Cognitive Wireless Networking

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. 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. 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. Elements of Cognition • Spectrum Sensing • Monitors signal activities • Detectssignals • 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 • Adaptssensing period/time/frequency, stopping rule, etc.

5. What to Expect from Cognition? • Technically, • Efficient spectrum utilization • Smarter spatial reuse • Coexistence 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. 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

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

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

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. 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. 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. 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 (safevs. aggressive) mode transmission scheduling based on PU channel-usage pattern estimation 12

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

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. 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. 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. Cognitive Networking Research in RTCL at Michigan 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)

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

20. 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) GCE PEE MLME (MAC) RME PLME (PHY) MME

21. CR Components & Architecture, cont’d • 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) VACANCY VACANCY VACATE 3 states GCE SCAN SCAN SCAN LISTEN LISTEN

22. Maximal discovery via periodic sensing Sensing-time TIi Sensing-period TPi • Find optimal Tpi’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: 3 1,3 1,3 3 3 1 1 1 2 1,2 2 1 2 2 sensing: logical ch: Disrupted reuse Periodic sensing Discovered opportunities time

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

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

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

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

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

28. Incumbent Protection via In-band sensing • GOALS • Broadband wireless access in rural area • Protect incumbents (DTV, uPhone) • Detectability requirements: • IDT, CDT, PMD/PFA • 2) Promote QoS (for CPEs) •  Minimal sensing overhead 155 km (keep-out radius) TV transmitter • 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) CPEs BS 33(typical) -100km

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

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

31. Spatio-Temporal Diversity in RSSs • OBSERVATIONS • Location-dependent sensor heterogeneity • Temporal variations due to measurement error • How to select sensors and schedule sensing?

32. Optimal Sensing Framework • SEQUENTIAL HYPOTHESIS TESTING PROBLEM • Find an optimal set of cooperating sensors • At each sensing period n, update decision statistic Λn, compare it with predefined thresholds • Stop scheduling sensing when Λn reaches the thresholds • Minimize sensing overhead while guaranteeing the detection requirements

33. Performance Evaluation • SENSING SCHEDULING • SENSOR SELECTION • Reduce sensing while meeting detectability requirement • Sensor selection further reduces the sensing overhead

34. Attack-Tolerant Distributed Sensing in CRNs • THREAT • 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) • CHALLENGE • Openness of PHY/MAC layer in SDR devices • No cooperation between primary and secondary networks • OBJECTIVE • To withstand falsified sensing reports from malicious or • faulty sensors • KEY IDEA • Leverage spatial RSS correlation due to shadow fading to filter abnormal sensing reports

35. Spatially-correlated shadow fading • REMARKS • 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. Attack-Tolerant Sensing • FRAMEWORK • MAIN COMPENENTS • 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

37. Anomaly Detection • CORRELATION-BASED FILTER • Derive conditional pdfof neighbors’ sensing results • Cross-checks the abnormality of neighboring sensors’ reports • 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

38. Performance Evaluation • TYPE-1 ATTACK • TYPE-2 ATTACK • Successfully tolerates both type-1 and type-2 attacks

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) User arrivals ON Departure from service OFF Leased Channels ON OFF CR HotSpot

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) nk: # of class-k users • in service • (2)Spectrum demand = k

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

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

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. 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 (<3%), while keeping SU utilization high (>94%) on avg. 45

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 (~1¹s) for networking system calls 46

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