Use Cases: 3GPP Long Term Evolution (LTE) and. Wireless Sensor Networks (WSNs)

Transcription

1 Use Cases: 3GPP Long Term Evolution (LTE) and Self-Organizing Page 1

2 3GPP Long Term Evolution (LTE) Page 2

3 LTE (3.9G) and LTE-A(4G) High data rates (100Mbps (1Gbps LTE-A) downlink and 50Mbps uplink) Low latency Support Mobility No more RNC (Radio Network Controller) RNC functionalities are moved in enodeb X2 interface for seamless mobility (i.e. data/context forwarding) and interference management Page 3

4 Costs CAPital EXpenditures (CAPEX) determine the direction and level of investment 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 and quality of services provided OPerational EXpenditures (OPEX) : running cost Growing wireless markets imply gowing OPEX Page 4

5 Drivers For Self-Organization High complexity and high number of parameters Operation of heterogeneous networks Expanding number of Base Stations (BSs) Introducing of home evolved NodeBs (enodebs) leads to a huge number of nodes to be operated in multi-vendor scenarios OPEX is expanding Reduction of OPEX requires reducing human interactions by Configuring and optimizing the network automatically while allowing the operator to be the final control instance High quality must be ensured SONs are essential Page 5

6 Drivers For Self-Organization SON can improve the network performance and quality of service. This can be achieved through applying different techniques that can optimize the performance of the network. Unlike 2G and 3G, in next generation mobile communication networks, there will be no need for RNC. Therefore the enodebs will be more interactive. There is room for cooperative management of base stations among different operators. Page 6

7 Functionalities Of SONs Page 7

8 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 failuer detection Cell outage detection... Self-Planning (dynmic re-computation) Page 8

9 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 Page 9

10 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 Page 10

11 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, etc. Page 11

12 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, do some actions to avoid performance degradation Page 12

13 Architectures Of SONs Page 13

14 Requirements & Taxonomy Support of network sharing between network operators Providing an easy transition from operator controlled to autonomous operation Three architecture Centralized SON Distributed SON Hybrid SON Page 14

15 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 Cons OAM is vendor specific (multi-vendor optimization is problematic) Not applicable for situations where selforganization tasks should be fast OAM Centralized OAM SON SON SON OAM Itf-N enb enb Page 15

16 Distributed SON SON functionalities reside in the enb at the lower level of network architecture Fully autonomous distributed RAN optimization Pros Applicable for situations where selforganization task should be achieved fast Cons Hard to deploy and manage X2 interfaces should be extended OAM Centralized OAM OAM Itf-N enb enb SON SON x2 Page 16

17 Hybrid SON Idea is to push some of the SON functionalities on the enb itself and some on OAMs Pros Allowance for a high degree of automation guarantee, control and inspection Cons Hard to deploy and manage Requiring of multiple interfaces extensions OAM Centralized OAM SON SON SON OAM Itf-N enb enb SON SON x2 Page 17

18 Use Cases Page 18

19 What Are The Use Cases Defined In 3GPP? Automatic Neighbor Relation (ANR) Coverage and capacity optimization Energy saving Interference reduction Physical cell-id automatic configuration Mobility robust optimization Mobility load balancing optimization Random Access Channel (RACH) optimization Page 19

20 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 an in advance actions maybe done to optimize the performance (ressources reservation) enb3 x2 x2 x2 enb2 enb4 Page 20

21 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 measurent results Configuration of X2 interfaces between enbs Connection setup with neighbor enbs ANR optimization Update as new enbs join/disjoin the network How to accurately optimize the neighbor relation is still an open issue till now Some steps work in preoperational state, while some others work in operational state Page 21

22 Mobility robust optimization Power Reduce the number of HO-related Radio Link Failure (RLF) Reduce the HO-related issues that lead to degradation in the QoS. Time Failures due to too late HO triggering Failures due to too early HO triggering Failures due to HO to a wrong cell Page 22

23 Too Late HO Page 23

24 Too Early HO Page 24

25 HO To wrong cell Page 25

26 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 Non LTE coverage LTE cell smaller than planned LTE coverage Page 26

