An Introduction to 3G Monte-Carlo simulations within ProMan

Transcription

1 An Introducton to 3G Monte-Carlo smulatons wthn ProMan responsble edtor: Hermann Buddendck AWE Communcatons GmbH Otto-Llenthal-Str. 36 D Böblngen Phone: Fax: Issue Date Changes V1.0 Feb Frst verson of document

2 Monte Carlo UMTS system smulator 1 1 Motvaton System smulatons are requred for the network plannng process on order to come to an cost effectve nvestment n ar nterface network nfrastructure. Ths s acheved by reducng the number of Node B (and stes) to a mnmum, stll fulfllng capacty and servce qualty constrants. System smulatons help to evaluate moble network performance n dfferent envronments wth dfferent confguratons or network layouts. Performance ndcators of nterest are network capacty and servce avalablty. There are manly three approaches for the smulaton of UMTS networks: dynamc smulatons, sem-dynamc smulatons (Monte Carlo smulator) and statc smulatons. Dynamc System Smulatons One way to model the network behavor s to use dynamc system smulatons wth qute realstc models for most aspects and effects (power control, soft handover, moblty,...), and to smulate the tme-varant behavor of the network. Detaled results can be obtaned by usng ths method, but t s very tme consumng. It s manly used to valdate or optmze small parts of the network or to perform parameter studes to tune the network settngs. Statc Smulaton / Analytc Approach To speed up the requred smulaton tme and to enable the smulaton of larger areas the consderaton of ndvdual mobles and dscrete moble dstrbutons has to be gven up, and the network capacty s to be predcted wth analytcal methods. Therefore, n a second approach (statc or analytcal) the moble termnals are consdered to be dstrbuted contnuously over the smulaton area,.e. for each pxel of the smulaton area a knd of fractonal moble s assumed, and the complete smulaton area has to be scanned only once. Wth ths statc/analytc approach the capacty of a gven UMTS FDD network layout can be estmated based on the propagaton condtons n a very fast way. Ths s very useful for a frst rough network plannng wth a few teratons necessary to fnd a proper network layout that fulflls your needs. Monte-Carlo Smulatons Another opton for network smulatons s to use Monte-Carlo (MC) smulatons wth less detaled models (compared to the dynamc approach explaned above): tme varant effects are not consdered but many (ndependent) snapshots wth random moble dstrbutons are evaluated. These smulatons are faster than dynamc smulatons but to get statstcally relable results many snapshots have to be carred out and the smulaton tme depends on the number of snapshots and on the number of moble statons. Practcal examples show that the sze of the area that can be consdered s lmted to a few hundred cells. The Monte Carlo (MC) method conssts n repeatng an experence many tmes wth dfferent randomly determned data n order to draw statstcal conclusons. It can be appled for moble networks smulaton. In ths case the repeated experence s called a snapshot and represents a set of Moble Statons n the network wth random poston, state and parameters. The results gven by a hgh number of snapshots are consdered to be representatve of all the possble states of the network. The am of a Monte Carlo smulator s to provde an analyss tool that allows the fast and accurate evaluaton of the performance of a UMTS network durng the plannng and optmzaton phases.

3 Monte Carlo UMTS system smulator 2 Fgure 1-1: Comparson of the dfferent categores of UMTS system smulators 2 Smulaton Approach The results gven by the Monte-Carlo smulator are essentally related to two concepts: The coverage (proporton of the area n whch a communcaton lnk can be successfully establshed) The capacty (maxmum traffc supported by the network) The man features of the Monte Carlo system smulator are the followng: Fast coverage and capacty predcton Mean nose rse and mean power per cell evaluaton Power dstrbuton hstograms for DL and UL Determnaton of the maxmum capacty of the network 2.1 Locaton dependent traffc The traffc densty can be defned ether homogeneous for the complete smulaton area or locaton dependent. Ths latter possblty can be realzed by loadng a clutter map together wth a table, whch translates the traffc or morpho class to a traffc densty (Erl./sqkm). Durng the Monte-Carlo Smulaton the dscrete mobles are generated accordng to these specfed traffc denstes. 2.2 CDMA downlnk orthogonalty factor The CDMA downlnk orthogonalty (OF) factor has an mportant nfluence on the cell capacty because the level of ntra-cell-nterference s scaled wth ths factor. Ths s to consder the loss n CDMA code orthogonalty due to multpath propagaton. So the grade of orthogonalty manly depends on the channel profle. In general ProMan offers three possbltes to set the orthogonalty factor: constant OF LOS/NLOS dependng OF CIR dependng OF The last two optons requre ray-optcal propagaton models to determne whether a possble recever locaton,.e. a pxel, s n Lne of Sght (LOS) condton, or to compute the Channel Impulse Response (CIR). As the determnaton of the CIR wth the propagaton model s qute tme consumng, ths feature elmnates a part of the Monte-Carlo smulators benefts compared to full dynamc smulatons (although t has to be performed only once for each fxed antenna/cell confguraton and t s avalable va look-up tables afterwards). So n most cases the OF wll be set to a constant (average) value.

