05 wo np02 e1_1 umts capacity estimation-64

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05 wo np02 e1_1 umts capacity estimation-64

  1. 1. UMTS Capacity Estimation ZTE University
  2. 2. Content      UMTS Service mode Common Capacity Design Methods Uplink Capacity Estimation Downlink Capacity Estimation Estimation Examples
  3. 3. CS Domain Service Model Call Duration Call Setup  Call Release Call Duration Call Setup Call Release Key parameter: call frequency, call duration, blocking probability  Average Erlang = call frequency ×duration / 3600
  4. 4. PS Domain Service Model Session (WWW) Click Click Call Session (WWW) Click Call Dormant Active Packet Call (Web Page) Dormant Active Active Packet Active Packet
  5. 5. PS Domain Service Model  Dormant status and Active status conversion  Every session can contain several packet calls, different data services and different user types have different features  Resource occupied by packet call varies alone with the burst transmission
  6. 6. PS Service Model - Example service Bearer Mean Mean Mean Reading rate(k packet packets calls/s time bps) size(byte) in a call ession between calls(second) Email 64 480 32 2 5 www 144 480 25 5 5 Download 64 480 62 2 5 MMS 480 32 2 5 480 267 1 0 64 Streaming 384
  7. 7. PS Domain Service Model Parameter Name Parameter definition Unit DL Bit rate Downlink service bit rate kbps DL Mean Packet Size Mean downlink packet size Mean downlink packet quantity Mean calls of downlink session Transmission duration between downlink calls Mean packets in one downlink session Downlink service quality requirement Downlink activating factor Byte DL Mean # Packets DL Mean Calls/session DL Reading time between calls DL Mean packets in a call DL BLER DL PS Activity Factor second
  8. 8. PS Domain Service Model UL Bit rate Uplink service bit rate kbps UL Mean Packet Size Mean uplink packet size Byte UL Mean # Packets Mean uplink packet quantity Mean calls of uplink session Transmission duration between uplink calls Mean packets in one uplink session Uplink service quality requirement Uplink activating factor UL Mean Calls/session UL Reading time between calls UL Mean packets in a call UL BLER UL PS Activity Factor BHSA Busy hour attempt sessions second
  9. 9. Service Category Service type Basic characteristic Example Conversation The time relationship between information entities in the stream must be kept, session mode (small delay, strict delay jitter requirement) Voice, video phone Streaming The time relationship between information entities in the stream must be kept Multimedia data stream Interactive Request/response mode, data integrity must be kept Web browser, internet game Background Data integrity must be kept, high delay tolerance Email download in background
  10. 10. User Group Classification   Classification principle Based on user consumption capability and consumption behavior User type High-end Group features High income group, enterprises and managers. Providing high rate access service. Medium-end General enterprises and some high income consumers. Providing information inquiry, mobile entertainment and mobile financial services. Lower-end  Middle income class and students. Providing data services such as SMS and some mobile game services Note: User groups are distinguished by service type, service rate, service quality and service intensity.
