Self-organisation in future mobile cellular networks

Transcription

1 FP7 ICT-SOCRATES Self-organisation in future mobile cellular networks Remco Litjens TNO ICT, Delft, The Netherlands

2 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 2/

3 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 3/

4 INTRODUCTION Wikipedia Self organisa2on is a process of a4rac2on and repulsion in which the internal organiza2on of a system, normally an open system, increases in complexity without being guided or managed by an outside source. Another a4empt (in the specific context of telecommunica2on networks) Self organisa2on is the automated (without human interven2on) adapta2on or configura2on of network parameters (in a broad sense), in response to observed changes in the network, traffic, environment condi2ons and/or experienced performance. Some examples may help 4/

5 SELF-ORGANISATION IN EXISTING NETWORKS Example 1: Transmission Control Protocol Operates end-to-end on the transport layer Automatically adapts source transfer rate to end-to-end congestion level Limits amount of data in transit Slow start phase is followed by congestion avoidance phase AIMR Additive Increase, Multiplicative Decrease TCP TCP IP IP IP IP MAC/RLC MAC/RLC MAC/RLC MAC/RLC PHY PHY PHY PHY SOURCE NODE DESTINATION NODE 5/

6 SELF-ORGANISATION IN EXISTING NETWORKS Example 1: Transmission Control Protocol Operates end-to-end on the transport layer Automatically adapts source transfer rate to end-to-end congestion level Limits amount of data in transit Slow start phase is followed by congestion avoidance phase AIMR Additive Increase, Multiplicative Decrease NO PACKET TIMEOUT: CONGESTION WINDOW + 1 CONGESTION WINDOW / 2 6/

7 SELF-ORGANISATION IN EXISTING NETWORKS Example 2: Routing in ad hoc networks Automatic detection of connectivity Automatic establishment of routes Automatic rerouting upon node failure DESTINATION NODE SOURCE NODE 7/

8 SELF-ORGANISATION IN EXISTING NETWORKS Example 2: Routing in ad hoc networks Automatic detection of connectivity Automatic establishment of routes Automatic rerouting upon node failure DESTINATION NODE SOURCE NODE 8/

9 SELF-ORGANISATION IN EXISTING NETWORKS Example 3: Uplink transmit power control in UMTS networks Default case Fixed transmit power Near-far effect! Battery drainage! 1 st Self-optimisation loop Adjust transmit power to meet SINR target Responds to multipath fading variations 2 nd Self-optimisation loop Adjust SINR target to meet BLER target Adapts to user velocity 3 rd Self-optimisation loop Adjust BLER target to meet end-to-end packet loss target? Adjust power control steps? Adjust power control heartbeat? UE SINR NodeB BLER RNC 9/ inner loop power control outer loop power control

10 SELF-ORGANISATION IN EXISTING NETWORKS Example 3: Uplink transmit power control in UMTS networks outer loop power control responds to a velocity increase transmit power path gain inner loop power control follows mul2path fading 10/

11 INTRODUCTION Context of this presentation Mobile cellular communications networks LTE access technology Long Term Evolution (E-UTRAN) Currently under standardisation Focus on radio access network UMTS 2011? LTE + HSPA GPRS 1994 GSM 1989 NMT NMT OBLB 11/

12 INTRODUCTION Current networks are largely manually operated Separation of network planning and optimisation (Non-)automated planning tools used to select sites, radio parameters Over-abstraction of applied technology models Manual configuration of sites Radio (resource management) parameters updated weekly/monthly Performance indicators with limited relevance Time-intensive experiments with limited operational scope Delayed, manual and poor handling of cell/site failures Future wireless access networks will exhibit a significant degree of self-organisation Self-configuration, self-optimisation, self-healing, Broad attention 3GPP, NGMN, SOCRATES, 12/

