KTH ROYAL INSTITUTE
OF TECHNOLOGY
Analysis and optimization of Centralized Sequential
Channel Sensing in Cognitive Radio Networks
(invited)
Hossein Shokri-Ghadikolaei, Forough Yaghoubi, and Carlo Fischione
Email: [email protected], [email protected], and [email protected]
Automatic Control Department and ACCESS Linnaeus Centre
Outline
• 
Introduction
§  Motivation
§  Spectrum mobility
§  Contribution
• 
Modeling and Performance Evaluation
§  System model
§  Single user case
§  Multiuser case
• 
Numerical results
• 
Concluding remarks
Motivations of cognitive radios
•  New emerging applications need higher bit rates,
Ø  e.g., VoIP, IPTV, online games, video streaming, etc.
•  Idea: using larger bandwidth to achieve higher data rates.
•  Problem: electromagnetic bandwidth is a fixed resource.
•  Cognitive radio network (CRN)
§  Introduce an intelligent and adaptive wireless system
Average spectrum usage pattern in a period of 10min [1]
spectrum allocation chart
Introduction FCC
of cognitive
radios (CRs) and
§  Sense its surrounding environment
Ref: R. W. Broderson,
A. Wolisz, D. Cabric, S. M. Mishra, and D.
Ref: www.ntia.doc.gov/osmhome/allochrt.pdf
Willkomm. (2004) White paper: CORVUS: A cognitive radio
the approach
changes
inofthe
allocation
for usage
virtualspectrum
unlicensed spectrum.
§  Reconfigure its transmission policy based on a cognitive engine
§  Introduce policy
two type of users: primary and secondary
Introduction
3/14
Spectrum mobility
•  Vacating a channel upon PU return
•  Finding a proper set of channels for pursuing the communications
Spectrum
handoff
Two problems:
1.  When should an SU perform handoff ?
2.  Considering a narrowband spectrum sensing, which channel should
be sensed first?
Sequential channel sensing and sensing order
Introduction
4/14
Contributions
•  Deriving main performance measures for single-user case
•  Proposing novel Markov model for evaluating main performance
metrics in multi-user case
•  Formulating an optimization algorithm for maximizing throughput while
keeping under control the average interference
•  Investigating the impact of several data fusion rules on the performance
of sequential channel sensing
Introduction
5/14
•  Introduction
•  Modeling and Performance Evaluation
•  Numerical results
•  Concluding remarks
System model
•  Slotted CR network
•  Centralized decision maker
•  Sequential channels sensing
•  Imperfect spectrum sensing
•  Single user case by setting number of SUs to 1.
RTk = remained time for
transmission
CR = constant transmission
rate
Modeling and Performance Evaluation
7/14
Multiuser case
The average throughput and average interference
time are
No 3channel
is found
21 is assigned
SU
to possibly
transmit on channel 32δ1
where
Ns: number of SUs
δ: Maximum number of channels
can be sensed
Modeling and Performance Evaluation
: probability of successful transmission
of the SU sm at the channel cn
: probability of interference between the
SU sm at the PU cn
8/14
•  Introduction
•  Modeling and Performance Evaluation
•  Numerical results
•  Concluding remarks
Simulation set-up
•  Simulation parameters from IEEE 802.22
•  Monte Carlo simulation for 1e6 time slots
Numerical Results
10/14
Impact of sensing time
•  Sensing-throughput tradeoff
•  Saturation of the throughput
Numerical Results
11/14
Impact of cooperation
Higher throughput
Lower
throughput
per
in OR
SU,
rule
higher CRNdue
throughput
Ø  Substantial
performance
improvement
to the optimal design
Numerical Results
12/14
•  Introduction
•  Modeling and Performance Evaluation
•  Numerical results
•  Conclusions
Conclusions
•  Cognitive radio (CR) concept, as a potential communication
paradigm, can mitigate the spectrum scarcity problem
•  Optimal designing of sequential channel sensing enhances
network operation
•  We focused on the modeling, performance evaluation, and
optimization of the SUs performing sequential channel sensing
with a centralized decision maker
•  OR rule outperforms AND rule in terms of average throughput
•  Developing a low complexity algorithm for solving the
optimization problem is a future direction.
Concluding remarks
14/14
KTH ROYAL INSTITUTE
OF TECHNOLOGY
Analysis and optimization of Centralized Sequential
Channel Sensing in Cognitive Radio Networks
(invited)
Hossein Shokri-Ghadikolaei, Forough Yaghoubi, and Carlo Fischione
Email: [email protected], [email protected], and [email protected]
Automatic Control Department and ACCESS Linnaeus Centre