Cognitive Radio: From Promise to Reality · Cognitive Cycle determine which portions of the...
Transcript of Cognitive Radio: From Promise to Reality · Cognitive Cycle determine which portions of the...
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Cognitive Radio: From Promise to Reality
José F. de [email protected]
LAND – Laboratory for Modelling, Analysis and Development of Networks and Computer Systems
Universidade Federal do Rio de Janeiro (UFRJ)
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Cognitive Radio: Motivation
Amplitud
e (dBm)
Heavy Use Heavy Use
Medium UseSparse Use
Frequency (MHz)
Fixed Spectrum Assignment Spectrum Utilization
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Cognitive Radio: DSA Enabler
� J. Mitola III and G. Q. Maguire, Jr., “Cognitive Radio: Making Software Radios More Personal,” IEEE Personal Commun., vol. 6, no. 4, Aug. 1999, pp. 13–18.
Dynamic Spectrum
Allocation
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What is a Cognitive Radio (CR)?
� capability to use or share the spectrum in an opportunistic and intelligent manner
� advantages:� allows for real-time spectrum management
� significantly increases spectrum efficiency
� CR should be able to change (by software) its transmitter parameters based on interaction with the environment in which it operates� it senses the RF environment and modifies frequency,
bandwidth, power or modulation
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Cognitive Cycle
� determine which portions of the spectrum is available and detect the presence of licensed users (Spectrum Sensing)
� select the best available channel (Spectrum Decision)
� coordinate access to this channel with other users (Spectrum Sharing)
� vacate the channel when a licensed user is detected (Spectrum Mobility)
Spectrum
Sharing
Spectrum
Sensing
Spectrum
Decision
Channel Capacity
Primary User
Detection
RF
Stimuli
Spectrum Hole
Radio Environment
Spectrum
Mobility
Decision Request
Transmitted Signal
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CR Evolution
� sensing based� regulator guidelines recommend a high sensitivity (-120 or -114 dBm)
� increases the costs of the equipment
� several blind and signal specific feature-based techniques� energy detector, spectral correlation, time-domain cyclostationarity, eigenvalue
decomposition, pilot detection, high order statistics analysis
� geo-location based (White Space DB - WSDB)� the database determines the free channels for operation
� cheaper cognitive radios (no sensing)
� waste of temporal opportunities� more appropriate to TV bands (TVWS)
� challenges on the database management, maintenance and information exchange
� hybrid: sensing + geo-location� database-aided sensing or sensing-aided database
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Existing WSDBs
Google Spectrum Database
Spectrum Bridge
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Existing WSDBs
� WSDB@UFRJ � General Architecture
8
source 1
source 2
source n
WS DB
.
.
.
.
.
.
Regulation
agency
STEL
WSDB
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WSDB use cenarios
� Rural Broadband� 802.22 (Wi-FAR) and 802.11af
� IoT� Weightless, SigFox and 802.15.4m
� LTE femto cells� in the 3.5GHz radar band
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Some Research Activities on CRs
� sensing
� channel sensing order problem
� proactive sensing for spectrum handoff
� DSA in the radar bands
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Channel Sensing Order
� single radio and multiple channels with temporal opportunities
� when channels have varying capacity and occupation� sensing order has a great impact in the performance
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Channel Sensing Order
� intuitive channel sequences� decreasing order of capacity/occupation
� the theory of optimal stopping can provide the best sequence to be followed� but requires a priori knowledge of the moments of the random
variables that characterize the channels
� and presents a high computational complexity
� our proposal uses a reinforcement learning (RL) machine� does not require any a priori knowledge about the channels
� with a proper modeling we can� reduce the state space of the problem (Q-table)