13 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 13/

14 DRIVERS Technogical perspective Complexity of future/contemporary wireless access networks Multitude of tuneable parameters with intricate dependencies Multitude of radio resource management mechanisms on different time scales Complexity is needed to maximise potential of wireless access networks Higher operational frequencies Multitude of cells to be managed Growing suite of services with distinct char tics, QoS req ments Heterogeneous access networks to be cooperatively managed Common practice in network planning and optimisation labour-intensive operations delivering suboptimal solutions! Enabler The multitude and technical capabilities of base stations and terminals to perform, store, process and act upon measurements increases sharply 14/

15 DRIVERS Market perspective Increasing demand for services Increasing diversity of services Traffic characteristics, QoS requirements Need to reduce time-to-market of innovative services Reduce operational hurdles of service introduction Pressure to remain competitive Reduce costs (OPEX/CAPEX) Enhance resource efficiency Keep prices low 15/

16 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 16/

17 VISION Minimise human involvement in planning/optimisation Significant automation of network operations Key components Self-configuration Self-healing Self-optimisation con2nuous loop triggered by incidental events 17/

18 VISION Self-configuration Incidental, intentional events Plug and play installation of new base stations and features Self-healing Download of initial radio network parameters, neighbour list generation, transport network discovery and configuration, Incidental, non-intentional events Cell outage detection Alarm bells Triggers compensation Cell outage compensation Automatic minimisation of coverage/capacity loss con2nuous loop triggered by incidental events 18/

19 VISION Self-optimisation Measurements Performance indicators Network, traffic, mobility, propagation conditions Gathering via UEs, enodebs, probes Optimal periodicity, accuracy, format depends on parameter/ mechanism that is optimised Automatic tuning Smart algorithms process measurements into parameter adjustments E.g. tilt, azimuth, power, RRM parameters, NCLs In response to observed changes in conditions and/or performance In order to provide service availability/quality most efficiently Triggers/suggestions in case capacity expansion is unavoidable triggered by incidental events con2nuous loop 19/

20 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 20/

21 EXPECTED GAINS OPEX reductions Primary objective! Less human involvement in Network planning/optimisation Performance monitoring, drive testing Troubleshooting About 25% of OPEX is related to network operations x00 million savings potential per network 21/

22 EXPECTED GAINS and/or CAPEX reductions Via delayed capacity expansions Smart enodebs may however be more expensive and/or performance enhancements Enhanced service availability (robustness/resilience), service quality 22/

23 EXPECTED GAINS and/or CAPEX reductions Via delayed capacity expansions Smart enodebs may however be more expensive and/or performance enhancements Enhanced service availability, service quality 23/

24 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 24/

25 USE CASES Definition of use cases To guide development of solutions Algorithms Performance aspects Impact on standards and operations To help determine requirements Technical requirements Performance Complexity Stability/robustness Timing Interaction Architecture/scalability Required measurements Business requirements Faster roll-out of LTE networks Simplified operational processes Easy deployment of new services End user quality/cost benefits 25/

26 USE CASES Non-exhaustive use case list Self-optimisation 26/ Radio network optimisation Interference coordination Self-optimisation of physical channels RACH optimisation Self-optimisation of Home enodeb GOS/QoS-related optimisations AC/CC/PS optimisation Link level retx scheme optimisation Coverage hole detection/compensation Handover related optimisation Handover parameter optimisation Load balancing Neighbour cell list Others Reduction of energy consumption TDD UL/DL switching point Management of relays and repeaters Spectrum sharing MIMO Self-configuration Automatic NCL generation Intell. selecting site locations Automatic generation of default parameters for NE insertion Network authentication Hardware/capacity extension Self-healing Cell outage prediction Cell outage detection Cell outage compensation

27 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 27/

28 USE CASE: AUTOMATIC NEIGHBOUR CELL LIST GENERATION Self-configuration/-optimisation use case NCL indicates potential handover target cells Typically limited to 32 cells Missing neighbours induces call dropping or excessive interference Undesired neighbours cause unnecessary measurements Self-optimisation based on e.g. UE s signal strength reports enb scans of neighbours Call drops, handover failures Handover stats: used neighbours Triggers Site/cell addition Poor performance Periodic optimisation B: {A,D,E} E: {B,D} A: {B,C,D} D: {A,B,C,E} C: {A,D} 28/

