Meeting the Challenges of Planning and Optimising Multi system RANs Meeting the Challenges of Planning and Optimising Multi System RANs Julian Buhagiar Product Manager Actix Ltd julian buhagiar@actix[.]
Meeting the Challenges of Planning and Optimising Multi-System RANs Julian Buhagiar Product Manager Actix Ltd julian.buhagiar@actix.com Agenda Developing end-to-end 2/2.5/3G network dimensioning, capacity planning & traffic engineering Optimising traffic distribution between network layers Meeting the challenges of modelling multi-system networks - channel types - coverage thresholds - interference Developing models for data traffic in multi-system networks - spatial distribution - data traffic services Achieving maximum capacity and QoS for voice and data traffic How we develop end-to-end network dimensioning, capacity planning & traffic engineering? If only…we could optimise a cluster in the same time as this afternoon’s session and be confident in the results… The objectives of optimising networks for quality, capacity and coverage… High level goals Generating new revenue Meeting rollout obligations Increasing customer base While: Reducing required new sites Reducing/delaying capex Reducing opex Reducing churn due to quality Reducing time to launch Maintaining (or reducing in some cases) current headcount Technical excellence for the sake of technical excellence is not a requirement Coverage, capacity and quality in GSM, EGPRS & UMTS In GSM, EGPRS and UMTS all three are fundamentally joined together by the link budget: UMTS CPICH EcNo EGPRS BEP GSM RxQual Coverage – target availability, propagation characteristics Capacity – design loading, design user density Quality – service(s) EbNo and traffic The combination of all three leads to a single set of the network design targets UMTS Soft Handover Overhead EGPRS Cell Reselection GSM Hard Handover Overhead UMTS CPICH RSCP EGPRS CValue GSM RxLev UMTS Pilot Pollution EGPRS Low C/I GSM Low C/I Specific challenges for optimisation of UMTS Capacity for a given equipment quantity is dependent on network spectral efficiency The proportion of network loading available for traffic decreases when interference increases It is possible to increase network capacity with no additional infrastructure deployment, or damage capacity by adding additional infrastructure UMTS operates on a single frequency There is no frequency planning option to hide the interference problems caused by poor spectral efficiency of the network plan The next cheapest options are significantly more expensive and time consuming Optimising capacity and coverage Impact of modifiying average intercell interference on throughput vs range From Holma and Toskala: ⎛ Eb ⎞ ⎜ N ⎟ 0⎠j ⎝ η DL = ∑ν j 1−α + i ⎛W ⎞ j =1 ⎜ R ⎟ j⎠ ⎝ The only improvement we can make in downlink loading (for a given quality) is by optimising intercell interference (average i) A 0.3 dB reduction in average RSSI can lead to a 4% increase in capacity on the downlink with the same coverage [( 900 ) ] This is equivalent to reducing the average i from 0.45 to 0.35 Range (m) N 800 0.35 0.45 0.55 0.65 700 600 600 700 800 900 Throughput (kbps) Estimates from scanner data indicate typical values of average i to be at least 0.38 to 0.50 Planning vs measured data Automatic Cell and Frequency Planning tools help in decreasing interference levels and improving coverage This ensures better coding scheme usage and higher capacity Measured data improves the accuracy of the solution identification process As a result we will be able to: Reduce rework Reduce drive testing Reduce implementation costs Reduce analysis time Reduce the number of iterations we require prior to launch, or to resolve a problem Reducing time to market and/or Reducing time to problem resolution 100 90 80 70 Interference 60 50 lev els 40 [%] 30 20 10 100 900 90 840 800 700 59 Traffic [Erlang] 574 554 600 500 400 300 200 Initial network CellOpt ACP CellOpt ACP+AFP 300 198 Number of radios 205 100 Initial network CellOpt ACP CellOpt ACP+AFP Delta and model based solutions compared Delta Based Improvement Approach Shortcut Accuracy Typically 4.5dB standard deviation Sampling Like-for-like statistics compared to measurements Information Coverage Certainty about measured areas Automatic vs Interactive Interactive Data not uniform - unsuitable for full automation Local knowledge and constraints part of process Use Scenarios Fine Tuning Course Optimisation Validation of coarse/break-out optimisation Delta and model based solutions compared Model Based Improvement Approach Step Improvement Accuracy Down to 7dB standard deviation with improvements Sampling Inherent difference in statistics calculation Information Coverage Automatic vs Interactive Use Scenarios Uniform data but less certain Data in uniform grid – suitable for automation Possibility of breakout from local optima Local knowledge and constraints require prior input and post-validation Initial Planning Coarse Optimisation Break-out Optimisation Optimize Inter-system Handover parameters CPICH RSCP of 3G macro cell drops rapidly after passing the 100dBm mark by: Between ~ 10 dB per second for a vehicle moving along the ramp leading to basement car park Between 10 ~ 20 dB per second upon entering the underground