Uplink Call Admission Control Techniques for Multimedia Packet Transmission in UMTS WCDMA System

Size: px
Start display at page:

Download "Uplink Call Admission Control Techniques for Multimedia Packet Transmission in UMTS WCDMA System"

Transcription

1 Upln ll Admsson onrol Technues for Mulmed Pce Trnsmsson n UMTS WDMA Sysem Angel Hernández Soln, Anono Vldovnos Brdjí, Fernndo sdevll Plco 2 Elecroncs Engneerng nd ommuncons Dp. Unversy of Zrgoz. (Spn) nhersol@pos.unzr.es 2 Sgnl Theory nd ommuncons Dp. Polyechnc Unversy of lon (UP); Brcelon (Spn) Absrc- The hrd generon cellulr newor, UMTS, provdes lrge opporunes o offer mulmed pplcons nd servces h mee end o end uly of servce (QoS) reuremens, n more effcen wy, consderng pce d rnsmsson mode. However, hs reures o desgn n effcen flow conrol nd cll dmsson conrol (A). Desgn of A hs specl dffcules gven h cpcy n DMA vres ccordng wh he number of cve users nd he level of nerference. Tng no ccoun he sochsc nure of mulmed rffc nd he chnges n he vlble cpcy n he DMA sysem, n effecve cpcy reues s derved nd used o chrcerze he resources reured by ech moble user n order o mee s respecve QoS reuremens. These reuremens re expressed n erms of re nd dely consrns n ddon o he sgnl o nerference ro. Afer seng n effecve cpcy hreshold for he sysem, lner pproxmon s used o fnd he ol moun of resources reured by ll he moble users lredy dmed n he sysem nd he new connecon reues. Pce rnsmsson s consdered for boh rel me nd non rel me servces whle cenrlzed demnd ssgnmen lgorhm hs been mplemened n order o provde QoS n he pce level. ompuer smulon resuls re gven o demonsre he performnce of he proposed mehod. I. ITRODUTIO The connecon dmsson conrol (A) for moble communcons s one of he mos mporn engneerng ssues n order o gurnee sysem effcency nd uly of servce (QoS) o connecon orened servces. In nerference lmed DMA sysems, good nerference hndlng by rdo resource llocon schemes plys n mporn role o enhnce he performnce nd o ncrese he sysem cpcy. In hs wy, A ndrecly conrols nerference by lmng he number of users n he sysem n order h he negoed sgnl o nerference ro vlues cn be cheved by ech exsng connecon. Wheres, n pce me scle, he medum ccess conrol/flow conrol blnces he sysem nerference nd rrnges rnsmssons on frme o frme bss by schedulng users ccordng wh her QoS reuremens n erms of he mxmum llowed dely, re nd b error re. In ny cse, coordnon beween A nd flow conrol s reured o mee QoS nd conseuenly he resuls obned from pce level hve o be en no ccoun n order o nfer n effcen mehod for A. The flexbly of DMA ccess n ddon o he pce rnsmsson mode mproves sysem effcency bu ncreses he complexy of he A. Gven h n DMA cpcy vres due o chnges n he nerference he desgn of A hs ssoced wo mn desgn problems: ) o se n effecve A hreshold o gurnee he QoS for n negron of vrous servce ypes, nd b) o cheve he mxmum effcency n he sense of resources usge. In fc, A relly operes wo dfferen levels: he frs one chrcerzed by he pce level consrns such s pce loss, dely jer or verge dely nd he second one h llows he sysem cpcy o be shred mong he vrous rffc ypes nd/or o proec he hndoff connecons from he new connecons. Severl reserch sudes hve ddressed hese ssues by mens of sc nd dynmc prorzon nd shrng scheme, ssoced wh sc or dpve reserve of resources. The problem of seng n effecve A hreshold hs been ddressed before n ue lo of wors [4][5][6][]. In generl, wo A syles cn be found n hese references: A h derve n verge cell cpcy bsed on he llowed number of cve connecons nd A bsed on he rnsmed power or he ol nerference level. [4] presens useful expressons for sysem cpcy evluon. However, snce he erlng cpcy s clculed bsed only on blocng re, he communcon uly s no gurneed nd s relon wh erlng cpcy s lef uncler. A dsrbuon funcon of nerference s deermned n order o evlue he blocng probbly. [4] shows h he men nd vrnce of oher-cell nerference cn be pproxmed by he nrcell nerference mulpled by consn coeffcen f. In [5] expressons of he QoS (pce loss) nd GoS (grde of servce) s funcons of rffc nensy nd A hresholds hve been derved lhough only ype of servce s consdered. In [6], n effecve cpcy (re), ng no ccoun he sochsc nure of rffc nd he dely reuremens n mulmed scenro, s used o chrcerze he resources reured by mulmed servces. The proposed mehod, hough useful, hs been shown o be conservve. In [] n nerference-bsed A sregy s proposed. A new user s no dmed by he upln A f he new resulng ol nerference level s hgher hn hreshold vlue. Ths hreshold s he sme s he mxmum upln nose rse nd cn be se by rdo newor plnnng. Alhough hs mehod could gurnee he sgnl o nerference ro for ll dmed connecons, pce level uly consrns n erms of dely reuremens re no ssured. oe h he mos cpcy nlyss concenre smply on rnsmsson chrcerscs nd do no consder he rel me operon of he sysem under sochsclly vryng rffc lod. On he oher hnd, none of he prevous references consder specfc MA proocols n order o provde dfferened QoS n he pce level. We hve nroduced hs specfc fcor. ompuer smulon resuls obned from pce level hve been evlued n order o nfer n effecve cpcy hreshold. In prculr, our sudy hs been ssoced o he Wdebnd ode Dvson Mulple Access (WDMA) upln defned n Unversl Moble Telecommuncon Sysem (UMTS) erresrl rdo ccess (UTRA) nd lmed o he upln. Trnsmssons of rel-me nd non rel-me servces hve been performed n pcezed form over he physcl d chnnel (DPDH) where cenrlzed demnd