4 Monte Carlo UMTS system smulator 3 OF (value) meanng 1 perfect orthogonalty (1 path channel) 0..1 reduced orthogonalty 0 no orthogonalty at all Table 1: Defnton of Orthogonalty factor wthn ProMan Typcal values for the orthogonalty factor are OF = 0.5 for the ITU Vehcular A profle and 0.9 for the ITU Pedestran A channel profle. For the constant OF model an approprate (mean) value for the expected OF has to be used. 2.3 Servce Mx The smulator supports the defnton of dfferent servces and moble dfferent moble type categores. Wthn the traffc specfcaton (ether homogenous or locaton dependent, see above) each servce can be consdered ndvdually, so that t s possble to defne an arbtrary servce mx. The most mportant parameters related to the servce are the followng: Btrate (UL/DL) max. lnk power (UL/DL) requred E b /N o (to ensure servce qualty) traffc parameters (Erl./sqkm) 2.4 System modelng The am of the downlnk computaton s to allocate the downlnk transmt powers n order to match the servce downlnk E b /N 0 requrements. The followng scheme (Fgure 2-1) should help you to fgure out the context of the calculaton of the downlnk power for MS(). The am of the uplnk computaton s fndng the uplnk transmt power,.e. whch power the moble staton has to use to communcate wth ts servng base staton. The followng pcture depcts the context of the prevous calculaton of the uplnk transmt power. Fgure 2-1: Downlnk problem Fgure 2-2: Uplnk problem The requred E b /N o for each Moble Staton s known by lnk level smulaton and s provded by the system suppler. It s defned as:

5 Monte Carlo UMTS system smulator 4 E PTX a b BS (), G = N (1 α) I + I + P DL, MS o own other N (1) Where: E b /N o s the sgnal to nose rato (on a net bt bass) P TX s the requred transmt power for the lnk (Servng sector of a Base Staton BS() of Moble Staton to MS a BS(), s the lnear attenuaton for the path BS() to MS G s the processng gan α s the downlnk orthogonalty factor for the consdered lnk (UL: α = 0) I own s the nterference level from the own cell I other s the nterference level from the other cells The prevously mentoned nterference levels are gven by the followng: downlnk uplnk I own= PTX a j BS (), + PCCH a (2) I own= BS( ) BS (), PTX a (3) UL, j j, BS ( ) j _ served _ by _ BS ( ) j_ served_ by_ BS( ) j j I other= PTX a u, j u, + PCCH a (4) I u u, other= PTX a (5) UL u BS () j _ served _ by _ u, j j, BS ( ) j other_ cell 2.5 Lnear Equaton System Comng from the fundamental equatons shown n the prevous chapter, a lnear system wth the lnk transmsson powers as solutons can be bult. Ths system can be descrbed usng a matrx notaton. P TX s the vertcal vector contanng the desred solutons for all transmsson powers. Usng the fundamental equatons the followng coeffcents can be determned: downlnk uplnk AP TX = B (6) C PTX = D (7) E B = (1 α) P a + P + a P b CCHBS ( ) BS (), N No DL, MS u BS() u, CCHu A Eb abs (), (1 α ); (1) No MS E = a ;(2) b, j BS( j), No MS a BS (), G;(3) (8) (10) D C E b = P (9) N No UL, BS ( ), j Eb a No UL, BS ( ) = a G;(2) BS (), jbs, ( ) ;(1) (11) wth: (1) s vald f the MS j s dfferent from the MS and belongs to the same cell as the MS ;.e. they have the same servng sector BS()=BS(j). (2) s used when the MS j belongs to another cell,.e. BS(j) BS() (3) s used when =j wth: (1) s vald f j s dfferent from (2) s used when =j

