Radio network planning and optimisation for umts 2nd edition phần 10 ppsx

The conclusion drawn from the results is that the autotuning of cell-based downlink link maxima and load targets improves significantly the system performance as measured with throughput particularly in comparison with cautious or incorrect parameter settings. Therefore, the feature is a promising candidate for implementation into the NMS. 9.3.6 Capacity Optimisation and Traffic Balancing In the following sections some mechanisms to share resources between circuit switched and packet switched traffic or between cells is discussed. A similar logic can be applied to GPRS as demonstrated in Chapt er 10. The traffic control mechanism between systems, inter-system handover, is introduced in Chapter 4. 9.3.6.1 Autotuning of P-CPICH Power The primary objective of the methods presented in this section is to minimise the usage of power resources for the P-CPICH, while ensuring good enough P-CPICH coverage. This is even more important if the power levels of all other common channels are set with respect to P-CPICH power – i.e., higher amounts of power resources can be saved and more traffic served. The original work is presented in [36]. Method for Autotuning P-CPICH defines the power of the P-CPICH in the cell. Increasing or decreasing the pilot power makes the cell larger or smaller. Thus, the tuning of pilot powers can be applied to balance cell load among neighbouring cells and, additionally, to provide sufficient signal reception for the terminals. The common pilot coverage issues are discussed in Section 9.3.3.3. In the rule-based method of [36] the pilot power of a cell was increased or decreased by 0.5 dB if the cell load was significantly lower or higher than the neighb our cell load as indicated by statistics in Section 9.3.3, Equation (9.14). If the load was not Advanced Analysis Methods and Radio Access Network Autotuning 561 Table 9.8 Micro 46-cell scenario: results for circuit switched speech and packet switched traffic. Measure Parameter setting —————————————————————————————————————————— PtxTarget PtxTarget PtxTarget PtxTarget 33 dBm, 33 dBm, 35.5 dBm, 35.5 dBm, fixed offset DL link fixed offset DL link 5.5 dB maxima 5.5 dB maxima tuned tuned Number of ended RT CS calls 14472 13696 15057 14674 Probability of degraded RT CS BLER 7.0% 2.0% 18.9% 3.8% RT CS blocking probability 8.2% 13% 4.5% 6.9% RT CS throughput [kbps/cell] 500 473 520 507 NRT PS throughput [kbps/cell] 264 255 542 494 DL total throughput [kbps/cell] 764 728 1063 1001 significantly unbalanced among the cells, but the pilot signal reception was significantly lower or higher than the target, the pilot power was increased or decreased by 0.5 dB, respectively. Pilot power was limited between 3% and 15% of the maximum BS power. Pilot power control actions are presented in Table 9.9. In Table 9.9 t (load balance) and c (coverage balance) are calculated as follows. The test statistic of the difference between own-cell and neighbour cell loads was obtained using: t 0 ¼ m 1 À m 2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi v 1 N k þ v 2 X i6¼k N i v u u t ð9:23Þ Statistic t 0 was quantified to three levels of load balance: t ¼ À1; t 0 < À2 0; À2 t 0 2 1; t 0 > 2 8 < : ð9:24Þ The terminals in the sector reported the received E c =I 0 of the pilot. For each reported E c =I 0 cell-specific co unter N ecio was incremented. If E c =I 0 exceeded À18 dB, counter N over was also incremented. The counters were reset at the point of pilot power adjustment as shown in Table 9.9. The test statistic of the difference between cell coverage and target coverage, C , was calculated according to: c 0 ¼ N over À N ecio Á C ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N ecio Á C Áð1 ÀCÞ p ð9:25Þ The target coverage was set to C ¼ 0.98. As above, statistic c 0 was quantified to three levels of coverage balance: c ¼ À1; c 0 < À2 0; À2 c 0 2 1; c 0 > 2 8 < : ð9:26Þ 562 Radio Network Planning and Optimisation for UMTS Table 9.9 Pilot power control actions. Load balance, t Coverage balance, c Pilot power change and counter reset À1 À1 Increase pilot and reset all counters À1 0 Increase pilot and reset load counters À1 1 Increase pilot and reset load counters 0 À1 Increase pilot and reset coverage counters 0 0 No change and no reset 0 1 Decrease pilot and reset coverage counters 1 À1 Decrease pilot and reset load counters 1 0 Decrease pilot and reset load counters 1 1 Decrease pilot and reset all counters Results from Simulations A full set of results