2 ssgnmen proocol hs been mplemened o negre servces wh QoS. oe h hs proposl dffers from pce rnsmsson modes defned for he momen n UTRA (shor nfreuen pce on RAH nd shor number of successve frmes on ommon Pce hnnels (PH) []). However, we hve consdered he menoned lernve n order o rnsm lrge or freuen d pces whou conen for code. In hs cse, ech MS, whch hs pces wng o rnsm, seups dedced code usng n nl Rndom Access reues, wheren he ype nd rffc prmeers re specfed. Then, he newor evlues he reues nd decdes f he necessry resources cn be provded o he MS n order o suppor s QoS. Once he dedced chnnel (code) s ssgned, he MS s no llowed o sr he rnsmsson, needs o w h he bse son (BS) specfes he mes n whch cn rnsm. Boh MS rnsmed powers nd rnsmsson res my be consdered s conrollble resources by he newor. Thus, he schedulng dscplnes BS, n coordnon wh mnmum rnsmed power creron, re responsble for rrngng pce rnsmssons whn her specfed re reuremens nd dely olernces, beng chnnel condons of ndvdul users n mnd. The purpose of he mnmum power conrol creron s o mee he B Error Re (BER) of smulneously rnsmed pces, ssgnng n opmum level o he rnsmed power from ll he MS s n such wy h nerference cused o oher cells s mnmzed nd hus hroughpu s mxmzed. Besdes, he level of nerference BS s lwys mnned under hreshold whle MS oupu powers re consrned. Severl schedulng sreges bsed on sc nd dynmc prores hve been consdered nd proved o perform dfferene QoS. A deled descrpon of he proocol nd he schedulng sreges cn be found n [2]. However, some ssumpons bou power ssgnmen re revewed n secon II n order o suppor he nlyss of he effecve user cpces nd he hreshold sysem cpcy. Once he effecve A hreshold s se, we evlue he performnce of vrble chnnel reservon A polcy bsed on he esmed poson nd movemen of mobles sons (MS s). Ths esmon s performed consderng mesuremens repored by he MS. ompuer smulon resuls demonsre he effcency of he proposed A hreshold n mulmed scenro nd lso he effecve hndoff prory provded by he proposed dsrbued-dpve A polcy. II. ALL ADMISSIO THRESHOLD The se of reuremens ssoced wh MS loced n cell : mxmum dely nd dely jer, mnmum re r, nd mxmum b (BER) or bloc (BLER) error res, cn be mpped no n euvlen (E b /o)., consrn denoed by γ : Eb o wh, W r I, n, P, h, γ + I + η W ex, I ex Pl, ' hl, ' l, o.., P I n,, h, () where E b s he b energy, o he ol nerference densy receved he BS, η o he herml nose specrl densy, I n,. nd I ex, he nrcell nd nercell nerference respecvely, P, he rnsmed power ssoced o he MS loced n cell, h, he ph loss beween MS nd he BS, he number of users n he cell nd W he vlble bndwdh n he cell (chp re). I hs been shown h, consderng h power oupu consrns re ppled o MS, 0 < P, < p,, mx, he power conrol problem n cell s fesble f nd only f [3]: W η( / + j r γ j ( + f ( )) j j η( mx ( I f I (2) ex, ηow, n, ( ) W mn + j p, mxh r γ j We consder mx ( s he mxmum vlble cpcy n he cell, he consumed cpcy by MS nd η( s he reserved cpcy needed o gurny he power consrns o MS s n frme. A mnmum vlue of prmeer η eul o 0. s consdered n order o lm he mxmum ol power receved he BS I ol,. If condon (2) s cheved for se of res nd E b / o vlues, hen he power cn be obned usng (3). ηow wh P res ( + f ) j (3) ˆ W j h, ( + res( r γ, oe h n cern frme, where sch s he number of scheduled users, he relon (4) s ssfed. mx sch res η( 0 + f + f ( ) (4) The ro beween ner nd nrcell nerference f(, nd he chnnel condon should be nown n order o cheve he mnmum rnsmed power creron wh ccurcy. Ro f( s esmed from he ro mesured n he prevous frme. Tng no ccoun condon (2), schedulng he BS rrnges rnsmssons of MS s ccordng o lgorhms reled wh dely nd re reuremens. MS s whch cn no mee Eb/o reuremens re delyed lhough could be n conc wh he bse hrough he Dedced onrol hnnel (DPH) (o perform closed power conrol). Unused resources (power nd nsn of rnsmsson) re ssgned o he res of he users. oe h he wse of cpcy ssoced o DPH mus be dded n (2). In ddon o he schedulng sreges bsed on dely nd re reuremens, we hve consdered chnnel se dependen schedulng lgorhm bsed on he reured vlue of prmeer η [2]. MS s whch reure hgh vlue of η could be delyed n order o provde perms o more users. Once we hve descrbed heorecl expressons for cpcy n he pce level me scle, we wll expln he developmen of he effecve A. In our cse, unle [4][5][6], he scheduler lms he number of MS s rnsmng n frme, hus, pror, every MS hvng perm o rnsm mus rech s Eb/o

3 consrns (condon (2) s ssfed). So, only mperfecons n he esmon of ro f( could preven, gven h lmos perfec chnnel esmon could be ssumed hns o DPH. Thus, users cn only no ssfy her QoS consrns reled wh dely consrns. Rel me servces drop pces h exceed dely consrns, so specfc drop probbly consrn s mposed s QoS creron. On he oher hnd, smulon resuls show h nl cpcy mx ( mnns he sme sscl dsrbuon, whch hs been shown o correspond o gmm dsrbuon, wh he sme men n vrnce ndependenly of he number of cve users n he sysem, beng only dependen of he propgon prmeers, closed power conrol nccurcy nd he spl dsrbuon of MS s whn he cell. However, n order o perform A, we use n lernve mesure of he cpcy (5), more dependen of curren cve users, whch provde more ccure bound. n sch η f (5) Smulons show h n ( cn be chrcerzed by F-dsrbuon, gven h he produc of f( nd sch cn be modeled s produc of Gmm dsrbuons. Ths dsrbuon s mnned consderng eher one ype of rffc or n negron of rel me nd non rel me servces. However, for he momen, n order o smplfy he nlyss, we wll only consder rel me servces, nd only one ype of rffc. oe h f here re MS s dmed n cell, he probbly of cve users my be expressed s bnoml dsrbuon (6), where ρ s he cvy fcor ssoced o clss MS s. Dsrbuon of cve users s no he sme s dsrbuon of scheduled users. However, whle s below he A hreshold (chrcerzed by he mxmum number of MS llowed, mx, ) hey could be consdered eul, whch smplfes he prmeers of he F-dsrbuon ssoced o n. Pu, ( ) ρ ( ρ ) (6) onsderng he dsrbuon of he vlble cpcy n,, he dely probbly cn be clculed s he frcon of ll cve users h cn no rnsm due o he bsence of vlble cpcy n frme. (7): j Pu, ( ) pn ( m) dm 0 j 0 0 Pdely (7) P ( ) 0 u. (p n s he pdf. of n. ) If we ssume no dely olernce, he mxmum number of llowed users, consderng hreshold o he P dely, represens n dmsson regon bound, gven h n fc, n hs cse, P dely s eul o he droppng probbly. However, hs ssumpon s very conservve gven h we cn ssume some olernce o he dely o lmos ny rel me servce. Insed of, n effecve cpcy,,ef (nroducng he dely nfluence) mus be derved n order o provde more ccure resul. Probbly ε s he probbly h dely ssoced o pces of ny connecon belongng o clss exceed dely reuremens. So, f we re ble o obn he dely dsrbuon of he ndvdul sources, he cpcy of he sysem n erms of number of MS s ( mx ) cn be clculed s (8) nd, n ddon, he effecve cpcy ssoced o MS of clss s (9): mx ef mx( Pr[ dely > Dmx ] ε ) (8) 0: [ mx ] mx, E / (9) where E[ mx ] s he men of he cpcy mx, esmed n perod of me. To obn mx, we need o compue he dely dsrbuon for ndvdul sources consderng her BLER nd servce re reuremens, he vrble wreless cpcy nd he error conrol scheme (selecve repe lgorhm s mplemened). To consder ll hese fcors ogeher n sngle nlycl model seems nccessble, so we hve mde he nlyss n wo seps. The probbly h dely does no exceed dely reuremens s compued s he probbly h cpcy ssgned o he ndvdul connecons, n me nervl eul o dely olernce Dmx couned from he nsn of he pce rrvl o he ueue ( ), s hgher hn he cpcy reured o rnsm ll pces wng n he ueue when pce rrves, ( ) (0). Pr [ dely > Dmx ], Pr( sg, { }, (, + Dmx ) ( )* ) [ (, + Dmx) n * / ( ) n ] sg, { } n (0) Pr Pr( ( ) n ) The probbly () [, + Dmx) n * / ( n ] Pr }( sg, { ) () s clculed usng model h ncludes he selecve repe scheme, he cpcy ssgned o connecon clculed dependng on he dely probbly clculed n (7), P d, nd he error probbly reled o he Eb/o reuremen, P e. lculus of P e ncludes he effecs of he lle devons n he Eb/o due o errors n he esmon of ro f( nd chnnel condons (hey hve been ssclly modeled). Ths model consders h ll pces wng o rnsm mus receve servce whn her specfc dely reuremen. On he oher hnd, he probbly of ueue occupon when pce rrves s clculed from n nlycl model h consders he evoluon n he ueue occupon when he source doesn genere rffc nd when he source s n cve se (remember h rffc s ssumed o be bursy). The nercon beween sources nd he chnges n he ssgned cpcy due o chnges n rffc dsrbuons re ncluded. Dely olernce of users s only represened by he mxmum sze llowed o he ueue nd no ddonl consderons re en no ccoun. A deled descrpon of he nlycl models for ueue nd dely dsrbuon cn be found n [4]. The exenson for n negron of severl rel me users wh dfferen BLER nd re reuremen s mmede consderng he effecve cpcy compued seprely for ech ype of rffc. So, lner