subway tunnel Must ensure HO process is completed before link to macro 3G cell is lost within the tunnel or carpark Early trigger for CPICH RSCP HO threshold is required (eg -95dBm) MinRxlev of target 2G cell (in tunnel/carpark) should be lower than typical value Early trigger may cause unnecessary ISHO to other 2G macro cells on street level and frequent compressed mode measurement 3G macro cell involved must have proper adjacency plan Longer-term capacity improvement Micro cells for hot spots Shared channels (HSDPA, HSUPA) for I&B traffic resource occupation only during transmission (minimum occupation time 2ms) good user experience (fast switching, low latency) efficient resource usage Smart antennas increases air interface capacity availability? costs? How does this optimization translate to Network KPIs? Availability KPIs: Attach set up time Attach success rate Detach success rate Mobility KPIs: LAU and RAU set up time LAU and RAU success rate Cell reselection time Accessibility KPIs: PdP Context set up time PdP Context success rate DNS and application server access and many others How does this optimization translate to Application KPIs? Other common indicators: DNS Lookup Time, TCP Packet Loss, Retransmission and Duplication, TCP RTT, TCP Connect and Session Completion Rate Solutions to enhance EDGE services Problem FTP throughput low Non-optimised algorithms affect performance FTP Uploads 50 12 Wrong neighbour definition! 45 10 35 Kbps 30 25 20 16 14 15 13 8 Eventually it moves to MCS-6… Level and quality are good! 10 10 Number of % 40 10 Session App kbps UL C/I LLC throughput BLER RLC throughput Cell reselections Stuck on MCS-3 Diagnosis Link adaptation too slow to react Solution Re-tune LA parameter setting And throughput doubles (30 kbps)! Let’s filter on that Upload Too many cell reselections! Although the level is good for a long time Link Adaptation and Coding Schemes By measuring and correlating quality, BLER, coding scheme usage and the resulting throughput we can verify that parameter changes have improved the algorithms Different routes and clutter environments should be used Statistics on required C/I C value per Coding Scheme Better parameters should move the distribution to higher MCS and throughput values Radio analysis Problem Unstable throughput FTP Downloads Resulting throughput: 90 50 80 45 70 40 RLC = 41 kbps, Appl = 31 kbps 35 60 30 50 25 dB and % Kbps 40 20 30 Ping Pong cell reselections 15 20 10 10 2 Peak throughput is 100 kbps! Cell ID App kbps DL LLC DL throughput RLC DL throughput # cell reselections C/I DL BLER Throughput BLER Diagnosis bad neighbour definition coverage overlaps Resulting in Cell Reselections, Interference and high BLER Solution Revise neighbour definition and decrease interference and coverage overlaps using automatic cell and frequency planning tools C/I < 10 dB in this area Too many neighbours with similar level Long distance server TCP analysis The mobile buffer limit is reached and the throughput is affected Problem Throughput is 62 kbps instead of a potential application throughput of over 100 kbps (2 timeslots are used) and the average RTT is well over sec (measured on Gb) Eventually retransmissions get to the mobile Service summary Window size 16 kB Diagnosis TCP packet loss and out of sequence increases the number of packets waiting and the mobile buffer saturates thus provoking stall situations Throughput slow to ramp up Solution Increase of receiving window size (to 25-30 kB) TCP slow start revision Throughput is unstable in both Um and Gb TCP slow start Bytes on Gb Bytes on Um Good level Good quality (BEP) What would benefit the analysis? Antenna Tuning process Analyze effects on antenna changes directly from drive-test data View entire cluster effects due to single sector change in azimuth, beamwidth, power Verify changes with drive tests What would benefit the analysis? Automated process Select files to analyze Instantly view your important KPIs Diagnose any identified problems What would benefit the analysis? Automated Diagnosis Drill down to each detected critical fault Examine L3 and RRC messages prior to fault Useful for automated diagnosis, benchmarks and verification procedures What would benefit the analysis? Mapping tools Use mapping tools to synchronize radio with fixed interface Perform holistic radio analysis by providing spatial (map) with temporal (events) views Useful for automated diagnosis, benchmarks and verification procedures Conclusions Optimise coverage and capacity in UMTS by reducing inter cell interference Both automation and delta analyses are complementary in practical processes Manage iRAT handovers via SHO reduction Use different profiles for access parameters in indoor & tunnel cells Use process-driven software to facilitate planning and troubleshooting of multi-RAB management Thank you for your attention Julian Buhagiar Julian.buhagiar@actix.com +44 (0) 7834 26 5126 sales@actix.com +44 (0) 20 8735 6300 Actix We provide solutions for performance engineering of wireless voice and data services Founded in 1990 Global company covering all wireless technologies 200+ staff 250+ customers, 200+ wireless operators Over 5000 active users worldwide Offices in UK, USA, Hong Kong, Sweden, China, Japan