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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
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