  11. 11. Service Penetration    Percentage of user distribution in different application environments are different Percentage of high-end, middle-end and lower-end users in different application environments are different Service model statistic characteristic relates to percentages mentioned above A B C D Total 10% 30% 30% 30% High End 30% 10% 5% 0% Medium End 40% 50% 40% 10% Low End 30% 40% 55% 90%
  12. 12. Traffic Analysis for Single Subscriber  CS Domain Service Mean busy type hour calls Activate Mean factor speed (kbps) Mean busy hour erl per user Tel. 1.25 72 0.5 12.2 0.025 Video phone  Mean call duration 0 (lower end) 54 0.05 (medium end) 0.1 (high end) 1 64 0 (lower end) 0.00075 (middle end) 0.0015 (high end) Mean busy hour Erl. Per user=mean busy hour calls*mean call duration/3600
  13. 13. Traffic Analysis for Single Subscriber  PS Domain Service type   Mean packet size (byte) Mean packets in a call Low-end user 50% 0.01 480 25 5 4.8 75% 0.02 480 25 5 9.6 High-end user  BHSA Medium end user Web service Penetration rate Mean Busy hour calls/s throughput ession per user (kbit) 100% 0.03 480 25 5 14.4 Node: penetration rate means the percentage of UEs which support this service in total UEs. Busy hour throughput per user = BHSA* mean calls in a session *mean packets in a call*mean packet size*8/1000 Equivalent Erl = Busy hour throughput per user / (Bearer rate *3600)
  14. 14. Traffic Analysis for Single Subscriber  The average traffic according to the Service Model in each transmission environment is :  Average traffic for each subscriber = ∑ Ratio of subscriber group* Service penetration * average traffic of this group
  15. 15. Content      UMTS Service mode Common Capacity Design Methods Uplink Capacity Estimation Downlink Capacity Estimation Estimation Examples
  16. 16. UMTS Network Dimensioning Procedure Input:system load requirment and coverage requirement Downlink coverage estimation Uplink coverage estimation Quantity of BSs satisfying uplink coverage Uplink capacity estimation Quantity of BSs satisfying uplink capacity Quantity of BSs satisfying downlink coverage Compare the results and evaluate the larger one Based on power Quantity B of channels availably provided by every cell on the downlink Quantity A of channels to be provided by every cell on the downlink No A<B Yes End Add BSs Downlink capacity estimation Based on traffic type
  17. 17. Capacity Estimation Procedure  Hybrid service intensity analysis   Uplink capacity estimation   The UMTS system provides multiple services and the hybrid service intensity analysis makes the system capacity consumed by various services equivalent to that consumed by a single service. Estimate the NodeB number that meets the service demand based on the hybrid service intensity analysis. Downlink capacity estimation  It is a verification process. The NodeB transmission power formula is used to calculate the channel number that can be provided by the current NodeB scale so as to verify whether this channel number can meet the capacity requirement, and if it cannot, stations need be added.
  18. 18. Common Capacity Design Methods  Equivalent Erlangs method  Post Erlang-B method  Campbell method
  19. 19. Equivalent Erlangs Method   Principle: Make a service equivalent to another service and calculate the total Erl. Example     Service A: 1 channel for each connection and the total is 12 erl. Service B: 3 channels for each connection and the total is 6 erl. If 1 erl service B = 3 erl service A, altogether 30 erl service A shall be equivalent and 39 channels shall be required (under 2% blocking rate). If 3 erl service A = 1 erl service B, altogether 10 erl service B shall be equivalent and 17 service B channels shall be required (equal 17*3=51 service A channels under 2% blocking rate).
  20. 20. Equivalent Erlangs Method Capacities meeting the same GoS are different Low speed service equivalent + 2 Erl low speed service 1 Erl high speed service High speed service equivalent The calculation result is related to the equivalent mode
  21. 21. Post Erlang-B Method  Principle: Calculate the capacity required by each service respectively and add them.  Example      Service A: 1 channel for each connection and the total is 12 erl. Service B: 3 channels for each connection and the total is 6 erl. Service A requires 19 channels (under 2% blocking rate). Service B requires 12 service B channels (equal 12*3=36 service A channels, under 2% blocking rate). These two services require 19+36=55 channels
  22. 22. Post Erlang-B Method  Suppose services A and B are the same kind, where,    Based on the Post Erlang-B method       Service A: 1 channel for each connection and the total is 12 erl. Service B: 1 channel for each connection and the total is 6 erl. Service A requires 19 channels (under 2% blocking rate). Service B requires 12 channels (under 2% blocking rate). Altogether 19+12=31 channels are required. Based on traditional Erlang-B method The total traffic of services A and B is 12+6=18 erl and altogether 26 channels are required under 2% blocking rate. Required channel number estimated through the Post Erlang-B method is too large.
