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E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 1 E3E3 Intelligent Management Strategies for Network Segments of Cognitive, High- Speed, B3G.

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Presentation on theme: "E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 1 E3E3 Intelligent Management Strategies for Network Segments of Cognitive, High- Speed, B3G."— Presentation transcript:

1 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 1 E3E3 Intelligent Management Strategies for Network Segments of Cognitive, High- Speed, B3G Infrastructures Dr. George Dimitrakopoulos Prof. P. Demestichas, Mr. A. Saatsakis University of Piraeus Department of Digital Systems Piraeus, GREECE, e-mail: gdimitra@unipi.gr

2 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 2 E3E3 Outlook  Current Trends in Wireless Communications  The B3G Era  Reconfigurable segments  Capabilities and Requirements  Legacy Management Functionality  Description  Indicative Simulation Results  Enhancements for Introducing Cognition  K-Nearest Neighbor Algorithm  Incorporation in generic Functional Architecture (FA)  Way forward – Mapping on Long Term Evolution (LTE) System Architecture  Conclusions

3 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 3 E3E3 B3G World: Overview  Heterogeneous network infrastructure (Radio Access Technologies – RATs)  Mobile  WMAN, WLANs  Short range connectivity  Reconfigurable segments  How can this infrastructure be managed?  Application level QoS  Traffic (user, application, session) distribution to network segments  Selection of optimal configurations of the network segments  Flexibility required  Future regarding applications is unpredictable  New RATs introduced  New technologies at the network layer  Complexity due to heterogeneity of network infrastructure  Each segment is seen as a reconfigurable segment  May be SDR or “Software Adaptable Networks”  Select the appropriate configurations taking into account context (incl. traffic, mobility, interference), profiles, policies  Entail flexibility

4 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 4 E3E3 B3G world: Reconfigurable Segments  Configuration = {S/W for RAT, frequency, other parameters...}  Reconfiguration = selection of optimum configurations  Online configuration  Cross-layer implications (PHY/MAC, IP, TCP, application  Focus on PHY/MAC layers  Maintain RAT, change spectrum E.g., legacy system operated in new spectrum  Change RAT, maintain spectrum E.g., new system operated in legacy spectrum  Change RAT, change spectrum E.G. Flexible spectrum management  Reconfiguration model More expensive than the “single technology” model Less expensive than the sum of the cost of individual technologies Fast, effective, stable, scalable and reliable techniques are needed for anticipating ever- changing conditions

5 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 5 E3E3 Legacy Management Functionality: Overview  Design and development of schemes for showcasing the benefits of reconfigurable transceivers in terms of resource efficiency, when trying to accommodate a given user traffic.  Dynamic Network Planning and Management (DNPM) performs the planning, implementation and monitoring of RAT, spectrum and radio resource allocations  Developed within E2R project (1 st and 2 nd phase)

6 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 6 E3E3 Legacy Management Functionality Description: Input (Context, Profiles, Policies) and Output Discovery Profiles OptimizationReconfiguration Context Policies Monitoring  Context  Monitoring: senses information related to the network segment/element and its environment  Discovery: estimates the capabilities that can be achieved by alternate configurations of the transceivers of the element  Profiles  Acquisition and maintenance of information (data and knowledge) on the managed element and the users  Policies (constraints, rules, strategies)  Optimization functionality is needed in order to obtain the optimum (re)configurations  RAT and spectrum selection Cross-layer optimization of network’s performance  RAT-specific management part (for CDMA and OFDMA) Improvement of spectrum and radio resources utilization

7 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 7 E3E3 Legacy Management Functionality: Optimization