29 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 29/

30 USE CASE: SELF-OPTIMISATION OF ADMISSION CONTROL Admission control Key radio resource management mechanism Objective is to admit as many calls as possible such that service quality requirements can be satisfied Typical admission control rule current load new call load cell capacity c(t) admission threshold for HO/RT admission threshold for dis2nct margins for e.g. FR/RT, FR/NRT, HO/RT, HO/NRT, 2me 30/

31 USE CASE: SELF-OPTIMISATION OF ADMISSION CONTROL Key challenges How to determine cell capacity = non-trivial!! How to determine the new call load = non-trivial!! How to set the margins? Too low means inadequate QoS Too high means excessive blocking Save resources for unpredictable/ uncontrollable variations in resource usage User activity User mobility location affects resource usage Propagation effects Give sufficient preference to handover calls Sufficient depends on degree of mobility, which may vary during the day/week Give sufficient preference to real-time calls Little tolerance w.r.t. (temporary) QoS degradation Optimal margin depends on occasional downgradability of non-real-time traffic Self-optimisation!! 31/

32 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 32/

33 USE CASE: CELL OUTAGE MANAGEMENT Self-healing use case Control parameters Operator policy: Coverage, QoS Compensa2on Cov. map es2ma2on Detec2on Measurements 33/ 33/20

34 USE CASE: CELL OUTAGE MANAGEMENT Cell outage detection Cell outages enodeb failure, cell failure, physical signal/channel failure May not be detected for hours or even days May require manual analysis and unplanned site visits Automatic detection of failures Generate alarms for automated compensation and manual repair Indicate location, type and urgency of outage Minimise detection time, probability of missed detection and false alarm Measurements UE measurement reports: pilots, interference levels enb hard/software reports, carried load, call drops, 34/

35 USE CASE: CELL OUTAGE MANAGEMENT Cell outage compensation Automatic compensation of failures Optimise regional coverage, capacity and/or quality Control parameters Power settings Downtilt, azimuth(?) Beamforming Scheduler s fairness parameter Intra/inter-RAT handover parameters, load balancing Neighbour cell lists 35/

36 USE CASE: CELL OUTAGE MANAGEMENT Achieved gains local revenue manual detection time repair time enodeb dies CASE WITHOUT BEFORE SELF-HEALING SITE OUTAGE enodeb revived time 36/

37 USE CASE: CELL OUTAGE MANAGEMENT Achieved gains local revenue manual detection time repair time enodeb dies CASE WITHOUT SELF-HEALING enodeb revived time repair time local revenue CASE WITH SELF-HEALING regained revenue due to cell outage detection regained revenue due to cell outage compensation otherwise missed revenue time 37/

38 USE CASE: CELL OUTAGE MANAGEMENT On going work Scenarios for cell outage compensation Impact of enodeb density and load More compensation potential in a dense capacity-driven network layout Impact of service type More compensation potential in an area with predominantly low-bandwidth service, e.g. VoIP telephony Impact of outage location More compensation potential if a cell/site outage occurs at the inner part of an LTE island Also study impact of user mobility, propagation aspects, spatial traffic distribution, UE class Controllability & observability Algorithm development Impact on 3GPP specifications 38/

39 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 39/

40 USE CASE: SELF-OPTIMISATION OF HOME enodebs Home enodebs Femto-cell deployed to enhance coverage/qos in homes, offices, Low-cost WLAN-type base station, connected to DSL broadband modem Operates LTE radio access technology in operator s licensed spectrum Used to improve coverage and/or capacity in small areas Closed versus open access 40/

41 USE CASE: SELF-OPTIMISATION OF HOME enodebs Key characteristics Potentially large number of home enodebs Small coverage areas, typically few users May be turned on/off and moved frequently Not physically accessible for operators Operate on a same/separate frequency from macro-cells self-configuration/ -optimisation needed 41/

