QoS and Power Consumption Analysis OFCooperative Multicast Scheme with Cell Zooming

QoS and Power Consumption Analysis of Cooperative Multicast Scheme with Cell ZoomingKyeongMin Lee1, Joohyung Lee2 , GwangHui Park3 and Jun Kyun Choi4 1-4 Department of Electrical Engineering, KAIST Deajeon, Republic of Korea 1-3 {kmlee0922,joohyung08,detectiveks}@kaist.ac.kr 4 jkchoi59@Abstract — As the demand for high-quality multimedia service over wireless networks has increased wireless multicast communication has been researched for many years in efforts to achieve high throughput. Recently, cooperative multicast scheduling scheme was proposed with the goal of achieving high throughput with good fairness. However, although much attention has been directed towards energy efficient communication, energy efficient cooperative multicast design has not received much consideration. Part of the reason for this is that significant relay power consumption is required for user cooperation. In this paper, we model Quality of Service (QoS) of multicast in terms of outage probability when the cooperative multicast scheme is adopted. From the model, we obtain analytical results for QoS and power consumption under a varying number of users and cell coverage of the Base Station (BS). Finally, the analytical models can be used to find the op timal BS's transmission p ower by p redicting system p erformance if system configurations are given. This can help system deployment and system optimization. Keywords- cooperative communication, multicast, QoS, power consumption, cell coveragecommunication multicast with selective relay is the only published work on this aspect [3]. As one solution for green cellular networks, cell zooming was proposed by adjusting cell coverage according to varying traffic load[4]. Here, cell coverage of the BS is an important factor, because it directly affects the BS’s transmission power. In this paper, we analyze a conventional cooperative multicast scheme that employs selective relay and cell zooming. In this model, the number of users in a multicast group and adjustable cell coverage is an important parameter that affects QoS and power consumption. For instance, increasing the number of users is effective in expanding cell coverage through cooperative transmission. It leads to reduced BS transmission power by maintaining the same QoS satisfaction. However, it also causes additional relay power consumption for user cooperation. For this reason we concentrated on this analysis to provide intuition of QoS and power performance when a cooperative multicast is adopted. This paper describes the relationship between the ratio of guaranteeing QoS and power consumption under changing cell coverage and total number of MSs in the multicast group. From this study, the analytical models can be used to find the optimal BS transmission power by predicting system performance if system configurations are given. This can help system deployment and system optimization. The remainder of this paper is organized as follows. Section II introduces the system model. Section III describes the solution of mathematical problems related to the system. Section IV presents the analysis equations for a WiMAX system together with numerical results. Section V concludes the paper. II. SYSTEM MODELI.INTRODUCTIONThe demand for high-quality multimedia services has increased as well as real-time multimedia data such as VoIP (Voice over IP). It is necessary to guarantee Quality of Service (QoS) for successful transfer. In addition, band efficiency should be maximized in order to provide a certain level of service at reasonable prices. The multicast transmission can reduce bandwidth consumption by transmitting the same data to a number of users in a multicast group simultaneously. However, because there is serious bandwidth inefficiency for supporting the worst channel user of the multicast group, the cooperative multicast scheme is considered a promising solution that alleviates this problem with good fairness. Based on a cooperative multicast, it makes it possible to achieve extension of coverage from relay of Mobile Stations (MSs) and can provide more users with