8 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 8 E3E3 Legacy Management Functionality: Results (1)  Configuration of monitoring, discovery, profiles and policies information by means of a custom developed traffic generator  Focus on a cell served by an element of the network segment Users are uniformly distributed Users request 3 services –Audio-call, –Video-streaming (including applications such as IPTV and mobile TV) –Web-browsing. 9 reference traffic cases In each case there is a traffic mix. Average number of sessions is depicted in each row  Finally, elements are equipped with three reconfigurable transceivers, each of which may select among various configurations.  Audio call service  Only one reference quality level (utility equals to 1) has been defined 64kbps.  Video-streaming and web-browsing services  Five reference quality levels (utilities equal to 2, 4, 8, 16 and 32) 64, 128, 384, 512, or 1024 kbps, respectively. Test Cases Video Streaming Sessions Web Browsing Sessions Audio Call Sessions 10 0 120 21 4 110 33 7 100 45 10 90 58 12 80 613 12 70 716 14 60 810 15 50 928 12 40

9 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 9 E3E3 Legacy Management Functionality: Results (2)  Optimal choice of configurations  Allocation of technologies/spectrum to transceivers of an element/segment  Allocation of demand to technologies  Allocation of applications to quality levels  Criterion: Objective function representing aggregate utility volume minus cost  Results Analysis  Gains depend on the transceivers reconfiguration capabilities  Gains in user satisfaction up to 60% Gains in CAPEX up to 40% Case 8: (c1,c2,c3) Case 8: (c1,c2,c2)

10 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 10 E3E3 Legacy Management Functionality: Results (3)  Set of transceivers configured to CDMA  Requirement for intelligent allocation of demand among the available 3G transceivers  Demand allocation policies considered = percentages of demand allocated per available carrier Service-based policies Location-based policies  Criterion for selection of the best allocation policy Minimization of the averaged sum of all powers received/transmitted by each reconfigurable transceiver Balancing of loading factors among carriers

11 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 11 E3E3 Enhancement with cognition capabilities  Way towards cognitive networks  Retain information from interactions with environment  Transform this information to knowledge and experience  Respond/act based on this information  Basic principles  Exploitation of reconfigurable platforms  Evolution of the legacy management functionality  What is exactly needed in terms of management ???

12 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 12 E3E3 Work areas for introducing cognition  Incorporation of the presented functionality in any learning model with a feedback loop is more than important  Enhance DNMP with learning techniques in order to obtain the self-management functionality for cognitive wireless network segments sM-CgWNS)  DNPM + learning = sM-CgWNS Ways to achieve this: 1.Context Acquisition: Identification of known contexts (context recognition) through pattern matching algorithms »Pattern matching: find the problem-pattern that matches the most with the current one in order to deploy its known solution, skipping the optimization procedure (k-Nearest Neighbor algorithm - k-NN) 2.Decision Making: Reinforcement learning for rating the behavior of configurations

13 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 13 E3E3 The Target of Cognitive Networks Management O(u 2 ) O(r t*u )  What are the gains?  DNPM searches for solution from the scratch while Enhanced Context Acquisition exploits known solutions using Pattern Matching. Applying already known solution means: –1) Reduce complexity imposed by large number of RATs, resources,load »Enhanced Context Acquisition complexity: O(u 2 ), where u denotes the number of users »DNPM complexity (worst case): O(r t*u ), where r denotes the number of available RATs, t denotes the number of transceivers –2) Decrease the overall time needed for the efficient network adaptation

14 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 14 E3E3 Enhanced Context Acquisition and Cognitive Decision Making  Enhanced Context Acquisition  Problem statement Time consuming network optimization Increased complexity due to high number of network configurations Repetition of similar environment problems  Problem solution Check if currently encountered context has been addressed in the past How? Use of pattern matching (k Nearest Neighbor – kNN algorithm) Apply “known” solutions skipping network optimization procedure  Cognitive Decision Making  Problem Statement Selected configurations perform in a certain way. They may meet the promises completely or up to a certain percentage Certain configurations should be preferred due to policies, past performance (stable/reliable)  Problem solution “Remember” the performance of the configuration for each different context How? Use Reinforcement Learning Influence decision making: select allowed configurations that exhibit stable and reliable behavior

15 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 15 E3E3 K-Nearest Neighbor Algorithm  A pattern matching algorithm: K-Nearest neighbor Algorithm The target: Find the pattern with a known solution which matches best to the current problem Training procedure: Find configurations for standard traffic patterns to act as the pattern pool. Matching Criteria: Distance among users with the same profile and service

