QoS-Aware Spectrum Sharing in Cognitive Wireless Networks

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "QoS-Aware Spectrum Sharing in Cognitive Wireless Networks"

Transcription

1 QoS-Aware Spectrum Sharng n Cogntve reless Networks Long Le and Ekram Hossan Abstract e consder QoS-aware spectrum sharng n cogntve wreless networks where secondary users are allowed to access the spectrum owned by a prmary network provder. The nterference from secondary users to prmary users s constraned to be below the tolerable lmt. Also, sgnal to nterference plus nose rato (SINR) of each secondary user s mantaned hgher than a desred level for QoS nsurance. hen network load s hgh, admsson control needs to be performed to satsfy both QoS and nterference constrants. e propose an admsson control algorthm whch s performed jontly wth power control such that QoS requrements of all admtted secondary users are satsfed whle keepng the nterference to prmary users below the tolerable lmt. hen all secondary users can be supported at mnmum rates, we allow them to ncrease ther transmsson rates and share the spectrum n a far manner. e formulate the jont power/rate allocaton wth max-mn farness crteron as an optmzaton problem. e show how to transform t nto a convex optmzaton problem so that ts globally optmal soluton can be obtaned. Numercal results show that the proposed admsson control algorthm acheves performance very close to the optmal soluton. Also, mpacts of dfferent system and QoS parameters on the network performance are nvestgated for both admsson control and rate/power allocaton problems. I. INTRODUCTION Implementaton of emergng hgh-speed wreless applcatons requres exponental growth n spectrum demand. However, t has been reported that current utlzaton of allocated spectrum can be as low as 15 % [1]. Thus, there s an ncreasng nterest n developng effcent method for spectrum management and sharng whch s encouraged by both ndustry and FCC authorty [2]. Ths motvates to explot the spectrum opportuntes n space, tme, frequency whle protectng users of the prmary network owner from excessve nterference due to opportunstc spectrum access [3]. In fact, t s requred that an nterference lmt correspondng to an nterference temperature level be mantaned at the recevng ponts of the prmary network. The key challenge n cogntve rado networks s how to construct spectrum access/sharng schemes such that users of the prmary network (wll be called prmary users n the sequel) are protected from excessve nterference due to secondary spectrum access and QoS performance of secondary users are guaranteed. A graph-theoretc model for spectrum sharng/access among secondary users was proposed n [4] where dfferent objectve functons were nvestgated. The formulaton of channel allocaton problem usng game theory was proposed n [5]. In ths work, the proposed utlty functons capture the nterference perceved by one user on each channel and/or the nter- Long Le and Ekram Hossan are wth the Department of Electrcal and Computer Engneerng, Unversty of Mantoba, nnpeg, MB, Canada. E-mal: {long, ference ths user creates for neghborng ones. However, prmary users were not explctly protected from nterference due to spectrum access of secondary users. In [6], heurstc-based channel and power allocaton algorthm was proposed where nterference constrant for prmary users was consdered. In ths paper, we present a spectrum sharng framework for cogntve CDMA wreless networks wth explct nterference protecton for prmary users and QoS constrants for secondary users. Secondary users have mnmum transmsson rates wth requred QoS performance n terms of SINR (or equvalent bt error rate (BER)) and maxmum power constrants. hen the network load s hgh, an admsson control algorthm s proposed to guarantee QoS constrants for secondary users and nterference constrants for prmary users. hen all secondary users can be supported, we present a jont rate and power allocaton soluton wth QoS and nterference constrants. The rest of ths paper s organzed as follows. System models and problem defnton are presented n Secton II. Admsson control algorthm for spectrum access of secondary users s proposed n Secton III. The power/rate allocaton formulaton s presented n Secton IV. Numercal results are presented n Secton V. Secton VI concludes the paper. II. SYSTEM MODELS AND PROBLEM DEFINITION e consder the spectrum sharng problem among unlcensed users (secondary users) and lcensed users (prmary users). The problem consdered n ths paper apples to both centralzed networks (e.g., cellular networks) and dstrbuted networks (e.g., ad hoc and sensor networks). For ease of presentaton, we wll dscuss dfferent problem aspects n the context of centralzed settng most of the tme. e assume that there are a number of prmary and secondary users communcatng wth ther partners smultaneously. Here, the term user wll be used broadly where t can be a moble node or base staton/access pont n centralzed networks or smply a moble node n ad hoc networks. The CDMA technology wll be assumed although the model can be extended for other technologes as well. Smultaneous communcatons among users (.e., both prmary and secondary users) wll nterfere wth each other. The enttes we wll work wth are communcaton lnks each of whch s a par of users communcatng wth each other. e wll refer to communcaton lnks belongng to secondary networks as secondary lnks. e wll also consder the nterference constrants at the recevng nodes of prmary networks whch wll be referred to as prmary recevng ponts n the sequel. e assume that each prmary recevng pont can tolerate a maxmum nterference level. Also, secondary lnks have desred QoS performance n terms of BER. Fg. 1 llustrates the transmsson settng consdered n ths paper. One possble example for the 3563

