1. Self-Management for Unified Heterogeneous Radio Access Networks ISWCS 2015 Twelfth International Symposium on Wireless Communication Systems Brussels, Belgium August 25, 2015

2. SON coordination: conflict detection and conflict resolution Ovidiu Iacoboaiea Orange Labs

3. Outline  Storyline  SON Conflict Resolution (SONCO-R) – Reinforcement Learning  SON Conflict Detection (SONCO-D) – Naive Bayesian Classifier  Conclusions and Future Work 3 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015

4. SEMAFOUR Vision 4 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 Decision Support System SERVICE PROVIDER NETWORK OPERATOR HETEROGENEOUS NETWORK General Network- Oriented Objectives Network Status related to General Network- Oriented Objectives Integrated SON Management Physical Network Resources Physical Network Resources Physical Network Resources Policy-Based SON Management Monitoring & Diagnosis Operational SON Coordination Multi-RAT / Multi-layer SON WLAN Mgmt. 2G / 3G SON LTE SON UNIFIED SELF-MANAGEMENT SYSTEM

5. SONCO Storyline 5 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 SON 1 … SON 2 SON N SON MANAGEMENT get network KPIs SON Conflict Detection NETWORK SON Conflict Resolution set network parameters parameter update requests set network parameters  SON functions are meant to – Automate network parameter tuning / optimization in order to reduce CAPEX – Relieve the human operator from manual optimization tasks and reduce OPEX  Having several independent SON functions running concurrently may lead to conflicts, eg: common tuned parameters, input/measurements interdependence  The SONCO should detect, diagnose and resolute/arbitrate the SON conflicts. SONCO

6. 6 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 MOSON Evolutionary SON coordinator SON1(1) ND, traffic, UE dropping SON Change request Manual change Planning tool SON controllers SON1(T) Site 1 Site T SON2(1) SON2(T) SON3(1) SON3(T) OSS Changes to be executed Operator priorities Special events ND Updates

7. SONCO-R: Concept 7 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 SON 1 SON 2 time SON update requests Current NW config Action SONCO-R Actor Learner SON enabled Network SON enabled Network Reinforcement Learning Framework The SONCO-R decides which update requests are accepted and which are denied:  Update requests contain a criticalness indication (reflects how far the SON instance is from achieving its target KPIs).  The decision shall ensure fairness between different SON Function instances.  To anticipate the optimal decision, we use Reinforcement Learning employing a Regret based on the criticalness

8. SONCO-R: Concept 8 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 SON 1 SON 2 time SON update requests Current NW config Action SONCO-R Actor Learner SON enabled Network SON enabled Network Reinforcement Learning Framework The SONCO-R decides which update requests are accepted and which are denied:  Update requests contain a criticalness indication (reflects how far the SON instance is from achieving its target KPIs).  The decision shall ensure fairness between different SON Function instances.  To anticipate the optimal decision, we use Reinforcement Learning employing a Regret based on the criticalness

9.  2 SON Functions instantiated in each and every cell: – MLB: tuning the Cell Individual Offset (CIO) – MRO: tuning the CIO and the HandOver (HO) Hysteresis (HYS)  There is a parameter conflict on the CIO SONCO-R: Results 9 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 High priority to MLB High priority to MRO Average LoadNo. Too Late HOs [#/min]No. Ping-Pongs [#/min]

10. SONCO Storyline 10 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 SON 1 … SON 2 SON N SON MANAGEMENT get network KPIs SON Conflict Detection NETWORK SON Conflict Resolution set network parameters parameter update requests  SON functions are meant to – Automate network parameter tuning / optimization in order to reduce CAPEX – Relieve the human operator from manual optimization tasks and reduce OPEX  Having several independent SON functions running concurrently may lead to conflicts, eg: common tuned parameters, input/measurements interdependence  The SONCO should detect, diagnose and resolute/arbitrate the SON conflicts. SONCO

11. SONCO-D: Symptoms, Cause Dictionary 11 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 Evaluate Network Measurements/KPIs (symptoms) network fault ? Cause diagnosis:identify the misconfigured SON function Potential Causes (C): -SON 1 -SON 2 -…… symptoms (S1,S2…) -setting 1 -setting 2 ……… -setting 1 -setting 2 ……… max C P(C ∣ S 1, S 2,…) no yes ?  network fault = bad network KPIs The SONCO-D has to detect and diagnose network faults generated by the SON functions:  The causes are considered to be the misconfigurations of the SON functions.  Relevant symptoms have to be identified to allow for the inference of the cause.  To establish the link between causes and symptoms, we employ a Naïve Bayesian Classifier

12.  Bayes’ Rule: – for any cause C in the cause dictionary. – given the symptoms S 1, S 2, …  Assumptions: – symptoms are independent given the cause. – one cause at a time SONCO-D: Concept 12 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 SONCO-D Inference method: max C P(C|S) Inference method: max C P(C|S) Learner: P(C),P(S|C) Learner: P(C),P(S|C) labeled data (S,C) unlabeled data (S) solution deployment P(C ∣ S 1, S 2,…) = P(C) P(S 1,S 2 … ∣ C) P(S 1,S 2 …) P(C) Π i P(S i ∣ C) P(S 1,S 2 …) = Naïve Bayesian Classifier

13. SONCO-D: Results 13 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 % of correct cause identification  3 SON Functions instantiated in each and every cell: – (MLB via) CRE : tuning the Cell Individual Offset (CIO) – MRO: tuning the HandOver Hysteresis (HOH) – eICIC: tuning the number of Almost Blank Sub-frames (ABS)  There are several potential measurement conflicts: Cause diagnosis cause dictionary

14. Conclusions and Further Work 14 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015 SONCO-R: The Reinforcement Learning algorithm could be proven to have the necessary capabilities for tuning the change request decision with respect to priorities / weights defined by the operator SONCO-D: The Naïve Bayesian Classifier is a promising solution for the conflict detection / diagnosis. The more details are used for describing the cause the more difficult it is to correctly identify it Conclusions Create more SONCO friendly SON Functions Update the SONCO consequently Example: SON Functions raise intelligent and honest alarms to help the SONCO in the conflict detection / diagnosis and resolution Future Work

15. POSTERS on the SEMAFOUR use cases Don’t miss! 15 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015

16. Thank you for your attention www.fp7-semafour.eu 16 Workshop on Self-Management for Unified Heterogeneous Radio Access Networks, August 25, 2015