guaranteed QoS [1], [2]. However, there has been a lack of consideration of energy efficient cooperative multicast design, despite that it solves the problems of both throughput and fairness. However, significant relay power consumption is required for user cooperation. To the best of our knowledge, our preliminary work regarding an energy efficient cooperativeIn this section, we introduce our system model in a WiMAX system. Fig.1 describes the system model with a cooperative multicast scheme. In this case, R0 is the maximum transmission distance of the BS with limited power. Here, the determined transmission power’s coverage R of the BS is changeable within R0. When the BS transmitted the data, white MSs that are in the cell coverage R receive the data without error (this means that the MS satisfies QoS). Then, if there are black MSs that are not able to receive data from the BS due to a poor channel condition in their coverage r0, they relay the978-1-4673-4728-0/12/$31.00 ©2012 IEEE238APCC 2012A. Path loss model In order to analyze and ev path loss model to obtain distance. In our scheme we shadow fading. The decibel path loss equa of distance [6].TABLE INOTATIONSR0 R r0 n Pbase PmobileMaximum radius of BS Radius of BS Radius of MS The number of MS Transmission power of BS Transmission power of MSTotal power consumption Ptotal is given as the summation of the BS transmission power and the power consumed by all MSs that participated in relay transmission. This can be written asPtotal = Pbase + E[relay ] ´ Pmobile(11)In phase II, MSs that received a lower threshold SNR in phase 1 can receive data with a sufficient SNR from the nearest MS that is transferred completely in phase I. We define Prrelay as the ratio of additional QoS satisfied users in phase 2. Prior to solving Prrelay, however, we must find A(R,r,r0) in Figure 2, as delineated in the following equation [7].A( R, r , r0 ) = p R 2 ´ 2q1 2q + p r0 2 ´ 2 - rR sin q1 2p 2p = R 2q1 + r0 2q 2 - rR sin q1-1Pbase is the transmission power of the base station varying with radius R and Pmobile is a fixed value of the mobile station’s transmission power consumption. To evaluate Ptotal, we must find the value of E[relay] . It is the predicted number of mobile stations that are transmitting data to another nearby mobile station that cannot receive data in phase I. It is affected by variation of the radius R and the total number of mobile stations, n. In order to evaluate E[relay] , we evaluate B(R,r,r0) in Figure 2 as follows:B( R, r , r0 ) = p r0 2 ´ 2f2 2f - p R 2 ´ 1 + rR sin f1 2p 2p = r0 2f2 - R 2f1 + rR sin f12 2 2(7)(12)R 2 + r 2 - r0 2 R sin q1 where, q1 = cos ( ), q 2 = sin -1 ( ) 2 Rr r0When a MS user is far from the BS with distance r (r>R), QoS cannot be satisfied with the receiving data in phase I. Hence, if it wants to receive data from the relay transmission, there must be one or more MS that have already received data in phase I located in the vicinity. Hence, PrA is the probability of finding a relay node when the user is located in r(r>R) and is equal to the probability of there existing one or more mobile users in area A(R,r,r0). We define PrA asæ A( R, r , r0 ) ö PrA = 1 - ç1 ÷ p R0 2 ø èn -1R + r - r0 ) 2 Rr R sin f1 i ) r < R 2 - r0 2 , f2 = sin -1 ( ) r0 where, f1 = cos -1 ( ii ) otherwise;f2 = p - sin -1 (R sin f1 ) r0(8)The predicted number of participating relay MSs can be obtained by multiplying n (total number of MSs) by the pdf of MSs engaged in cooperative communication. From equations (5) and (12), the predicted number of MSs that transfer data for cooperative communication is obtained as follows:Accordingly, PrRelay is the probability not only that a MS exists within the radius r (r>R), but also that it received data from other MSs in phase 2 as follows:PrRelay = ò (Prr ´ PrA )drR R0n -1 é R 2r ì ï æ B( R, r , r0 ) ö ü ï ù E[relay ] = n ´ ê ò 1 1 (13) í ç ÷ ý dr ú 2 2 p R0 ø ï ú ê R-r0 R0 ï î è þ û ëTABLE Ⅱ SYSTEM PARAMETERS=R0òRn -1 2r ì A( R, r , r0 ) ö ü ï æ ï 1 1 í ý dr ç ÷ R0 2 ï è p R0 2 ø ï î þ(9)From equations (6) and (9), PrQoS is the ratio of users who are satisfied with QoS among all users. It can be obtained by the summation of the