cognitive wireless networking

Cognitive Wireless Networking

Kang G. Shin

Real-Time Computing LaboratoryEECS Department

The University of MichiganAnn Arbor, MI 48109-2121

http://www.eecs.umich.edu/~kgshin

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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

Wirelessmedium

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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

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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 accessCR

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

▪ 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

USRP1 USRP2 SORAThroughput 400 bytes/s 16 kbps 15 MbpsBandwidth 2Mhz 768 kHz Standard

802.11gPHY CSMA Pulse-modu-

latedCDMA OFDM

Intercon-nect

USB Gigabit Ether-net

PCI

Cognition Engine Integration Architecture of Cognition Elements with Legacy Systems

Cognition-based Network Design

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Cognition Engine

Includes key elements in achieving awareness

Enables unified cognition for wireless networks

Cognition EngineEnhanced Utiliza-

tionBetter QoS Support

Environment/App MonitoringEnvironment/App Learning

System/App AdaptationOptimal Decision

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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’dIntegration 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

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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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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

• 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

MLME (MAC)

PLME (PHY)

RME

MME

GCE PEE

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)

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3 statesGCE

VACANCY

SCAN LISTEN

VACANCY

SCAN LISTEN

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

Maximal discovery via periodic sensing

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:

logical ch:

Disrupted reuse time

Sensing-time TIi

Discovered opportunities

Periodic sensing

Sensing-period TPi

1 3 1,3

1 1,3

1 2 1,2 2 31 32 2

sensing:

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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

vacationchannel 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

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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 TI

i / Pidlei

Heterogeneous case suboptimal sequenceSatisfying 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

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

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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%

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Incumbent Protection via In-band sensing

TV transmitter

155 km (keep-out radius)

BS

33(typical)

-100km

CPEs

GOALSBroadband wireless access in rural area1) Protect incumbents (DTV,

uPhone) Detectability requirements: IDT, CDT, PMD/PFA2) 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


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

min

imal

sen

sing

ove

rhea

d

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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?

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

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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

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

SEQUENTIAL HYPOTHESIS TESTING PROBLEM

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Reduce sensing while meeting detectability requirement

Sensor selection further reduces the sensing overhead

SENSING SCHEDULING SENSOR SELECTION

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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

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

FRAMEWORK

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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

CORRELATION-BASED FILTER

Derive conditional pdf of neighbors’ sensing results

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Successfully tolerates both type-1 and type-2 attacks

TYPE-1 ATTACK TYPE-2 ATTACK

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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 OFFON): which user to evict from the service?

▪ Approach: Semi-Markov Decision Process (SMDP)

CR HotSpot

User arrivals

Departure from

service

ONOFF

ONOFF

Leased Channels

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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

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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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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

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

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cognitive wireless networking

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