for the optimisation of pilot power using a rule-based method is presented in [36]. The mixed macro-cell and micro-cell scenario depicted in Section 9.3.2 was used in the simulations. Table 9.10 shows the improvement of downlink packet data performance measures obtained with the autotuning method. Table 9.11 shows that the average downlink total transmission powers (PtxTotal) increased slightly when rule-based optimisation was applied. The initial P-CPICH power values were 5% of the total maximum transmission power in each cell – i.e., 200 mW for micro-cells and 1 W for macro-cells. Increased total BS powers explain the improved performance of rule- based optimisation. This can be taken as an indication that the load was more evenly distributed. As the target pilot coverage was 98%, the resul ts in Table 9.11 show that the coverage deteriorated with autotuning. Coverage could be improved by increasing its weight in the cost function and by adjusting the rule priorities. The resul ts corroborated that the fixed setting of the pilot power –i.e., by default to 5% of the maximum BS power – is a warranted choice. Coverage was sufficient and the packet data performance was close to that obtained with the pilot control. Thus, pilot power control may only benefit performance in congested cells. However, load balancing, which was clearly attained, can benefit single cells whose performance is not reflected in the total network performance but which subjectively can be highly significant. The total BS powers show the effect of load balancing obtained with the pilot power control. Mean macro-cell total power moved closer to the target of 10 W and the standard deviation of the power decreased. The decrease of standard deviation was shown in micro-cells as well. Advanced Analysis Methods and Radio Access Network Autotuning 563 Table 9.10 Improvement of packet data performance with pilot power optimisation compared with initial pilot power setting. Rule-based method [%] Total throughput 4 Active session throughput 21 Allowed bit rate 31 95th percentile of packet delay 5 Table 9.11 Performance results of pilot power optimisation. No optimisation Rule-based method Macro PtxTotal (std) [W] 9.0 (2.0) 9.4 (1.3) Micro PtxTotal (std) [W] 1.9 (0.3) 1.9 (0.2) Macro pilot power (std) [W] 1 (0) 1.6 (0.9) Micro pilot power (std) [W] 0.2 (0) 0.24 (0.15) 1 ÀMacro coverage [%] 1.6 2.3 1 ÀMicro coverage [%] 1.3 3.5 These results suggest that, first, the balancing of load among cells and aiming to achieve a specific coverage level is feasible using the simple heuristic rules that control pilot power. Second, the pilot power control method improves the air interface performance. Finally, the method is a valid means for improving network operability by its automation. In [19] another optimisation technique for adjusting pilot powers in CDMA systems is presented. The idea in this technique is to reduce the unused pilot signals seen by mobiles, thus reducing the number of pilot powers within a certain margin relat ive to the strongest pilot. The results in [19] showed that the low ering of pilot power pollution gives some improvement in downlink link coverage and capacity in addition to reduction in deployment efforts spent in optimising pilot powers. 9.3.6.2 Autotuning of Dedicated Capacity for Non-Real Time Services or for High-speed Downlink Packet Access The basic RRM without the dedicated capacity for NRT services allows only one threshold for any traffic when performing admission control for the new entering RAB, when modifying an existing RAB or when performing packet scheduling. This means that RT and NRT traffic will use the same entry criteria and in case the cell is fully loaded with RT traffic there will be no room for NRT traffic at all. With the dedicated NRT traffic capacity feature the operator can guarantee at least some capacity for NRT traffic as well. The dedicated NRT traffic capacity feature provides uplink and downlink target power thresholds for RT and NRT traffic separately. This feature improves the QoS, because it provides a possibility to guarantee some capacity for NRT traffic on a cell- by-cell basis. The capacity reservation for NRT traffic requires support in NEs in terms of algorithms and configuration parameters to do the resource reservations in practice. In Figure 9.37 the idea underlying dedicated NRT capacity reservation is presented. In phases A, B and C both RT and NRT traffic are getting the needed capacity, and there are no traffic restrictions. In phase D NRT traffic