27 Coverage & Capacity Optimization LTE coverage holes and no alternative RAT Significant call drops due to coverage holes Page 27

28 Coverage & Capacity Optimization Isolated LTE cells Coverage blackouts in network s border areas Page 28

29 Coverage & Capacity Optimization Solution Update the BS parameters such as height, tilt and Tx power Page 29

30 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 possible throughout the 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 if users are away from home enb and closed subscriber group cells Page 30

31 Interference Reduction Goal Improving the network performance by means of reducing the interference between its equipments Works in operational state Many limitations due to the applied frequency band Interference depends on frequency band characteristics Solutions Decrease enbs density Hard to apply due to the capacity decrease and the existence of home enbs that are not under the control of the network operator Power control and/or reconfigure the wireless setup Interference cancellation, coordination and randomization Page 31

32 Physical Cell-ID Automatic Configuration Goal Automatically configure the physical Cell-ID (collision and confusion free assignment of physical Cell-ID) Works in preoperational state A part of self-configuration procedure Main limitation is that there is only 504 physical Cell-IDs available Solution enb-based solution (distributed solution) OAM-based solution (centralized solution) Page 32

33 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 measurements 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 Page 33

34 Conclusions Future mobile communication networks will be much more dynamic and hard to manage SONs are a necessity Optimize the performance Reduce OPEX Three Architecture for SON Centralized, distributed and Hybrid Very important: SONs should allow the network operator to be capable of doing any required changes Page 34

35 Summary (what do I need to know) Why Self-organization in next generation networks? How can self organization be used in different use case such as ANR Mobility rubostness Energy saving PCI Page 35

36 References Self-Organizing Networks (SON):Concepts and Requirements, 3GPP TS V0.3.1 ( ) LTE Operations and Maintenance Strategy, white paper %20Operators/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 TR36902 v1.2.0, June 2009 Page 36

37 Page 37

38 Network Large Size Mostly Static Data-centric Private Nets. Node Limited resources Battery No Global ID Applications Habitat Monitoring Smart home, building, metering (smart Grid) Surveillance and rescue.. Page 38

39 SENSOR NETWORKS ARCHITECTURE Internet, Satellite, Sink Task Manager Page 39

40 Characteristics of sensor nodes Low cost*, size, and weight per node Limited resources Computing : microcontroller Storage: few KB of RAM, 100s of KB of flash. Communication: low range, poor connectivity, sometimes only broadcasting. Normally rely on batteries. Page 40

41 Characteristics of sensor nodes Prone to failures More use of broadcast communications instead of point-to-point Nodes do not have a global ID such as an IP address The security, both on physical and communication level, is more limited than in classical wireless networks Page 41

42 CHARACTERISTICS OF WSNs Very large number of nodes Nodes need to be close to each other Asymmetric flow of information Communications are triggered by queries or events Limited amount of energy Mostly static topology Page 42

43 APPLICATIONS Page 43

44 Military Applications: Monitoring friendly forces, equipment and ammunition Battlefield surveillance Reconnaissance of opposing forces and terrain Targeting Battle damage assessment Nuclear, Biological and Chemical (NBC) attack detection and reconnaissance Page 44

45 Environmental Applications Tracking the movements of birds, small animals, and insects Monitoring environmental conditions that affect crops and livestock Chemical/biological detection Pollution study Precision agriculture Flood detection, and Forest fire detection. Page 45

46 Habitat Monitoring Great Duck Island in Maine. Page 46

47 Health Applications Providing interfaces for the disabled Integrated patient monitoring Diagnostics Telemonitoring of human physiological data Tracking and monitoring doctors and patients inside a hospital, and Drug administration in hospitals Page 47

48 Smart Roads Traffic monitoring, accident detection, recovery assistance Finding out empty parking lots in a city, without asking a server (car-to-car communication) Vehicle tracking and detection Page 48

49 Smart Grid Monitoring product quality Factory Floor Automation Constructing smart homes Constructing smart office spaces Smart spaces Page 49

50 WSN is able to offer customers and utilities a convenient, cost-effective way to monitor energy creation in real-time, as well as manage the deployed system componentsuality Factory Floor Automation Constructing smart homes Constructing smart office spaces Smart spaces Smart Grid Page 50

