Self-Organization in LTE

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1 Self-Organization in LTE Andreas Mitschele-Thiel Cellular Communication Systems Page 1

2 Outline Introduction Functionalities of Self-Organizing Networks (SONs) Architectures of SONs Use Cases Coordination References Cellular Communication Systems Page 2

3 Introduction Cellular Communication Systems Page 3

4 Motivation TOTal EXpenditures (TOTEX) comprise CAPital EXpenditures (CAPEX) investments telecommunications carriers make (in network equipment as well as services) CAPEX is based on a combination of two primary factors Number of customers served Volume of services provided OPerational EXpenditures (OPEX) running cost Cellular Communication Systems Page 4

5 OPEX per Revenues Cellular Communication Systems Page 5

6 OPEX Details Our focus Cellular Communication Systems Page 6

7 Drivers for Automation & Self-Organization Multiple and heterogeneous networks (GSM, UMTS, LTE) High complexity of systems and hugh number of system parameters Expanding number of Base Stations (BSs) Introducing of femto cells, home enbs leads to a huge number of nodes (from multi vendors) to be operated Network OPEX is increasing Reduction of Network OPEX requires reducing human interactions by configuring and optimizing the network automatically while allowing the operator to be the final control instance High quality (network utilization and customer satisfaction) must be ensured SON is essential Cellular Communication Systems Page 7

8 Functionalities of SONs Cellular Communication Systems Page 8

9 Recap: Self-Organizing Systems (Theory) Local system control C S1 C S2 Local interactions (environment, neighborhood) C S3 C S4 Simple local behavior C S5 C S6 Cellular Communication Systems Page 9

10 Functionalities Of SONs Self-Configuration (plug and play) Self-Optimization (auto-tune) Self-Healing (auto-repair) Auto-setup Auto-neighbor detection... Coverage & capacity Mobility robustness Load balancing... HW/SW failure detection Cell outage detection... Self-Planning (dynmic re-computation) Cellular Communication Systems Page 10

11 Self-Configuration Definition The process where newly deployed enbs are configured by automatic installation procedures to get the necessary basic configuration for system operation. Works in preoperational state How Create logical associations with the network Establishment of necessary security contexts (providing a secure control channel between new elements and servers in the network) Download configuration files from a configuration server (using NETCONF protocol) Doing a self-test to ensure that everything is working as intended Cellular Communication Systems Page 11

12 Self-Configuration 1. IP address allocation, selfconfiguration subsystem detection GW 4. Transport and radio configuration enb enb enb Self-configuration subsystem Normal OAM subsystem OAM subsystem Cellular Communication Systems Page 12

13 Self-Optimization Definition The process where User Equipments (UE) and enbs performance measurements are used to auto tune the network. Works in operational state How Optimizing the configuration while taking into account regional characteristics of radio propagation, traffic and UEs mobility Analysis of statistics and deciding what are optimal parameters Detecting problems with quality, identifies the root cause and automatically takes remedial actions Examples: neighbor list optimization, coverage optimization, HO optimization, load balancing Cellular Communication Systems Page 13

14 Self-Healing Definition The process enabling the system detecting the problems by itself and mitigating them whilst avoiding user impact and reducing maintenance costs. Works in operational state End-to-end service recovery time should be < 1 sec How Automated fault detection Root cause identification Recovery actions application If fault cannot be resolved, take some actions to avoid performance degradation Cellular Communication Systems Page 14

15 SON Optimization vs. RRM Environment Measure Learn rules Decide RRM decisions (fast, local): CAC, ConC HO, IC/IS HO MLB ICIC MBMS Mode selection PRB assignment Act SON (slow, global): Learning of the optimal rules to make fast decisions for the specific environment Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel Group Page 15

16 Architectures of SONs Cellular Communication Systems Page 16

17 Requirements & Taxonomy Providing an easy transition from operator-controlled (open loop) to autonomous (closed loop) operation Support of network sharing between network operators Three architectures Centralized SON Distributed SON Hybrid SON Cellular Communication Systems Page 17

18 Centralised SON SON algorithms are executed in the OAM System SON functionalities reside in a small number of locations at a high level in the architecture Pros Easy to deploy and to manage Global system view Cons OAM is vendor-specific (multi-vendor optimization is problematic) Not applicable to situations where selforganization tasks should be fast Centralized OAM SON SON SON OAM OAM Itf-N enb enb Cellular Communication Systems Page 18

