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Cognitive Wireless Networking Kang G. Shin Real-Time Computing Laboratory EECS Department The University of Michigan Ann Arbor, MI 48109-2121

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Presentation on theme: "Cognitive Wireless Networking Kang G. Shin Real-Time Computing Laboratory EECS Department The University of Michigan Ann Arbor, MI 48109-2121"— Presentation transcript:

1 Cognitive Wireless Networking Kang G. Shin Real-Time Computing Laboratory EECS Department The University of Michigan Ann Arbor, MI 48109-2121 http://www.eecs.umich.edu/~kgshin

2 2 Today’s Wireless Networking  Exponential growth of wireless access demands ▪ Multimedia & other QoS applications ▪ Diverse network uses – commercial, public, military  “Paradigm shift” in network design ▪ Static, environment/app-agnostic  dynamic and adaptive Wireless medium

3 3 Cognition : key to future networking  What is cognition? ▪ Awareness of surrounding environment and apps, which are often subject to: ▪ Random noise, fading, heterogeneous signal attenuation ▪ Diverse app types and criticalities  Why cognition? ▪ Spectrum is a limited resource ▪ Traditional network designs are not efficient ▪ New research directions, e.g., Dynamic Spectrum Access ▪ DSA requires cognition ▪ One-fits-all doesn’t apply

4 4 Elements of Cognition  Spectrum Sensing ▪ Monitors signal activities ▪ Detects signals ▪ Energy or feature detection  Environmental/App Learning ▪ Learns network dynamics and app requirements ▪ Channel quality and usage patterns (e.g., ON/OFF, SNR) ▪ Apps needs (e.g., delay, bw, jitter)  System/App Adaptation ▪ Adapts system/app configurations/parameters ▪ Adapts sensing period/time/frequency, stopping rule, etc.

5 5 What to Expect from Cognition?  Technically, ▪ Efficient spectrum utilization ▪ Smarter spatial reuse ▪ C oexistence of heterogeneous networks  Economically, ▪ Extra benefit to legacy users ▪ Opportunistic spectrum auction/leasing ▪ Cheaper service to CR users ▪ CR Hotspots – cheaper Internet access CR HotSpot

6 6 Software-Defined Radios (SDRs)  Key to cognition! ▪ Reconfigurable in real time (e.g., USRP, SORA, WARP)  Today’s SDR Devices ▪ Different PHY layers cannot account for the throughput differences ▪ Slow USB interface results in significant lag between carrier sense and transmission ▪ PHY and MAC layers need to tolerate processing delays USRP1USRP2SORA Throughput400 bytes/s16 kbps15 Mbps Bandwidth2Mhz768 kHzStandard 802.11g PHYCSMA Pulse-modulatedCDMAOFDM InterconnectUSBGigabit EthernetPCI

7  Cognition Engine  Integration Architecture of Cognition Elements with Legacy Systems Cognition-based Network Design

8 8 Cognition Engine  Includes key elements in achieving awareness  Enables unified cognition for wireless networks Cognition Engine Enhanced Utilization Better QoS Support Environment/App Monitoring Environment/App Learning System/App Adaptation Optimal Decision

9 9 Environment/App Monitoring  Signal detection ▪ PHY-layer monitoring of signal activities  Adaptive selection of method for signal detection ▪ Energy detection – more sensitive to SNRwall ▪ Feature detection – usually longer sensing-time  Monitoring of application QoS needs ▪ Applications can provide QoS hints, e.g., bandwidth, e2e delay, jitter

10 10 Environment/App Learning  Spectrum-usage pattern inference ▪ Infer ON/OFF channel-usage patterns ▪ Methods: ML, Bayesian, and entropy-based estimation  Signal profiling ▪ Based on received signal strength (RSS)  Application QoS estimation & prediction ▪ Applications may have stringent & diverse QoS needs ▪ History-based estimation/prediction using explicit hints and network-state awareness

11 11 System/App Adaptation  Spectrum-sensing scheduling ▪ Policy-aware: ▪ Meet FCC’s requirement on sensing for primary user protection ▪ Bandwidth-aware: ▪ Maximal or fast discovery of idle channels  Spectrum-aware user admission/eviction control ▪ Commercial CR Access Points ▪ Multiple user classes (with different spectrum demands) ▪ Time-varying spectrum resources (ON  OFF) ▪ Optimal user admission/eviction control ▪ To maximize profits