4 pproxmon cn be used o fnd he ol resources reured by ll he MS s. When n negron of rel me servces wh dfferen dely reuremens s consdered, lner pproxmon hs been shown o be lso vld. However, could be necessry, n some cses, o defne lle reserve of cpcy n order o preven lle devons from he lner pproxmon. oe h he effecve cpcy ssoced o non rel me servces s clculed consderng her men b re n cpcy. In frs nsnce, we cn consder convenonl A h llows new user no he rdo ccess newor f condon (2) s ssfed. [ ], ef + new, ef < E mx (2) cell where new, ef s he effecve cpcy of he new user. III. ALL ADMISSIO OTROL TEHIQUES We hve consdered n dpve A polcy o evlue he performnce of he proposed A hreshold. In order o proec hndoff clls we perform vrble resource reservons for hndoff clls dependng on he MS mobly behvor nd we compre he resuls wh complee shrng pproch. ll dmsson decson s performed n dsrbued mnner ner o h used n [2][3] for fxed cpcy sysems. Ech BS mes n dmsson decson by exchngng se nformon wh he djcen cells perodclly. BS s esme he fuure MS hndoffs usng nformon of power nd uly mesuremens repored by ech MS. We ssume h he power mesuremens wll come from he curren nd djcen cells. The conrol sysem nows whch of he djcen cells re poenl cnddes o hndoff, especlly when chnnel degrdon becomes mporn. A MS plced n degrdon re, defned n he lms of he cell, hs hgh probbly o hndoff whn me nervl. Usng nformon bou he evoluon n he power levels receved by MS n hs regon, could be possble o predc n some cses he drecon of movemen of he MS nd preven n some cses unnecessry resource reservon. Therefore, when MS s predced o hndoff, n ndcon of cpcy reserve eul o,ef s sen o he predced desnon n order o pre-lloce resources for he expeced hndoff. Reserves re nerchnged perodclly beween cells. A reservon my be no longer reured nd cncelled. A hndoff cll, wh effecve cpcy new_hndoff,ef, s cceped f: [ ], ef + new _ hndoff, ef < E mx (3) cell wheres new cll s only dmed f: cell ef + new, ef + pr _ dj _ hndoff j dj _ cells [ ], < E (4) where pr_dj_hndoff s he reserve of cpcy ssoced o he MS s from he djcen cells. oe h hndoff reuess re reed eully ndependenly wheher specfc MS hd mde reservon or no. All he reserved cpcy ny nsn s used n complee shred wy o serve hndoff reuess. Afer successfully hndoff, he reserve of cpcy, f vlble, s decresed n he mx correspondng,ef. IV. SIMULATIO MODEL AD RESULTS We propose cellulr sysem model composed by 9 hexgonl cells (rdus2km). Wrp-round echnue s used o vod border effec. Only nerference from he frs-er of djcen cells s consdered. Mcrocell propgon model proposed n [7] s doped for ph loss. Log-normlly dsrbued shdowng wh sndrd devon of σ 8dB s dded ccordng wh he model proposed n [8]. Addonlly, mul-ph fdng envronmen proposed n [9] s consdered. db nenn gn nd herml nose power of 03dBm re ssumed [7]. MS s hve mxmum oupu power of 27 dbm ccordng wh clss 2 defned n [9], nd move wh speed beween 25 o 50Km/h. Inl loclzon s seleced rndomly (unform dsrbuon). MS cn chnge s drecon of movemen whn n ngle of ±π/6 ech s nd 2π ech 20s. Two nd of rffc sources: Rel Tme Servces (lss I. nd lss I.2 d servces wh dely consrns of 300 ms 50ms respecvely) nd on-rel Tme Servces (lss II - D servces wh non-dely consrns) re consdered. onvoluonl codng re ½ ogeher wh rernsmsson scheme (ARQ) re used o cheve BLER0-2 n boh cses. Rel-me sessons re bsed on Pce lls wh number of pces exponenlly dsrbued wh men 35 pces (lss I.) nd 40 pces (lss I.2), whle servce of 36ps (rnspor bloc of 360bs nd rel rnsmsson re of 20bps) s ssumed. Averge ner-pce rrvl me s 0ms, whle pce cll ner rrvl me s exponenlly dsrbued wh men s. On he oher hnd we ssume h ech user generes sngle connecon/cll nd connecon/clls rrve o he sysem ccordng o Posson process of nensy λ. ll lengh s exponenlly dsrbued wh men 80s nd user leves he sysem s soon s he cll ends. on-rel me rffc sources re bsed on he model presened n [0]. 32Kbps d re servces hve been consdered. We presen some resuls n order o ssess he performnce of he connecon dmsson conrol. Fg compres he droppng probbles compued wh models (7) (Pr_dely_model) nd (0) (Pr_drop_model) nd smulons resuls when only clss I. MS s re consdered. Dely probbly s fr from droppng probbly obned n he smulon. However, he droppng probbly clculed when dely olernce s ncluded n he model s very close o hose obned n he smulon. Smlr resuls re obned for lss I.2. Fg. 2 shows cll dmsson lms when n negron of clss I. nd clss I.2 nd droppng probbly of % s consdered s uly creron (n hs fgure hndoff hs no been consdered n smulons). In Fg. 4,5,6,7 compuer smulons show, for he sme combnon of MS, he effcency of he proposed A mehod boh consderng homogeneous (Fg. 4 nd 6()) nd heerogeneous rffc dsrbuons n cells (Fg 5, 6(b) nd 7) nd mobly beween cells. Droppng probbly s lwys mnned under uly consrns (Fg. 6 nd 6b). On he oher hnd, vrble reservon (Fg. 4 nd 5) provdes good resuls (hndoff re s ner o λ/4.).