6 Monte Carlo UMTS system smulator Coverage estmaton It was prevously mentoned that the MS generaton phase of the algorthm ncludes a coverage check. The algorthm used durng ths verfcaton s detaled n ths chapter Plot channel based coverage crterons In UMTS systems the coverage s manly dependng on the common plot channels (CPICH) parameters and the nterference level. Here, a MS s consdered to be n the coverage zone f the followng two constrants are fulflled: The receved CPICH level s above a predefned lmt (for example -90dBm) CPICH carrer to nterference rato s above the threshold (for example -14dB) As the transmt powers are not a pror known, assumptons have to be made n order to estmate the nterference level. The smulator assumes that the DL and UL transmt powers are maxmum, and the resultng nterferences s scaled by a predefned scalng factor Uplnk related coverage crterons The condton checked by the smulator s the ablty for a MS to communcate wth ts servng BS wth a suffcent uplnk E b /N 0 rato assumng that the MS s transmttng wth full power and the nterference n the network s fxed (predefned). The pont s that the nterference contrbutons are unknown at ths step of the calculatons and therefore a fxed load defned by the user defned maxmum load factor and a scalng factor s used to assumes a typcal nterference level. 2.7 Capacty determnaton For the capacty determnaton only the MS n the coverage zone are consdered. Ths enables a separate evaluaton of capacty and coverage problems. The requred transmt powers n DL and UL drectons are determned accordng to the lnear equaton system. Then the snapshot can be evaluated,.e. t s checked f all MS of ths snapshot can be served by the network. Practcally t means that some constrants are tested for both UL and DL. The snapshot s declared vald f all these condtons are fulflled. Wth the percentage of vald snapshots the probablty for a certan capacty can be estmated based on the traffc load. Usually a fxed number of 200 snapshots s performed as t turned out wthn many smulatons that more snapshots do not ncrease the relablty of the statstcal results Downlnk snapshot evaluaton The downlnk transmt powers are determned to fulfll the servce downlnk E b /N 0 requrements. A vald snapshot must fulfll the followng constrants: The downlnk transmt powers must be n the range [0..P max,dl ] The total transmt power for the BS must not be exceeded by the sum of all lnks powers Uplnk snapshot evaluaton The UL transmt powers are computed accordngly to match the uplnk servce E b /N 0 for all MS. A vald snapshot must fulfll the followng condton: All determned transmsson power must be n the range [0..P max,ul ]

7 Monte Carlo UMTS system smulator 6 Fgure 2-3: Snapshot operatons 3 Examples smulaton To demonstrate the performance of the Monte Carlo smulator, some examples and case studes wll now be presented. The frst example llustrates the case of a bad scenaro that has to be mproved wth the help of the Monte Carlo smulator. 3.1 Optmzaton method The coverage nformaton provded by the smulator s calculated under typcal traffc condtons. You can defne the condtons of ths evaluaton by adaptng the maxmum load value factor. If you don t want to consder the effect of the coverage you can smply set ths parameter to zero. A consequence of ths method for the coverage estmaton s that the coverage results provded are ndependent of the traffc generated durng the smulaton: they depend only of the fxed load factor. Of course the capacty determned later on depends on the resultng coverage ( as t s well known for WCDMA networks). In ths smulaton model s has to be assured (by the user) that the assumed load (n coverage predcton) matches roughly the load determned by the capacty evaluaton. 3.2 The example scenaro A sngle smulaton s very useful n order to have a fast nsght n the network for a defned traffc level. It provdes nterestng nformaton about the behavor of the network n ths state. For example, let s magne that you need to estmate the qualty of the followng layout of a network. You want to test the network for a hgh data rate servce (384kbps for DL, 64kbps for UL). You expect at least 60% of vald snapshots for DL and UL for 25 users n the whole network (1.2 user per cell) and t s requred that more than 90% of the area s covered for a typcal load of 70%.

8 Monte Carlo UMTS system smulator 7 Fgure 3-1: Example network layout n a dense urban area (Pars France). Extract of the result fle: Parameters: Thu Dec 08 14:33: Number of snapshots: 100 Traffc scale factor: Average Number of MS: 25.0 Repartton of the generated MS per servce: Servce 384 -> % Coverage for Servce 384: Coverage -> % Nr MS (accordng to traffc parameters) -> <=> 1.19 users per cell Mean Nr MS n the coverage zone -> <=> 1.19 users per cell Mean CPICH C/I -> db Mean throughput per cell -> DL: kb/s UL: kb/s Capacty: Mean total Nr of MS per cell: 1.19 Mean total throughput per cell: DL: kb/s UL: kb/s Result of the smulatons: Downlnk -> 18 % of the Snapshots are vald Uplnk -> 100 % of the Snapshots are vald Mean power per Cell: mw <=> dbm (mean over all BS of all vald snapshots) Mean Nose Rse per Cell: <=> db (average over all BS of all vald snapshots) Influence of the CPICH level On the prevous example, the Monte-Carlo smulator ndcates that the layout and network parameters should be mproved because the capacty of the network does not satsfy the requrements. You can try to decrease the CPICH level n order to mnmze the nterferences. Of

