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
Today’s Wireless Networking
 Exponential growth of wireless access demands
▪ Multimedia & other QoS applications
▪ Diverse network uses – commercial, public, military
Wireless
medium
 “Paradigm shift” in network design
▪ Static, environment/app-agnostic  dynamic and adaptive
2
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
3
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.
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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
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Software-Defined Radios (SDRs)
 Key to cognition!
▪ Reconfigurable in real time (e.g., USRP, SORA, WARP)
 Today’s SDR Devices
USRP1
USRP2
SORA
Throughput
400 bytes/s
16 kbps
15 Mbps
Bandwidth
2Mhz
768 kHz
Standard 802.11g
PHY
CSMA Pulse-modulated CDMA
Interconnect USB
OFDM
Gigabit Ethernet PCI
▪ 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
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Cognition-based Network Design
 Cognition Engine
 Integration Architecture of Cognition Elements
with Legacy Systems
Cognition Engine
Cognition Engine
Better QoS Support
Enhanced Utilization
Environment/App Monitoring
Environment/App Learning
System/App Adaptation
Optimal Decision
 Includes key elements in achieving awareness
 Enables unified cognition for wireless networks
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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
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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
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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
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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
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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
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System Integration Architecture
Implementation & deployment of cognition
▪ Needs a well-defined integration architecture
▪ Different from traditional (full layer-based) design
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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
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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
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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
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Cognitive Networking Research
in RTCL at Michigan
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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)
CNR Group @RTCL
• 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
CR Components & Architecture
 Main Components

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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)
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GCE
PEE
RME
MLME (MAC)
MME
PLME (PHY)
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
VACATE
GCE
3 states
SCAN
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LISTEN
Maximal discovery via periodic sensing
Sensing-time TIi
Sensing-period TPi
Ch 1:
Ch 2:
Ch 3:
sensing:
logical ch:
1
Periodic sensing
3
1,3
1
1,3
1
Discovered opportunities
1
2
2
2
Disrupted reuse
1,2 2
3 3
time
 Find optimal Tpi ’s – Tradeoff b/w discovery & disruption:
▪ Frequent sensing  (1) more idle channels discovered, but
(2) more disruption in utilizing opportunities
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Performance Evaluation


Discovered ≥98% of the analytical maximum (AORmax)
≤22% more opportunities than non-optimal schemes
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Fast discovery via reactive sensing
 Reactive sensing – discover opportunities at channel vacation
reactive sensing
ON
reused channel
channel vacation
opportunity found
Ch 1
OFF
Ch 2
Ch 3
Opportunity discovery latency  seamless service provisioning
 Find: optimal sensing sequence for minimal latency
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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
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Backup channel management
 Goal: manage a list of backup channels
▪ A subset of out-of-band channels
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?
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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
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Incumbent Protection via In-band sensing
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
TV transmitter
WE PROPOSED (MobiCom’08)
CPEs
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)
BS
33(typical)
-100km
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Performance Evaluation
minimal sensing overhead
Results
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
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
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
 KEY IDEA

Exploit spatio-temporal variations in received primary signal
strengths (RSSs) among sensors
 HOW?
 Optimal sensor selection
 Use sensors with high performance
 RSS-profile-based detection rule
 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
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Spatio-Temporal Diversity in RSSs
 OBSERVATIONS


Location-dependent sensor heterogeneity
Temporal variations due to measurement error
 How to select sensors and schedule sensing?
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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
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Performance Evaluation
 SENSING SCHEDULING
 SENSOR SELECTION
 Reduce sensing while meeting
detectability requirement
 Sensor selection further reduces
the sensing overhead
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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
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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
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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
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Anomaly Detection
 CORRELATION-BASED FILTER

Derive conditional pdf of 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
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Performance Evaluation
 TYPE-1 ATTACK
 TYPE-2 ATTACK
 Successfully tolerates both type-1 and type-2 attacks
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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)
Departure
from
service
CR HotSpot
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ON
OFF
ON
OFF
Leased
Channels
User
arrivals
Performance Evaluation
Test Conditions
▪ Channel capacity = 5
▪ 2 channels
▪ 2 user classes
(1) nk: # of class-k users
in service
(2) Spectrum demand = k
 Observation
▪ No threshold behavior (unlike in time-invariant resources)
▪ Intentional blocking of arrivals (unlike in complete-sharing)
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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
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Performance Evaluation
 In comparison with DCB, a CSMA/CA based
broadcast protocol
▪ PDR and delay in lossy wireless networks:
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Performance Evaluation, cont’d
▪ PDR and delay as a function of source rate
(indicating maximum supportable throughput)
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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%
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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.
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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
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Conclusion
 Cognition-based network design is key to the nextgeneration 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
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