16 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 16 E3E3 Indicative publications per management area I. Derivation of policies for CDMA and OFDM segments K. Tsagkaris, G. Dimitrakopoulos, P. Demestichas, "Policies for the Reconfiguration of Cognitive Wireless Infrastructures to 3G Radio Access Technologies", ACM/Springer Wireless Networks journal, to appear. II. Bayesian networks for discovery function G.Dimitrakopoulos, K.Tsagkaris, K.Demestichas, E.Adamopoulou, P.Demestichas, “A Management Scheme for Distributed Cross-Layer Reconfigurations in the Context of Cognitive B3G Infrastructures”, Computer Communications journal, to appear. III. Optimisation - Greedy strategy K. Tsagkaris, G. Dimitrakopoulos, P. Demestichas, A. Saatsakis, “Distributed Radio Access Technology Selection for Adaptive Networks in High-Speed, B3G Infrastructures”, International Journal of Communication Systems, October 2007 IV. K-NN for traffic estimation: under preparation V. Moving towards Cognition G. Dimitrakopoulos, P. Demestichas, K. Tsagkaris, A. Saatsakis, K. Moessner, M. Muck, D. Bourse, “Emerging Management Concepts for Introducing Cognition in the Wireless B3G World”, Springer Wireless Personal Communications journal, to appear

17 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 17 E3E3 Incorporation into the E2R Functional Architecture (FA)  Categorization of functionality into Management and Control plane  Management Plane Dynamic Network Planning and Management – DNPM Advanced Spectrum Management – ASM Meta Operator – MO Traffic Estimator – TE  Control Plane Joint Radio Resource Management – JRRM  Direction towards standardization (ETSI)

18 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 18 E3E3 Overview of Future Steps related to FA             Input for our work Functional Architecture - FA Mapping Management and Control Long Term Evolution - LTE on LTE System Architecture on Network Elements, Architecture

19 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 19 E3E3 LTE System Architecture

20 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 20 E3E3 Mapping on LTE System Architecture 3GPP Access CM (DNPM, ASM, Self-x) CC (from FA) Serving Gateway CC CM CC CM Extension CM=cognitive management CC = cognitive control

21 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 21 E3E3 LTE Network Architecture CM CC CM CC CM CC CM CC CM CC CM=cognitive management CC = cognitive control

22 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 22 E3E3 LTE evolved Node B (eNB) and SA Gateways  Radio Resource Management Functions  Radio Bearer Control – RB Control  Radio Admission Control  Connection Mobility Control  Dynamic Resource Allocation (Scheduling)

23 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 23 E3E3 Cognitive Management (DNPM, ASM, Self-x) Mapping on evolved Node B (eNB) and GWs Cognitive Control (part of FA) Cognitive Management (DNPM, ASM, Self-x) ( S1 – M )

24 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 24 E3E3 Wrap up - Conclusions  B3G era disposes high levels of complexity  Valid option to tackle complexity is the design of wireless infrastructures exploiting reconfiguration capabilities  Legacy Management Functionality (description, simulations)  Further enhancements Incorporation of cognitive networking principles  Incorporation in generic Functional Architecture (FA)  Towards Standardization Activities (ETSI)  Mapping on LTE SA  Significant reduction in CAPEX and OPEX  Increase in response velocity  Increase in user satisfaction

25 E 3 Chinese CRS Workshop – Beijing, May 26-30, 2008 Slide 25 E3E3 Future Plans  Study of 3 cases for cognitive management  Cognitive management may be allocated to already identified functions  Cognitive management will most likely lead to upgrades in the interfaces between already identified functions  Cognitive management may lead to the identification of new functions  Performance of machine learning techniques shall be improved  Exploitation of solutions at a level of lower complexity  Further reduction of complexity  Encompass wireless mesh internetworking aspects in functionality  Impact on power and capacity constraints, signalling


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