5 Probbly 0.04 Dely Prob. Smulon Resuls Dely Prob. Model Droppng Prob. Smulon Resuls Droppng Prob. Model lss I.. umber of users lss I.2. umber of users Pce Droppng Probbly Fg.. Dely nd pce droppng probbly Smulon Theorcl lss I.. umber of users Fg. 2. A lms. Inegron clss I. nd I ew cll rrvl re: lss I. 20/80 clls/s. lss I. 20/80 clls/s. S A. Pce droppng. lss I.2 S A. Pce droppng. lss I ew ll Offered Trffc (Erl/cell.). lss II Fg. 3. Pce droppng probbly consderng n negron of rel me nd non rel me servces. Probbles ew cll rrvl re lss I. 30/80 clls/s. S A. Pb lss I. S A. Ploss lss I. S A. Pb lss I.2 S A. Ploss lss I.2 VR A. Pb lss I. VR A. Ploss lss I. VR A. Pb lss I.2 VR A. Ploss lss I /80 44/80 52/80 60/80 68/80 76/80 ew ll Arrvl re (clls/s.). lss I.2 Fg. 4. S vs VR A. ew cll blocng nd hndoff droppng probbly vs. offered rffc. Pce Droppng Probbly Pce Droppng Probbly ew cll rrvl re lss I. 30/80 clls/s. S A. Pce droppng. lss I.2 VR A. Pce Droppng. lss I.2 Fg. 6. S vs VR A. Pce droppng probbly consderng homogeneous () nd heerogeneous (b) rffc dsrbuon n cells. Probbles enrl cell. ew cll rrvl re lss I. 30/80 clls/s. lss I.2 46/80 clls/s. 0-2 S A. Pb lss I. S A. Ploss lss I. S A. Pb lss I.2 S A. Ploss lss I.2 VR A. Pb lss I. VR A. Ploss lss I. VR A. Pb lss I.2 VR A. Ploss lss I Ro beween dycen nd cenrl cell new cll rrvl re Fg. 5. S vs VR A. ew cll blocng nd hndoff droppng probbly consderng heerogeneous rffc dsrbuon n cells. 36/80 44/80 52/80 60/80 68/80 76/ ew ll Arrvl re (clls/s.). lss I.2 ) 0.4 enrl cell. ew cll rrvl re 0.35 mx (men). enrl ell. lss I. 30/80 clls/s. lss I.2 46/80 clls/s. consumed (men). enrl cell. efecve (men). enrl cell. 0.3 mx (men). Adjcen ell. consumed (men). Adjcen cell. S A. Pce droppng. lss I.2. enrl_cell S A. Pce droppng. lss I.2. Adjcen cells efecve (men). Adjcen cell VR A. Pce Droppng. lss I.2. enrl_cell Ro beween dycen nd cenrl cell new cll rrvl re Ro beween dycen nd cenrl cell new cll rrvl re b) pcy enrl cell. ew cll rrvl re lss I. 30/80 clls/s. lss I.2 46/80 clls/s. Fg.7. Dsrbuons of vlble nd consumed cpcy, consderng heerogeneous rffc dsrbuon n cells. Fg. 7 shows he evoluon of he vlble nd consumed cpcy n ddon o he effecve cpcy compued when heerogeneous rffc dsrbuon s consdered. oe h lhough mx cpcy s dfferen n cells, droppng probbly (Fg. 6b) chnges ccordng wh vlues shown n Fg. 7. Smlr resuls re obned when n negron of rel nd no rel me rffc s ssumed (Fg. 3 shows droppng probbly). Alhough resuls re no shown here, he evluon of he nerference levels shows h n nerference-bsed A n he wy of [] resuls n n underesmon of he cpcy. Inerference hreshold mus be se ccordng wh dely reuremens of MS nd dped o e no ccoun he me vrons on rffc sources dsrbuons. V. OLUSIOS In hs pper, we hve evlued he performnce of A mehod for he W-DMA reverse ln, where pce rnsmsson hs been consdered for rel me nd non rel me servces. A cenrlzed demnd ssgnmen proocol hs been mplemened n he pce level n order o provde QoS. Resuls obned from pce level provde useful nformon n order o nduce cpcy bound. Dely consrns ssoced o ndvdul rffc clsses re consdered n order o se n ccure hreshold of A. The resuls show h he proposed A hreshold gurnee sfe dmsson boh consderng unform or non-unform rffc dsrbuons n cells, gven h pce droppng probbly s lwys mnned under he QoS consrns. On he oher hnd, he proposed vrble reservon polcy gurnees QoS nd GoS. VI. AKOWLEDGMETS Ths wor hs been suppored by he grns IYT TI nd TI from he Mnsry of Scence nd Technology of Spnsh Governmen nd FEDER. VII. REFEREES [] 3GPP TS MA Proocol Specfcon,v.3.6, Dec [2] A. Hernández Soln, A. Vldovnos Brdjí, F. sdevll Plco, Performnce Anlyss of Pce Schedulng Sreges for Mulmed Trffc n WDMA, Proc. IEEE VT 2002 Sprng. [3] A. Smph, S. Kumr nd J. M. Holzmn, Power onrol nd Resource Mngemen for Mulmed DMA Wreless Sysem, Proc. IEEE PIMR 95. [4] A. M. Verb nd A. J. Verb, Erlng pcy of power conrolled DMA sysem, IEEE JSA, vol., no. 3, Aug. 993, pp [5] Y. Ishw,. Umed, pcy Desgn nd Performnce of ll Admsson onrol n ellulr DMA Sysems, IEEE JSA, vol. 5, no 8. Oc. 997, pp [6] J. Q. J. h, W. Zhung,, pcy Anlyss for onnecon Admsson onrol n Indoor Mulmed DMA Wreless ommuncons. Wreless Personl ommuncons 2, 2000, pp , Kluwer. [7] 3GPP TS RF Sysem Scenros. v.3..0., Jun [8] A. J. Verb, A. M. Verb, Sof hndoff exends DMA cell coverge nd ncreses reverse ln cpcy, IEEE JSA, vol. 2, no 8, Oc. 994, pp [9] 3GPP TS 25.0 UTRA (UE) FDD; Rdo Trnsmsson nd Recepon, v.3.5.0, Mr [0] Unversl Moble Telecommuncons Sysem (UMTS). Selecon procedures for he choce of rdo rnsmsson echnologes of he UMTS, UMTS Techncl Repor 30.03, v Apr [] H. Holm, A. Tosl, WDMA for UMTS. Rdo Access For Thrd Generon Moble ommuncons, John Wley &Sons, 2000, pp. 25. [2] M. ghshneh, M. Schwrz, Dsrbued ll Admsson onrol n Moble/Wreless ewors, IEEE JSA. vol. 4., no4, My 996, pp [3] M. Olver Rer, Admsson onrol Sregy Bsed on Vrble Reservon For Wreless Mul-Med ewor., Proc. WPM 99. [4] A. Hernández Soln, A. Vldovnos Brdjí, F. sdevll Plco, pcy Anlyss nd ll Admsson Technues for DMA Pce Trnsmsson Sysems, Proc. IEEE MW 2002.

Supporting information How to concatenate the local attractors of subnetworks in the HPFP

Supporting information How to concatenate the local attractors of subnetworks in the HPFP n Effcen lgorh for Idenfyng Prry Phenoype rcors of Lrge-Scle Boolen Newor Sng-Mo Choo nd Kwng-Hyun Cho Depren of Mhecs Unversy of Ulsn Ulsn 446 Republc of Kore Depren of Bo nd Brn Engneerng Kore dvnced

More information

Lecture 4: Trunking Theory and Grade of Service (GOS)

Lecture 4: Trunking Theory and Grade of Service (GOS) Lecure 4: Trunkng Theory nd Grde of Servce GOS 4.. Mn Problems nd Defnons n Trunkng nd GOS Mn Problems n Subscrber Servce: lmed rdo specrum # of chnnels; mny users. Prncple of Servce: Defnon: Serve user

More information

Simplified Variance Estimation for Three-Stage Random Sampling

Simplified Variance Estimation for Three-Stage Random Sampling Deprmen of ppled Sscs Johnnes Kepler Unversy Lnz IFS Reserch Pper Seres 04-67 Smplfed rnce Esmon for Three-Sge Rndom Smplng ndres Quember Ocober 04 Smplfed rnce Esmon for Three-Sge Rndom Smplng ndres Quember