42 USE CASE: SELF-OPTIMISATION OF HOME enodebs Key challenge: interference/coverage optimisation Objectives Coverage should be large enough to include e.g. attic, balcony, garden, Interference caused to macro-cell should be limited Example: UE in femto-cell coverage area but not allowed access may cause/ experience excessive interference to/from femto-cell Example: UE served by femto-cell experience severe DL interference from macrocell, while macro-cell may experience significant UL interference from femto-cell Control parameters Up/downlink transmit power Scheduling parameters, e.g. time-frequency domain separation MIMO parameters 42/

43 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 43/

44 CHALLENGES Development of effective self-organisation methods imposes quite a few challenges Measurements What data? What frequency? Tuned to urgency? Trade-off: signalling cost vs achieved performance Appropriate processing to determine network state Detection/handling of erroneous/ malicious reports Effectiveness of self-organisation Multi-objective optimisation Intricate parameter dependencies Frequency of adjustments Mutual timing prevent oscillations Centralised vs distributed control Timely detection, swift response 44/

45 CHALLENGES Development of effective self-organisation methods imposes quite a few challenges Dealing with delayed feedback Feedback upon control actions is not immediate Effects of control decisions or due to natural variations Reliability Actions must be reliable No human sanity checks or revision of actions Operator must trust the system when giving up direct control Gradual introduction Shape the network architecture Incorporation in actual systems Protocols, interfaces, architecture 45/

46 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 46/

47 SELF-OPTIMISATION APPROACHES Generic approach 3GPP specifica2on Assessment Criteria Measurements Control Parameters Scenarios Operator Policy Controllability & Observability Algorithm Specifica2on methodologies Algorithm Development Algorithm Assessment Implementa2on 47/

48 SELF-OPTIMISATION APPROACHES Generic approach In the process of algorithm development, the Real Network can be replaced by a simulator Network parameters Real Network Network Sta2s2cs Op2misa2on Algorithm 48/

49 SELF-OPTIMISATION APPROACHES Approach 1: Simulation-based optimisation Optimisation Algorithm exploits a Network Simulator for what if analyses, i.e. test potential parameter adjustments before application in the Real Network Equivalent to current approach to off-line optimisation Specific problem imposes requirements on speed and accuracy Network parameters Real Network Network Sta2s2cs Predicted Network Sta/s/cs Op2misa2on Algorithm Network Simulator Proposed Parameter Se3ngs 49/

50 SELF-OPTIMISATION APPROACHES Approach 2: Off-line optimised real-time controller Real-Time Controller rapidly responds to changes Periodic off-line tuning of Real-Time Controller by Optimisation Algorithm Particularly suitable when quick response is needed Real-Time Controller may largely simplify dependencies/impact Reliable, stable but suboptimal for complex use cases? Network parameters Real Network Network Sta2s2cs Updated Controller Se3ngs Real Time Controller Op2misa2on Algorithm Controller s Performance Sta/s/cs 50/

51 SELF-OPTIMISATION APPROACHES Approach 3: On-line optimised real-time controller Periodic/continuous on-line tuning of Real-Time Controller Example combining admission control and reinforcement learning Reference CAC scheme with an RL-based self-optimisation layer on top, optimising the CAC parameters Integrated RL-based CAC scheme, directly optimising the mapping of system state to admission/rejection decision Network parameters Real Network Network Sta2s2cs Adap2ve Real Time Controller Involves random intialisation and training phase with randomised actions Inherent degree of black box character limits trustworthiness 51/

52 SELF-OPTIMISATION APPROACHES Approach 4: Adaptive model-based optimisation Optimisation Algorithm exploits a Network Model for what if analyses (assess performance impact of potential parameter adjustments) or direct derivation of the optimal parameters, before parameter application in the Real Network. The Network Model is tuned based on measurements. For example, feeding estimated propagation/coverage maps into automated planning tool in order to optimise tilts and azimuths Network parameters Real Network Network Sta2s2cs Proposed Network Parameters Adap2ve Network Model Solve the model Op2misa2on Algorithm Predicted Network Sta/s/cs 52/