51 WSNs, How To Self-Organize? Supervised self-organization An overlay layer of supervision (can be provided by the user or an overlay management system) Better described with centralized self-organization Unsupervised self-organization Little or no interaction with the ultimate user/ management system Better described with distributed self-organization Hybrid self-organization Inherits properties of both, supervised and unsupervised Page 51

52 Where Is Self-Organization Required? Goal: Enable WSNs to adapt themselves based on their environment (not being application-specific) Self-organization techniques are required in many tasks, such as Deployment and topology control of WSNs Address management Channel access Routing Power efficiency Quality of Service (QoS )... Page 52

53 Deployment of WSNs Page 53

54 Deployment Issues! What Does it all mean? Question How should sensor nodes be deployed so that a required QoS is guaranteed? Challenges Which parts of the area should be covered to detect particular events? What number of sensor nodes is needed and where should they be placed physically? Page 54

55 Deployment Issues! What Does it all mean? Adequate deployment simplifies other tasks Clustering, access to the medium, routing, etc. Deployment problem can be complicated if Cost minimization is required Some parts of the area need to be covered better than others Nodes of different types and sensing capabilities are present The area is not a two dimensional plane Page 55

56 Research Challenges Relocation of nodes positions and may roles A strategy for inter-nodes communication to reposition themselves is essential (self-organization efficient network) Nodes deployment in three dimensional space Most existing solutions assume the deployment to take place in two dimensional space In real terrain, the deployment is three dimensional and is a NP hard complexity problem Page 56

57 Channel Access Control (MAC) Page 57

58 Requirements MAC mechanism should provide Energy conservation Fairness High throughput and low delay Scalability Robustness against frequent topology change High degree of self-organization.. Restrictions Limited energy, computational, and communication resources in WSNs Page 58

59 Power (mw) POWER CONSUMPTION RADIO SENSOR CPU TX RX IDLE SLEEP Page 59 59

60 Taxonomy Contention-based protocols Allow nodes to independently access to a shared medium Nodes are not required to form a cluster or certain topology Suitable for applications with rather unpredictable events occurrence, network topology and network mobility Pros Cons Good scalability in term of new nodes joining the network Organization of sleep and wake-up phases is complicated For energy efficiency, control overhead is required to keep neighbors synchronized Idle listening, collisions, overhearing, etc. Page 60

61 Taxonomy Schedule-based (TDMA-based) protocols Time is divided into time slots, each is assigned to a node Suitable for stationary networks with almost predictable traffic Pros Cons Avoid collisions, idle listening and schedules sleep without overhead Dynamically changing the frame length and time slot assignments in a cluster in difficult (node changes or inclusions) Poor scalability and poor mobility Effective slot assignment in multi-hop networks is also challenging. Moreover, inter-cluster communication is complicated High quality time synchronization is required Page 61

62 S-MAC Contention-based protocol S-MAC provides mechanisms to circumvent idle listening, collisions, and overhearing Does not require more than 1 wireless interface Page 62

63 S-MAC Each node alternates between a fixed-length listen and a fixed-length sleep period according to its schedule The listen period of S-MAC can be used to receive and transmit packets Neighbors are coordinate, so that their listen periods start at the same time Page 63

64 S-MAC Each neighbor (B or C) wishing to transmit a SYNCH packet picks one of the time slots randomly and starts transmitting if no signal was received in any of the previous slots CTS Synch B B and C goes back into sleep mode and waits for A s next wakeup Phase 1 Synch phase A RTS Synch C A knows a neighbor B and C s schedule, A can wake at appropriate times and send its own SYNCH packet to B/C A listens for RTS packets from neighboring nodes. Phase 2 RTS phase Node A transmits a CTS packet if an RTS packet was received in the previous phase Phase 3 CTS phase Page 64

65 S-MAC Nodes use RTS/CTS handshake and maintains a NAV variable NAV is used to switch off a node to avaoid overhearing Schedule of node A and it s neighbours can be synchronized, i.e A can reach all with a single synch S-MAC allows the neighbouring nodes to agree on the same schedule and thus forms a virtual cluster Virtual cluster solely refers to the exchange of schedules, not data Page 65