19 Distributed SON SON functionalities reside in the enb, i.e. at the lowest level of the network architecture Fully autonomous distributed RAN optimization Pros Applicable for situations where selforganization task should be achieved fast Cons Hard to deploy and manage Extension of X2 interfaces needed Centralized OAM OAM OAM Itf-N enb enb SON SON Cellular Communication Systems Page 19

20 Hybrid SON Idea is to implement some of the SON functionalities in the enbs and others in the OAM Pros Best exploit of the benefits of SONs Allowance for a high degree of automation, control and inspection Cons Hard to deploy and manage Extension of multiple interfaces needed Centralized OAM SON SON SON OAM OAM Itf-N enb enb SON SON Cellular Communication Systems Page 20

21 Use Cases Cellular Communication Systems Page 21

22 SON Use Cases (R9) Physical cell-id automatic configuration (PCI) Automatic Neighbor Relation (ANR) Coverage and capacity optimization (CCO) Energy saving Interference reduction Inter-cell interference coordination (ICIC) Random Access Channel (RACH) optimization Mobility load balancing optimization (MLB) Mobility robust optimization (MRO) See 3GPP TR v for details Cellular Communication Systems Page 22

23 Physical Cell-ID Automatic Configuration Goal Automatically configure the physical Cell-ID (collision and confusion free assignment of physical Cell-ID) from a limited space of 504 physical Cell-IDs Problem: Collision: neighbor enb has same PCI as serving cell Confusion: two neighbors have the same PCI which may result in a HO to the wrong cell Works in preoperational state A part of self-configuration procedure Solutions enb-based solution (distributed solution) OAM-based solution (centralized solution) Cellular Communication Systems Page 23

24 Physical Cell-ID Automatic Configuration enb-based solution (distributed solution) enb chooses an arbitrary Cell-ID enb instructs UEs to do measurements, collects and analyses measurement results enb starts communicating with neighbors using X2 interfaces In case the enb has detected a conflict, a new Cell-ID is assigned and the procedure is repeated again OAM-based solution (centralized solution) enb instructs UEs to do measurements, collects and sends the results to the OAM The OAM assigns a Cell-ID to the enb Cell-ID assigning procedure may require doing updates to other enbs in the network Cellular Communication Systems Page 24

Automatic Neighbor Relation (ANR) Relations between neighbor enbs should be carefully determined since they affect the network performance Handoff performance, call dropping probability, etc.

25 Automatic Neighbor Relation (ANR) Relations between neighbor enbs should be carefully determined since they affect the network performance Handoff performance, call dropping probability, etc. enb1 The mobiles residing in the range of enb2 may move to either enb1 or enb3 in-advance actions maybe done to optimize the performance (resources reservation) enb3 x2 x2 x2 enb2 enb4 Cellular Communication Systems Page 25

26 Automatic Neighbor Relation (ANR) ANRs covers following steps Neighbor cell discovery enb instructs UEs to do measurements New joined enbs are detected based on the analysis of measurement results Configuration of X2 interfaces between enbs Connection setup with neighbor enbs ANR optimization Update as new enbs join/disjoin the network Some steps work in preoperational state, while some others work in operational state Cellular Communication Systems Page 26

27 Coverage & Capacity Optimization Goal Maximizing the capacity while ensuring coverage requirements Holes-free coverage Improved capacity with given resources Works in operational state 3 Cases LTE coverage holes within other Radio Access Technologies (RATs) QoS degradation due to frequent inter-rat handoffs LTE coverage LTE cell smaller than planned Non-LTE coverage Cellular Communication Systems Page 27

28 Coverage & Capacity Optimization LTE coverage holes without alternative RAT Significant call drops due to coverage holes Non coverage Cellular Communication Systems Page 28

29 Coverage & Capacity Optimization Isolated LTE cells Coverage blackouts in network s border areas Non coverage Cellular Communication Systems Page 29

30 Coverage & Capacity Optimization Solution Update the BS parameters such as height, azimuth, tilt and Tx power Cellular Communication Systems Page 30

31 Energy Saving Goal Reduction of OPEX by saving energy resources Works in operational state How can energy be saved Tx power optimization Minimal saving but through the whole day Switching-off some of the Tx of a cell Possible where antenna diversity is not required Complete enb switch-off Maximum saving but possible only during low load times Also applicable to home enbs and closed subscriber group cells if users are away High latency for switch-on Cellular Communication Systems Page 31