More information

Advanced Electromechanical Systems (ELE 847)

Advanced Electromechanical Systems (ELE 847) (ELE 847) Dr. Smr ouro-rener Topc 1.4: DC moor speed conrol Torono, 2009 Moor Speed Conrol (open loop conrol) Consder he followng crcu dgrm n V n V bn T1 T 5 T3 V dc r L AA e r f L FF f o V f V cn T 4

More information

Origin Destination Transportation Models: Methods

Origin Destination Transportation Models: Methods In Jr. of Mhemcl Scences & Applcons Vol. 2, No. 2, My 2012 Copyrgh Mnd Reder Publcons ISSN No: 2230-9888 www.journlshub.com Orgn Desnon rnsporon Models: Mehods Jyo Gup nd 1 N H. Shh Deprmen of Mhemcs,

More information

Dynamic Power Allocation and Routing for Time Varying Wireless Networks

Dynamic Power Allocation and Routing for Time Varying Wireless Networks Dynmc Power Allocon nd Roung for Tme Vryng Wreless Neworks Mchel J. Neely hp://we.m.edu/mjneely/www MIT LIDS: mjneely@m.edu Asrc We consder dynmc roung nd power llocon for wreless nework wh me vryng chnnels.

More information

Memory Size Estimation of Supercomputing Nodes of Computational Grid using Queuing Theory

Memory Size Estimation of Supercomputing Nodes of Computational Grid using Queuing Theory Inernonl Journl of Compuer Applcons (0975 8887) Volume 8 No., Ocober 00 Memory Sze Esmon of Supercompung Nodes of Compuonl Grd usng Queung Theory Rhul Kumr Insue of Envronmen & Mngemen Lucknow, Ind. Dr.

More information

ANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES DATA

ANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES DATA Tn Corn DOSESCU Ph D Dre Cner Chrsn Unversy Buchres Consnn RAISCHI PhD Depren of Mhecs The Buchres Acdey of Econoc Sudes ANOTHER CATEGORY OF THE STOCHASTIC DEPENDENCE FOR ECONOMETRIC MODELING OF TIME SERIES

More information

Chapter 2: Evaluative Feedback

Chapter 2: Evaluative Feedback Chper 2: Evluive Feedbck Evluing cions vs. insrucing by giving correc cions Pure evluive feedbck depends olly on he cion ken. Pure insrucive feedbck depends no ll on he cion ken. Supervised lerning is

More information

Numerical Simulations of Femtosecond Pulse. Propagation in Photonic Crystal Fibers. Comparative Study of the S-SSFM and RK4IP

Numerical Simulations of Femtosecond Pulse. Propagation in Photonic Crystal Fibers. Comparative Study of the S-SSFM and RK4IP Appled Mhemcl Scences Vol. 6 1 no. 117 5841 585 Numercl Smulons of Femosecond Pulse Propgon n Phoonc Crysl Fbers Comprve Sudy of he S-SSFM nd RK4IP Mourd Mhboub Scences Fculy Unversy of Tlemcen BP.119

More information

Lecture 2 M/G/1 queues. M/G/1-queue

Lecture 2 M/G/1 queues. M/G/1-queue Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly

More information

An improved statistical disclosure attack

An improved statistical disclosure attack In J Grnulr Compung, Rough Ses nd Inellgen Sysems, Vol X, No Y, xxxx An mproved sscl dsclosure c Bn Tng* Deprmen of Compuer Scence, Clforn Se Unversy Domnguez Hlls, Crson, CA, USA Eml: bng@csudhedu *Correspondng

More information

e t dt e t dt = lim e t dt T (1 e T ) = 1

e t dt e t dt = lim e t dt T (1 e T ) = 1 Improper Inegrls There re wo ypes of improper inegrls - hose wih infinie limis of inegrion, nd hose wih inegrnds h pproch some poin wihin he limis of inegrion. Firs we will consider inegrls wih infinie

More information

Electromagnetic Transient Simulation of Large Power Transformer Internal Fault

Electromagnetic Transient Simulation of Large Power Transformer Internal Fault Inernonl Conference on Advnces n Energy nd Envronmenl Scence (ICAEES 5) Elecromgnec Trnsen Smulon of rge Power Trnsformer Inernl Ful Jun u,, Shwu Xo,, Qngsen Sun,c, Huxng Wng,d nd e Yng,e School of Elecrcl

More information

FINANCIAL ECONOMETRICS

FINANCIAL ECONOMETRICS FINANCIAL ECONOMETRICS SPRING 07 WEEK IV NONLINEAR MODELS Prof. Dr. Burç ÜLENGİN Nonlner NONLINEARITY EXISTS IN FINANCIAL TIME SERIES ESPECIALLY IN VOLATILITY AND HIGH FREQUENCY DATA LINEAR MODEL IS DEFINED

More information

A Kalman filtering simulation

A Kalman filtering simulation A Klmn filering simulion The performnce of Klmn filering hs been esed on he bsis of wo differen dynmicl models, ssuming eiher moion wih consn elociy or wih consn ccelerion. The former is epeced o beer

More information

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files)

Decompression diagram sampler_src (source files and makefiles) bin (binary files) --- sh (sample shells) --- input (sample input files) . Iroduco Probblsc oe-moh forecs gudce s mde b 50 esemble members mproved b Model Oupu scs (MO). scl equo s mde b usg hdcs d d observo d. We selec some prmeers for modfg forecs o use mulple regresso formul.

More information

Direct Current Circuits

Direct Current Circuits Eler urren (hrges n Moon) Eler urren () The ne moun of hrge h psses hrough onduor per un me ny pon. urren s defned s: Dre urren rus = dq d Eler urren s mesured n oulom s per seond or mperes. ( = /s) n

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

Developing Communication Strategy for Multi-Agent Systems with Incremental Fuzzy Model

Developing Communication Strategy for Multi-Agent Systems with Incremental Fuzzy Model (IJACSA) Inernonl Journl of Advnced Compuer Scence nd Applcons, Developng Communcon Sregy for Mul-Agen Sysems wh Incremenl Fuzzy Model Sm Hmzeloo, Mnsoor Zolghdr Jhrom Deprmen of Compuer Scence nd Engneerng

More information

Query Data With Fuzzy Information In Object- Oriented Databases An Approach The Semantic Neighborhood Of Hedge Algebras

Query Data With Fuzzy Information In Object- Oriented Databases An Approach The Semantic Neighborhood Of Hedge Algebras (IJCSIS) Inernonl Journl of Compuer Scence nd Informon Secury, Vol 9, No 5, My 20 Query D Wh Fuzzy Informon In Obec- Orened Dbses An Approch The Semnc Neghborhood Of edge Algebrs Don Vn Thng Kore-VeNm

More information

Motion Feature Extraction Scheme for Content-based Video Retrieval

Motion Feature Extraction Scheme for Content-based Video Retrieval oon Feure Exrcon Scheme for Conen-bsed Vdeo Rerevl Chun Wu *, Yuwen He, L Zho, Yuzhuo Zhong Deprmen of Compuer Scence nd Technology, Tsnghu Unversy, Bejng 100084, Chn ABSTRACT Ths pper proposes he exrcon

More information

Privacy-Preserving Bayesian Network Parameter Learning

Privacy-Preserving Bayesian Network Parameter Learning 4h WSEAS In. Conf. on COMUTATIONAL INTELLIGENCE, MAN-MACHINE SYSTEMS nd CYBERNETICS Mm, Flord, USA, November 7-9, 005 pp46-5) rvcy-reservng Byesn Nework rmeer Lernng JIANJIE MA. SIVAUMAR School of EECS,