2 where total nterference at the prmary recevng pont j should be smaller the tolerable lmt. e wll assume that transmsson rate of secondary lnk can be adjusted n an allowable range wth mnmum and maxmum values are R mn and R max, respectvely. Also, power of secondary lnk s constraned to be smaller than the maxmum lmt P max. Fg. 1. Secondary users Prmary users Communcaton lnk Interference to prmary user Spectrum sharng among prmary and secondary users. nvestgated network settng s where prmary users communcate wth ther BS n the uplnk drecton n a cellular network and secondary users communcate wth each other n an ad hoc mode. Here, the total nterference that secondary lnks create at each BS of the prmary network should be smaller that the tolerable level. A. QoS and Interference Constrant Modelng Assume that there are M prmary recevng ponts and N secondary communcaton lnks n the consdered geographcal area. Let us denote the channel gan from the transmttng node of secondary lnk to recevng node of secondary lnk j by j, whle the channel gan from the transmttng node of secondary lnk to prmary recevng pont j as j,. If N denotes the total nose and nterference at the recevng sde of secondary lnk, for wreless access based on CDMA, the correspondng effectve bt-energy-to-nose spectral densty rato can be wrtten as [9] µ = R P N j=1,j = g(s) P j + N (1) where s the spectrum bandwdth, R s the transmsson rate of secondary lnk. Here, /R s the processng gan whch s usually requred to be larger than a partcular value. The processng gan s smply equal to one for other multple access technologes such as FDMA and µ denotes the SINR. In the sequel, we wll abuse a bt by referrng µ as SINR n all cases. Now, f a partcular modulaton scheme s employed, there wll be an explct relaton between BER and SINR. Thus, for a specfc requred BER level of secondary lnk, µ s requred to be larger than a correspondng value γ. Hence, the QoS requrement for secondary lnk can be expressed as µ γ, =1, 2,,N. (2) Now, let T j be the maxmum nterference level tolerable by prmary recevng pont j. The nterference constrant for prmary recevng pont j can be wrtten as =1 j, P T j, j =1, 2,,M (3) B. Admsson Control Problem Here, we are nterested n the scenaro where a number of secondary lnks wsh to access the spectrum wth mnmum transmsson rate (.e., R = R mn ) and both the QoS requrements (n (2)) as well as the nterference constrants (n (3)) need to be satsfed. The problem s how to choose the subset of requestng lnks wth maxmum sze such that the constrants n (2) and (3) are both satsfed. C. Jont Rate and Power Allocaton Problem hen the network load s low, all requestng secondary lnks wth mnmum transmsson rates can be supported whle satsfyng both QoS and nterference constrants n (2), (3). If t s the case, secondary lnks would ncrease ther transmsson rates above the mnmum values and share the spectrum n a far manner. For farness ssue, we adopt the max-mn crteron whch ams to maxmze the transmsson rate of the secondary lnk wth a mnmum transmsson rate. e wll arrange power, rate and other quanttes of all secondary lnks nto the correspondng vectors for notatonal convenence. For example, P wll denote a column vector whose element P s the transmsson power of secondary lnk. The jont rate and power allocaton problem can be stated as maxmze {mn R } R mn R R max (4) P P max, and the constrants n (2), (3). e wll show how to solve both admsson control problem as well as jont rate and power allocaton problem n the followng sectons. III. ADMISSION CONTROL ALGORITHM As has been mentoned n Secton II, we wll consder the admsson control problem when the network load s hgh and all secondary lnks transmt wth ther mnmum rate (f admtted). Now, usng equaton (1), we can rewrte the QoS constrant n (2) as follows: P j=1,j = γ R mn P j + γr mn N, =1, 2,,N. (5) The constrants for all secondary lnks can be wrtten n the matrx form as follows: (I F )P u (6) where I s an dentty matrx of order N N, u s a column vector whch can be wrtten as ( ) γ 1 R1 mn N 1 u = g, γ2r 2 mn N 2 (s) 1,1 g,, γnr N mn N N (s) 2,2 N,N 3564

3 where (.) denotes the matrx/vector transpose. And F s an N N matrx whose (, j)-th element s γ R mn F =, f j. 0, f = j A. Constraned Power Control Recall that we are nterested n the scenaro where not all N secondary lnks can be admtted nto the network whle satsfyng both QoS and nterference constrants stated n (2), (3). e wll frst focus on the power allocaton problem under maxmum power constrant (.e., P P max ) and QoS constrants and gnore the nterference constrants for a whle. In [8], the authors proposed an effcent teratve power control algorthm whch can be mplemented dstrbutvely. Specfcally, let P (t) and P (t+ t) be the power levels of secondary lnk after two consecutve power updates at tme nstants t and t+ t, respectvely. The power of secondary lnk s updated as follows: { P (t + t) =mn P max,p (t) γ } (7) µ (t) where µ (t) s the nstantaneous SINR at the recevng sde of secondary lnk at tme nstant t whch can be wrtten as µ (t) = P (t) R N j=1,j = g(s) P j(t)+n It was shown n [8] that ths power control algorthm converges to the fxed pont soluton of P = mn {P max,fp + u} (8) whch wll be referred to as statonary power vector. Let Ω be the set of secondary lnks and P Ω be the statonary power vector when the power algorthm wth the rule as n (7) s run wth secondary lnk set Ω. From the results of [7], we have the followng facts: Fact 1: If all secondary lnks n Ω can be supported (.e., the power control algorthm n (7) results n a statonary power vector P Ω satsfyng QoS constrants n (2)), the QoS constrants wll be satsfed wth equalty. Fact 2: If a subset Ω 0 Ω s the set of secondary lnks whch are not supported wth statonary power vector P Ω, then P Ω = P max for Ω 0. Now, let us defne the followng nterference measures : α (P Ω ) = P Ω j, + N g(s) P Ω (9) γ R j=1,j = β (P Ω ) = j=1,j = P Ω j + N g(s) γ R P Ω (10) D Ω (P Ω ) = Ω β (P Ω ). (11) e can easly see that D Ω (P Ω )= β (P Ω )= α (P Ω ). (12) Ω Ω e can also see that f the QoS constrant for secondary lnk s satsfed wth equalty, then β (P Ω )=0. Also, D Ω (P Ω )=0 f and only f all secondary lnks n Ω are supported. In general, we have β (P Ω ) 0 and the value of β (P Ω ) reflects the degree n whch the QoS constrant for secondary lnk s volated. Also, t s ntutve that α (P Ω ) quantfes the aggregate relatve nterference that secondary lnk creates for other lnks n Ω. In [7], the authors proposed several removal algorthms whch am at maxmzng the number of lnks whch can be admtted nto the network whle satsfyng the QoS requrements. Among these proposed algorthms, SMIRA and SMART(R) are the two most effcent ones. e have observed through smulaton that these two algorthms acheve very close performance; therefore, we wll only descrbe the SMIRA algorthm here. Note that the nterference measures defned n (9)-(11) are not the same wth those n [7]. However, the sprt of the SMIRA algorthm remans the same n ths paper. In fact, SMIRA algorthm runs the power control algorthm n (7) and removes lnks from the network one by one and untl the remanng set of lnks can be supported. The removal crteron of SMIRA s as follows: = argmax Ω { max ( α (P Ω ),β (P Ω ) )}. (13) Intutvely, SMIRA algorthm removes the lnk whch volates QoS constrants the most and/or creates the largest amount of nterference to other lnks n each step. Thus, t can potentally remove the least number of lnks from the network. B. Admsson Control wth QoS and Interference Constrants In our spectrum sharng problem, besdes QoS constrant, admsson and power control should be done such that nterference constrants for prmary lnks stated n (3) are also satsfed. e have the followng result on the complexty of ths admsson control problem. Proposton 1: The admsson control problem wth QoS and nterference constrants s NP-hard. Proof: It was shown n [7] that the admsson control wth only QoS constrants s NP-hard. Because the admsson control wth QoS constrants s a specal case of that wth both QoS and nterference constrants (they become the same when I j for j =1,,M). Therefore, our nvestgated admsson control problem s also NP-hard. Because of the complexty of the problem, we propose a lowcomplexty admsson control algorthm s ths sub-secton. The proposed algorthm also removes the worst lnk one-by-one. In each step, we perform the power control algorthm as n (7) and remove one lnk untl the remanng set satsfes both QoS and nterference constrants. Here, the key ssue s to construct a removal crteron whch acheves good overall performance. Because there are two dfferent knds of constrants, we consder the followng cases n each removal step. Case 1: Interference constrants for all prmary recevng ponts stated n (3) are satsfed but QoS constrants n (2) are volated In ths case, we employ the SMIRA algorthm as presented n Secton III.A. 3565

