scope zone are served by planning the radio assets on information channels. All activity is served by the imperatives set by the BS's greatest limit. D. Sleep state:

In which a few or the majority of the little cell equipment is either completely transitioned or they work in their comparing low power modes. The components to be transitioned rely on the specific equipment plan, additionally as on the vitality sparing algorithmic program utilized. The blend of calculations with the equipment conjointly delineates the time it takes to flip between the Ready state and Sleep state. III. PROPOSED METHODOLOGY

The proposed work fundamentally concentrate on an effective execution of advanced design for computerized flag processors to upgrade the execution. The majority of the operations performed with the assistance of formats can be executed inside the quickening agent module without meddling the processor. A format might be characterized as a specific equipment unit or a gathering of fastened unit. An information stream diagram (DFG) is a chart which speaks to an information conditions between various operations .In this productive layouts for DSP, for example, FFT and FIR channels are mapped into engineering as preparing component. In the mapped design Loop Back calculation is proposed to decrease the region, power and gives the improved engineering. The proposed design is appeared in Fig.1. A bunching approach that goes inside the far established area based measurements, the consequences of the time-shifting BS stack. Bunching empowers intra-group coordination among the base ÂŠ 2017, IJARIDEA All Rights Reserved

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

stations with the end goal of advancing the downlink execution through load adjusting. Inside each group, the BSs receive a shrewd rest wake instrument to lessen the vitality utilization. In light of between bunch impedance, the groups must be constrained to compete with each other to settle on choices on enhancing the vitality effectiveness by means of a dynamic decision of rest and dynamic states. We cast the issue of dynamic rest state determination as a non-agreeable amusement between groups of BSs. To determine this diversion, we propose a dispersed algorithmic program utilizing ideas from Gibbs-testing. A. Arrange Model:

Consider the downlink transmission of a remote system comprising of an arrangement of little cell base stations (SBSs) B = f1;: : ; Bgunderlaid on a full scale cell organize. We accept that the BSs are consistently appropriated over a two-dimensional system format and we let x be any area on the two-dimensional plane measured as for the inception. More-over, let Lb be the scope territory of BS b with the end goal that any given client gear (UE) at a given area x is served by BS b if x 2 Lb. Besides, we consider that all BSs are groupedinto an arrangement of bunches C = fC1;:: : ; CjCjg in which any group C 2 C comprises of an arrangement of BSs who can collaborate with oneanother. Take note of that jCj means the cardinality of the set C. Here, we expect that, inside a given bunch C 2 C, the BSs permit to proficiently offload the UEs among each other and empower rest mode while keeping up the UEs' nature of administration. Give Ib a chance to be the transmission marker of BS b with the end goal that Ib = 1 demonstrates the dynamic state while Ib = 0 mirrors the sit out of gear or rest state. Here, we accept that, in dynamic express, every BS willnonzero energy to detect the UEs in its region Array factor calculation AF(theta)=AF(theta)+ An*exp(j*n*2*pi*d*(cos(deg2rad(theta))-cos(theta_zero*pi/180))) ; Energy dissipated is calculated through, AF(theta)=AF(theta) + An*exp(j*n*2*pi*d*(cos(deg2rad(theta))-cos(theta zero*pi/180))) ; B. Cluster Formation And In cluster co-ordination:

For clustering, we map the system into a weighted graph G = (B; E) Here, the set of BSs B represents the nodes while E represents the links between BSs. The key step in clustering is to identify similarities between network elements (i.e. BSs) in which BSs with similar features grouped. This will allow to perform coordination between BSs with little signalling overhead. Here, we proposed number of techniques to calculate the similarities between BSs based on their locations and loads. C. Cluster formation:

Adjacency based neighbourhood and Guassian similarity Consider the graph G = (B; E). The set of edges E indicates to characterize the presence of a link between nodes b and b0 BS) b 2 B in the Euclidean space. The links between the nodes are weighted based on their similarities. Since nearby BSs are more likely to cooperate, we use the Gaussian similarity metric as a means to compute the weight between two nodes b; b0. D. Load based dissimilarities:

Clustering, load-based clustering provides a more dynamic manner of grouping neighbouring BSs in terms of traffic load. Therefore, the load based similarity between BSs b and b0 can be computed.