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

( ) [ ] MAP Decision Rule

( ) [ ] MAP Decision Rule Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure

More information

Multi-load Optimal Design of Burner-inner-liner Under Performance Index Constraint by Second-Order Polynomial Taylor Series Method

Multi-load Optimal Design of Burner-inner-liner Under Performance Index Constraint by Second-Order Polynomial Taylor Series Method , 0005 (06) DOI: 0.05/ mecconf/06700005 ICMI 06 Mul-lod Opml Desgn of Burner-nner-lner Under Performnce Index Consrn by Second-Order Polynoml ylor Seres Mehod U Goqo, Wong Chun Nm, Zheng Mn nd ng Kongzheng

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X

Interval Estimation. Consider a random variable X with a mean of X. Let X be distributed as X X ECON 37: Ecoomercs Hypohess Tesg Iervl Esmo Wh we hve doe so fr s o udersd how we c ob esmors of ecoomcs reloshp we wsh o sudy. The queso s how comforble re we wh our esmors? We frs exme how o produce

More information

EEM 486: Computer Architecture

EEM 486: Computer Architecture EEM 486: Compuer Archecure Lecure 4 ALU EEM 486 MIPS Arhmec Insrucons R-ype I-ype Insrucon Exmpe Menng Commen dd dd $,$2,$3 $ = $2 + $3 sub sub $,$2,$3 $ = $2 - $3 3 opernds; overfow deeced 3 opernds;

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

Hidden Markov Model. a ij. Observation : O1,O2,... States in time : q1, q2,... All states : s1, s2,..., sn

Hidden Markov Model. a ij. Observation : O1,O2,... States in time : q1, q2,... All states : s1, s2,..., sn Hdden Mrkov Model S S servon : 2... Ses n me : 2... All ses : s s2... s 2 3 2 3 2 Hdden Mrkov Model Con d Dscree Mrkov Model 2 z k s s s s s s Degree Mrkov Model Hdden Mrkov Model Con d : rnson roly from

More information

September 20 Homework Solutions

September 20 Homework Solutions College of Engineering nd Compuer Science Mechnicl Engineering Deprmen Mechnicl Engineering A Seminr in Engineering Anlysis Fll 7 Number 66 Insrucor: Lrry Creo Sepember Homework Soluions Find he specrum

More information

Physics 201 Lecture 2

Physics 201 Lecture 2 Physcs 1 Lecure Lecure Chper.1-. Dene Poson, Dsplcemen & Dsnce Dsngush Tme nd Tme Inerl Dene Velocy (Aerge nd Insnneous), Speed Dene Acceleron Undersnd lgebrclly, hrough ecors, nd grphclly he relonshps

More information

CS 268: Packet Scheduling

CS 268: Packet Scheduling Pace Schedulng Decde when and wha pace o send on oupu ln - Usually mplemened a oupu nerface CS 68: Pace Schedulng flow Ion Soca March 9, 004 Classfer flow flow n Buffer managemen Scheduler soca@cs.bereley.edu

More information

S Radio transmission and network access Exercise 1-2

S Radio transmission and network access Exercise 1-2 S-7.330 Rdio rnsmission nd nework ccess Exercise 1 - P1 In four-symbol digil sysem wih eqully probble symbols he pulses in he figure re used in rnsmission over AWGN-chnnel. s () s () s () s () 1 3 4 )

More information

Optimization of Pollution Emission in Power Dispatch including Renewable Energy and Energy Storage

Optimization of Pollution Emission in Power Dispatch including Renewable Energy and Energy Storage eserch Journl of Appled cences, Engneerng nd Technology (3): 59-556, I: -767 Mxwell cenfc Orgnzon, ubmed: Aprl, Acceped: My, Publshed: ecember, Opmzon of Polluon Emsson n Power spch ncludng enewble Energy

More information

0 for t < 0 1 for t > 0

0 for t < 0 1 for t > 0 8.0 Sep nd del funcions Auhor: Jeremy Orloff The uni Sep Funcion We define he uni sep funcion by u() = 0 for < 0 for > 0 I is clled he uni sep funcion becuse i kes uni sep = 0. I is someimes clled he Heviside

More information

MODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB

MODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB MODELLING AND EXPERIMENTAL ANALYSIS OF MOTORCYCLE DYNAMICS USING MATLAB P. Florn, P. Vrání, R. Čermá Fculy of Mechncl Engneerng, Unversy of Wes Bohem Asrc The frs pr of hs pper s devoed o mhemcl modellng

More information

Linear Response Theory: The connection between QFT and experiments

Linear Response Theory: The connection between QFT and experiments Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure

More information

Motion. Part 2: Constant Acceleration. Acceleration. October Lab Physics. Ms. Levine 1. Acceleration. Acceleration. Units for Acceleration.

Motion. Part 2: Constant Acceleration. Acceleration. October Lab Physics. Ms. Levine 1. Acceleration. Acceleration. Units for Acceleration. Moion Accelerion Pr : Consn Accelerion Accelerion Accelerion Accelerion is he re of chnge of velociy. = v - vo = Δv Δ ccelerion = = v - vo chnge of velociy elpsed ime Accelerion is vecor, lhough in one-dimensionl

More information

UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS. M.Sc. in Economics MICROECONOMIC THEORY I. Problem Set II

UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS. M.Sc. in Economics MICROECONOMIC THEORY I. Problem Set II Mcroeconomc Theory I UNIVERSITY OF IOANNINA DEPARTMENT OF ECONOMICS MSc n Economcs MICROECONOMIC THEORY I Techng: A Lptns (Note: The number of ndctes exercse s dffculty level) ()True or flse? If V( y )

More information

Political Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019.

Political Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019. Polcal Economy of Insuons and Developmen: 14.773 Problem Se 2 Due Dae: Thursday, March 15, 2019. Please answer Quesons 1, 2 and 3. Queson 1 Consder an nfne-horzon dynamc game beween wo groups, an ele and

More information

THE EXISTENCE OF SOLUTIONS FOR A CLASS OF IMPULSIVE FRACTIONAL Q-DIFFERENCE EQUATIONS

THE EXISTENCE OF SOLUTIONS FOR A CLASS OF IMPULSIVE FRACTIONAL Q-DIFFERENCE EQUATIONS Europen Journl of Mhemcs nd Compuer Scence Vol 4 No, 7 SSN 59-995 THE EXSTENCE OF SOLUTONS FOR A CLASS OF MPULSVE FRACTONAL Q-DFFERENCE EQUATONS Shuyun Wn, Yu Tng, Q GE Deprmen of Mhemcs, Ynbn Unversy,

More information

Applied Statistics Qualifier Examination

Applied Statistics Qualifier Examination Appled Sttstcs Qulfer Exmnton Qul_june_8 Fll 8 Instructons: () The exmnton contns 4 Questons. You re to nswer 3 out of 4 of them. () You my use ny books nd clss notes tht you mght fnd helpful n solvng

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

Background and Motivation: Importance of Pressure Measurements

Background and Motivation: Importance of Pressure Measurements Imornce of Pressre Mesremens: Pressre s rmry concern for mny engneerng lcons e.g. lf nd form drg. Cvon : Pressre s of fndmenl mornce n ndersndng nd modelng cvon. Trblence: Velocy-Pressre-Grden ensor whch

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More information

Comb Filters. Comb Filters

Comb Filters. Comb Filters The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of