9 Monte Carlo UMTS system smulator 8 course the mpact on coverage has to be observed. The effect of the CPICH power at the BS can be checked by makng an addtonal smulaton wth a CPICH level dvded by two compared to the prevous smulaton (now: 30dBm). A part of the result fle s lsted below Coverage Servce 384 : Coverage -> % Nr MS (accordng to traffc parameters) -> <=> 1.19 users per cell Mean Nr MS n the coverage zone -> <=> 1.18 users per cell Mean CPICH C/I -> dB Mean throughput per cell-> DL: kb/s UL: kb/s Capacty Servce 384 : Mean total Nr of MS per cell: 1.19 Mean total throughput per cell: DL: kb/s UL: kb/s Result of the smulatons: Downlnk -> 65 % of the Snapshots are vald Uplnk -> 100 % of the Snapshots are vald Mean power per Cell: [mw] <=> dbm (mean over all BS of all vald snapshots) Mean Nose Rse per Cell: <=> db (average over all BS of all vald snapshots) From the results the nfluence of the CPICH power level can be clearly seen: 65% of vald snapshots for DL nstead of 18% wth the frst smulaton) mpact on coverage s neglgble (99% nstead of 99.84%) Influence of the orthogonalty factor In the prevous smulatons the orthogonalty factor (quantfyng the negatve mpact of the multpaths propagaton on the performances of the network n downlnk) was set to 60%. Assumng a more optmstc value for the orthogonalty factor (e.g. 80%) the followng results are obtaned (CPCIH power agan 30dBm): Coverage Servce 384 : Coverage -> % Nr MS (accordng to traffc parameters) -> <=> 1.24 users per cell Mean Nr MS n the coverage zone -> <=> 1.23 users per cell Mean CPICH C/I -> dB Mean throughput per cell-> DL: kb/s UL: kb/s Capacty Servce 384 : Mean total Nr of MS per cell: 1.24 Mean total throughput per cell: DL: kb/s UL: [kb/s] Result of the smulatons: Downlnk -> 78 % of the Snapshots are vald Uplnk -> 100 % of the Snapshots are vald Mean power per Cell: mw <=> dbm (mean over all BS of all vald snapshots) Mean Nose Rse per Cell: <=> db (average over all BS of all vald snapshots)

10 Monte Carlo UMTS system smulator 9 You can notce that the prevous change has the followng consequences on the smulaton results: 78% of downlnk vald snapshots nstead of 65% whereas the traffc has been ncreased smlar coverage, mean power per cell, and nose rse values If the orthogonalty factor was set to 1.0 (perfect orthogonalty) 97% of the snapshots would be vald for DL under the same condtons. Ths shows the great mpact of the downlnk orthogonalty factor. The low capacty of ths network s due to the smple desgn of the layout (orentaton of the antennas, locaton of the base statons). 3.3 Evaluaton of maxmum capacty Performng several smulatons wth the parameters as descrbed above and addtonally wth ncreasng network load the followng results are obtaned. It can be seen, that n ths (not optmzed) network scenaro a cell capacty of about 400kbps s predcted f 90% of the UE dstrbutons should be served. For a hgher cell throughput,.e. more mobles, the probablty to serve a certan constellaton decreases, but there are stll (some) advantageous constellaton that stll can be served. That s why there s a soft degradaton n the servng probablty. Furthermore the resultng MS and BS power dstrbutons are depcted n the fgure below. Fgure 3-2: Percentage of successful snapshots for DL (left) and average DL power per cell (rght). Fgure 3-3: Example for the nose rse output (wth ncreasng traffc load)

11 Monte Carlo UMTS system smulator 10 Fgure 3-4: DL lnk power dstrbuton (left) and UE power dstrbuton (rght) 4 Propagaton Predcton The 3G Monte-Carlo smulaton approach s based on the predcted path loss matrces for all base statons. So after havng defned a network scenaro, frst the propagaton predcton has to be performed, and n a second step the network predcton (.e. 3g Monte-Carlo smulaton) can be run. In small scenaros the propagaton may be predcted for each transmtter for the complete smulaton area. Consderng large areas a predcton radus can be defned for each transmtter. Note that the defnton of the predcton radus has to ensure suffcent overlap of neghborng cells. Ths overlap s not only requred for best server determnaton, but also for nterference consderatons,.e. the overlap should be desgned to account for the nterference of approx. two rngs of nterferng cells at each pxel. That means that the predcton radus should be approx. 2-3 tmes the nter-ste dstance. Many dfferent propagaton models are avalable wthn ProMan. Dependng on the scenaro, the envronment, and the avalable database the best sutable propagaton model must be chosen. The Monte Carlo smulator s able to handle the propagaton results generated by all avalable models. Further nformaton about the propagaton models can be found on the followng web ste: Furthermore t should be mentoned that addtonal nformaton about the propagaton models s also avalable n form of applcaton notes. See the followng web ste: 5 Further Informaton For further nformaton you are nvted to vst AWE Communcatons webste or to send an e-mal to the responsble edtor of ths document net@awe-communcatons.com