experiences capacity shortage 564 Radio Network Planning and Optimisation for UMTS Planned target load Maximum guaranteed NRT traffic capacity ABCDEF RT Traffic NRT Traffic Planned target load Maximum guaranteed NRT traffic capacity ABCDEF RT Traffic NRT Traffic NRT Traffic RT Traffic Figure 9.37 Conceptual presentation of the operation of dedicated non-real time traffic capacity reservation. and new RT RAB setups are rejected until NRT traffic gets the capacity it requires. At point E RT traffic experiences blocking. NRT traffic is allocated the capacity that is left over from the RT traffic. At F both traffic types experience blocking, new RT RAB setups are rejected and NRT traffic is given the maximum guaranteed capacity. Optimisation of dedicated NRT traffic capacity would take care that the threshold controlling the size of the dedicated territory would be adaptive. Some of the resources available in the uplink and downlink can be dedicated to NRT traffic. During heavy load a tradeoff between RT traffic blocking and NRT traffic queuing can be performed. One possible method is to attach costs to blocked and queued bearers. Autotuning can be done so that the dedicated NRT traffic capacity is increased if the cost of queued bearers is significantly higher than the cost of blocked bearers, and correspondingly decreased if the cost of queued bearers is significantly lower than the cost of blocked bearers. Similarly, HSDPA functionality shares the physical and logical resources in terms of power and codes with Dedicated Channels (DCHs). Should the HSDPA performance be degraded due to lack of power resources or codes, a similar method to the one above should reallocate physical and logical resources optimising the performance of HSDPA channels and of DCHs. 9.3.6.3 Intra-frequency Traffic Balancing Using Cell Individual Offsets As discussed in Chapter 4 handovers within the UTRA-FDD system can be classified as intra-frequency handovers and inter-frequency handovers. In intra-frequency soft handover, an MS is allowed to connect simultaneously to several BSs, which are added or removed from the terminal’s active set by applying relative handover thresholds. The most important ones are the addition threshold, the addition timer, the dropping threshold and the dropping timer. In principle, if a received P-CPICH E c =I 0 from a new BS is within a window defined by the addition threshold relative to the best serving BS’s E c =I 0 for a time period longer than the addition timer, it is added into the user’s active set. When the P-CPICH E c =I 0 from a BS in the active set is lower than the P-CPICH E c =I 0 of the best serving BS by a margin defined by the dropping threshold that BS is removed from the active set. Typically, the measurement quantity is P-CPICH E c =I 0 but it can also be path loss. Further, a Cell Individual Offset (CIO) value can be used to make one neighbouring cell more attractive than another. This is demonstrated with Figure 9.38. In order to make the handover to a cell with P-CPICH 3 happen earlier, an offset is applied to manipulate the terminal’s decision. The offset raises the P-CPICH 3 curve. The terminal measures the E c =I 0 levels of the pilot signals of neighbouring cells. The terminal initiates changing of the active set by sending a measurement report and an ASU request to the RNC. The reporting conditions have the following general form: CPICHðmonitoredÞþAdjsEcNoOffsetðbest; monitoredÞ > ReportingCriterionðCPICHðbestÞÞ ð9:27Þ where CPICHðmonitoredÞ and CPICHðbestÞ are the measurement results (E c =I 0 ) of the monitored cell and the best active set cell, respectively; and Advanced Analysis Methods and Radio Access Network Autotuning 565 AdjsEcIoOffsetðbest; monitoredÞ is the cell individual offset added to the measured E c =I 0 of the monitored cell. It is specific to the primary cell in the active set – i.e., there is an offset for each neighbour of a cell. The neighbour set for a specific combination of cells in the active set is a union or intersection of the neighbour sets of the individual active set cells formed using a particular method. The maxi mum size of the combined neighbour set is 32 cells. When an ASU is made, the terminal gets signalled the new neighbour set and CIO- related information. The idea of congestion relief is to utilise these CIOs to force traffic from a highly congested cell to neighbouring cells that are less loaded (see Figure 9.39). Such a situation applies, for example, in the business areas of city centres. A few highly