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

A Random variable is a real valued function defined over the sample space of a random experiment. The values of a random variables correspond to the outcomes of the random experiment. ... When X takes values in the given interval (a,b) it is continuous variable in that interval. Properties of probability distribution Let f(x) be a probability distribution. Then 1.f(x) ≥ 0, for all values of x. 2. Σ f(x) = 1. Calculating an Equilibrium Concentration: aA+bB⇌ cC+dD aA+bB⇌ cC+Dd Kc=[C]c[D]d/[A]a[B]b Mean deviation = Σ|x − µ|N Σ is Sigma, which means to sum up || (the vertical bars) mean absolute value basically to ignore minus signs x is each value (such as 3 or 16) µ is the mean (in our example µ = 9) N is the number of values (in our example N = 8) First Derivative formula and the slope is calculated by, Put in f(x+ ∆x) and f(x): Simplify x^2 and -x^2 cancel Simplify more (divide through by ∆x) And then as ∆x heads towards 0 we get. F. Inter and intra bunching:

Bunching BSs fills two principle needs: (i).reducing intra-group obstruction and (ii) productively offload UEs from BSs which need to rest to dynamic BSs. Impedance diminishment builds the BS limit in which BSs are competent to serve bigger number of UEs. A BS can turn OFF in the event that it doesn't serve UEs or it can offload the serving UEs to different BSs. Accordingly, BSs in a bunch have higher opportunity to change OFF or to bolster the BSs who should be turned OFF. Once the groups are framed, the vigorously stacked BS inside the bunch is chosen as the bunch head. The capacity of a bunch make a beeline for arrange the transmissions among the group individuals by allotting orthogonal asset hinders between them in the time-space. Thus, the whole heap of the bunch is dispersed between its individuals and orthogonal asset designation mitigates intra-group impedance. Because of actuality that the BSs inside a group can facilitate, the whole bunch can be viewed as a solitary super BS which serves all the UEs inside its region Permitting UE offloading among BSs inside the group empowers rest mode for BSs while guaranteeing the UE fulfillment. The offloading is completed with the goal of limiting the bunch stack. This decreases the quantity of UEs presented with low rates. Give MC a chance to be the arrangement of UEs related with the arrangement of BSs in bunch C. Give zbm a chance to be the pointer which characterizes the availability between UE m 2 MC and BS b 2 C, i.e. zbm = 1 if UE m served by BS b and, zbm = 0 generally. Therefore, for a given arrangement of BS transmission controls, the casual issue of blended whole number straight program (MILP) G. ON/OFF Strategy

Given the arrangement of the groups, our next objective is to propose a self-sorting out answer for (5a)- (5f) in which each bunch of BSs separately changes its transmission parameters in view of neighborhood data. To do as such, we utilize a lament based learning approach [8], in which the proposed arrangement comprises of two interrelated parts: client affiliation and bunch savvy BS transmission enhancement. © 2017, IJARIDEA All Rights Reserved

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

H. Algorithm:

Algorithm 1 Dynamic Clustering and BS Switching ON/OFF 1: Input: ^uC(t); ^rC(t) and _C(t) for t = 0 and 8C 2 C, and ^_(t) 2: while true do 3: t ! t + 1 Intra-cluster operations: 4: Action selection: aCi (t) = f__Ci (t ďż˝ 1)_, (23) 5: Load advertising ^_(t) and anchor BS selection: (5) 6: Per cluster UE scheduling: (17)-(19) Inter-cluster operations: 7: Calculations: _Ci (t); _Ci_(t)_; uCi_(t)_ 8: Update utility and regret estimations, and probability: 9: ^uCi (t + 1); ^rCi (t + 1); _Ci (t + 1), (22) 10: Update load estimations: (6) 11: if t N 2 Z+ then 12: Update clusters C. 13: end if 14: end while I. Load-Based User Association:

At the point when more number of clients are towards a specific construct station the heap in light of that base station increments there by building up more number of interlinks. For this situation on and of methodology is built up where the base station which has more load quits accepting further correspondence and the closest base station begins getting the occupied activity. J. Model and issue definition:

At the point when the client enters the SCN or a client does not fulfill with respect to the administration from at present serving base station, it movements to another competitor base station who can guarantee the nature of the administration. This may prompt over-burdening BSs and lower phantom efficiencies. In this manner, a more intelligent system in which the BSs promote their heap to all client gear inside their scope zone is alluring. Under typical recurrence task conditions, the obtaining channel task utilizes the settled channel task calculation, however in the event that no radio channels stay accessible, it will get channels from its neighboring cells. Little cells offload remote activity from BSs by overlaying a layer of little cell APs, which really diminishes the normal separation between the transmitters and clients, in this manner bringing about lower proliferation. Instability in measured parameters-These imperatives are because of hub glitch, gathering/sending inaccurate information, coveted information getting blended with clamor and hub situation. K. Utilizations of remote systems:

Remote systems encourage the observing and controlling of physical conditions from remote areas, with upgraded exactness. They have applications in an assortment of fields, for example, natural checking, military purposes and assembling of detecting data in unfriendly areas. Thick systems for condition detecting and information gathering. Sensors are outfitted with both information preparing and correspondence capacities the prime favorable position ÂŠ 2017, IJARIDEA All Rights Reserved

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

of sensors is their ability to work unattended in cruel conditions. Remote sensor systems are generally utilized as a part of modern, restorative, purchaser and military applications.

Fig.1. Clustered covered region

They are referred to as “learning with k-mean clustering “learning with spectral clustering”, and “learning with P2PBSclustering” hereinafter, respectively. For all these threeclustering methods, the clusters remain unchanged for an interval of N = 100. L. Created network:

Fig.2. Created network

Forour simulations, we consider a single macrocellunderlaidwith an arbitrary number of SBSs and UEs uniformlydistributed over the area. All the BSs share the entire spectrumand thus, suffer from co-channel interference.

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

Fig.3. Energy harvested cluster selection

Conventional network operation referred to hereinafter as“classical approach” in which BSs always transmit. For further comparisons, we consider a random BS ON/OFF switching with equal probability and finally an uncoordinated learning based ON/OFF mechanism without forming clusters. These are referred to as “random ON/OFF” and “learning without clusters”.

Fig.4. Free Space Vs Attenuation

As the distance from source increases attenuation gets raised and power vs distance graph indicates that as the distance is increased the amount of received power gets reduced.This condition can be achieved with dynamic clustering where as in case of static we couldn’t achieve this condition. [16] proposed a principle in which another NN yield input control law was created for an under incited quad rotor UAV which uses the regular limitations of the under incited framework to create virtual control contributions to ensure the UAV tracks a craved direction. Utilizing the versatile back venturing method, every one of the six DOF are © 2017, IJARIDEA All Rights Reserved

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

effectively followed utilizing just four control inputs while within the sight of un demonstrated flow and limited unsettling influences.

Fig.5. Distributed learning analysis

The proposed learning approaches with clustering yield larger number of BSs consuming less energyclustering allows to better offload traffic and, subsequently, improve overall efficiency.

Fig.6. Energy efficiency comparison

Moreover, for highly-loaded networks, the spectral clustering approach reduces the cost compared to all three clustering mechanisms. Although P2P-SB clustering is a decentralized clustering method, the cost reduction of learning with P2P-SB is higher than the centralized k-mean clustering for highly loaded networks. ÂŠ 2017, IJARIDEA All Rights Reserved

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

Fig.7. Cumulative function

The CDF of the BSs’ load for 10 SBSs and 50 UEs. Here, the random ON/OFF method exhibits a large number of BSs with low load. Similar to the CDF of BSs’ energy consumption, Fig. shows that the proposed learning and the dynamic clustering yield a higher number of switched-OFF BSs.

Fig.8. Performance of node corresponding to area covered

Fig.8. shows that the changes in _ have the same effects on both systems with 30 UEs and 60 UEs. It canbe noted that the benefit of clustering to reduce the average cost rincreases with the number of UEs compared to learning without IV. CONCLUSION

A dynamic group in view of/OFF instrument for little cell base stations. Bunching enables grouped base stations to facilitate their transmission while the groups contend with each other to decrease a for every bunch in view of their vitality utilization and time stack because of © 2017, IJARIDEA All Rights Reserved