12 12 System Adaptation, cont’d  Application-aware DSA optimization ▪ DSA parameters (e.g., sensing time & interval) are adaptively updated based on applications’ QoS demand  Collision-aware transmission scheduling ▪ Collision resolution, instead of collision avoidance  DSA transmission scheduling ▪ Goal: Achieve good PU-safety vs. SU-efficiency tradeoff ▪ Dual (safe vs. aggressive) mode transmission scheduling based on PU channel-usage pattern estimation 12

13 13 Optimal Decision  Existence of opportunities ▪ H0: no primaries exist  there are opportunities ▪ H1: primaries exist  no opportunity  Reliable distributed sensing ▪ Attack-tolerant cooperative sensing ▪ Protect sensor networks from (e.g., spoofing) attacks ▪ Detection/filtering of abnormal sensing reports ▪ Mal-functioning or compromised sensors

14 14 System Integration Architecture Implementation & deployment of cognition ▪ Needs a well-defined integration architecture ▪ Different from traditional (full layer-based) design 14

15 15 System Integration Arch, cont’d Integration architecture consists of:  Cognition Interface (CI) ▪ Provides interface API to each cognition mechanism ▪ Seamlessly integrates with OS protocol stack, applications, and other cognition mechanisms  Cross-layer Interaction Framework (CLIF) ▪ Provides “awareness” management in system/network ▪ Consists of Repository, Parameter Mapper, and Trigger Manager 15

16 16 Cognition Interface  Defines communication mechanisms between cognition engine and existing network stack  API functions provided for ▪ Export/import & management of awareness parameters ▪ Registering trigger events 16

17 17 Cross-Layer Interactions  Provides abstraction for cognition protocol implementation & deployment  Consists of: ▪ Repository - stores awareness parameters ▪ Trigger Manager - registers predicates of parameters, and generates notification events ▪ Parameter Mapper - manages routines that define relationship between awareness parameters 17

18  CR Components & Architecture  Maximal Opportunity Discovery via Periodic Sensing  Fast Opportunity Discovery via Periodic Sensing  Incumbent Protection via In-band Sensing  Optimization Framework for Cooperative Sensing  Attack-Tolerant Distributed Sensing in CRNs  Spectrum-Aware User Control  Collision-Aware Transmission Scheduling  Context-Aware Spectrum Agility (CASA)  Spectrum-Conscious WiFi (SpeCWiFi)  System Integration Architecture (SIA) Cognitive Networking Research in RTCL at Michigan

19 Current Members PhD students: Eugene Chai, Hyoil Kim, Ashwini Kumar, Alex Min, Michael Zhang, Xinyu Zhang Post docs: Jaehuk Choi Recent Alums PhD graduates: Chun-Ting Chou Post docs: Young-June Choi, Bechir Hamdaoui CNR Group @RTCL

20 MLME (MAC) PLME (PHY) RME MME GCEPEE CR Components & Architecture  Main Components  RME: Resource Management Entity  MME: Measurement Management Entity  GCE: Group Coordination Entity  PEE: Policy Enforcement Entity  Resource Management Entity (RME)  Maintains Spectral Opportunity Map (SOM)  Status of each channel  SOM is updated by  scanning (MME) and  exchanging SOMs (b/w RMEs) 20

21 21 3 states GCE VACANCY SCANLISTEN VACANCY SCANLISTEN VACATE SCAN  Group Coordination Entity (GCE)  Synchronize channel vacation  Exchange spectrum-usage information  Described by three states  SCAN: scan a channel (MME)  LISTEN: check returning incumbent (MME)  VACATE: vacate channel (GCE) CR Components & Architecture, cont’d

22 Maximal discovery via periodic sensing  Find optimal T p i ’s – Tradeoff b/w discovery & disruption: ▪ Frequent sensing  (1) more idle channels discovered, but (2) more disruption in utilizing opportunities Ch 1: Ch 2: Ch 3: logical ch: Disrupted reuse time Sensing-time T I i Discovered opportunities Periodic sensing Sensing-period T P i 1 31,3 1 121,2 2 3 1 3 22 sensing: 22