4 Case 2: Interference constrants for prmary recevng ponts stated n (3) are volated Note that ths case covers both scenaros where QoS constrants n (2) are volated or not. In ths case, we would remove the lnk whch volates both QoS and nterference constrants the most n each step. Now, we defne the measure whch quantfes degree of volaton at prmary recevng pont j as follows: η j (Ω) = T j =1 j, P Ω. (14) e propose a removal algorthm wth the followng removal metrc M η j (Ω) = argmax Ω D Ω (P Ω )+ M k=1 η k(ω) g(p) j, P Ω j=1 D Ω (P Ω ) + D Ω (P Ω )+ M k=1 η k(ω) max j, P Ω, j Ω,j = g(s) j Ω,j = j Ω,j = P Ω j In fact, j, P Ω denotes the total nterference that secondary lnk creates to other secondary lnks whle j Ω,j = g(s) P j Ω s the total nterference receved at the recevng end of lnk. In addton, j, P Ω denotes the nterference that secondary lnk creates for prmary recevng pont j. Recall that D Ω (P Ω ) quantfes the degree of volaton for QoS constrants and η j (Ω) quantfes the degree of volaton for the nterference constrant of prmary recevng pont j. Therefore, the proposed crteron removes n each step the secondary lnk whch creates the largest amount of nterference for prmary recevng ponts and other secondary lnks n the weghted average sense. As a result, t would potentally remove the least number of secondary lnks from the network. e wll refer to ths algorthm as nterference-aware SMIRA (I-SMIRA) n the sequel. The computaton complexty of I-SMIRA s just O(N 2 ) whch s qute acceptable. IV. JOINT RATE AND POER ALLOCATION OPTIMIZATION hen the network load s low, all secondary lnks can be admtted nto the network and they would ncrease ther transmsson rates above the mnmum values. In essence, we wsh to solve the optmzaton problem stated n (4). The decson varables are transmsson rates R and powers P. e wll show how to transform ths problem nto a convex optmzaton problem where globally optmal soluton can be obtaned. e would lke to note that the jont rate and power allocaton for cellular CDMA networks has been an actve research topcs over the last several years. e refer the readers to [9] and references theren for exstng lterature on the problem. However, the work n [9] s one of the frst papers whch adapt the problem to the ad hoc network settng. Here, the objectve s to mnmze the maxmum servce tme on dfferent transmsson lnks. In ths paper, we proceed one step further by solvng the jont rate and power allocaton problem n the spectrum sharng context where nterference constrants for prmary recevng ponts are taken nto account. Now, the objectve functon n (4) s equvalent to mnmze {max 1/R }. By ntroducng a new varable t and wrtng down all the constrants explctly, the optmzaton problem (4) s equvalent to mnmze t 1/R t, =1, 2,,N R P P N γ, =1, 2,,N. j=1,j = g(s) Pj+N N =1 g(p) j, P T j, j =1, 2,,M R mn R R max, =1, 2,,N P P max, =1, 2,,N (15) The optmzaton problem n (15) s not convex. However, we can transform t nto a geometrc program whch can be solved effcently (chapter 4, [10]). Now, we show how to transform the optmzaton problem (15) nto a geometrc program whch can be transformed nto a convex optmzaton problem. Specfcally, optmzaton problem n (15) s equvalent to mnmze t t 1 R 1 1, =1, 2,,N R P 1 N j=1,j = g(s) γ N j, =1 R mn R 1 (R max (P max P j + γn R P 1 1, =1, 2,,N T j P 1, j =1, 2,,M 1, =1, 2,,N ) 1 R 1, =1, 2,,N ) 1 P 1, =1, 2,,N (16) Now, defnng P = e x, R = e y and t = e s, substtutng these new varables nto (16), and takng ln n both the objectve and the constrant functons, we acheve a convex optmzaton problem whch can be solved by the standard nteror pont algorthm [10]. e have the followng property on the soluton of jont rate and power allocaton problem. Proposton 2: The optmal soluton of the jont rate and power allocaton problem satsfes R = R j,, j. Proof: Ths can be proved by contradcton followng a procedure smlar to the one for proposton 3 n [9]. Hence, the rate and power allocaton problem acheves perfectly far rate for all secondary lnks n the sense that optmal transmsson rates for all lnks are the same. V. NUMERICAL RESULTS e present the numercal results for a smple network settng as shown n Fg. 1. Assume that prmary users communcate wth ts BS n the uplnk drecton (.e., a sngle cell s consdered). Transmttng nodes of secondary lnks are randomly located n a rectangular area and the BS of the prmary network s located at the center of the rectangular area. The sze of the rectangular area s 2000m 2000m. Also, recevng node of each secondary lnk s generated randomly n a 1000m 1000m rectangle wth ts transmttng node beng at the center