S.Praveen Kumar et al., International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA] Vol.2, Issue 2,27 April 2017, pg. 67-77

their movement. To beat this, we have proposed a dispersed calculation and an intra-group coordination strategy utilizing which base stations pick their transmission modes with least overhead. Our proposed bunching strategy utilizes data on both the areas of BSs and their capacity of dealing with the movement and powerfully shape the groups so as to enhance the general execution. For bunching, both incorporated and decentralized grouping systems are presented and the exhibitions are contrasted and load based and remove based methodologies which are static to that of dynamic grouping approach. REFERENCES [1]ComScore, “Mobile future in focus 2013,” Tech. Rep., Feb. 2013. [Online]. Available: http://www.comscore.com/Insights/Presentations and Whitepapers/2013/2013 Mobile Future in Focus [2] M. W. Arshad, A. Vastberg, and T. Edler, “Energy efficiency improvement through pico base stations for a green field operator,” in Proc. IEEE Wireless Communications and Networking Conf. (WCNC), Paris, France, Apr. 2012, pp. 2197–2202. [3] P. Gonzalez-Brevis, J. Gondzio, Y. Fan, H. V. Poor, J. Thompson, I. Krikidis, and P.-J. Chung, “Base station location optimization for minimal energy consumption in wireless networks,” in Proc. IEEE Vehicular Technology Conf. (VTC), Budapest, Hungary, May 2011, pp. 1–5. [4] K. Son, H. Kim, Y. Yi, and B. Krishnamachari, “Base station operation and user association mechanisms for energy-delay tradeoffs in green cellular networks,” IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1525–1536, Sep. 2011. [5] Y. Q. Bian and D. Rao, “Small Cells Big Opportunities,” Huawei, Tech. Rep., Feb. 2014. [Online]. Available: www.huawei.com/ilink/en/ download/HW 330984 [6] B. Rengarajan and G. de Veciana, “Practical adaptive user association policies for wireless systems with dynamic interference,” IEEE/ACM Trans. Netw., vol. 19, no. 6, pp. 1690–1703, Dec. 2011. [7] S. Zhou, A. J. Goldsmith, and Z. Niu, “On optimal relay placement and sleep control to improve energy efficiency in cellular networks,” in Proc. IEEE Intl. Conf. on Communications (ICC), Kyoto, Japan, 2011, pp. 1–6. [8] S. Bhaumik, G. J. Narlikar, S. Chattopadhyay, and S. Kanugovi, “Breathe to stay cool: adjusting cell sizes to reduce energy consumption,” in Proc. ACM Special Interest Group on Data Communications on Green Netw. (SIGCOMM), New Delhi, India, 2010, pp. 41 – 46. [9] H. C¸elebi, N. Maxemchuk, Y. Li, and I. Guvenc¸,¨ “Energy reduction in small cell networks by a random on/off strategy,” in Proc. IEEE Global Communications Conf. (GLOBECOM) Workshop, Atlanta, GA, USA, Dec. 2013. [10] Y. S. Soh, T. Q. Quek, and M. Kountouris, “Dynamic sleep mode strategies in energy efficient cellular networks,” in Proc. IEEE Intl. Conf. on Communications (ICC), Budapest, Hungary, Jun. 2013, pp. 3131–3136. [11] H. Kim, H. Y. Kim, Y. Cho, and S.-H. Lee, “Spectrum breathing and cell load balancing for self organizing wireless networks,” in Proc. IEEE Intl. Conf. on Communications (ICC) Workshop, Budapest, Hungary, Jun. 2013, pp. 1139–1144. [12] L. Rokach and O. Maimon, Data Mining and Knowledge Discovery Handbook. Springer US, 2005, ch. Clustering Methods, pp. 321–352. [13] U. von Luxburg, “A tutorial on spectral clustering,” CoRR, vol. abs/0711.0189, 2007. [Online]. Available: http://arxiv.org/abs/0711.0189 [14] R. Jacob, S. Ritscher, C. Scheideler, and S. Schmid, Algorithms and Computation. Springer Berlin Heidelberg, 2009, ch. A Self-stabilizing and Local Delaunay Graph Construction, pp. 771–780. [15] E. Ogston, B. Overeinder, M. van Steen, and F. Brazier, “A method for decentralized clustering in large multi-agent systems,” in Proc. Intl. Joint Conf. on Autonomous Agents and Multiagent Systems (AAMAS), Melbourne, Australia, 2003, pp. 789–796. [16] Christo Ananth,"A NOVEL NN OUTPUT FEEDBACK CONTROL LAW FOR QUAD ROTOR UAV",International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications[IJARIDEA],Volume 2,Issue 1,February 2017,pp:18-26. [12] P.H. Petersen, “Resistance to High Temperature”, A.S.T.M. Special Technical Publication, No. 169-A; pp. 290 ff. [17] W. Khaliq, “Performance characterization of high performance concretes under fire conditions (Ph.D. thesis), Michigan State University, 2012.