23 Performance Evaluation Discovered ≥98% of the analytical maximum (AOR max ) ≤22% more opportunities than non-optimal schemes 23

24 Fast discovery via reactive sensing  Reactive sensing – discover opportunities at channel vacation channel vacation ON OFF Ch 1 Ch 2 Ch 3 opportunity found  Find: optimal sensing sequence for minimal latency Opportunity discovery latency  seamless service provisioning reactive sensing reused channel 24

25 Optimal sensing sequence  At channel vacation: ▪ N out-of-band channels ▪ Capacity C i ▪ P idle i : channel availability (probability of idleness) ▪ B : amount of bandwidths to discover at channel vacation  N! possible sequences (NP-hard)  Homogeneous case ( C i =C )  optimal sequence Sorting channels in ascending order of T I i / P idle i  Heterogeneous case  suboptimal sequence Satisfying necessary condition for optimality 25

26 Backup channel management  Goal: manage a list of backup channels ▪ A subset of out-of-band channels channel export channel import out-of-band channel Backup Channel List (BCL) Candidate Channel List (CCL) Q1: How to form BCL Initially? Q2: How/When to update BCL? channel swap 26

27 Performance Evaluation Delay Type-I: opportunities discovered at first round search Delay Type-II: opportunities discovered at successive retries (1) Optimal Sensing Sequence(2) BCL Update 47% (enhanced) 40% 76% 91% 27

28 Incumbent Protection via In-band sensing TV transmitter 155 km (keep-out radius) BS 33(typical) -100km CPEs GOALS Broadband wireless access in rural area 1) Protect incumbents (DTV, uPhone)  Detectability requirements: IDT, CDT, PMD/PFA 2) Promote QoS (for CPEs)  Minimal sensing overhead WE PROPOSED (MobiCom’08) 1) 2-tiered clustered sensor networks To support collaborative sensing Maximal cluster size (radius) Maximal sensor density 2) In-band sensing scheduling algorithm Optimal sensing-time Optimal sensing-period Better detection method: (energy vs. feature) 28

29 Performance Evaluation  Energy detection vs. Feature detection, applying optimal sensing time/period  aRSS threshold : avg. RSS, above which energy detection is better  aRSS energy min : avg. RSS, above which energy detection is feasible, to overcome SNRwall Results minimal sensing overhead 29

30 30 Optimization Framework for Cooperative Sensing  G OAL  To detect the existence of a primary signal as fast as possible with high detection accuracy with minimal sensing overhead  K EY I DEA  Exploit spatio-temporal variations in received primary signal strengths (RSSs) among sensors  H OW ?  Base station manages spatial RSS profile of sensors  Optimal stopping time for sensing  Sequential analysis based on measured RSSs  Optimal sensor selection  Use sensors with high performance  RSS-profile-based detection rule  Measured RSSs are compared to the profile

31 31 Spatio-Temporal Diversity in RSSs  O BSERVATIONS  Location-dependent sensor heterogeneity  Temporal variations due to measurement error  How to select sensors and schedule sensing?

32 32 Optimal Sensing Framework  At each sensing period n, update decision statistic Λn, compare it with predefined thresholds  Find an optimal set of cooperating sensors  Minimize sensing overhead while guaranteeing the detection requirements  Stop scheduling sensing when Λn reaches the thresholds  S EQUENTIAL H YPOTHESIS T ESTING P ROBLEM

33 33 Performance Evaluation  Reduce sensing while meeting detectability requirement  Sensor selection further reduces the sensing overhead  S ENSING S CHEDULING  S ENSOR S ELECTION

34 34 Attack-Tolerant Distributed Sensing in CRNs  T HREAT  Malicious/malfunctioning sensors can manipulate sensing results, thus obscurinhg the existence of a primary signal  Waste of spectrum opportunities (Type-1 Attack) or excessive interference to primaries (Type-2 Attack)  C HALLENGE  Openness of PHY/MAC layer in SDR devices  No cooperation between primary and secondary networks  O BJECTIVE  To withstand falsified sensing reports from malicious or faulty sensors  K EY I DEA  Leverage spatial RSS correlation due to shadow fading to filter abnormal sensing reports