5 Number of addmtted lnks N = 5 N = 7 Optmal Removal 3.5 I SMIRA SMIRA Desred SINR (db) Fg. 2. Average number of accepted lnks versus desred SINR (for I = 5N 0 ). The channel gans are modeled as = K 0.µ (p).(d(p) ) 4, where d (s) = K 0.µ (s).(d(s) ) 4, are the corre- and d(p) spondng dstances, µ (s) and µ(p) are random Gaussan varables wth zero mean and standard devaton equal 6 db, K 0 = 10 3 whch captures system and transmsson effects such as antenna gan, carrer frequency, etc. The total nose and nterference at the recevng node of all secondary lnks s chosen to be N = N 0 = The maxmum transmsson power on secondary lnks s P max = 0.1. The spectrum bandwdth s = 5.12 MHz. e wll denote the tolerable nterference lmt at the prmary recevng pont (.e., BS) as I. The mnmum transmsson rate on secondary lnks s R mn = 64 Kbps and maxmum transmsson rate s R max = /PG where PG s the mnmum processng gan. For each smulaton run, the locatons of secondary lnks (.e., transmttng and recevng nodes) are generated randomly. The measure of nterest s obtaned by averagng over 10 3 smulaton runs. The average number of accepted lnk versus the desred SINR for each secondary lnk (.e., γ ) s shown n Fg. 2 for SMIRA, I-SMIRA algorthms and optmal removal. The result for optmal removal s obtaned by an exhaustve search wth the least number of removed lnks. As s evdent from ths fgure, the performance of I-SMIRA algorthm s very close to that of optmal removal. Also, I-SMIRA algorthm acheves much hgher performance than SMIRA algorthm. Ths s due to the fact that I-SMIRA captures both QoS and nterference constrants whle SMIRA only takes care of the QoS constrants. Note that I-SMIRA algorthm has much lower computatonal complexty than the optmal removal. In addton, the number of accepted lnks decreases wth the desred SINR. Ths s because a smaller number of lnks should be accepted to keep the congeston level low enough so that hgher desred SINR can be acheved. In Fg. 3, we show the total throughput versus the mnmum processng gan for dfferent sets of constrants. As expected, the more strngent the QoS and nterference constrants are, the lower the total throughput that can be acheved. Also, when the mnmum processng gan ncreases, the throughput gap between these two curves n Fg. 3 becomes smaller. In fact, when the mnmum processng gan ncreases, the maxmum transmsson rate decreases (because R max Total throughput (Kbps) Fg. 3. N=5) = /PG). Thus, when SINR = 10dB, I = 20N 0 SINR = 15dB, I = 5N Mnmum processng gan Throughput per secondary lnk versus mnmum processng gan (for the mnmum processng gan s hgh enough (e.g., close to 40), the throughput s more lmted by the maxmum transmsson rate so the mpacts of QoS and nterference constrants dmnsh. VI. CONCLUSIONS e have presented a soluton approach to the spectrum sharng problem n cogntve wreless networks. In partcular, an admsson control algorthm has been proposed whch ams to remove the least number of secondary lnks so that both QoS constrants n terms of desred SINR for accepted lnks and nterference constrants for prmary lnks are satsfed. e have also formulated the jont rate and power allocaton problem for the secondary lnks as an optmzaton problem wth both QoS and nterference constrants. Numercal results shown the superor performance of the proposed admsson control algorthm. Also, several nterestng mpacts of system, QoS and nterference constrant parameters on network performance were nvestgated and dscussed. REFERENCES [1] FCC. Spectrum polcy task force report, FCC Nov [2] FCC. Facltatng opportuntes for flexble, effcent, and relable spectrum use employng cogntve rado technologes, notce of proposed rule makng and order, FCC Dec [3] Q. Zhao and B. M. Sadler, A survey of dynamc spectrum access: Sgnal processng, networkng, and regulatory polcy, IEEE Sgnal Processng Mag., to appear. [4] H. Zheng and C. Peng, Collaboratve and farness n opportunstc spectrum access, n Proc. IEEE ICC 05. [5] N. Ne and C. Comancu, Adaptve channel allocaton spectrum etquette for cogntve rado networks, n Proc. IEEE DySPAN 05. [6] A. T. Hoang and Y. -C. Lang, A two-phase channel and power allocaton scheme for cogntve rado networks, n Proc. IEEE PIMRC 06. [7] M. Andersn, Z. Rosberg, and J. Zander, Gradual removals n cellular PCS wth constraned power control and nose, ACM/Baltzer reless Networks J., vol. 2, no. 1, pp , [8] S. A. Grandh and J. Zander, Constraned power control, reless Personal Commun., vol. 1, no. 4, [9] A. Muqattash, M. Krunz, and T. Shu, Performance enhancement of adaptve orthogonal modulaton n wreless CDMA systems, IEEE J. Sel. Areas Commun., vol. 24, no. 3, pp , Mar [10] S. Boyd and L. Vandenberge, Convex Optmzaton, Cambrdge Unversty Press,

Downlink Power Allocation for Multi-class. Wireless Systems

Downlink Power Allocation for Multi-class. Wireless Systems Downlnk Power Allocaton for Mult-class 1 Wreless Systems Jang-Won Lee, Rav R. Mazumdar, and Ness B. Shroff School of Electrcal and Computer Engneerng Purdue Unversty West Lafayette, IN 47907, USA {lee46,

More information

Joint Resource Allocation and Base-Station. Assignment for the Downlink in CDMA Networks

Joint Resource Allocation and Base-Station. Assignment for the Downlink in CDMA Networks Jont Resource Allocaton and Base-Staton 1 Assgnment for the Downlnk n CDMA Networks Jang Won Lee, Rav R. Mazumdar, and Ness B. Shroff School of Electrcal and Computer Engneerng Purdue Unversty West Lafayette,

More information

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS 21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS

More information

AN optimization problem to maximize the up-link

AN optimization problem to maximize the up-link IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 57, NO. 8, AUGUST 29 2225 Power and Rate Control wth Outage Constrants n CDMA Wreless Networks C. Fschone, M. Butuss, K. H. Johansson, and M. D Angelo Abstract

More information

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

Enabling P2P One-view Multi-party Video Conferencing

Enabling P2P One-view Multi-party Video Conferencing Enablng P2P One-vew Mult-party Vdeo Conferencng Yongxang Zhao, Yong Lu, Changja Chen, and JanYn Zhang Abstract Mult-Party Vdeo Conferencng (MPVC) facltates realtme group nteracton between users. Whle P2P

More information

Communication Networks II Contents

Communication Networks II Contents 8 / 1 -- Communcaton Networs II (Görg) -- www.comnets.un-bremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP

More information

Coordinated Denial-of-Service Attacks in IEEE 802.22 Networks

Coordinated Denial-of-Service Attacks in IEEE 802.22 Networks Coordnated Denal-of-Servce Attacks n IEEE 82.22 Networks Y Tan Department of ECE Stevens Insttute of Technology Hoboken, NJ Emal: ytan@stevens.edu Shamk Sengupta Department of Math. & Comp. Sc. John Jay

More information

Delay-Throughput Enhancement in Wireless Networks with Multi-path Routing and Channel Coding

Delay-Throughput Enhancement in Wireless Networks with Multi-path Routing and Channel Coding Delay-Throughput Enhancement n Wreless Networs wth Mult-path Routng and Channel Codng Kevan Ronas, Student Member, IEEE, Amr-Hamed Mohsenan-Rad, Member, IEEE, Vncent W.S. Wong, Senor Member, IEEE, Sathsh

More information

A User-Centric Approach for Dynamic Resource Allocation in CDMA systems based on Hopfield Neural Networks

A User-Centric Approach for Dynamic Resource Allocation in CDMA systems based on Hopfield Neural Networks A User-Centrc Approach for Dynamc esource Allocaton n CDA systems based on Hopfeld eural etworks. García. Agustí J. érez-omero Unverstat ompeu Fabra (UF) Unverstat oltècnca de Catalunya (UC) Barcelona

More information

Multimedia Content Delivery in Millimeter Wave Home Networks

Multimedia Content Delivery in Millimeter Wave Home Networks Multmeda Content Delvery n Mllmeter Wave Home Networks Bojang Ma, Student Member, IEEE, Hamed Shah-Mansour Member, IEEE, and Vncent W.S. Wong, Fellow, IEEE Abstract Mllmeter wave mm-wave communcaton s

More information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

More information

Stochastic Games on a Multiple Access Channel

Stochastic Games on a Multiple Access Channel Stochastc Games on a Multple Access Channel Prashant N and Vnod Sharma Department of Electrcal Communcaton Engneerng Indan Insttute of Scence, Bangalore 560012, Inda Emal: prashant2406@gmal.com, vnod@ece.sc.ernet.n

More information

"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *

Research Note APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES * Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC

More information

Downlink Resource Allocation and Pricing for Wireless Networks

Downlink Resource Allocation and Pricing for Wireless Networks Downlnk Resource Allocaton and Prcng for Wreless Networks Peter Marbach and Randall Berry Abstract Ths paper consders resource allocaton and prcng for the downlnk of a wreless network. We descrbe a model