  23. 23. Post Erlang-B Method Capacities meeting the same GoS are different 1 Erl service A + 1 Erl service B The calculation result is too pessimistic 1 Erl service A and 1 Erl service B
  24. 24. Campbell Method  Principle: Make multiple services equivalent to a virtual service and calculate the capacity on the basis of the virtual service.   c    erl a ci erli ai2 i i i i Ci  ai  Capacity  c OfferedTra  ffic  c c  capacity. factor a  mean * n v  var iance * n ai  amplitude.of .service.i Ci  capacity.of .service.i
  25. 25. Campbell Method  Example    Service A: 1 channel for each connection and the total is 12 erl. Service B: 3 channels for each connection and the total is 6 erl Mean & variance  erl  a    erl  a  i i i 2 i  12 1  6  3  30  12 12  6  32  66
  26. 26. Campbell Method  Capacity factor c  66 c   2.2  30  Virtual traffic α 30 Offered Traffic    13.63 c 2.2  21 channels (virtual channels) are required to meet the virtual traffic under 2% blocking rate.
  27. 27. Campbell Method  Under 2% blocking rate, channel number required by each service is shown as follows:  Service A: C1  (21  2.2)  1  47  Service B: C 2  (21  2.2)  3  49  Different channel numbers are required to meet the GOS requirements of diversified services. Compared with the former two methods, the calculation result through the Campbell method is more reasonable. 
  28. 28. Campbell Method  If the reference service is the voice service: Amplitudeservice Rservice * Eb / Noservice * v service  Rvoice * Eb / Novoice * vvoice
  29. 29. Content      UMTS Service mode Common Capacity Design Methods Uplink Capacity Estimation Downlink Capacity Estimation Estimation Examples
  30. 30. Uplink Load Analysis  Eb/No the receive signal in the NodeB must reach Eb/No required by the service demodulation. Pj W ( Eb / No) j   v j R j I total  P j W: indicates the chip rate. vj: indicates user j’s activation factor. Rj: indicates user j’s data rate. Pj : indicates user j’s signal receive power Itotal: indicates total broadband receive power with the thermal noise power included in the NodeB.
  31. 31. Uplink Load Analysis  The receive power at the NodeB receive end should meet the following formula so that the user signal can meet the demodulation requirement: 1 Pj  1  W ( Eb No ) jRjvj Define a connection load factor Lj: Lj   Itotal  LjItotal Pj Itotal 1  1 W ( Eb No ) jRjvj The total receive power of all N users from one cell is: N N P  L I j 1 j j 1 j total
  32. 32. Uplink Load Analysis  The total receive power at the NodeB receive end consists of three parts: I tatal  Pin  Pother  PN P indicates the total interference power of in-cell users. in P indicates the total interference power of out-cell users. other PN indicates the NodeB thermal noise power. Neighbor cell’s interference factor I  i= Other cell interference /Local cell interference 
  33. 33. Uplink Load Analysis  The total user receive power of the NodeB: N Pin  Pother  (1  i )  L I j tatal j 1 Define the noise lifting as the ratio of total broadband receive power to the noise power of the NodeB: I total I tatal NR    PN I tatal  Pin  P other 1 N 1  (1  i ) L j 1 j
  34. 34. Uplink Load Analysis  Define the uplink load factor to be: UL  (1  i ) N  j 1  N L j  (1  i )  1 W j 1 1  ( Eb / No) j R j v j The noise lifting can be represented to be: 1 NR  1  UL NR(dB)  10 LOG10 (1  UL )
  35. 35. Uplink Load Analysis  The uplink capacity is limited by interference increase: 11 10 9 noise rise(dB) 8 7 6 5 4 3 Cantonese 2 25 30 35 40 45 user number 50 55 60 65 Shanghai dialect Minnan dialect mandarin
  36. 36. Uplink Capacity Estimation   In the case of a single service, evaluate the channel quantity provided by every cell according to the load formula and further evaluate the total number of base stations satisfying the uplink capacity requirement. To budget composite traffic, based on the Campbell algorithm, make different services consumption on the system resource equivalent to the single service consumption on the system resource, and then evaluate the quantity of channels to be provided by every cell according to load formula, and further evaluate the number of base stations satisfying the composite traffic requirement.