loaded cells might serve certain office buildings, and the surrounded cells are low loaded during business hours. Thus these surrounding cells can be taken to serve the business complex with the proposed method. The offs ets between two cells A and B – i.e., AdjsEcIoOffsetðA; BÞ and AdjsEcIoOffsetðB; AÞ are adjusted if the ratios of blocked calls differ significantly between the cells. Blocking can be measured and evaluated in several ways – for instance, as: . soft blocking due to insufficient power resources (downlink total transmission power exceeding its target level); 566 Radio Network Planning and Optimisation for UMTS Figure 9.38 Cell individual offset. A positive offset is applied to P-CPICH 3 before event evaluation in the terminal. Figure 9.39 Conceptual presentation of the congestion relief logic. After optimisation the average behaviour is the same, but the blocking performance is improved for the highly congested cell. . hard blocking due to insufficient hardware or logical (codes) resources; or . hard and soft blocking combined, soft handover overhead-related information – if abnormal, increased blocking may be due to a poorly set AdditionWindow which is indicated to the user. The user then may wish to perform AdditionWindow optimisation first. The proposed control method gives the best gain if insufficient hardware resources caused the blocking but the method is able to balance the load from soft-blocked cell s to other cells as well. The combination of hard and soft blocking as the blocking measure is the best solution. The algorithm collects blocking statistics during specified hours and/or load conditions and, as soon as significant differences between the blocking of a cell pair is detected, ha ndover event-triggering parameters between the cells are adjusted in order to balance the load with handover actions. With conservative setting of control method parameters, the method reacts slowly to differing blocking ratios. If the average blocking ratio in a cell pair is 2%, the number of samples required for detecting a blocking ratio difference with sufficient statistical accuracy is some hundreds in both cells. When a blocking ratio difference bigger than a certain threshold is detected, the parameters for event-triggered measurement reporting are changed slightly; the change in the CIO values is a function of the difference between the blocking ratio of the cells in question. The control method inherently assumes that the downlink is the limiting direction with respect to power resources. If the load is high in the uplink, the control actions of the method can cause terminals to run out of power. Thus, control actions should be made cautiously with the operator monitoring the cells with high uplink load. Load-balancing Process CIOs are a tool to move the cell border. Thus, adjusting the offsets can reduce traffic in congested cells and increase traffic in low loaded cells. Traffic in congested cell A is moved to a neighbouring less loaded cell B by decreasing AdjsEcIoOffsetðB; AÞ and increasing AdjsEcIoOffsetðA; BÞ. Decreasing AdjsEcIoOffsetðB; AÞ inhibits soft handovers from cell B to cell A – i.e., cell A is more difficultly added to and more easily dropped from the acti ve set when the user is close to cell B. Increasing AdjsEcIoOffsetðA; BÞ makes users close to cell A favour cell B in the soft handover, which moves traffic from other neighbours of cell A to cell B as well. The algorithm may have the following internal parameters that the user can ad just: . Normal – the level of blocking ratio that can still be considered normal. Its value must not be zero. . Step – the adjustment step of the CIOs in decibels. Its range begins with 0.1 dB; it can be a function of the differences in cell-blocking ratios. . Max – the maximum absolute value of the CIOs. . Threshold – the threshold for indicating a significan t difference in the blocking ratios. The parameter determines the sensitivity required to make an offset adjustment. Its conservative values lie between 2% to 3% in absolute terms. However, setting it closer to zero can increase adaptation speed without significant adverse effects. Advanced Analysis Methods and Radio Access Network Autotuning 567 Control is performed for the selected group of cells periodically. The actual value of the period is not crucia l and it can be as often as is practically possible; a change is made when a significant difference in the blocking rates is detected. The control algorithm is described using the steps in the following list, but, before optimisation, the planned CIOs of a selected group of cells, C, are stored in the reference con figura- tion management database: 1. Iterate Steps 2 to 10 for all cell pairs (c 1 ; c 2 ) in the cell group selection. Not e:Donot repeat steps for a cell pair (c 2 ; c 1 )if(c 1 ; c 2 ) is already processed. 