53 SELF-ORGANISATION METHODOLOGIES Non-exhautive list of potentially applicable optimisation techniques neural networks metamodels random search sample path expert systems fuzzy Q learning neuro evolu2on of augmen2ng topologies perturba2on analysis ordinal op2misa2on branch & bound open loop control itera2ve learning control fuzzy logic clique detec2on Markov decision processes gradient based methods propor2onal integral deriva2ve control automa2c control gene2c programming non gradient based methods model predic2ve control neural swarming reinforcement learning 53/

54 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 54/

55 NGMN Cooperation among world-leading mobile network operators General objective To collect and promote operator requirements and recommendations for future mobile networks Establish clear performance targets, fundamental recommendations and deployment scenarios Operational Efficiency project SON WP is a continuation of Project 12 Develop operator vision on self-organisation Ensure that self-organisation capabilities become an inherent part of the initial design of future systems Push vendors to fulfil operator requirements regarding the implementation of particular solutions Concrete activities include Industry conferences, vendor workshops White papers, 3GPP contributions 55/

56 3GPP Standardisation of E-UTRAN (LTE) Self-Optimising Networks Introduced in Q2 07 Considered primarily in TSGs RAN2/3, SA5 Releases R8 (frozen) Some auto-configuration concepts included Automatic neighbour relation, Automatic physical cell ID discovery R9 (tentatively frozen 12/2009) See also Key RAN3 work items Coverage and capacity optimisation, Interference reduction, Load balancing, Coverage hole management and cell outage management, Handover optimisation Self-configuring and self-optimizing network use cases and solutions, 3GPP Technical Report , maintained by RAN3 56/

57 SOCRATES Overview Self-Optimisation and self-configuration in wireless networks Self-configuration, self-optimisation, self-healing 3-year duration: from 01/01/2008 until 31/12/2010 Effort: 378 person months, EU IST FP7-ICT /

58 SOCRATES Scope Technological focus: 3GPP E-UTRAN (LTE) Radio (resource management) parameters, e.g. pilot power, antenna tilt, neighbour cell lists, SHO/CC/CAC/scheduling parameters, Consortium 58/

59 SOCRATES Objectives Development of novel concepts, methods and algorithms for the effective self-organisation of wireless access networks Specification of the required information, its statistical accuracy and the methods of retrieval incl. the needed protocol interfaces Validation and demonstration of the developed concepts and methods for selforganisation through extensive simulation experiments, assessing the established capacity/coverage/quality enhancements, and the attainable O/ CAPEX reductions Assessment of the operational impact of the developed concepts and methods for self-organisation, with respect to the network operations, e.g. radio network planning and capacity management processes Influence on 3GPP standardisation and NGMN activities 59/

60 SOCRATES Evolutionary approach Quantitative character Development of methods and algorithms Quantitative assessment Simulation of scenarios Contacts and cooperation FP7 E3, 4WARD, EFIPSANS, EURO-NF,. COST GPP, NGMN, WWRF 60/

61 SOCRATES Come and see us at the joint workshop * on Self-organisation for beyond 3G wireless networks at ICT Mobile Summit 09 in Santander, Spain 61/

62 OUTLINE Introduction Drivers Vision Expected gains Use cases Automatic neighbour cell list generation Admission/congestion control Cell outage management Self-optimisation of Home enodebs Challenges Approaches Who is who? Concluding remarks 62/

63 CONCLUDING REMARKS Self-organisation key approach to reduce O/CAPEX cost-effective provisioning of high-quality services reduce time-to-market of new features, services Key components Self-configuration Self-optimisation Self-healing Exciting challenges Effectiveness, reliability, stability Measurements, interfaces, protocols, architectures Involved parties/projects NGMN, 3GPP, GANDALF, E3, SOCRATES,, you? 63/

64 SELF-ORGANISATION IN HANOI TRAFFIC 64/

65 QUESTIONS? 65/