� achieve a performance close to the optimal
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Results Normalized to the Optimal Sequence
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On-going Work on Channel Sensing Order
� RL convergence problem� exploration x exploitation tradeoff
� multiple users� independent versus cooperative agent learners
� spectrum utilization efficiency and fairness
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Proactive Sensing for Spectrum Handoff
� spectrum handoff� in order to free the channel after PU arrival
� i.e. reactive handoff
� SU needs to sense other channels� sensing order matters
� however, by doing proactive sensing on the remaining N-1 channels, we can � estimate PU activity (i.e. two-state continuous-time Markov
Chain)
� gather channel state information, and then
� determine a handoff sensing sequence that optimizes some criteria
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Handoff Sensing Sequence
� the order in which channels are sensed matters� e.g. probability of being idle (PIDLE): lesser time to
handoff
� e.g. expected transmission time (tE): longer time before handing off again
� proposal� expected interference time (tI): lesser interference to
the PU� considering the misdetection probability (PMD)
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Assessed Criteria
Name Criterion Reference
kim [Kim and Shin 2008]
duan [Duan and Li 2011]
lee [Lee and Akyildiz 2011]
proposed [CrownCom 2012]
PIDLE ⋅ tE ⋅Td(tE )
PIDLE
tE
PIDLE ⋅ tE
tI
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Proactive Sensing Interval Variation
� channel switching delay = 0.15 s� PMD = 0.01
0
0.02
0.04
0.06
0.08
0.1
25 50 100 150
inte
rfer
ence
frac
tion
proactive sensing interval
leekim
duanproposed
PMD = 0.2PMD = 0.1
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PMD Variation
channel switching delay = 0.15 sproactive sensing interval = 50 s
0
0.02
0.04
0.06
0.08
0.1
0.01 0.1 0.2 0.3
inte
rfer
ence
frac
tion
PMD
leekim
duanproposed
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Proactive Sensing Interval Variation
� channel switching delay = 0.15 s� PMD = 0.01
0.4
0.5
0.6
0.7
0.8
0.9
1
25 50 100 150
succ
essf
ul u
tiliz
atio
n
proactive sensing interval
leekim
duanproposed
PMD = 0.2PMD = 0.1
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On-going Work on Spectrum Handoff
� proactive handoff� free the channel before the PU arrival
� eliminate unpredictable channels� use of entropy
� use spectrum measurement traces
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Dynamic Spectrum Access in Radar Bands
� huge space of opportunities
� spatial & temporal opportunities
� PU is easy to detect1400960 27001710 3500 5000 5850 MHz2170
L CS3G4G2G
> 2GHz
No
temporal
and spatial
WSs
Temporal
WSsSpatial WSs
Co-located
transmitter and
receiver
High transmit
power
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The Aggregate Interference Problem
� caused by multiple SUs
� static threshold� multiple interference is not considered
� proposal: a cooperative method between PU and SUs� combination of DFS-T: temporal & spatial opportunities
� an additional mechanism to prevent Aggregate Interference (AI)� dynamic threshold which evolves with AI
� cooperation between PU and SUs
� PU measures the amount of interference and broadcasts new ����� SU updates ���� information
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References
� [IEEE LCN 11] Mendes, A. C., Augusto, C. H., Silva, M. W. R., Guedes, R. M., and de Rezende, J. F. – “Channel Sensing Order for Cognitive Radio Networks Using Reinforcement Learning”, in IEEE LCN’2011, Bonn, Germany, October 2011.
� [CrownCom 12] Coutinho, P. S., Silva, M. W. R., and de Rezende, J. F. – "Detection Error Aware Spectrum Handoff Mechanism for Cognitive Radios“, in CrownCom’2012, Stockholm, Sweden, June 2012.
� [ITS14] de Souza Lima, C., Paisana, F., de Rezende, J. F., and DaSilva, L. A. – “A cooperative approach for dynamicspectrum access in radar bands”, in ITS’2014, São Paulo, SP, Brazil, July 2014.
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Thank You.
José F. de [email protected]
LAND – Laboratory for Modelling, Analysis and Development of Networks and Computer Systems
Universidade Federal do Rio de Janeiro (UFRJ)