2. Obtain the measured blocking ratios for cells (c 1 ; c 2 ) from the performance management database and KPI calculation engine. 3. Obtain the current CIO values from the configuration management database. 4. Check for situations when blocking ratios are not greater than Normal. If the blocking statistics show that cell blocking is at an allowed level and the difference in blocking ratios between cells is less than a pre-defined threshold then move the offsets towards their initial planned values in the reference database (provided that values differ) and continue with Step 10. Otherwise, continue with the next step. 5. Retrieve the KPI for the average blocking ratio. 6. Compute the deviation, D, using the blocking ratios (B 1 ; B 2 for cells 1 and 2, respectively; N 1 ; N 2 being the number of samples, N limit being, e.g., 5). This is obtained using: D ¼ B 1 À B 2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  BBð1 À  BBÞ  1 N 1 þ 1 N 2  s ð9:28Þ where  BB is the average blocking. However, D is zero if: min½  BB; ð1 À  BBÞ ÁN 1 < N limit or min½  BB; ð1 À  BBÞ ÁN 2 < N limit ð9:29Þ 7. Compute the change in the CIOs: DOffset ¼ Step if D > Threshold ÀStep if D < ÀThreshold 0 otherwise ( ð9:30Þ in which Step is the CIO adaptation step. 8. Compute the new CIOs with range checking. 9. If both computed new CIOs of cells c 1 ; c 2 differ from their current setting, change the CIOs and provision the change to network. 10. If there are unprocessed cell pairs, take the next one and continue with Step 2. 11. Reset the congestion measurement counters and KPIs of the cells whose CIOs were changed. It is the operators’ choice what level of congestion to allow and tolerate even after the congestion relief algorithm. The proposed method is not an answer to all blocking- related problems, but it can solve certain traffic hotspot-type situations. Another identified application area for CIO optimisation is for areas along highways. The soft handovers of a mobile user can be controlled by prioritising the adjacency definitions using offset values. Cells intended to cover the highway are higher prioritised 568 Radio Network Planning and Optimisation for UMTS in handover evaluation and thus unnecessary ASUs (cell addition and immediate deletion again), involving cells aside the highway not intended for highway usage but which can be locally received, can be avoided. This would reduce signalling load and would be especially beneficial for fast-moving mobiles. 9.4 Summary In this chapter advanced an alysis methods for cellular ne tworks were introduced. NMS level intelligence is needed in order to cope with the challenges arising from the increased amount of traffic and new mobile services. Further, some WCDMA- specific automation examples were presented. The presented methods bring first of all operational efficiency, owing to the high level of process, analysis and decision logic automation. Second, with automation network performance is improved and network resources are used more efficiently. References [1] ETSI, TS 100.908, v8.10.0, GSM Technical Specification 05.02: Digital Cellular Telecom- munications System (Phase 2þ); Multiplexing and Multiple Access on the Radio Path, 2001. [2] Vehvila ¨ inen, P. (2004). Data mining for managing intrinsic quality of service in digital mobile telecommunications networks. Thesis (Doc.Tech.), Tampere University of Technology. [3] Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P., From data mining to knowledge discovery: An overview. In: U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (eds), Advances in Knowledge Discovery and Data Mining, pp. 1–34, MIT Press, 1996. [4] Laiho, J., Raivio, K., Lehtima ¨ ki, P., Ha ¨ to ¨ nen, K. and Simula, O., Advanced Analysis Methods for 3G Cellular Networks, Report A65, Publications in Computer and Informa- tion Science, Helsinki University of Technology, 2002. Modified version resubmitted to IEEE Transactions on Wireless Communications end 4/2002. [5] Han, J. and Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001. [6] Ho ¨ glund, A.J., Hatonen, K. and Sorvari, A.S., A computer host-based user anomaly detection system using the self-organising map. Proc. IEEE-INNS-ENNS