More information

A Computer Technique for Solving LP Problems with Bounded Variables

A Computer Technique for Solving LP Problems with Bounded Variables Dhaka Unv. J. Sc. 60(2): 163-168, 2012 (July) A Computer Technque for Solvng LP Problems wth Bounded Varables S. M. Atqur Rahman Chowdhury * and Sanwar Uddn Ahmad Department of Mathematcs; Unversty of

More information

Energy Efficient Routing in Ad Hoc Disaster Recovery Networks

Energy Efficient Routing in Ad Hoc Disaster Recovery Networks Energy Effcent Routng n Ad Hoc Dsaster Recovery Networks Gl Zussman and Adran Segall Department of Electrcal Engneerng Technon Israel Insttute of Technology Hafa 32000, Israel {glz@tx, segall@ee}.technon.ac.l

More information

Graph Theory and Cayley s Formula

Graph Theory and Cayley s Formula Graph Theory and Cayley s Formula Chad Casarotto August 10, 2006 Contents 1 Introducton 1 2 Bascs and Defntons 1 Cayley s Formula 4 4 Prüfer Encodng A Forest of Trees 7 1 Introducton In ths paper, I wll

More information

ROSA: Distributed Joint Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks

ROSA: Distributed Joint Routing and Dynamic Spectrum Allocation in Cognitive Radio Ad Hoc Networks ROSA: Dstrbuted Jont Routng and Dynamc Spectrum Allocaton n Cogntve Rado Ad Hoc Networks Le Dng Tommaso Meloda Stella Batalama Mchael J. Medley Department of Electrcal Engneerng, State Unversty of New

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

Economic-Robust Transmission Opportunity Auction in Multi-hop Wireless Networks

Economic-Robust Transmission Opportunity Auction in Multi-hop Wireless Networks Economc-Robust Transmsson Opportunty Aucton n Mult-hop Wreless Networks Mng L, Pan L, Mao Pan, and Jnyuan Sun Department of Electrcal and Computer Engneerng, Msssspp State Unversty, Msssspp State, MS 39762

More information

Minimum Energy Coding in CDMA Wireless Sensor Networks

Minimum Energy Coding in CDMA Wireless Sensor Networks IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 8, NO., FEBRUARY 009 985 Mnmum Energy Codng n CDMA Wreless Sensor Networks C. Fschone, Member, IEEE, K. H. Johansson, Member, IEEE, A. Sangovann-Vncentell,

More information

One-Shot Games for Spectrum Sharing among Co-Located Radio Access Networks

One-Shot Games for Spectrum Sharing among Co-Located Radio Access Networks One-Shot Games for Spectrum Sharng among Co-Located Rado Access etwors Sofonas Halu, Alexs A. Dowhuszo, Olav Tronen and Lu We Department of Communcatons and etworng, Aalto Unversty, P.O. Box 3000, FI-00076

More information

M3S MULTIMEDIA MOBILITY MANAGEMENT AND LOAD BALANCING IN WIRELESS BROADCAST NETWORKS

M3S MULTIMEDIA MOBILITY MANAGEMENT AND LOAD BALANCING IN WIRELESS BROADCAST NETWORKS M3S MULTIMEDIA MOBILITY MANAGEMENT AND LOAD BALANCING IN WIRELESS BROADCAST NETWORKS Bogdan Cubotaru, Gabrel-Mro Muntean Performance Engneerng Laboratory, RINCE School of Electronc Engneerng Dubln Cty

More information

The Greedy Method. Introduction. 0/1 Knapsack Problem

The Greedy Method. Introduction. 0/1 Knapsack Problem The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton

More information

Efficient On-Demand Data Service Delivery to High-Speed Trains in Cellular/Infostation Integrated Networks

Efficient On-Demand Data Service Delivery to High-Speed Trains in Cellular/Infostation Integrated Networks IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. XX, NO. XX, MONTH 2XX 1 Effcent On-Demand Data Servce Delvery to Hgh-Speed Trans n Cellular/Infostaton Integrated Networks Hao Lang, Student Member,

More information

Resource Control for Elastic Traffic in CDMA Networks

Resource Control for Elastic Traffic in CDMA Networks Resource Control for Elastc Traffc n CDMA Networks Vaslos A. Srs Insttute of Computer Scence (ICS) Foundaton for Research and Technology - Hellas (FORTH) P.O. Box 1385, GR 711 1, Heraklon, Crete, Greece

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Data Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network *

Data Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network * JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819-840 (2008) Data Broadcast on a Mult-System Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,

More information

Dynamic Resource Allocation and Power Management in Virtualized Data Centers

Dynamic Resource Allocation and Power Management in Virtualized Data Centers Dynamc Resource Allocaton and Power Management n Vrtualzed Data Centers Rahul Urgaonkar, Ulas C. Kozat, Ken Igarash, Mchael J. Neely urgaonka@usc.edu, {kozat, garash}@docomolabs-usa.com, mjneely@usc.edu

More information

Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application

Performance Analysis of Energy Consumption of Smartphone Running Mobile Hotspot Application Internatonal Journal of mart Grd and lean Energy Performance Analyss of Energy onsumpton of martphone Runnng Moble Hotspot Applcaton Yun on hung a chool of Electronc Engneerng, oongsl Unversty, 511 angdo-dong,

More information

1 Approximation Algorithms

1 Approximation Algorithms CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons

More information

Distributed Interference Pricing for OFDM Wireless Networks with Non-Separable Utilities

Distributed Interference Pricing for OFDM Wireless Networks with Non-Separable Utilities Dstrbuted Interference Prcng for OFDM Wreless Networs wth Non-Separable Utltes Changxn Sh, Randall A. Berry, and Mchael L. Hong Department of Electrcal Engneerng and Computer Scence Northwestern Unversty,

More information

Recurrence. 1 Definitions and main statements

Recurrence. 1 Definitions and main statements Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.