32 Interference Reduction Goal Improving the network performance by means of reducing the interference between its equipment Works in operational state Limitations due to the applied frequency band Interference depends on frequency band characteristics and given environment (terrain, buildings, etc.) Solutions Decrease enbs density using same frequency band Hard to apply due to the capacity decrease and existence of home enbs not fully controlled by the network operator Power control and/or reconfiguration of the wireless setup Interference cancellation, coordination and randomization Cellular Communication Systems Page 32

33 Inter-Cell Interference Coordination Soft frequency reuse: reuse > 1 at cell edges Cellular Communication Systems Page 33

34 RACH Optimization RACH is an uplink unsynchronized channel for initial access or uplink synchronization RACH is involved in many situations Connection setup, radio link failure, handover, etc. Delay to access to RACH influences many other tasks Call setup/handoff delay and success rate Capacity of the whole network (due to physical resources reserved for RACH) Cellular Communication Systems Page 34

35 RACH Optimization Delay to access to RACH depends on current network parameters Transmit power, handover threshold, etc. changing network parameters requires optimizing the RACH Solution enb does measurements E.g. random access delay, random access success rate, random access load Based on measurements, RACH parameters are optimized RACH physical resources RACH persistence level and backoff control RACH transmission power control, etc. Cellular Communication Systems Page 35

36 Load Balancing Normal load Overload Cellular Communication Systems Page 36

37 Load Balancing Overloaded Cells Cellular Communication Systems Page 37

38 Load Balancing Strategies 1. Downlink (DL) power modification, i.e. pilot power and/or antenna tilt - Degrades indoor coverage in reduced power cells - Requires over provisioning of power amplifiers in increased power cells 2. Handover (HO) parameter modification + Overcomes the cons of DL power modification method - Load balancing (LB) can only be achieved if neighbors have free resources Cellular Communication Systems Page 38

39 Mobility Load Balancing (MLB) LB optimization by modifying HO parameters Advance HO in case of overloaded cell Delay HO in case of normal loaded cell Scheme typically works better for slowly moving mobiles Cellular Communication Systems Page 39

40 Handover Algorithm (RSRP t +CIO t ) (RSRP s +CIO s ) > Hys RSRP [dbm] CIO S Source Cell s CIO t Target Cell t RSRP: Reference Signal Received Power Hys: Hysteresis CIO: Cell Individual Offset TTT: Time to Trigger P: Preparation time Hys Start of TTT HO Decision P HO Command Time Cellular Communication Systems Page 40

41 MLB Optimization Cell Load information RSRP information MLB Algorithm CIO RSRP: Reference Signal Received Power CIO: Cell Individual Offset Cellular Communication Systems Page 41

Handover Optimization Filtered RSRP [db] Optimize HO performance amidst mobility Hence Mobility Robustness Optimization Parameters Hysteresis (Hys) Time to Trigger (TTT) Hys A3 entry

42 Handover Optimization Filtered RSRP [db] Optimize HO performance amidst mobility Hence Mobility Robustness Optimization Parameters Hysteresis (Hys) Time to Trigger (TTT) Hys A3 entry condition [1] (RSRP t +CIO t ) (RSRP s +CIO s ) > Hys [1] 3GPP E-UTRA Radio Resource Control (RRC) Protocol specification (Release 8) TS V ( ) Cellular Communication Systems Page 42

Mobility Robustness Optimization (MRO) Aim: Maintaining few HOs and HO oscillations (Ping-Pongs), minimize Radio Link

cell B inside cell A s coverage or UE handed over before cell B is steadily better than cell A HO Triggering RLF HO

902 V0.0.1, Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Self-configuration and self-optimizing

43 Mobility Robustness Optimization (MRO) Aim: Maintaining few HOs and HO oscillations (Ping-Pongs), minimize Radio Link Failures (RLF) due to [2]: RLF Late HOs: UE leaves coverage cell before HO is complete Early HOs: island coverage of cell B inside cell A s coverage or UE handed over before cell B is steadily better than cell A HO Triggering RLF HO to wrong cell: improper settings between cells A and B UE handed to cell C when should have been handed to cell B E.g. due to PCI confusion HO Triggering RLF [2] 3GPP TR V0.0.1, Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Self-configuration and self-optimizing network use cases and solutions Cellular Communication Systems Page 43

MRO: Approaches in Literature Studies have applied expert knowledge control loops to search through the Hys-TTT parameter space [3,4,5,6] Two possible Hys-TTT parameter search strategies Diagonal &