International Joint Conf. on Neural Networks (IJCNN 2000), Vol. 5, pp. 411–416, 2000. [7] Pyle, D., Data Preparation for Data Mining, Morgan Kaufmann, 1999. [8] Breiman, L., Friedman, J., Olshen, R. and Stone, C., Classification and Regression Trees, Chapman & Hall/CRC Press, 1984. [9] Vesanto, J. and Alhoniemi, E., Clustering of the self-organising map, IEEE Transactions on Neural Networks, 11(3), pp. 586–600, 2000. [10] Laiho, J., Radio network planning and optimisation for WCDMA, Thesis (Doc. Tech.), Radio Laboratory, Helsinki University of Technology, July 2002. [11] Laiho, J., Kylva ¨ ja ¨ , M. and Ho ¨ glund, A., Utilisation of advanced analysis methods in 3G networks, IEEE VTS Proc. of Vehicular Technology Conf., Spring 2002, Birmingham, Alabama, pp. 726–730. Advanced Analysis Methods and Radio Access Network Autotuning 569 [12] Kohonen, T., Self-organising Maps, Springer-Verlag, 1995. [13] Kohonen, T., Analysis of processes and large data sets by a self-organising method, Proc. of 2nd International Conf. on Intelligent Processing and Manufacturing of Materials, 1999, vol. 1, pp. 27–36. [14] Kohonen, T., Oja, E., Simula, O., Visa, A. and Kangas, J., Engineering applications of the self-organising map, Proceedings of the IEEE, 84(10), October 1996, pp. 1358–1384. [15] Kohonen, T., New developments and applications of self-organising maps, Proc. of Inter- national Workshop on Neural Networks for Identification, Control, Robotics, and Signal/ Image Processing, 1996, pp. 164–172. [16] Ahola, J. Alhoniemi, E. and Simula, O., Monitoring industrial processes using the self- organising map, Proc. of IEEE Midnight Sun Workshop on Soft Computing Methods in Industrial Applications, 1999, pp. 22–27. [17] Ha ¨ ma ¨ la ¨ inen, S., Holma, H. and Sipila ¨ , K., Advanced WCDMA radio network simulator, Proc. of PIMRC 1999, Aalborg, Denmark, October 1997, pp. 509–604. [18] Raivio, K., Simula, O. and Laiho J., Analysis of mobile radio access network using the self- organising map, Proc. of IEEE International Conf. on Data Mining, San Jose, California, November/December 2001, pp. 457–464. [19] Vesanto, J., Himberg, J., Alhoniemi, E. and Parhankangas, J., SOM Toolbox for Matlab 5, Report A57, Helsinki University of Technology, 2000. [20] Vehvila ¨ inen, P, Ha ¨ to ¨ nen, K. and Kumpulainen, P., Data mining in quality analysis of digital mobile telecommunications network, Proc. of XVII IMEKO World Congress, Dubrovnik, Croatia, June 22–27, 2003, pp. 684–688. [21] Suutarinen, J., Performance measurements of GSM base station system. Thesis (Lic.Tech.), Tampere University of Technology, 1994. [22] Ha ¨ to ¨ nen, K., Kumpulainen, P. and Vehvila ¨ inen, P., Pre- and post-processing for mobile network performance data, In: R. Tuokko (ed.), Automaatio03, Seminaaripa ¨ iva ¨ t [Automa- tion Makes it Work]: Automaation sovellukset ja ka ¨ ytto ¨ kokemukset, September 9–11, 2003, pp. 311–316, Finnish Society of Automation. [23] Ha ¨ to ¨ nen, K., Laine, S. and Simila ¨ , T., Using the LogSig-function to integrate expert knowledge to Self-Organising Map (SOM) based analysis, IEEE International Workshop on Soft Computing in Industrial Applications, Birmingham University, New York, June 23– 25, 2003, pp. 145–150. [24] Johnson, R.A. and Wichern, D.W., Applied Multivariate Statistical Analysis (4th edn), Prentice Hall, 1998. [25] Everitt, B.S., Cluster Analysis, Edward Arnold, 1993. [26] Davies, D.L. and Bouldin, D.W., A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1(2), pp. 224–227, April 1979. [27] Rousseeuw, P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, 20, November 1987, 53–65. [28] McGill, R., Tukey, J.W. and Larsen, W.A., Variations of boxplots, The American Statistician, 32, pp. 12–16, 1978. [29] Ha ¨ ma ¨ la ¨ inen, S., Slavina, P., Hartmann, M., Lappetelainen, A., Holma, H. and Salonaho, O., A novel interface between link and system level simulations, Proc. ACTS Summit 1997, Aalborg, Denmark, October 1997, pp. 599–604. [30] Ho ¨ glund, A. and Valkealahti, K. Automated optimisation of key WCDMA parameters, Journal of Wireless Communications and Mobile Computing, in press. [31] Olofsson, H., Magnusson, S. and Almgren, M., A concept for dynamic neighbor cell list planning in a cellular system, Proc. 7th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’96), pp. 138–142. 570 Radio Network Planning and Optimisation for UMTS [...]... this chapter 10. 