More information

The Characterization of Jones Polynomial. for Some Knots

The Characterization of Jones Polynomial. for Some Knots Inernon Mhemc Forum,, 8, no, 9 - The Chrceron of Jones Poynom for Some Knos Mur Cncn Yuuncu Y Ünversy, Fcuy of rs nd Scences Mhemcs Deprmen, 8, n, Turkey m_cencen@yhoocom İsm Yr Non Educon Mnsry, 8, n,

More information

4.8 Improper Integrals

4.8 Improper Integrals 4.8 Improper Inegrls Well you ve mde i hrough ll he inegrion echniques. Congrs! Unforunely for us, we sill need o cover one more inegrl. They re clled Improper Inegrls. A his poin, we ve only del wih inegrls

More information

Contraction Mapping Principle Approach to Differential Equations

Contraction Mapping Principle Approach to Differential Equations epl Journl of Science echnology 0 (009) 49-53 Conrcion pping Principle pproch o Differenil Equions Bishnu P. Dhungn Deprmen of hemics, hendr Rn Cmpus ribhuvn Universiy, Khmu epl bsrc Using n eension of

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

Robustness Experiments with Two Variance Components

Robustness Experiments with Two Variance Components Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

A NEW INTERPRETATION OF INTERVAL-VALUED FUZZY INTERIOR IDEALS OF ORDERED SEMIGROUPS

A NEW INTERPRETATION OF INTERVAL-VALUED FUZZY INTERIOR IDEALS OF ORDERED SEMIGROUPS ScInLhore),7),9-37,4 ISSN 3-536; CODEN: SINTE 8 9 A NEW INTERPRETATION O INTERVAL-VALUED UZZY INTERIOR IDEALS O ORDERED SEMIGROUPS Hdy Ullh Khn, b, Nor Hnz Srmn, Asghr Khn c nd z Muhmmd Khn d Deprmen of

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More information

An Intelligent Agent Negotiation Strategy in the Electronic Marketplace Environment

An Intelligent Agent Negotiation Strategy in the Electronic Marketplace Environment An Inellgen Agen Negoon regy n he Elecronc Mreplce Envronmen Mlm Lou, Ionn Rouss, Lmbros Pechlvnos 3 Technologcl Educonl Insue of Wesern Mcedon, Deprmen of Busness Admnsron, Kol, Kozn 5, GREECE e-ml: lou@elecom.nu.gr

More information

INVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES

INVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES Brodogrdnj/Shpuldng Volume 65 Numer 3, 214 Ferd Ckc Muhsn Aydn ISSN 7-215X eissn 1845-5859 INVESTIGATION OF HABITABILITY INDICES OF YTU GULET SERIES IN VARIOUS SEA STATES UDC 629.5(5) Professonl pper Summry

More information

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005

Dynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005 Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc

More information

Journal of Theoretical and Applied Information Technology.

Journal of Theoretical and Applied Information Technology. Journal of heorecal and Appled Informaon echnology 5-9 JAI. All rghs reserved. www.ja.org NEW APPROXIMAION FOR ANDOFF RAE AND NUMBER OF ANDOFF PROBABILIY IN CELLULAR SYSEMS UNDER GENERAL DISRIBUIONS OF

More information

System Design and Lift Traffic Analysis

System Design and Lift Traffic Analysis Sysem Desgn nd Lf Trffc Anlyss IMechE CPD Cerfce Course 7 Dec, 06 Idel Knemcs () 3 Idel Knemcs () 4 Idel Knemcs (3) Tme for Jerk Acc Jerk v 5 Idel Knemcs (4) 6 7 Tme ken o comlee ourney of dsnce d wh o

More information

Remember: Project Proposals are due April 11.

Remember: Project Proposals are due April 11. Bonformtcs ecture Notes Announcements Remember: Project Proposls re due Aprl. Clss 22 Aprl 4, 2002 A. Hdden Mrov Models. Defntons Emple - Consder the emple we tled bout n clss lst tme wth the cons. However,

More information

Jordan Journal of Physics

Jordan Journal of Physics Volume, Number, 00. pp. 47-54 RTICLE Jordn Journl of Physcs Frconl Cnoncl Qunzon of he Free Elecromgnec Lgrngn ensy E. K. Jrd, R. S. w b nd J. M. Khlfeh eprmen of Physcs, Unversy of Jordn, 94 mmn, Jordn.

More information

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng

More information

Obtaining the Optimal Order Quantities Through Asymptotic Distributions of the Stockout Duration and Demand

Obtaining the Optimal Order Quantities Through Asymptotic Distributions of the Stockout Duration and Demand he Seond Inernonl Symposum on Sohs Models n Relbly Engneerng Lfe Sene nd Operons Mngemen Obnng he Opml Order unes hrough Asympo Dsrbuons of he Sokou Duron nd Demnd Ann V Kev Nonl Reserh omsk Se Unversy

More information

Concise Derivation of Complex Bayesian Approximate Message Passing via Expectation Propagation

Concise Derivation of Complex Bayesian Approximate Message Passing via Expectation Propagation Concse Dervon of Complex Byesn Approxme Messge Pssng v Expecon Propgon Xngmng Meng, Sheng Wu, Lnlng Kung, Jnhu Lu Deprmen of Elecronc Engneerng, Tsnghu Unversy, Bejng, Chn Tsnghu Spce Cener, Tsnghu Unversy,

More information

Principle Component Analysis

Principle Component Analysis Prncple Component Anlyss Jng Go SUNY Bufflo Why Dmensonlty Reducton? We hve too mny dmensons o reson bout or obtn nsghts from o vsulze oo much nose n the dt Need to reduce them to smller set of fctors

More information

AN INTRODUCTORY GUIDELINE FOR THE USE OF BAYESIAN STATISTICAL METHODS IN THE ANALYSIS OF ROAD TRAFFIC ACCIDENT DATA

AN INTRODUCTORY GUIDELINE FOR THE USE OF BAYESIAN STATISTICAL METHODS IN THE ANALYSIS OF ROAD TRAFFIC ACCIDENT DATA AN INTRODUCTORY GUIDELINE FOR THE USE OF BAYESIAN STATISTICAL METHODS IN THE ANALYSIS OF ROAD TRAFFIC ACCIDENT DATA CJ Molle Pr Eng Chef Engneer : Trffc Engneerng Deprmen of Economc Affrs, Agrculure nd

More information

A Novel Efficient Stopping Criterion for BICM-ID System

A Novel Efficient Stopping Criterion for BICM-ID System A Novel Effcen Soppng Creron for BICM-ID Sysem Xao Yng, L Janpng Communcaon Unversy of Chna Absrac Ths paper devses a novel effcen soppng creron for b-nerleaved coded modulaon wh erave decodng (BICM-ID)

More information

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions II The Z Trnsfor Tocs o e covered. Inroducon. The Z rnsfor 3. Z rnsfors of eleenry funcons 4. Proeres nd Theory of rnsfor 5. The nverse rnsfor 6. Z rnsfor for solvng dfference equons II. Inroducon The

More information

Software Reliability Growth Models Incorporating Fault Dependency with Various Debugging Time Lags

Software Reliability Growth Models Incorporating Fault Dependency with Various Debugging Time Lags Sofwre Relbly Growh Models Incorporng Ful Dependency wh Vrous Debuggng Tme Lgs Chn-Yu Hung 1 Chu-T Ln 1 Sy-Yen Kuo Mchel R. Lyu 3 nd Chun-Chng Sue 4 1 Deprmen of Compuer Scence Nonl Tsng Hu Unversy Hsnchu