  37. 37. R99/HSUPA mixed calculation  During the uplink capacity calculation ,decide how much uplink load will be designed in R99 and HSUPA  By simulation, calculate how much PS throughput can be carried by HSUPA  Calculate how much of the remaining PS service to be carried by R99
  38. 38. R99 Uplink Capacity Algorithm Calculate the quantity of equivalent voice channels in the cell Calculate equivalent intensity of services Calculate the variance, average value and capacity factor of the composite service System virtual traffic A A/B Number of cells Quantity of virtual channels in the cell Virtual service capacity B of the cell
  39. 39. Content      UMTS Service mode Common Capacity Design Methods Uplink Capacity Estimation Downlink Capacity Estimation Estimation Examples
  40. 40. Downlink Load Analysis  To correctly demodulate useful signals, the UE must overcome interference from the following three aspects I tatal  (1   ) P  Pother  PN P represents total power of signals from current cell P represents total interference power of signals other from the outside of the cell PN represents thermal noise power from the UE  represents orthogonal factor of the downlink
  41. 41. Downlink Load Analysis  By referring to the derivation means of uplink load factor, denote the downlink load factors as follows:  DL  N v j 1 ( Eb / No) j j W / Rj [(1   )  i ] W represents chip rate at 3.84M chip/s vj represents activation factor of the user j R j represents bit rate of the user j  represents the average orthogonal factor in a cell i represents the average ratio of the NodeB power from other cell to that from this cell
  42. 42. Downlink Load Analysis  Total downlink power allocation N MS PBS _ TX   Eb   N  N 0j  W L j W j 1 Rj  1   DL Where, N MS represents the noise power spectrum density on the front of the receiver in the mobile station NMS  KT + NF  - 174 + NF L (suppose T = 290 K ) represents the average path loss of the cell
  43. 43. Downlink Load Analysis The downlink capacity is limited by transmission power of the base station 46 44 42 . . . Tx Power (dBm)  40 38 36 Downlink power Three users Two users One user Public channel 34 32 46 48 50 52 54 56 user number 58 60 62 64
  44. 44. Downlink Load and Scale Analysis  Estimate downlink capacity after analyzing the channel quantity required by uplink capacity, and observe whether the downlink can support the mobile station to work in the designated coverage area and its channel quantity reaches the channel quantity generated by the uplink  Calculate the quantity of equivalent voice channels to be provided by every cell  Calculate the quantity of equivalent voice channels availably provided by every cell  Compare the above two results
  45. 45. Content      UMTS Service mode Common Capacity Design Methods Uplink Capacity Estimation Downlink Capacity Estimation Estimation Examples
  46. 46. Assumed Conditions      Channel environment: downtown area TU 3 km/h System design load: 50% Voice service blocking rate: 2% Interference factor from the adjacent cell: 0.65 Area of the city zone: 40.8 square kilometers
  47. 47. Assumed Conditions Voice CS64 PS64/64 PS64/128 PS64/384 Uplink: Data rate(k) 12.2 64 64 64 64 Activity factor 0.67 1 1 1 1 Eb/No 4.2 2.87 1.6 1.6 1.6 Forecast traffic 3000 400 100 5 2 Voice CS64 PS64/64 PS64/128 PS64/384 Downlink: Data rate(k) 12.2 64 64 128 384 Activity factor 0.58 1 1 1 1 Eb/No 7.7 7.7 7.4 6.4 8 Forecast traffic 3000 400 100 35 20
  48. 48. Estimation Flow Chart Input: system load requirement and coverage requirement Downlink coverage estimation Uplink coverage estimation Quantity of base stations satisfying uplink coverage Quantity of base stations satisfying coverage requirement Uplink capacity estimation Quantity of base stations satisfying downlink coverage Compare the results and evaluate the larger one Quantity A of channels required by the cell Based on traffic model Based on power Quantity B of channels provided by the cell No Add base stations A<B Yes End