66 S-MAC Pros Circumvents idle listening, collision and overhearing Message passing approach of S-MAC reduces the latency of passing and entire message Cons In message passing approach, a single node can block the medium for a long time It is hard to adapt the length of the wakeup period to changing load situations, since this length is essentially fixed (as is the length of the listen period) Page 66

67 Routing Schemes Page 67

68 Constraints & Requirements Constraints Energy and bandwidth constrains Mostly no global addressing Requirements Energy-efficient and reliable routing mechanisms Maximizing the network lifetime Minimizing or even eliminating data traffic redundancy Efficient resource management Satisfying stationary as well as mobile sensor networks regardless if the events monitored are static or dynamic Cope with different data delivery modules (event-driven, query-driven, continuous and hybrid) Page 68

69 Taxonomy Routing protocols Data-centric routing protocols Hierarchical routing protocols Location-based routing protocols Network flow and QoS-aware routing protocols Page 69

70 Data-Centric Routing Protocols Basic idea Sink sends queries to certain regions and waits data from sensors located in that region Attribute-based naming is necessary to specify the properties of data required Pros Can be used for periodic monitoring Energy efficient by removing redundant transmissions Cons Not good for tracking application Page 70

71 Hierarchical Routing Protocols Basic idea Build a hierarchy among nodes and assign different roles to nodes Aim at maintaining energy consumption of sensor nodes either by enabling multi-hop communication within a particular cluster or by aggregation and fusion of data Pros Scalability Reduced number of transmissions Energy efficiency Data aggregation Page 71

72 Hierarchical Routing Protocols Cons Cluster head selection and cluster formation Example: LEACH Page 72

73 Clustering based Hierarichal Routing Phase 1: 2: 3: Cluster Intra-cluster Inter-cluster Head communication Selection r r BS Page 73

74 Location-Based Routing Protocols Geographic Routing Position,localization errors, Dead ends Local information Send data to N42-(x1,y1) (x1,y1) D2 D1? Node N32 N51 Position (x,y) (x,y) D2>D1 Page 74

75 Location-Based Routing Protocols Basic idea Distance between two nodes is calculated using location information Energy consumption resulting from transmitting a packet to a particular sensor node can be estimated (efficient energy utilization) Protocols designed for Ad hoc networks with mobility in mind may be applicable for sensor networks as well Pros Better routing decisions Table less Guaranteed delivery Page 75

76 Location-Based Routing Protocols Cons Getting location information is a costly operation Local maximum problem Page 76

77 Research Challenges Energy efficiency and robustness against node mobility Self-optimization and self-healing capabilities of routing mechanisms Page 77

78 Summary (what do I need to know) What is wireless sensor network? Some Applications of WSNs Characteristics of Sensor Networks? Why Self-organization WSN? Why Self-org in MAC Why self-org in Deployment and topology control Page 78

79 References I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, A Survey on Sensor Networks, IEEE Communications Magazine, vol. 40, no. 8, pp , August H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons, A. Awad, C. Sommer, R. German, and F. Dressler, Virtual Cord Protocol (VCP): A Flexible DHT-like Routing Service for Sensor Networks, in 5th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2008). Atlanta, GA: IEEE, September 2008, pp A. Awad, R. German and F. Dressler, "Exploiting Virtual Coordinates for Improved Routing Performance in Sensor Networks," IEEE Transactions on Mobile Computing, vol. 10 (9), pp , September M. Caesar, M. Castro, E. B. Nightingale, G. O Shea, and A. Rowstron, Virtual Ring Routing: Network routing inspired by DHTs, in ACM SIGCOMM Pisa, Italy: ACM, September Jin, Zhang, Jian-Ping, Yu, Si-Wang, Zhou, Ya-Ping, Lin, Guang, Li "A Survey on Position-Based Routing Algorithms in Wireless Sensor Networks, Algorithms 2, no. 1: Akkaya, K. and Younis, M., A survey of routing protocols in wireless sensor networks, Elsevier Ad Hoc Networks 2007, Vol. 3 I (3). pp Page 79