More information

Macroscopic quantum effects generated by the acoustic wave in a molecular magnet

Macroscopic quantum effects generated by the acoustic wave in a molecular magnet Cudnovsky-Fes-09034 Mcroscopc qunum effecs genered by e cousc wve n moleculr mgne Gwng-Hee Km ejong Unv., Kore Eugene M. Cudnovksy Lemn College, CUNY Acknowledgemens D. A. Grnn Lemn College, CUNY Oulne

More information

Reinforcement Learning for a New Piano Mover s Problem

Reinforcement Learning for a New Piano Mover s Problem Renforcemen Lernng for New Pno Mover s Problem Yuko ISHIWAKA Hkode Nonl College of Technology, Hkode, Hokkdo, Jpn Tomohro YOSHIDA Murorn Insue of Technology, Murorn, Hokkdo, Jpn nd Yuknor KAKAZU Reserch

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Modeling of Jitter Characteristics for the Second Order Bang-Bang CDR

Modeling of Jitter Characteristics for the Second Order Bang-Bang CDR Modelng of Jer hrcerscs for he Second Order Bng-Bng D H Adrng nd Hossen Mr Nm eceved Aug ; receved n revsed 8 Se ; cceed 3 Oc ABSA Bng-Bng clock nd d recovery BBD crcus re hrd nonlner sysems due o he nonlnery

More information

Attribute Reduction Algorithm Based on Discernibility Matrix with Algebraic Method GAO Jing1,a, Ma Hui1, Han Zhidong2,b

Attribute Reduction Algorithm Based on Discernibility Matrix with Algebraic Method GAO Jing1,a, Ma Hui1, Han Zhidong2,b Inernaonal Indusral Informacs and Compuer Engneerng Conference (IIICEC 05) Arbue educon Algorhm Based on Dscernbly Marx wh Algebrac Mehod GAO Jng,a, Ma Hu, Han Zhdong,b Informaon School, Capal Unversy

More information

Minimum Squared Error

Minimum Squared Error Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > 0 for ll smples y i solve sysem of liner inequliies MSE procedure y i i for ll smples

More information

1.B Appendix to Chapter 1

1.B Appendix to Chapter 1 Secon.B.B Append o Chper.B. The Ordnr Clcl Here re led ome mporn concep rom he ordnr clcl. The Dervve Conder ncon o one ndependen vrble. The dervve o dened b d d lm lm.b. where he ncremen n de o n ncremen

More information

FI 3103 Quantum Physics

FI 3103 Quantum Physics /9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon

More information

Minimum Squared Error

Minimum Squared Error Minimum Squred Error LDF: Minimum Squred-Error Procedures Ide: conver o esier nd eer undersood prolem Percepron y i > for ll smples y i solve sysem of liner inequliies MSE procedure y i = i for ll smples

More information

Tighter Bounds for Multi-Armed Bandits with Expert Advice

Tighter Bounds for Multi-Armed Bandits with Expert Advice Tgher Bounds for Mul-Armed Bnds wh Exper Advce H. Brendn McMhn nd Mhew Sreeer Google, Inc. Psburgh, PA 523, USA Absrc Bnd problems re clssc wy of formulng exploron versus exploon rdeoffs. Auer e l. [ACBFS02]

More information

Cubic Bezier Homotopy Function for Solving Exponential Equations

Cubic Bezier Homotopy Function for Solving Exponential Equations Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.

More information

Fall 2010 Graduate Course on Dynamic Learning

Fall 2010 Graduate Course on Dynamic Learning Fall 200 Graduae Course on Dynamc Learnng Chaper 4: Parcle Flers Sepember 27, 200 Byoung-Tak Zhang School of Compuer Scence and Engneerng & Cognve Scence and Bran Scence Programs Seoul aonal Unversy hp://b.snu.ac.kr/~bzhang/

More information

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose

More information

II. Light is a Ray (Geometrical Optics)

II. Light is a Ray (Geometrical Optics) II Lgh s a Ray (Geomercal Opcs) IIB Reflecon and Refracon Hero s Prncple of Leas Dsance Law of Reflecon Hero of Aleandra, who lved n he 2 nd cenury BC, posulaed he followng prncple: Prncple of Leas Dsance:

More information

3. Renewal Limit Theorems

3. Renewal Limit Theorems Virul Lborories > 14. Renewl Processes > 1 2 3 3. Renewl Limi Theorems In he inroducion o renewl processes, we noed h he rrivl ime process nd he couning process re inverses, in sens The rrivl ime process

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

M/G/1/GD/ / System. ! Pollaczek-Khinchin (PK) Equation. ! Steady-state probabilities. ! Finding L, W q, W. ! π 0 = 1 ρ

M/G/1/GD/ / System. ! Pollaczek-Khinchin (PK) Equation. ! Steady-state probabilities. ! Finding L, W q, W. ! π 0 = 1 ρ M/G//GD/ / System! Pollcze-Khnchn (PK) Equton L q 2 2 λ σ s 2( + ρ ρ! Stedy-stte probbltes! π 0 ρ! Fndng L, q, ) 2 2 M/M/R/GD/K/K System! Drw the trnston dgrm! Derve the stedy-stte probbltes:! Fnd L,L

More information

Chapter Lagrangian Interpolation

Chapter Lagrangian Interpolation Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and

More information

Let s treat the problem of the response of a system to an applied external force. Again,

Let s treat the problem of the response of a system to an applied external force. Again, Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem

More information

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25

Modeling and Predicting Sequences: HMM and (may be) CRF. Amr Ahmed Feb 25 Modelg d redcg Sequeces: HMM d m be CRF Amr Ahmed 070 Feb 25 Bg cure redcg Sgle Lbel Ipu : A se of feures: - Bg of words docume - Oupu : Clss lbel - Topc of he docume - redcg Sequece of Lbels Noo Noe:

More information

ISSN 075-7 : (7) 0 007 C ( ), E-l: ssolos@glco FPGA LUT FPGA EM : FPGA, LUT, EM,,, () FPGA (feldprogrble ge rrs) [, ] () [], () [] () [5] [6] FPGA LUT (Look-Up-Tbles) EM (Ebedded Meor locks) [7, 8] LUT

More information

To Possibilities of Solution of Differential Equation of Logistic Function

To Possibilities of Solution of Differential Equation of Logistic Function Arnold Dávd, Frnše Peller, Rená Vooroosová To Possbles of Soluon of Dfferenl Equon of Logsc Funcon Arcle Info: Receved 6 My Acceped June UDC 7 Recommended con: Dávd, A., Peller, F., Vooroosová, R. ().

More information

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon

More information

An Integrated Control Model for Managing Network Congestion

An Integrated Control Model for Managing Network Congestion 1 1 0 1 0 1 An Inegred Conrol Model or Mngng Nework Congeson Heng Hu* Deprmen o Cvl Engneerng Unversy o Mnneso 00 Pllsbury Drve S.E. Mnnepols MN Eml: huxxx@umn.edu (*Correspondng Auhor) Henry X. Lu Deprmen

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

A Time Truncated Improved Group Sampling Plans for Rayleigh and Log - Logistic Distributions

A Time Truncated Improved Group Sampling Plans for Rayleigh and Log - Logistic Distributions ISSNOnline : 39-8753 ISSN Prin : 347-67 An ISO 397: 7 Cerified Orgnizion Vol. 5, Issue 5, My 6 A Time Trunced Improved Group Smpling Plns for Ryleigh nd og - ogisic Disribuions P.Kvipriy, A.R. Sudmni Rmswmy

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu

More information