ratio of users who had guaranteed QoS in phase I and phase II as followsPrQoS = PrBase + PrRelay =R0 2r ì R2 ï æ A( R, r , r0 ) ö + ò 2 í1 - ç1 ÷ 2 R0 R R0 ï è p R0 2 ø î n -1Parameter Frequency Temperature Bandwidth Data rate BS transmission power MS transmission power Path loss exponentvalue 3.5Ghz 300K 10Mhz 300kbps 43dBm 13.3dBm 4IV.PERFORMANCE ANALYSISü ï ý dr ï þ(10)C. Proposed power consumption analysis with varying BS coverageIn this section, we present the numerical results of the proposed analysis in a WiMAX system. We consider streaming service of User Created Content (UCC) at an average speed of 300kbps. Detailed parameters are given in table II. According to the detailed parameters, we set R0 and r0 as 1km and 200m by using equation (4). The detailed calculation is as follows:240Pt = PL( Pathloss ) - PN0 + PR = 40log R - 76.7 (dBm)(4) (14)Power Consum ption dBm 45 40 35 30 25 20 0.0 BS N 10 N 100 N 1000Where, Pt is the transmission power. In this case, we consider a flat-earth model for the path loss model ( g = 4 ). In the same manner, we can extract the value of Pbase according to R. For guaranteeing the QoS, the receiving power must be greater than or equal to -88dBm for supporting the 300kbps streaming service, from equations (2) and (3). However, according to the mobile WiMAX standard, the sensitivity of a MT is around -82dBm [8]. Thus, the threshold value of the receiving power must be greater than or equal to -80dBm in order to guarantee QoS. A. Numerical results of QoS with varying number of users in a multicast groupOutage Probability 1.0 0.8 0.6 0.4 0.2 0.0 n 10000.20.40.60.81.0Fig. 4 Power consumption with varying the cell coverage with different nR 300 m R 500 m R 700 mConsequently, an increase of n results in large power consumption because more MSs must use power to transfer data. However, this does not necessarily mean that increasing n is a waste of power. Through the transmission of data by a MS in phase II, more and more mobile users can receive data with satisfactory QoS. Further analysis is needed to verify this. C. Relationship between QoS and total power consumptionC onsum ption dB m 4540020040060080035B S N 10 100 1000Fig. 3 Outage probability with varying the n with different R30N NOutage probability is ratio of users who cannot receive data during the whole transmission time. Outage probability = 1 – Pr{user with guaranteed QoS} As we can see from Fig.3 above, as the numbers of users becomes larger, the slope of outage probability approaches zero. Hence, if n is sufficiently large, the outage probability will be certain value. When n is small, the effect of cell coverage extension caused by cooperative communication is large. However, as n grows, the effect is diminished, since expanded cell coverage becomes overlapped. As a result, cell coverage will be expanded as much as r0=200m. In Fig.3 R = 300m, 500m, and 700m, and expanded radius of cell coverage will be 500m, 700m, and 900m if n is sufficiently large. As a result, the outage probability will be 0.75, 0.51, and 0.19. B. Numerical results of power analysis with varying cell coverage The following step is an analysis of power consumption. In our model, transmission power is a very important factor for performance evaluation. As can be seen in Fig. 4, we can determine the relationship cell coverage of the BS by summating the power of the BS and MSs.2520 0.00.20.40.60.81.0Fig. 5 Power consumption with QoS level with different nIn Fig. 5 we analyze the power consumption with the ratio of QoS satisfaction level. The key observation form Fig.4 is that even if n is increasing, power consumption does not necessarily rise. Fig. 5 shows that the case where n is 10 and 100 are more efficient as compared with transmission by only the base station. However, a n value of 1000 is too large for expanded cell coverage to keep pace with increasing power consumption, and thus shows poor power efficiency. From this result, the power consumption is reduced in a particular range of n, and we will find this value next. In addition, we can see from Figs. 4 and 5 that when R or the ratio of satisfied QoS is very small or large, the power consumption is