More information

U.C. Berkeley CS270: Algorithms Lecture 4 Professor Vazirani and Professor Rao Jan 27,2011 Lecturer: Umesh Vazirani Last revised February 10, 2012

U.C. Berkeley CS270: Algorithms Lecture 4 Professor Vazirani and Professor Rao Jan 27,2011 Lecturer: Umesh Vazirani Last revised February 10, 2012 U.C. Berkeley CS270: Algorthms Lecture 4 Professor Vazran and Professor Rao Jan 27,2011 Lecturer: Umesh Vazran Last revsed February 10, 2012 Lecture 4 1 The multplcatve weghts update method The multplcatve

More information

Joint Dynamic Radio Resource Allocation and Mobility Load Balancing in 3GPP LTE Multi-Cell Network

Joint Dynamic Radio Resource Allocation and Mobility Load Balancing in 3GPP LTE Multi-Cell Network 288 FENG LI, LINA GENG, SHIHUA ZHU, JOINT DYNAMIC RADIO RESOURCE ALLOCATION AND MOBILITY LOAD BALANCING Jont Dynamc Rado Resource Allocaton and Moblty Load Balancng n 3GPP LTE Mult-Cell Networ Feng LI,

More information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by 6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng

More information

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)

1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP) 6.3 / -- Communcaton Networks II (Görg) SS20 -- www.comnets.un-bremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes

More information

A Hybrid Systems Model for Power Control in Multicell Wireless Data Networks

A Hybrid Systems Model for Power Control in Multicell Wireless Data Networks A Hybrd Systems Model for Power Control n Multcell Wreless Data Networks Tansu Alpcan 1 and Tamer Başar 1 (alpcan, tbasar)@control.csl.uuc.edu Abstract We present a power control scheme based on noncooperatve

More information

Cooperative Load Balancing in IEEE 802.11 Networks with Cell Breathing

Cooperative Load Balancing in IEEE 802.11 Networks with Cell Breathing Cooperatve Load Balancng n IEEE 82.11 Networks wth Cell Breathng Eduard Garca Rafael Vdal Josep Paradells Wreless Networks Group - Techncal Unversty of Catalona (UPC) {eduardg, rvdal, teljpa}@entel.upc.edu;

More information

Optimization of Resource Allocation in Wireless Systems Based on Game Theory

Optimization of Resource Allocation in Wireless Systems Based on Game Theory Internatonal Journal of Computer Scences and Engneerng Open Access Research Paper Volume-4, Issue-1 E-ISSN: 347-693 Optmzaton of Resource Allocaton n Wreless Systems Based on Game Theory Sara Rah 1*, Al

More information

A generalized hierarchical fair service curve algorithm for high network utilization and link-sharing

A generalized hierarchical fair service curve algorithm for high network utilization and link-sharing Computer Networks 43 (2003) 669 694 www.elsever.com/locate/comnet A generalzed herarchcal far servce curve algorthm for hgh network utlzaton and lnk-sharng Khyun Pyun *, Junehwa Song, Heung-Kyu Lee Department

More information

Performance Analysis and Comparison of QoS Provisioning Mechanisms for CBR Traffic in Noisy IEEE 802.11e WLANs Environments

Performance Analysis and Comparison of QoS Provisioning Mechanisms for CBR Traffic in Noisy IEEE 802.11e WLANs Environments Tamkang Journal of Scence and Engneerng, Vol. 12, No. 2, pp. 143149 (2008) 143 Performance Analyss and Comparson of QoS Provsonng Mechansms for CBR Traffc n Nosy IEEE 802.11e WLANs Envronments Der-Junn

More information

Solutions to the exam in SF2862, June 2009

Solutions to the exam in SF2862, June 2009 Solutons to the exam n SF86, June 009 Exercse 1. Ths s a determnstc perodc-revew nventory model. Let n = the number of consdered wees,.e. n = 4 n ths exercse, and r = the demand at wee,.e. r 1 = r = r

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Properties of American Derivative Securities

Properties of American Derivative Securities Capter 6 Propertes of Amercan Dervatve Securtes 6.1 Te propertes Defnton 6.1 An Amercan dervatve securty s a sequence of non-negatve random varables fg k g n k= suc tat eac G k s F k -measurable. Te owner

More information

An MILP model for planning of batch plants operating in a campaign-mode

An MILP model for planning of batch plants operating in a campaign-mode An MILP model for plannng of batch plants operatng n a campagn-mode Yanna Fumero Insttuto de Desarrollo y Dseño CONICET UTN yfumero@santafe-concet.gov.ar Gabrela Corsano Insttuto de Desarrollo y Dseño

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

A Secure Password-Authenticated Key Agreement Using Smart Cards

A Secure Password-Authenticated Key Agreement Using Smart Cards A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,

More information

AN APPROACH TO WIRELESS SCHEDULING CONSIDERING REVENUE AND USERS SATISFACTION

AN APPROACH TO WIRELESS SCHEDULING CONSIDERING REVENUE AND USERS SATISFACTION The Medterranean Journal of Computers and Networks, Vol. 2, No. 1, 2006 57 AN APPROACH TO WIRELESS SCHEDULING CONSIDERING REVENUE AND USERS SATISFACTION L. Bada 1,*, M. Zorz 2 1 Department of Engneerng,

More information

A Lyapunov Optimization Approach to Repeated Stochastic Games

A Lyapunov Optimization Approach to Repeated Stochastic Games PROC. ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING, OCT. 2013 1 A Lyapunov Optmzaton Approach to Repeated Stochastc Games Mchael J. Neely Unversty of Southern Calforna http://www-bcf.usc.edu/

More information

A FRAMEWORK FOR EFFICIENT BANDWIDTH MANAGEMENT IN BROADBAND WIRELESS ACCESS SYSTEMS

A FRAMEWORK FOR EFFICIENT BANDWIDTH MANAGEMENT IN BROADBAND WIRELESS ACCESS SYSTEMS A FRAMEWORK FOR EFFICIENT BANDWIDTH MANAGEMENT IN BROADBAND WIRELESS ACCESS SYSTEMS BY BADER S. AL-MANTHARI A thess submtted to the School of Computng n conformty wth the requrements for the degree of

More information

Multi-Source Video Multicast in Peer-to-Peer Networks

Multi-Source Video Multicast in Peer-to-Peer Networks ult-source Vdeo ultcast n Peer-to-Peer Networks Francsco de Asís López-Fuentes*, Eckehard Stenbach Technsche Unverstät ünchen Insttute of Communcaton Networks, eda Technology Group 80333 ünchen, Germany

More information

Optimal portfolios using Linear Programming models

Optimal portfolios using Linear Programming models Optmal portfolos usng Lnear Programmng models Chrstos Papahrstodoulou Mälardalen Unversty, Västerås, Sweden Abstract The classcal Quadratc Programmng formulaton of the well known portfolo selecton problem,

More information

Lecture 3. 1 Largest singular value The Behavior of Algorithms in Practice 2/14/2

Lecture 3. 1 Largest singular value The Behavior of Algorithms in Practice 2/14/2 18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest

More information

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm

A hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):1884-1889 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel

More information

行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告

行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 行 政 院 國 家 科 學 委 員 會 補 助 專 題 研 究 計 畫 成 果 報 告 期 中 進 度 報 告 畫 類 別 : 個 別 型 計 畫 半 導 體 產 業 大 型 廠 房 之 設 施 規 劃 計 畫 編 號 :NSC 96-2628-E-009-026-MY3 執 行 期 間 : 2007 年 8 月 1 日 至 2010 年 7 月 31 日 計 畫 主 持 人 : 巫 木 誠 共 同

