Opportunistic Spectrum Access in Cognitive Radio Networks
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Transcript of Opportunistic Spectrum Access in Cognitive Radio Networks
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Opportunistic Spectrum Access in Cognitive Radio
Networks
Project Team: Z. Ding and X. Liu (co-PIs)
S. Huang and E. Jung (GSR)University of California, Davis
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(Well known) Motivations for Cognitive Radio Networks
WiMAX Base StationCellular tower
Smart Car
Public Safety Station tower
TV tower
AP
AP
AP
Wireless Sensor Network
Wireless Sensor Network
Wireless Sensor Network
Wireless Sensor Network
Smart House
Smart House
Smart House
Smart House
• Spectrum scarcity.• More wireless services.• Inefficient static spectrum
allocation. • Existence of a large
amount of under-utilized spectrum.
• Advantage of flexible and cognitive spectrum access scheme needed: cognitive radio.
Traditional Static Spectrum Allocation
100MHz 10GHz
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Opportunistic Spectrum Access
Radio tower
AP
House
Primary User
Secondary User
• Design Objectives: Non-intrusiveness Spectral efficiency Cost efficiency Decentralized
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Three basic access schemes
Collision! Success
PU Xmit
SU Xmit
Virtual Xmit
Vacation
Sensing Point
Overlapping time
Collision! Success
PU:
SU: VX
SU: KS
Collision! SuccessSU: VAC
PU -- primary user (licensee of the channel)SU -- secondary user (cognitive ratio)
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Problem Formulation• Assumptions: Exponentially distributed idle period General primary busy period distribution Perfect sensing Knowledge of average idle time/busy time
r
c
P
P
tsC
1
1
2
or, ,
.. max
• Constraint Metrics:Bounded collision probabilityBounded overlapping time
• Optimization problem:
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Fundamental limits of opportunistic spectrum access
• Primary channel with exponentially distributed idle period • Bounded collision probability constraints• Maximum achievable throughput of a secondary user
--- collision probability bound --- percentage of idle time (by primary users)
2C
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Comparison of VX and VAC
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Comparison of VX and KS
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Observations• VX, VAC and KS schemes have indistinguishable
throughput performance, under collision probability constraint;
• The smaller the packet length, the larger the throughput.
• The result can be extended to systems with multiple primary users and multiple secondary users (treat all secondary users as a “super” secondary user)
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Fixed length packet wins• Under the collision probability constraint, the secondary
user achieves the maximum throughput when it transmits fixed length packets
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Overhead Consideration
• Optimal packet length achieves trade-off between overhead and collision probability
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Relation between two constraint metrics
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Multi-band multiple secondary systems
• No synchronization between secondary users and primary users
• No control channel for secondary users• Collision probability constraint• Perfect sensing
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Two sensing strategies
Vacation
Randomly choose a
Channel to sense
Busy?
Transmit a packet
Virtual Transmit
N
Y
Vacation
Sensing All channel
All channel busy
Randomly choose an
idle channel
Virtual Transmit
N
Y
Transmit a packet
Random-Sensing All-Channel-Sensing
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Simulation result for Multi-band competitive systems
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Smart Antenna Technique Applied in Cognitive Radio Networks
• Design Objective: Maximize the QoS of SUs while protecting PUs Design MAC Protocols to take advantages of smart ante
nna technologies• System Setup: One primary Tx (PT), one primary Rx (PR) One cognitive Tx (CT) , one cognitive Rx (CR) PT and CT transmit simultaneously to PR and CR, respe
ctively• Performance metric: talk-able zone of CR
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System Model
pp
pc
cc
cp
cpdpcd
ccd
ppd
Cognitive Tx
Cognitive Rx
Primary Tx
Primary Rx
cppccccs
pccppppp
nshshy
nshshy
cpjidh iHiijij ,, ),( vw
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Optimal Beamforming Problem with Constraints
• Can be solved efficiently by convex optimization method
],[,2/1|)(|1|)(|
..
|)(|maxmin
cccccjcjc
ccc
cic
GGts
Gcpcicpcw
)()( vwHccG
manifoldarray :)(v
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A Typical Beamforming pattern of a Secondary TX
0 50 100 150 200 250 300 350-80
-70
-60
-50
-40
-30
-20
-10
0
2i
|Gs(
2i)|
in d
BBeamforming Pattern of Cognitive Tx
Cognitive Rx Primary Rx
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Simulation Results (1)
• PT uses omni-directional antenna
• PRs are evenly distributed over the area centered at PT
• Interference to PR is less than 0.1 of the received signal power
• Spectrum efficiency increased at least by:
0 500 1000 1500 2000 2500 3000 3500 4000 45000
500
1000
1500
2000
2500
3000
3500
4000
4500
c = 15dB
T = 6dBp = Pr[SINRc
T]
PT(omni-directional Ant.)
CT
p = 0.9
p = 0.7
p = 0.5
%64.40Area TotalArea Shaded
p
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Simulation Results (2)• PT uses Transmit beamformi
ng• PRs are evenly distributed ov
er the area centered at PT• Interference to PR is less tha
n 0.1 of the received signal power
• Spectrum efficiency increased at least:
0 500 1000 1500 2000 2500 3000 3500 4000 45000
500
1000
1500
2000
2500
3000
3500
4000
4500
PT (TXBF)
p = 0.9
p = 0.7
p = 0.5
CT
c = 15dB
T = 6dBp = Pr[SINRc T]%15.45
Area TotalArea Shaded
p
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Integration of MAC/PHY design in Cognitive Radio Networks
• Design Objective:Under the collision probability constraint,
increase the capacity of secondary usersA cross-layer approach• Channel modelsRich scattering environment: Rayleigh fading
MISO channel from CT to CR and PRRayleigh SISO fading channel from PT to PR
and CR
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Received signal model
• Idea: – when overlapping happens, primary user can decode i
ts signal as long as the interference power from secondary user is very small.
– Transmit beamforming helps in this scenario, since it can mitigate the interference to primary users;
]Pr[ 01*1
22
121
IIPP
vlvPP
cpcc
cc
• Collision probability:
d thresholceInterferen:
PR toCT from ceInterferen:
0I
Icp
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Simulation Result
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Conclusions• Opportunistic spectrum access of secondary
users can increase the spectrum efficiency of system
• Smart antenna technique enables concurrent transmission of primary users and secondary users, and reduces interference to primary user
• Integration of PHY/MAC layer can improve system’s spectrum efficiency