SATNAC PRESENTATION FINAL
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Africa – The Future Communications Galaxy Performance Evaluation of Channel Assembling Strategies with Multi-Class Secondary Users in Cognitive Radio Networks Ebenezer Esenogho and Tom Walingo Centre for Radio Access and Rural Technologies. Discipline of Electrical, Electronic and Computer Engineering. University Of KwaZulu-Natal.
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Transcript of SATNAC PRESENTATION FINAL
Africa – The Future Communications Galaxy
Performance Evaluation of Channel Assembling Strategies with Multi-Class Secondary Users in
Cognitive Radio Networks
Ebenezer Esenogho and Tom WalingoCentre for Radio Access and Rural Technologies.Discipline of Electrical, Electronic and Computer Engineering. University Of KwaZulu-Natal.
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OUTLINE
INTRODUCTION Cognitive Radio. Cognitive Radio Network. Channel Assembling. CHANNEL ASSEMBLING STRATEGIES & RELATED WORK MOTIVATION SYSTEM MODEL/ARCHITECTURE ALGORITHMS RESULTS AND DISCUSSIONS CONCLUSION FUTURE WORK
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INTRODUCTION
Cognitive Radio. Cognitive Radio Network. Channel Assembling.
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RELATED WORK AND PROPOSED CHANNEL ASSEMBLING STRATEGIES
RELATED WORK Lei Jiao, Frank Y. Li, and Vicent Pla “Dynamic Channel Aggregation Strategies
in Cognitive Radio Networks with Spectrum Adaptation” IEEE Globecom 2011 proceedings. 2011 pp.1-6 (Static and Dynamic)
PROPOSED CHANNEL ASSEMBLING STRATEGIES Immediate Blocking Strategy (IBS) Reassignment Based Strategy (RBS) Queuing Based Strategy (QBS)
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MOTIVATION
Realistic channel assembling (CA) strategies need : To consider different traffic class (real time and non-real time users) Consider the varying nature of a wireless link and mitigate schemes like
adaptive modulation and coding (AMC)
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SYSTEM MODEL/ARCHITECTURE
Primary User (TV)
class 0
Buffer
Primary User (TV)
class 0
PU TV mast
SU class 2
SU class 1
SU class 1
CRBS
SU class 2
Fig. 1 System Model/Architecture
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CONT. WIRELESS CHANNEL MODEL AND AMC
Fig. SNR Partitioning
Fig. 2 Wireless frame utilization
SNR Partitioning
Outage R2R1 R3
Bad Moderate Good
OFF Frame
DurationCh 1/ PU1
Ch 1/ PU2
Ch 1/ PU3
Ch 1/ PU1
:::::::::
Ch M/PU M
PU ON PU OFF
Slo1………………………………………………………...S
ON frame OFF frame (Tf)
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CONT.
ON(Buzy)
OFF(Idle)
Fig. 4. The ON-OFF channel usage model for primary users
The PU’s slot capacity, is given as
Where, Note that is the channel utilization ratio. The SU system capacity (slot capacity/OFF capacity) is given by
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ALGORITHM FOR IBS
• ; // test for resources• Admit = true; // admit • Else• Admit =false; // block • PU arrival, pre-empt • = true; // f • ;// enough resources • Admit = true: // admit • Else • Admit = false; // b • ; // PU arrival, pre-empt • = true; // d • end if • Go to start
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ALGORITHM FOR RBS
• ; // test for resources• Admit = true; // admit • else• test for new arrival or PU arrival• do ++ j: // adjusting and iterate over user resources • Admit = true: // admit • Else • Admit = false; // b • ; // test for resources• _admit = true; // admit • Else• go step 6• if all condition can not be meet• = false; // d • end if
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ALGORITHM FOR QBS
• ; // test for resources• admit = true; // admit • else• if • admit_queue = true (queue not full)• else • admit = false ;// queue full (block)• // enough resources • _admit = true; // admit • else• if • admit queue = true ;// (queue not full)• else • admit = false ;// queue full (block) • if • drop queue = true• if • drop queue = true• End if
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RESULTS AND DISCUSSIONS
PU arrival rate0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
SU c
lass
1 B
lock
ing
Prob
abilit
y
10-3
10-2
10-1
100
IBSRBSQBS
PU arrival rate0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
SU cl
ass 2
Bloc
king P
roba
bility
10-3
10-2
10-1
100
IBSRBSQBS
PU arrival0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
SU
cla
ss 1
Thr
ough
put
0.15
0.2
0.25
0.3
0.35
0.4
0.450.5
0.550.6
0.65
QBSIBS RBS
PU arrival0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
SU
cla
ss 2
Th
rou
gh
pu
t
0.2
0.3
0.4
0.50.6 QBS
IBS RBS
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CONT.
PU arrival0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
SU c
lass
1 D
ropp
ing/
FT p
roba
bility
10-2
10-1
100
101
RBSIBSQBS
PU arrival0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
SU cl
ass
2 Drop
ping/F
T prob
abilit
y
10-2
10-1
100
101
RBSIBSQBS
SU class 1 SNR[dB]0 5 10 15 20
SU cl
ass 1
capa
city
0
0.5
1
1.5
2
2.5IBSRBSQBS
SU class 2 SNR[dB0 5 10 15 20
SU cla
ss 2
Capa
city
0
0.5
1
1.5
2
2.5
IBSRBSQBS
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CONCLUSION
• In this work, compared the performance of three channel assembling techniques for cognitive radio network considering the dynamics of a wireless link with AMC in a multiclass SU traffic.
• The result obtained from our simulation shows that; the QBS scheme outperformed the RBS and IBS scheme amidst multiclass SUs in the sense that, it shows lower blocking and force termination probabilities.
• It demonstrates that AMC with queueing technique is a robust approach in improving dynamic channel allocation schemes.
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Assembling many slots/channels irrespective of the channel state can affect PER/BER especially in a dynamic wireless link. This forms the basis for our future work.
FUTURE WORK
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MAIN REFERENCES
Lei Jiao, Frank Y. Li, and Vicent Pla “Dynamic Channel Aggregation Strategies in Cognitive Radio Networks with Spectrum Adaptation” IEEE Globecom 2011 proceedings. 2011 pp.1-6
Indika A. M. Balapuwaduge, Lei Jiao, Frank Y. Li, and Vicent Pla “Channel Assembling with Priority-Based Queue in Cognitive Radio Network: Strategy and Performance” IEEE Transaction on wireless Communication, vol.13, NO. 2, FEBUARY 2014, Pp.630-644
Qingwen Liu, Shengli Zhou, and Georgios B. Giannakis, “Queuing With Adaptive Modulation and Coding Over Wireless Links: Cross-Layer Analysis and Design” IEEE Transactions On Wireless Communications, vol. 4, no. 3, May 2005.pp.
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THANK YOU