  49. 49. Uplink Coverage Estimation 1. Uplink budget Maximal emission power (dbm) Emission end Antenna gain (dbi) Human body loss (db) Effective emission power Thermal noise power spectrum density (dbm/HZ) Thermal noise power (dbm) Receiver noise coefficient (db) Receiver noise (dbm) Receiving end Interference margin (db) Bit rate (kbit) Processing gain (db) Receiving Eb/No (db) Receiver sensibility Antenna gain (dbi) Line loss Power control margin Soft handoff gain Other Shade fading margin Penetration loss Maximal path loss
  50. 50. Uplink Coverage Estimation 2. Calculate the cell coverage radius based on a specific propagation model: Path loss  k1  k2log(d)  k3Hms  k4log(Hms)  k5log(Heff) + k6log(Heff)log(d)  k7(diffraction loss)  clutter loss k1 k2 k5 k6 Heff Voice Radius (Km) 152.4 44.6 -13.82 -6.55 30 CS64 PS64 0.65 0.5 0.54 PS64/128 PS64/384 Uplink coverage is limited by the CS64 kps service 0.54 0.54
  51. 51. Uplink Coverage Estimation 3. Calculate the quantity of base stations required by uplink Coverage area of the three-sector base station 9 S 3R 2  1.95  0.5  0.5  0.488Km2 8 The quantity of base stations is 40.8/0.488=84
  52. 52. Uplink Capacity Estimation Equivalent intensity of each service Relative amplitude  bit rate for service  bit rate for amplitude 1 Eb Eb N0 N0 for service for amplitude  af Voice: 1 CS64: 64 x 1 x 100.287/12.2 x 0.67 x 10 0.42 = 5.76 PS64/64: 64 x 1 x 100.16/12.2 x 0.67 x 10 0.42 = 4.3 PS64/128: 64 x 1 x 100.16/12.2 x 0.67 x 10 0.42 = 4.3 Equivalent intensity of each service Quantity of equivalent voice channels in the cell Variance, mean and capacity factor of the composite service Virtual traffic A of the system Quantity of virtual channels in the cell A/B PS64/384: 64 x 1 x 100.16/12.2 x 0.67 x 10 0.42 = 4.3 Number of cells Virtual traffic A of the cell
  53. 53. Uplink Capacity Estimation Quantity of equivalent voice channels in the cell Equivalent intensity of each service Variance, mean and capacity factor of the composite service Virtual traffic A of the system Quantity of virtual channels in the cell Virtual traffic A of the cell A/B Number of cells  Mean mean   erli ai  3000  1  400  5.67  100  4.3  5  4.3  2  4.3  5766.1 i  Variance var iance   erli ai  3000  1  400  5.67 2  100  4.32  5  4.32  2  4.32  18271.7  Capacity factor  variance/mean  3.17 i  Virtual traffic of the system  mean/capacity factor  5766.1/3.17  1818.96(Erl)
  54. 54. Uplink Capacity Estimation Quantity of equivalent voice channels in the cell Equivalent intensity of each service Quantity of equivalent voice channels availably provided by the cell   (1  f ) * N  j 1 W 1 1 1 * * R v j Eb No Where,   50% and f  0.65 Variance, mean and capacity factor of the composite service Virtual traffic A of the system Quantity of virtual channels in the cell A/B Number of cells Get the quantity of equivalent voice channels N  54 Virtual traffic A of the cell
  55. 55. Uplink Capacity Estimation Equivalent intensity of each service  Quantity of virtual channels in every cell (Ci  ai ) Capacity  c Quantity of equivalent voice channels in the cell Variance, mean and capacity factor of the composite service Virtual traffic A of the system Quantity of virtual channels in the cell  (54  1)/3.17  16  Virtual traffic of every cell Look up the Erl B table, and provide 9.83Erl for 16 virtual channels in the case of 2% of call loss ratio Quantity of virtual channels in the cell A/B Number of cells Virtual traffic A of the cell
  56. 56. Uplink Capacity Estimation  Number of cells=Virtual traffic of the system/virtual traffic of every =1818.96/9.83=186  Number of three-sector base stations=186/3=62 Equivalent intensity of each service Quantity of equivalent voice channels in the cell Variance, mean and capacity factor of the composite service Virtual traffic A of the system Quantity of virtual channels in the cell A/B Number of cells Virtual traffic A of the cell
  57. 57. Downlink Capacity Estimation  Integrate uplink and downlink coverage budget and uplink capacity budget to determine that there are 84 base stations currently and authenticate whether downlink power meets the requirement. Determine the number of stations Virtual traffic of every cell Quantity of virtual channels in every cell Quantity A of channels to be provided by the cell End Yes NO A<B Add base stations Quantity B of channels availably provided by the ce;; Average traffic of every cell