44 MRO: Approaches in Literature Studies have applied expert knowledge control loops to search through the Hys-TTT parameter space [3,4,5,6] Two possible Hys-TTT parameter search strategies Diagonal & Diagonal - zigzag [4] A typical search through parameter space by evaluating HO performance for different configurations [6] [3] T Jansen, I Balan, I Moerman, T Kürner, Handover parameter optimization in LTE self-organizing networks, Proceedings of the IEEE 72nd Vehicular Technology Conference (VTC2010-Fall) (Ottawa, Canada, 2010). [4] I. Bălan, B. Sas, T. Jansen, I. Moerman, K. Spaey and P. Demeester, An enhanced weighted performance-based handover parameter optimization algorithm for LTE networks EURASIP Journal on Wireless Communications and Networking 2011, 2011:98. [5] Gao Hui and Peter Legg, Soft Metric Assisted Mobility Robustness Optimization in LTE Networks, Proceedings of the 9 th International Symposium on Wireless Communication Systems (ISWCS 2012), August 2012, pp.1-5. [6] S. Mwanje, N. Zia, A. Mitschele-Thiel, Self organised Handover parameter configuration for LTE, Proceedings of the 9 th International Symposium on Wireless Communication Systems (ISWCS 2012), August 2012, pp Cellular Communication Systems Page 44

MRO Alternative Approach: Q-Learning Challenge: MRO depends on user mobility and changes with time configurations should keep track Learn best configuration (for cell or system) for each mobility

45 MRO Alternative Approach: Q-Learning Challenge: MRO depends on user mobility and changes with time configurations should keep track Learn best configuration (for cell or system) for each mobility state Mobility in cell mean, spread,. 1.State Change Hys and/or TTT HO Aggregate Performance (HOAP) converges [7] Q-MRO 2. Action Network 3.Reward According to number of radio link failures, ping-pongs, HO successes [7] Stephen S. Mwanje, Andreas Mitschele-Thiel, Distributed Cooperative Q-Learning for mobility sensitive Handover Optimization in LTE SON, Proceeding of 2014 IEEE Symposium on Computers and Communications (ISCC 2014), Madeira, Portugal, Juni 2014 Cellular Communication Systems Page 45

46 Coordinating SON Use Cases Cellular Communication Systems Page 46

47 Use Cases are not Necessarily Independent! Example: impact of MLB on HO performance MLB results in advanced and delayed HOs which results in worse link conditions (for HO signaling) which may prompt HO performance optimization (MRO) to counteract by changing HO parameters uncoordinated execution of MLB and MRO may result in instabilities and oscillating behavior Cellular Communication Systems Page 47

48 Use Cases are not Necessarily Independent! General problem: A metric or goal (e.g. HO performance) is typically influenced by many parameters (e.g., TTT, Hys, Txpower, antenna azimut&tilt) Many parameters (CIO, Tx power, antenna azimut&tilt) have an impact on several goals (capacity, coverage, LB, HO performance) Cellular Communication Systems Page 48

49 Use Cases are not Necessarily Independent! Many SON functions optimize the same parameters System configuration System & Environment KPIs SON Function i Traffic ANR/ PCI ICIC Coverage Hole Mgmt. enb Insertion/ Removal CCO Energy Saving MLB MRO Network Parameters SON Functions Relay/ RepeaterMgm t Cellular Communication Systems Page 49

50 SON Design and Operational Challenge Use Cases (UCs) conflict within and across cells MRO MLB CELL 1A CCO. ICIC MRO Base Station 1 MLB MRO MLB CELL 2C CCO ICIC. Base Station 2 CELL 1B CCO. ICIC MLB: Mobility Load balancing (MLB) MRO: Mobility Robustness (Handover) Optimization CCO: Coverage and Capacity Optimization ICIC: Inter Cell Interference Coordination Possible conflicts/dependencies Intra-cell Inter cell, same UC Inter cell, different UCs Cellular Communication Systems Page 50

51 Example: Load Balancing vs. MRO Metric Value Conflict (MVC) Radio Link Failure rates olate HO (F L ) MRO Hys TTT HO Metrics oearly HO (F E ) Ping-Pong rate (P) HO rate (H) HO Aggregate Performance MVC MLB CIO Load Metric Overload leads to Reduced throughput user dissatisfaction No. of Unsatisfied Users Cellular Communication Systems Page 51

52 Example: Need for Coordination Both, No. of Unsatisfied users and HOAP performance degrades Post learning: HO degradation vs. change in No. of Unsatisfied users [8] System Ref QMRO QLB QMRO+ QLB Description Reference system with good HO performance Q-learning MRO solution Q-learning LB solution Both solution simultaneously active [8] Stephen S. Mwanje Coordinating Coupled Self-Organized Network Functions in Cellular Radio Networks, Doctoral Thesis, TU Ilmenau, Cellular Communication Systems Page 52