35 35 Spatially-correlated shadow fading  R EMARKS  RSSs are spatially-correlated under shadow fading  Large deviations can be easily detected  Form sensor clusters among sensors in proximity and cross-check validity of the reports

36 36 Attack-Tolerant Sensing  M AIN C OMPENENTS  Sensing manager: manages sensor cluster and schedule sensing periods  Attack detector: detects and discards abnormal sensing reports  Data-fusion center: decides on existence of a primary signal  F RAMEWORK

37 37 Anomaly Detection  If sensor i’s reports is flagged by more than x % of its neighbors, regard it as abnormal and discard/penalize it in the final decision  Cross-checks the abnormality of neighboring sensors’ reports  C ORRELATION- B ASED F ILTER  Derive conditional pdf of neighbors’ sensing results

38 38 Performance Evaluation  Successfully tolerates both type-1 and type-2 attacks  T YPE-1 A TTACK  T YPE-2 A TTACK

39 39 Spectrum-Aware User Control  CR HotSpots – commercial CR APs ▪ Provide wireless access (e.g., Internet) ▪ Lease channels from PUs (for opportunistic reuse) ▪ Time-varying channel availability (ON or OFF)  Goal: profit maximization ▪ Optimal admission and eviction control of CR end-users ▪ Eviction (at OFF  ON): which user to evict from the service? ▪ Approach: Semi-Markov Decision Process (SMDP) CR HotSpot User arrivals Departure from service ON OFF ON OFF Leased Channels

40 40 Performance Evaluation  Observation ▪ No threshold behavior (unlike in time-invariant resources) ▪ Intentional blocking of arrivals (unlike in complete-sharing) Test Conditions ▪ Channel capacity = 5 ▪ 2 channels ▪ 2 user classes (1) n k : # of class-k users in service (2) Spectrum demand = k

41 41 Collision-aware Transmission Scheduling  Iterative collision resolution (PHY layer)  Cognitive sensing and scheduling (MAC layer) ▪ Sense the identity of the packet in the air (PA) ▪ Transmit if PA has the same identity (seq and session id) as the packet to be sent

42 42 Performance Evaluation  In comparison with DCB, a CSMA/CA based broadcast protocol ▪ PDR and delay in lossy wireless networks:

43 43 Performance Evaluation, cont’d ▪ PDR and delay as a function of source rate (indicating maximum supportable throughput)

44 44 Context-Aware Spectrum Agility (CASA)  CASA is composed of: ▪ Application Monitoring element ▪ Application QoS Estimation & Prediction element ▪ Application-aware DSA optimization  CASA provides history-based DSA protocol optimization of DSA protocol parameters, ▪ e.g., reduce scanning duration according to e2e delay constraint  CASA improves SU QoS fulfillment by ≥35% 44

45 45 Spectrum-Conscious WiFi (SpeCWiFi)  SpeCWiFi consists of: ▪ Spectrum Sensing ▪ Spectrum-usage Pattern Estimation ▪ DSA Transmission Scheduling  Preliminary evaluation on a madwifi-based testbed  SpeCWiFi manages to keep PU interference low ( 94%) on avg. 45

46 46 System Integration Architecture (SIA)  SIA implemented in Linux kernel ▪ Repository and Trigger Manager implemented as loadable kernel modules ▪ Dynamic hash-tables used for data management  Cognition Interface implemented as DLL  For user-level applications, Application Adaptation Layer (AAL) implemented to minimize user-kernel crossings  Evaluation shows overhead to be minimal (~1 ¹s ) for networking system calls 46

47 47 Conclusion  Cognition-based network design is key to the next- generation wireless networking ▪ Dynamic spectrum resource management ▪ Environment/app-awareness  Two directions in Cognition-based design ▪ Cognition Engine – 4 elements to achieve awareness ▪ Integration Architecture – for compatibility with legacy systems  Still have a long way to go… http://kabru.eecs.umich.edu/bin/view/Main/RtclPapers 47


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