  58. 58. Downlink Capacity Estimation  Average traffic of various services in every cell CS64: 400/84/3  1.59 Erl PS64/64: 100/84/3  0.4 Erl PS64/128: 35/84/3  0.14 Erl PS64/384: 20/84/3  0.079 Erl Average traffic of every cell Quantity B of channels availably provided by the cell Voice: 3000/84/3  11.9 Erl Determine the number of stations Virtual traffic of every cell Quantity of virtual channels in every cell Quantity A of channels to be provided by the cell A<B Yes End
  59. 59. Downlink Capacity Estimation Determine the number of stations     mean  11.9+ 1.59  7.8 + 0.4  7.3 + 0.14 13.1+ 0.079  50 = 33.04  Variance of composite traffic Average traffic of every cell Virtual traffic of every cell Quantity of virtual channels in every cell Quantity A of channels to be provided by the cell var iance  11.9+ 1.59  7.82 + 0.4  7.32 + 0.14 13.12 + 0.079  50 2 = 355.19   Quantity B of channels availably provided by the cell  Virtual traffic of every cell Equivalent service intensity of each service on the downlink Voice: 1, CS64: 7.8, PS64/64: 7.3 PS64/128: 13.1, PS64/384: 50 Mean of composite traffic Traffic factor  capacity factor  variance/mean  355.19/33.04  10.75 Virtual service capacity of the cell  mean/capacity factor  33.04/10.75  3.07 (Erl) A<B Yes End
  60. 60. Downlink Capacity Estimation Determine the number of stations    Average traffic of every cell Quantity B of channels availably provided by the cell  Quantity of virtual channels in every cell Look up the Erl B table and obtain that the quantity of virtual channels required by 3.07 Erl virtual traffic is 7 Quantity of equivalent voice channels to be provided by every cell (Ci  ai ) Capacity  c Quantity of equivalent voice channels: 7  10.75  1  76 Virtual traffic of every cell Quantity of virtual channels in every cell Quantity A of channels to be provided by the cell A<B Yes End
  61. 61. Downlink Capacity Estimation  Calculate the quantity of channels availably provided by every cell based on power N PN * L * v j * ( Eb / No) j j 1 P N 1 v j 1 j * ( Eb / No) j W / Rj W / Rj Determine the number of stations Average traffic of every cell Virtual traffic of every cell [(1   j )   j ] P represents the maximum service transmission power, which is 13 W  j represents orthogoal factor, which is 0.6 for the multipath channel Quantity of virtual channels in every cell Quant ity B of chann els availa bly provid ed by the cell Quantity A of channels to be provided by the cell L represents the average path loss, which is evaluated by subtracting 6 dBm from the maximum path loss PN represents the noise power spectrum density on the front of the mobile A<B station receiver, and its value is -169 dBm End  j represents interference factor from an adjacent cell. It is 0.65 for the threesector antenna macro cell Obtain that the quantity of equivalent voice channels actually provided by every cell is 71 Yes
  62. 62. Downlink Capacity Estimation    Determine the number of stations Average traffic of every cell Quantity B of channels availably provided by the cell  Comparison The quantity of channels to be provided by every cell is 76 The quantity of channels actually provided by every cell is 71 There are 84 base stations currently, and it cannot satisfy downlink capacity requirement, and some stations should be added. Virtual traffic of every cell Quantity of virtual channels in every cell Quantity A of channels to be provided by the cell A<B Yes End
  63. 63. Downlink Capacity Estimation  Iterative calculation Number of base stations 83 85 76 76 70 71 86 72 71 87 72 71 88  Number of channels provided 69 84  Number of channels required 76 65 72 If there are 88 base stations, the uplink and downlink coverage capacity requirement can be met In the case, the base station coverage radius is 40.8 / 88 / 1.95  0.488 Km

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