similar to case of using only the BS. The reason for this is that when the radius of the BS is small, the probability of users being inside the cell coverage is also very small, and thus only a small amount of MSs transfer data to other MSs. Meanwhile, in the case where R is very large, few MSs are transmitting, as241most users already received data in phase I. The cause of this situation is that our model assumes there are no users outside of R0 coverage. D. Total power consumption within the selected QoSTotal Power Consum ption dBm 44 42 40 38 36 34 32 30 0 200 400 600 800 QoS 0.5 BS Power BS n 1000 M S of M Sthe case of large R. Specifically, as R becomes larger, the power consumption is increased substantially along with an increasing amount of cell coverage. V. CONCLUSIONFig. 6 Power consumption with varying R and n at 50% QoSTotal Power Consum ption dBm 44 42 40 38 36 34 32 30 0 200 400 600 800 QoS 0.8 BS Power BS n 1000 M S of M SThis paper has analyzed the satisfaction of QoS and power consumption constraints in a relay selective cooperative multicast scheme with cell zooming under a varying number of multicast group users and cell coverage of the BS. We obtained analytical results for QoS and power consumption. From the results, if the system configurations are given, we can find the optimal point of power consumption and predict system performance. This will help made decisions on whether the system should operate cooperative communication based on consideration of the users of the multicast group and the assigned BS coverage. From our study, if there is a moderate number of multicast group users, cooperative communication is a good solution to the recent communication problems of power consumption and efficient use of bandwidth. Our future work will study this scheme in greater detail. In order to prevent wasted relay transmission power, a more effective relay selection scheme will proposed. Furthermore, other detailed parameters such as antenna gain and height and shadow fading will be considered. ACKNOWLEDGMENT This work was supported by the IT R&D program of MKE/KEIT. [KI001822, Research on Ubiquitous Mobility Management Methods for Higher Service Availability] and [10039160, Research on Core Technologies for SelfManagement of Energy Consumption in Wired and Wireless Networks] REFERENCES[1] J Jakllari, G. Krishnamurthy, S. V. Faloutsos, M. Krishnamurthy, P. V. Ercetin,O. "A Framework for Distributed Spatio-Temporal Communications in Mobile Ad Hoc Networks” in Proc. Of IEEE INFOCOM 2006. Scaglione, A. Yao-Win Hong “Opportunistic large arrays: cooperative transmission in wireless multihop ad hoc networks to reach far distances” IEEE Transactions on Signal Processing.2003 Volume:51. Page:120-124 Lee, J. 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Nix, A “Mobile WiMAX system performance simulated versus experimental results” in Proc. OF IEEE PIMRC 2008Fig. 7 Power consumption with varying R and n at 80% QoSFrom Fig. 6 and Fig. 7 above we can determine values of n that provide a better QoS satisfaction level with lower power consumption. We wish to know what value of n or R provides the most efficient power consumption with the same level of QoS satisfaction. Fig. 6 and Fig. 7 target guarantee QoS satisfaction of 50% and 80% of users. Both figures have a point of optimal energy consumption. In Fig. 6 there is no cooperative communication, and R is approximately 707m. At the optimal point, where less than 4dBm of power is consumed, n is around 120 and the adjusted R is 600m. This means that until this optimal point the effect of cell coverage extension is fine. Afterwards, its efficiency is decreased. If n exceeds 650, the effect of cell coverage extension is worse than only BS transmission due to wasted power caused by too much cooperative transmission. In Fig. 7, the optimal point of n is at around 170 and R is 770m. In addition, the efficiency of cell coverage extension becomes poor from this point. However, in this case, cooperative communication is still useful even though n is 1000. As a result of this analysis, our cooperative multicast scheme is shown to be more effective in[2][3] [4] [5][6][7] [8]242。