More information

9.1 The Cumulative Sum Control Chart

9.1 The Cumulative Sum Control Chart Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s

More information

A FASTER EXTERNAL SORTING ALGORITHM USING NO ADDITIONAL DISK SPACE

A FASTER EXTERNAL SORTING ALGORITHM USING NO ADDITIONAL DISK SPACE 47 A FASTER EXTERAL SORTIG ALGORITHM USIG O ADDITIOAL DISK SPACE Md. Rafqul Islam +, Mohd. oor Md. Sap ++, Md. Sumon Sarker +, Sk. Razbul Islam + + Computer Scence and Engneerng Dscplne, Khulna Unversty,

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

A New Paradigm for Load Balancing in Wireless Mesh Networks

A New Paradigm for Load Balancing in Wireless Mesh Networks A New Paradgm for Load Balancng n Wreless Mesh Networks Abstract: Obtanng maxmum throughput across a network or a mesh through optmal load balancng s known to be an NP-hard problem. Desgnng effcent load

More information

denote the location of a node, and suppose node X . This transmission causes a successful reception by node X for any other node

denote the location of a node, and suppose node X . This transmission causes a successful reception by node X for any other node Fnal Report of EE359 Class Proect Throughput and Delay n Wreless Ad Hoc Networs Changhua He changhua@stanford.edu Abstract: Networ throughput and pacet delay are the two most mportant parameters to evaluate

More information

An Efficient Recovery Algorithm for Coverage Hole in WSNs

An Efficient Recovery Algorithm for Coverage Hole in WSNs An Effcent Recover Algorthm for Coverage Hole n WSNs Song Ja 1,*, Wang Balng 1, Peng Xuan 1 School of Informaton an Electrcal Engneerng Harbn Insttute of Technolog at Weha, Shanong, Chna Automatc Test

More information

BERNSTEIN POLYNOMIALS

BERNSTEIN POLYNOMIALS On-Lne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful

More information

Joint Scheduling of Processing and Shuffle Phases in MapReduce Systems

Joint Scheduling of Processing and Shuffle Phases in MapReduce Systems Jont Schedulng of Processng and Shuffle Phases n MapReduce Systems Fangfe Chen, Mural Kodalam, T. V. Lakshman Department of Computer Scence and Engneerng, The Penn State Unversty Bell Laboratores, Alcatel-Lucent

More information

Availability-Based Path Selection and Network Vulnerability Assessment

Availability-Based Path Selection and Network Vulnerability Assessment Avalablty-Based Path Selecton and Network Vulnerablty Assessment Song Yang, Stojan Trajanovsk and Fernando A. Kupers Delft Unversty of Technology, The Netherlands {S.Yang, S.Trajanovsk, F.A.Kupers}@tudelft.nl

More information

An Intelligent Policy System for Channel Allocation of Information Appliance

An Intelligent Policy System for Channel Allocation of Information Appliance Tamkang Journal of Scence and Engneerng, Vol. 5, No., pp. 63-68 (2002) 63 An Intellgent Polcy System for Channel Allocaton of Informaton Applance Cheng-Yuan Ku, Chang-Jnn Tsao 2 and Davd Yen 3 Department

More information

Introduction TSGR1#5(99)623. TSG-RAN Working Group 1 (Radio) meeting #5 Cheju, South Korea, June 1~4 th, Agenda Item: Smart Antenna Technology

Introduction TSGR1#5(99)623. TSG-RAN Working Group 1 (Radio) meeting #5 Cheju, South Korea, June 1~4 th, Agenda Item: Smart Antenna Technology TSG-RA Workng Group 1 (Rado) meetng #5 Cheu, South Korea, June 1~4 th, 1999 TSGR1#5(99)623 Agenda Item: Source: Ttle: CWTS WG1 Smart Antenna Technology Document for: Consderaton Introducton Followng the

More information

The eigenvalue derivatives of linear damped systems

The eigenvalue derivatives of linear damped systems Control and Cybernetcs vol. 32 (2003) No. 4 The egenvalue dervatves of lnear damped systems by Yeong-Jeu Sun Department of Electrcal Engneerng I-Shou Unversty Kaohsung, Tawan 840, R.O.C e-mal: yjsun@su.edu.tw

More information

AS the applications of wireless networks (such as cellular

AS the applications of wireless networks (such as cellular IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 5, NO. 12, DECEMBER 2006 3537 Decentralzed Rate Assgnments n a Mult-Sector CDMA Network Jennfer Prce, Student Member, IEEE, and Tara Javd, Member, IEEE

More information

J. Parallel Distrib. Comput.

J. Parallel Distrib. Comput. J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n

More information

A Dynamic Load Balancing Algorithm in Heterogeneous Network

A Dynamic Load Balancing Algorithm in Heterogeneous Network 06 7th Internatonal Conference on Intellgent Systems Modellng and Smulaton A Dynamc Load Balancng Algorthm n Heterogeneous etwork Zhxong Dng Xngjun Wang and Wenmng Yang Shenzhen Key Lab of Informaton Securty

More information

Solution of Algebraic and Transcendental Equations

Solution of Algebraic and Transcendental Equations CHAPTER Soluton of Algerac and Transcendental Equatons. INTRODUCTION One of the most common prolem encountered n engneerng analyss s that gven a functon f (, fnd the values of for whch f ( = 0. The soluton

More information

VoIP over Multiple IEEE 802.11 Wireless LANs

VoIP over Multiple IEEE 802.11 Wireless LANs SUBMITTED TO IEEE TRANSACTIONS ON MOBILE COMPUTING 1 VoIP over Multple IEEE 80.11 Wreless LANs An Chan, Graduate Student Member, IEEE, Soung Chang Lew, Senor Member, IEEE Abstract IEEE 80.11 WLAN has hgh

More information

Demand Response of Data Centers: A Real-time Pricing Game between Utilities in Smart Grid

Demand Response of Data Centers: A Real-time Pricing Game between Utilities in Smart Grid Demand Response of Data Centers: A Real-tme Prcng Game between Utltes n Smart Grd Nguyen H. Tran, Shaole Ren, Zhu Han, Sung Man Jang, Seung Il Moon and Choong Seon Hong Department of Computer Engneerng,

More information

When Network Effect Meets Congestion Effect: Leveraging Social Services for Wireless Services

When Network Effect Meets Congestion Effect: Leveraging Social Services for Wireless Services When Network Effect Meets Congeston Effect: Leveragng Socal Servces for Wreless Servces aowen Gong School of Electrcal, Computer and Energy Engeerng Arzona State Unversty Tempe, AZ 8587, USA xgong9@asuedu

More information

Dynamic Pricing for Smart Grid with Reinforcement Learning

Dynamic Pricing for Smart Grid with Reinforcement Learning Dynamc Prcng for Smart Grd wth Renforcement Learnng Byung-Gook Km, Yu Zhang, Mhaela van der Schaar, and Jang-Won Lee Samsung Electroncs, Suwon, Korea Department of Electrcal Engneerng, UCLA, Los Angeles,

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

More information

The example below solves a system in the unknowns α and β:

The example below solves a system in the unknowns α and β: The Fnd Functon The functon Fnd returns a soluton to a system of equatons gven by a solve block. You can use Fnd to solve a lnear system, as wth lsolve, or to solve nonlnear systems. The example below

More information

UTILIZING MATPOWER IN OPTIMAL POWER FLOW

UTILIZING MATPOWER IN OPTIMAL POWER FLOW UTILIZING MATPOWER IN OPTIMAL POWER FLOW Tarje Krstansen Department of Electrcal Power Engneerng Norwegan Unversty of Scence and Technology Trondhem, Norway Tarje.Krstansen@elkraft.ntnu.no Abstract Ths

More information

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts

Power-of-Two Policies for Single- Warehouse Multi-Retailer Inventory Systems with Order Frequency Discounts Power-of-wo Polces for Sngle- Warehouse Mult-Retaler Inventory Systems wth Order Frequency Dscounts José A. Ventura Pennsylvana State Unversty (USA) Yale. Herer echnon Israel Insttute of echnology (Israel)

More information

Heuristic Static Load-Balancing Algorithm Applied to CESM

Heuristic Static Load-Balancing Algorithm Applied to CESM Heurstc Statc Load-Balancng Algorthm Appled to CESM 1 Yur Alexeev, 1 Sher Mckelson, 1 Sven Leyffer, 1 Robert Jacob, 2 Anthony Crag 1 Argonne Natonal Laboratory, 9700 S. Cass Avenue, Argonne, IL 60439,

More information

Multivariate EWMA Control Chart

Multivariate EWMA Control Chart Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant

More information

Impact of Directional Receiving Antennas on Wireless Networks

Impact of Directional Receiving Antennas on Wireless Networks Impact of Drectonal Recevng Antennas on Wreless Networks Jean-Marc Kelf and Olver Smon Orange Labs 38-40 rue du Général Leclerc, 92130 Issy-Les-Moulneaux, France {jeanmarc.kelf, olver.smon}@orange.com

More information

Schedulability Bound of Weighted Round Robin Schedulers for Hard Real-Time Systems

Schedulability Bound of Weighted Round Robin Schedulers for Hard Real-Time Systems Schedulablty Bound of Weghted Round Robn Schedulers for Hard Real-Tme Systems Janja Wu, Jyh-Charn Lu, and We Zhao Department of Computer Scence, Texas A&M Unversty {janjaw, lu, zhao}@cs.tamu.edu Abstract

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v a 1 b 1 i, a 2 b 2 i,..., a n b n i. SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are

More information

Fault tolerance in cloud technologies presented as a service

Fault tolerance in cloud technologies presented as a service Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce INTRODUCTION Improvements n technques for vrtualzaton and performance

More information

Device-to-Device (D2D) Communication in Cellular Network - Performance Analysis of Optimum and Practical Communication Mode Selection

Device-to-Device (D2D) Communication in Cellular Network - Performance Analysis of Optimum and Practical Communication Mode Selection Ths full text paper was peer revewed at the drecton of IEEE Communcatons Socety subect matter experts for publcaton n the WCNC 2010 proceedngs. Devce-to-Devce (D2D) Communcaton n Cellular Network - Performance

More information

Scheduling and Resource Allocation in OFDMA Wireless Systems

Scheduling and Resource Allocation in OFDMA Wireless Systems Schedulng and Resource Allocaton n OFDMA Wreless Systems Janwe Huang, Vjay Subramanan, Randall Berry, and Rajeev Agrawal February 2009 Book chapter n Orthogonal Frequency Dvson Multple Access Fundamentals

More information

Efficient Bandwidth Management in Broadband Wireless Access Systems Using CAC-based Dynamic Pricing

Efficient Bandwidth Management in Broadband Wireless Access Systems Using CAC-based Dynamic Pricing Effcent Bandwdth Management n Broadband Wreless Access Systems Usng CAC-based Dynamc Prcng Bader Al-Manthar, Ndal Nasser 2, Najah Abu Al 3, Hossam Hassanen Telecommuncatons Research Laboratory School of

More information

On File Delay Minimization for Content Uploading to Media Cloud via Collaborative Wireless Network

On File Delay Minimization for Content Uploading to Media Cloud via Collaborative Wireless Network On Fle Delay Mnmzaton for Content Uploadng to Meda Cloud va Collaboratve Wreless Network Ge Zhang and Yonggang Wen School of Computer Engneerng Nanyang Technologcal Unversty Sngapore Emal: {zh0001ge, ygwen}@ntu.edu.sg

More information

An RFID Distance Bounding Protocol

An RFID Distance Bounding Protocol An RFID Dstance Boundng Protocol Gerhard P. Hancke and Markus G. Kuhn May 22, 2006 An RFID Dstance Boundng Protocol p. 1 Dstance boundng Verfer d Prover Places an upper bound on physcal dstance Does not

More information

Allocating Collaborative Profit in Less-than-Truckload Carrier Alliance

Allocating Collaborative Profit in Less-than-Truckload Carrier Alliance J. Servce Scence & Management, 2010, 3: 143-149 do:10.4236/jssm.2010.31018 Publshed Onlne March 2010 (http://www.scrp.org/journal/jssm) 143 Allocatng Collaboratve Proft n Less-than-Truckload Carrer Allance

More information

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582

NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING. Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 NMT EE 589 & UNM ME 482/582 ROBOT ENGINEERING Dr. Stephen Bruder NMT EE 589 & UNM ME 482/582 7. Root Dynamcs 7.2 Intro to Root Dynamcs We now look at the forces requred to cause moton of the root.e. dynamcs!!

More information

HYPOTHESIS TESTING OF PARAMETERS FOR ORDINARY LINEAR CIRCULAR REGRESSION

HYPOTHESIS TESTING OF PARAMETERS FOR ORDINARY LINEAR CIRCULAR REGRESSION HYPOTHESIS TESTING OF PARAMETERS FOR ORDINARY LINEAR CIRCULAR REGRESSION Abdul Ghapor Hussn Centre for Foundaton Studes n Scence Unversty of Malaya 563 KUALA LUMPUR E-mal: ghapor@umedumy Abstract Ths paper

More information

Blind Estimation of Transmit Power in Wireless Networks

Blind Estimation of Transmit Power in Wireless Networks Bln Estmaton of Transmt Power n Wreless Networks Murtaza Zafer (IBM Research), Bongjun Ko (IBM Research), Chatschk Bskan (IBM Research) an Ivan Ho (Imperal College, UK) Transmt-power Estmaton: Problem

More information

Downlink Scheduling and Resource Allocation for OFDM Systems

Downlink Scheduling and Resource Allocation for OFDM Systems 288 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 8, NO. 1, JANUARY 2009 Downlnk Schedulng and Resource Allocaton for OFDM Systems Janwe Huang, Member, IEEE, Vjay G. Subramanan, Member, IEEE, Rajeev

More information