Channel Assortment Strategy for Reliable Communication
in Multi-Hop Cognitive Radio Networks
Mubashir Husain Rehmani
Advisors: Aline Carneiro Viana, Hicham Khalife, and Serge Fdida
Cognitive Radio Networks (CRNs) and Channel Assortment in Multi-Hop CRNs
ñ Motivation
ñ Challenges of Channel Selection in CRNs
Limited available spectrum in today's wireless networks
Fixed spectrum assignment policy
Geographical and temporal utilization of spectrum varies
from 15%-85% (FCC)[1]
Results in the:
Inefficiency in spectrum usage
Creation of spectrum holes
ñ Cognitive Radio Nodes
Traffic pattern and channels’ occupancy of PR nodes
CR transmissions should not degrade the reception quality
of PR nodes
CR node should immediately interrupt its transmission
whenever a neighboring PR activity is detected [2]
ñ Best Channel?
Best strategy for all CR nodes is to dynamically switch to less PR
occupied channels
In CRNs, Cognitive Radio (CR) nodes:
Utilize free parts of unlicensed spectrum
Opportunistically exploit licensed band
IEEE WoWMoM 2010 PhD Forum
Channel selection is more challenging in CRNs
But
Generates contention and collision problems, as all CR nodes
compete for the same channel resource
ñ Why Channel Selection?
Channel selection plays a vital role in efficient and reliable
data dissemination
Wastes the valuable additional capacity on different channels that
the cognitive radio concept offers
Channel Assortment Strategy SURF
CR Occupancy Number of active CR nodes competing for the
channel
ñ Goal
How to find a good compromise between transmission
opportunity in terms of PR occupancy and the number of
CR neighbors
ñ SURF
Each CR node sense channels
Classify sensed channels in the decreasing order of
availability for transmission and/or overhearing
Approaches are present in [3] that provides PR occupancy
(PRO)
Assign weights to channels based upon PR occupancy
and CR occupancy (CRO)
PR Occupancy Ratio of the PR nodes utilizing the
channels over total number of PR nodes on that channel
Based on PR nodes that are in activity and is based
on PR nodes' state (i.e. if they are active – ON – or not –
OFF)
CRO= 1 – PRO
Channels’ weight is calculated based on formula:
Pw = e-PRO x CRO
We introduce Tenancy Factor ‘β’ which enables SURF algorithm
to avoid channels with high CR contentions
To allow nodes having a good compromise between number of
CR receivers and number of CR competing transmitters
Simulation Results
Comparison: SURF, RD, SB, and CA
SURF allows the message dissemination to 55% nodes in
the network
SURF guarantees the delivery of approximately 60% of
messages contrarily to less than 20% for RD and SB, and 80%
for CA
These results show the good level of network connectivity provided by SURF, suitable for reliable dissemination
Future Work
Investigate SURF performance under dynamic traffic by consideration of data rates and traffic volume generated by CR nodes
Optimize data dissemination delay
References
Prediction and history can also be accounted to enhance the performance
[1]
I. F. Akyildiz, W. -Y. Lee, M. C. Vuran, and S. Mohanty, “Next Generation/ dynamic spectrum access/ cognitive radio wireless networks: a survey”, Computer Networks: The International Journal of Computer and Telecommunications Networking,
Vol. 50, Issue 13, pp. 2127-2159, 2006
[2]
H. Khalife, S. Ahuja, N. Malouch, and M. Krunz, Probabilistic path selection in opportunistic cognitive radio networks”, in Proceeding of the IEEE GlobeCom Conference, 30 Nov-4 Dec 2008, pp. 1-5
[3]
T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications”, IEEE Communications Surveys and Tutorials, Vol. 11, No. 1, First Quarter 2009
M. H. Rehmani : Mubashir.Rehmani@lip6.fr
H. Khalife : Hicham.Khalife@labri.fr
(LIP6/UPMC)
(LaBRI/ENSEIRB)
A. C. Viana : Aline.Viana@inria.fr
S. Fdida: Serge.Fdida@lip6.fr
(ASAP/INRIA and TU-Berlin)
(LIP6/UPMC)