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Multi-channel Interference Measurement and Modeling

in Low-Power Wireless Networks

Guoliang Xing1,  Mo Sha2,  Jun Huang1  Gang Zhou3, Xiaorui Wang4, Shucheng Liu5

1Michigan State University, 2Washington University in St. Louis,

3College of William and Mary, 4University of Tennessee, Knoxville

5City University of Hong Kong

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Low-power Wireless Networks (LWNs)• Low communication power (10~100 mw)• Personal area networks

– ZigBee remote controls and game consoles, Bluetooth headsets….

• Wireless sensor networks– Environmental monitoring, structural monitoring, Industrial/home

automation

ZigBee thermostat (HAI ) industrial automation(Intel fabrication plant)

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Challenges

• LWNs are increasingly used for critical apps– Stringent requirements on throughput & delay

• Interference is often inevitable – Low throughput & unpredictable comm. delay– Worse for LWNs due to limited radio bandwidth

4

• Avoid interference by assigning links different channels– 802.15.4: 16 channels in 2.4-2.483

GHz, 5MHz separation

s2

r2

s1

r1 collisions

Mitigating Interference

signal power

frequency4

channel Xchannel Y

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Channels Are Overlapping!

signal power (dbm

)

0

-20

-40

-60

-80

-100Channel X

1 MHzChannel X+1Channel X-1

• Power leakage causes inter-channel Interference • Only 3 or 4 channels of ZigBee are orthogonal

theoretical channel bandwidth

Interference on adjacent channel

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Outline• Motivation

• Measurement-based interference modeling

• Lightweight interference measurement algorithm

• Extensions to channel assignment protocols

• Experimental results

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Strongly Overlapping Channels

• When two channels are close

• Received Signal Strength (RSS) grows nearly linearly with transmit power

s1

r1

channel 19, power level [0~31]

channel Y, received signal strength (RSS)

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Weakly Overlapping Channels• When two channels are not close

• RSS do not strongly correlate with transmit power

Sender periodically changes transmit power on channel 19

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Modeling Inter-Channel RSS

• Sender u on channel x and receiver v on channel y – Strongly correlated channels, sender transmit power P

RSS ( ux, vy, P ) = Au,x,v,y × P + Bu,x, v,y

– Weakly correlated channels, for given quantile α∊ [0,1]RSS ( ux, vy, α ) = X | Prob(RSS<X) = α

determined by measurements

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Outline• Motivation

• Measurement-based interference modeling

• Lightweight interference measurement algorithm

• Extensions to channel assignment protocols

• Experimental results

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Measurement Complexity

• RSS models need to be measured for each combination (sender ch. X, receiver ch. Y)

• Complexity is O(M2) for M overlapping channels– Complexity of measuring node S O(1)

• Our algorithm reduces the complexity to O(M)

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Lightweight Measurement Algorithm

S S

R

channel X channel Y

ZX,Y (dB)

BY,R (dB)

RSS (SX,RY,P)

• For any receiver R on channel Y

RSS (SX, RY, P) = P – ZX,Y – BY,R

ZX,Y -- sender Inter-channel signal power decay between ch. X and ch. Y

BY,R  -- intra-channel signal decay

• No channel switches for receiver if ZX,Y and BY,R are known!

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Measuring Spectral Power Density

• SPD is receiver-independent!– Randomly use M neighbors on M different channels– Measure inter-channel RSS models simultaneously

• Derive inter-channel decay ZX,Y for all channels {Y} ZX,Y = P – RSS (SX, RY, P) – BY,R

• Other nodes derive RSS models w/o channel switching

signal power

(dbm)

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Outline• Motivation

• Measurement-based interference modeling

• Lightweight interference measurement algorithm

• Extensions to channel assignment protocols

– Tree-based Multi-Channel Protocol [Wu et al., Infocom 08]

– Control based multi-channel MAC [Le et al., IPSN 07]

• Experimental results

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Tree-based Multi-Channel Protocol (TMCP) [Wu et al. 2008]

• Main idea– Partition the whole network into multiple vertex-disjoint subtrees– Allocate different channels to different subtrees

• Problems– Distance-based interference model– Minimization of “interference value” rather than throughput

BS

Channel XChannel Y

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Extending TMCP

• Apply our RSS models for interference assessment

• Assign channel c to maximize the current PRRs

Ti – subtree assigned channel iPRR(v, pv) – packet reception ratio from v to its parent, and

is obtain by our RSS model and PRR-SINR model• PRR considers both intra- and inter-tree interference

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Experimental Setup

• Implemented on TelosB with TinyOS-2.0.2

• 30 TelosB motes deployed in a 29×28 ft office

• Two different network topologies

• Five 3-node chains• Five 3-node clusters

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Accuracy of the SPD Algorithm

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Improvement of TMCP