1 GSM Packet Data Services This part of the chapter deals with issues relating to the planning of GPRS and Enhanced GPRS (EGPRS) services on the GSM network Data rate variability has introduced another variable into network planning This also affects air interface and transmission issues Radio Network Planning and Optimisation for UMTS Second Edition Edited by J Laiho, A Wacker and T Novosad... with downlink diversity scheme Radio Network Planning and Optimisation for UMTS 586 diversity The Eb =N0 and Es =N0 values are taken from simulations provided by the simulator presented in Section 10. 1.3.1 As can be seen from Figure 10. 10, downlink diversity increases throughput per timeslot for large ranges For the 3.9 km cell range the achievable throughput per timeslot for 8-PSK is about 26 kbps with... budget handover margin is changed from þ6 dB to 10 dB Note that the received level from the target cell must exceed À80 dBm for the handover to be allowed Radio Network Planning and Optimisation for UMTS 592 HoMarginPBGT = 6dB amhUpperLoadThreshold = 75% amhMaxLoadofTargetCell = 50% amhTrhoPbgtMargin= -10 dB TrhoTargetLevel = -80dBm Figure 10. 15 Illustration of BSC controlled traffic reason handover... such, network level simulations are required for the anticipated traffic profile and these should form the basis of the planning and optimisation processes and tools Other 3G Radio Access Technologies 589 Figure 10. 12 Net EGPRS throughput vs offered load (IR þ LA) Figures 10. 12 and 10. 13 show example output from such simulations [2], giving net throughput and LLC frame delay vs offered EGPRS load Simulations... as 3.9 km Radio Network Planning and Optimisation for UMTS 584 60 MCS-1 MCS-2 MCS-3 MCS-4 CS1 CS2 CS3 CS4 15 MSC5 50 Throughput [kbps] Throughput [kbps] 20 10 MSC6 MSC7 40 MCS8 MCS9 30 20 5 10 0 0 6.5 5.5 4.5 3.5 2.5 1.5 0.5 6 5 4 3 2 Cell range [km] (a) GMSK (b) 8-PSK Figure 10. 8 10. 1.5.2 1 Cell range [km] Cell range for different coding schemes Cell Range without Enhancements In Figure 10. 8 cell range... Methods and Radio Access Network Autotuning 571 [32] Love, R.T., Beshir, K.A,., Schaeffer, D and Nikides, R.S, A pilot optimisation technique for CDMA cellular systems IEEE VTS 50th Vehicular Technology Conf., VTC 1999, Fall 1999, Vol 4, pp 2238–2242 [33] 3GPP, TS 25.133, v3.50, Requirements for Support of Radio Resource Management, 2001 [34] Valkealahti, K., Hoglund A and Novosad, T., UMTS radio network. .. determined from network- level simulation results, such as the above, after design criteria are defined This typically involves the required net throughput or maximum allowable mean LLC delay For example, using the graphs above, and a re-use of 4/12, Figure 10. 13 Logical link control frame delay vs offered EDGE load (IR þ LA) 590 Radio Network Planning and Optimisation for UMTS the maximum offered load for the... provides control procedures, such as error correction and retransmission, to user data The protocols that should be considered when analysing the air interface performance are shown in Figure 10. 1, and are used between the Mobile Station (MS) and the Base Station Controller/Serving GPRS Support Node (BSC/SGSN) Radio Network Planning and Optimisation for UMTS 576 APP TCP/UDP IP SNDCP SNDCP LLC LLC RLC RLC... errorneous word 2nd transmission 2 2 2 2 2 2 2 2 2 2 2 2 + r = 1/1 3rd transmission 3 3 3 3 3 3 3 3 r = 1/1 transmitter 3 3 3 3 + 1 2 3 1 2 3 1 2 3 1 2 3 r = 1/3 decoding 1 1 0 1 correct word receiver Figure 10. 4 Operation of incremental redundancy Radio Network Planning and Optimisation for UMTS 580 At the receiver, the first transmission is received normally by performing depuncturing and decoding In... only IR For GMSK the corresponding throughputs per timeslot are 16.5 kbps and 13.5 kbps By introducing IR and downlink diversity, throughput can be trebled for the speech cell range from the original without any enhancements 10. 1.6 Capacity Planning Once link-level results are available, it is possible to estimate the air interface performance and perform network dimensioning based on a given network . variable into network planning. This also affects air inter face and transmission issues. Radio Network Planning and Optimisation for UMTS Second Edition Edited by J. Laiho, A. Wacker and T. Novosad. shown in Table 10. 2 [6]. In 574 Radio Network Planning and Optimisation for UMTS Table 10. 1 Standard convergence. Specification status GSM (ETSI SMG2) TIA/EIA 136 (TIA TR 45.3) (standardisation. timeslot 578 Radio Network Planning and Optimisation for UMTS BLER 0.01 0 .10 1.00 0 5 10 15 20 25 30 MCS-5 MCS-6 MCS-7 MCS-8 MCS-9 throughput [kbps] 0.0 10. 0 20.0 30.0 40.0 50.0 60.0 0 5 10 15 20