Proposed Approaches Functional parameter groups [9,10] Many parameters belong to a single group Optimize parameters in groups Coordination and control [9,10] Define rules for optimization of each set

53 Proposed Approaches Functional parameter groups [9,10] Many parameters belong to a single group Optimize parameters in groups Coordination and control [9,10] Define rules for optimization of each set of UCs and relationships Consider rules for the coordination of conflicts Temporal separation [11,12] Separate optimization of different UCs in time to improve learning [9] T. Jansen, et al, Embedding Multiple Self-Organization Functionalities in Future Radio Access Networks, 69th Vehicular Technology Conference, VTC2009-Spring, Barcelona, Spain, 2009 [10] SOCRATES Deliverable D5.9: Final Report on Self-Organization and its Implications in Wireless Access Networks, EU STREP SOCRATES (INFSO-ICT ), Dec2010 [11] Tobias Bandh, Lars Christoph Schmelz, Impact-time Concept for SON-Function Coordination, in Proceedings of the 9 th International Symposium on Wireless Communication Systems (ISWCS 2012), August 2012, pp [12] Kostas Tsagkaris, et al, SON Coordination in a Unified Management Framework, in Proceedings of the 77 th Vehicular Technology Conference, VTC2013-Spring, Dresden, Germany, 2013 Cellular Communication Systems Page 53

54 Spatial-temporal Scheduling [8] Spatio-temporal scheduling with UC accounting for effects to others Avoid concurrency among cells cluster cells in a multi-frame frame cluster 1 cluster 2 Cluster 3 Cluster 1 Cluster 2 multi-frame n multi-frame n+1 Cluster 1 multi-frame n+2 1 in very 7 cells is active Avoid concurrency among UCs - Allocate UCs time slots in a frame cell a cell a cell a MRO cell b cell b cell b Cluster 1 frame CCO MLB ICIC multi-frame n multi-frame n+1 multi-frame n+2 [8] Stephen S. Mwanje Coordinating Coupled Self-Organized Network Functions in Cellular Radio Networks, Doctoral Thesis, TU Ilmenau, Cellular Communication Systems Page 54

55 Conclusions Future mobile communication networks will be much more dynamic and hard to manage SONs are a necessity Optimize the performance (system performance and user QoS) Reduce OPEX Three architectures for SON Centralized, distributed and hybrid Challenge is the coordination of UCs New solutions based on cognition, machine learning and big-data Very important: SONs should allow the network operator to be the final instance keeping the control of any required changes (kind of autopilot functionality) Cellular Communication Systems Page 55

56 References LTE self-organizing networks (SON): network management automation for operational efficiency, edited by Seppo Hämäläinen et al. Self-organizing networks: self-planning, self-optimization and self-healing for GSM, UMTS and LTE, edited by Juan Ramiro et al. Self-Organizing Networks (SON):Concepts and Requirements, 3GPP TS V0.3.1 ( ) LTE Operations and Maintenance Strategy, white paper perators/lte/_document/static%20files/lte%20operability%20son%20white%20paper.pdf OAM Architecture for SON, 3GPP TSG SA WG5 & RAN WG3 LTE Adhoc, R ,13th 14th June 2007 Self-X RAN, Self-Organizing Networks, NEC's Proposals For Next-Generation Radio Network Management, February 2009 Self Organizing Networks: A Manufacturers View, ICT Mobile Summit Santander, Spain, June 2009 S. Feng, E. Seidel, Self-Organizing Networks (SON) in 3GPP Long Term Evolution, Next Generation Mobile Networks Beyond HSPA and EVDO, NGMN Alliance, December 2006 NGMN Recommendation on SON and O&M Requirements, NGMN Alliance, December 2008 NGMN Use Cases related to Self Organizing Network, Overall Description, NGMN Alliance, December 2008 E. Bogenfeld, I. Gaspard, Self-X in Radio Access Networks, end-to-end efficiency FP7 Project, December 2008 Self-organizing Networks (SON) in 3GPP Long Term Evolution, Nomor Research GmbH, May 2008 Self-configuring and Self-optimizing Network Use Cases and Solutions. 3GPP TR v9.3.1, R9, May 2011 SOCRATES, Cellular Communication Systems Page 56

Self-Organization in LTE

Self-Organization in LTE Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel M.Sc. Nauman Zia M.Sc. Stephen Mwanje Cellular Communication Systems Page 1 Outline Introduction Functionalities of Self-Organizing