The document summarizes the research scope and contributions of the author's PhD dissertation on medium access control (MAC) protocols for cognitive radio networks (CRNs). It discusses four past research areas: (1) CMAC protocol design with parallel spectrum sensing, (2) CMAC protocol with sequential sensing, (3) CMAC protocol with cooperative sensing, and (4) asynchronous full-duplex CMAC protocol. It also outlines three potential future research directions: (1) multi-channel MAC protocol design for full-duplex CRNs, (2) cross-layer CMAC and routing design for multi-hop CRNs, and (3) joint cognitive protocol and data processing design for smart grid applications.

1. 1
Medium Access Control for Dynamic
Spectrum Sharing in Cognitive Radio Networks

2. Asynchronous Full-Duplex MAC Protocol for
Cognitive Radio Networks
Joint Cooperative Spectrum Sensing and MAC Protocol
Design for Multi-channel CRNs
Research Scope and Contributions
2
Distributed MAC Protocol for Cognitive Radio Networks:
Design, Analysis, and Optimization
Channel Assignment With Access Contention Resolution
for Cognitive Radio Networks
Research Results, Future Directions and Conclusion

3. Future of wireless networks
3
Source: U.S. Department of Commerce
Source: Nokia
Source: Cisco

4. Dynamic Spectrum Access
4
 The dissertation focuses on Interweave
Spectrum Sharing paradigm.
Sources: http://www.gonzalo-vazquez-vilar.eu; [A]
[A] L. Giupponi, C. Ibars, ‘‘Cooperative Cognitive Systems,’’ in "Cognitive
Radio Systems", edited by Wei Wang, IN-TECH ISBN 978-953-307-021-6

5. • MAC protocol design integrating the
parallel spectrum sensing,
• Throughput Analysis & Optimization
CMAC protocol design
with parallel sensing
• Joint sequential sensing and access,
• Channel assignment,
• Throughput Analysis & Optimization
CMAC protocol with
sequential sensing
• Joint p-persistent MAC protocol &
DCSS in heterogeneous scenario,
• Throughput Analysis & Optimization
CMAC protocol with
cooperative sensing
• Distributed p-persistent CSMA
access and FD spectrum sensing
• Throughput Analysis & Optimization
Asynchronous full–
duplex CMAC protocol
5

6. Four Considered Settings
6
CMAC protocol design with parallel sensing CMAC protocol with sequential sensing
CMAC protocol with cooperative sensing
Source: I. F. Akyildiz, B. F. Lo, and R. Balakrishnan. Cooperative spectrum sensing in
cognitive radio networks: A survey. Phys. Commun., 4(1):40–62, March 2011.
CMAC protocol with FD sensing

7. 7
Conventional research
 assume perfect spectrum sensing
 consider analysis only
Our design objectives
 Joint sensing and access design
 Throughput analysis: Single- &
Multi-channel scenarios
 Optimization of sensing & access
parameters
System & network setting
 N pairs of SUs and M channels.
 Parallel sensing: Each SU senses all
channels by multiple sensors.
 SUs access all available channels.
 Collocated network: each available
channel can be used by 1 SU.

8. 8
Each fixed-size cycle T is divided into 3 phases
Phase 1-Spectrum sensing
* SUs sense all channels
* SU having vacant channels is active SU
Phase 2-Synchronization
Active SUs broadcast beacon signals.
Phase 3-Data transmission
* Active SUs randomly choose backoff
time and decrease backoff time counter.
* SU will start to transmit data when its
counter reaches 0.

9. 9
Normalized throughput
Average # of available channels
(E[l]/M=1: Single-channel scenario)
Prob. that n0 SUs contend
Conditional normalized throughput
average duration of generic slot time
Length of packet
Cycle time
prob. that a transmission is successful given that at least 1 SU transmits.
prob. that at least 1 SU transmits packets given that n0 SUs contend
Maximize throughput
Constraint to protect PUs
Constraints of sensing
time & contention window

10. 10
Proposition 1: The objective function satisfies the following properties
Initialize the parameter W = 1

11. Numerical results
11
(a) Single channel scenario, N = 15 (b) Multi-channel scenario, N = 10, M = 5.
Basic access mechanism and m = 4.

12. 12
Conventional research
 Considering sequential sensing with
• optimal channel sensing order optimization.
• random- and negotiation-based spectrum-sensing schemes.
Our contributions
 Formulate overlapping and non-overlapping channel assignment.
 Joint sequential sensing and access design for overlapping assignment.
 Analyze saturation throughput performance.
 Devise greedy algorithms.
 Further extensions: max–min fairness and imperfect sensing.
System & network setting
 Sequential sensing: each SU can sense only 1 channel at one time.
 Each SU can access at most one available channel.
⇒ Real-world hardware-constrained CRs.

13. Motivation
13
Throughput maximization problem can be written as:
Challenges: This problem is an MINLP problem!
Real-world
hardware-constrained CRs
Channel assignment:
Need to assign good channels to each SU
Non-overlapping assignment Overlapping assignment
Overlapping
channel
# SUs is comparable to # channels# SUs << # channels

14. Non-overlapping Channel Assignment
14
Alg. 1: Non-Overlapping Channel Assignment Algorithm
Step 1: Find best channel for each SU.
Step 2: Calculate increase of throughput (∆T) for each SU on its
best channel.
Step 3: Assign best channel to best user with maximum ∆T.
Step 4: Return to step 1 until all channels are assigned to SUs.

15. Overlapping Channel Assignment
15
Definition SU’s separate set is set of channels that are assigned for only this SU.
SU’s common set is set of channels that are assigned for this SU and others.
Each fixed-size cycle T is divided into 3 phases
Phase 1 - Synchronization
SUs exchange beacons.
Phase 2- Sensing
SUs sequentially sense assigned channels
Phase 3 - Contention & Transmission
If channels in separate set are vacant, SU randomly chooses one of them to transmit.
Otherwise, SU chooses one of channels in common set for contention.
⇒ using backoff mechanism.

16. Overlapping Channel Assignment Algorithm
16
Step 1: Run non-overlapping channel assignment Alg. 1.
Step 2: Estimate increase-in-throughput ∆Test for potential
overlapping channel assignment.
Step 3: Make 1 best channel assignment with maximum ∆Test.
Step 4: Return to step 2 until there is no potential channel
assignment.
⇒ Need to design joint overlapping channel assignment and MAC protocol.

17. Numerical results (1)
17
(a) For M = 2, (b) For M = 15

18. Numerical results (2)
18
(a) under max-min fairness, (b) under throughput maximization

19. 19
Conventional research
 The single-channel setting or the
homogeneous network scenario.
 The non-optimized window-based CSMA
MAC protocol.
 The design & optimization of cooperative
spectrum sensing parameters.
Our Contributions
 Joint p-persistent CSMA MAC protocol
& DCSS in heterogeneous scenario.
 Throughput analysis and optimization.
 Channel assignment algorithms.
 Further extensions: reporting errors.
System & network setting
 Each SU accesses at most 1
vacant channel
Collocated network: each vacant
channel can be used by 1 SU

20. Distributed Cooperative Spectrum Sensing
20
 Each SU is assigned in advance set of
channels.
 Each SU sequentially senses its assigned
channels.
 Sensing results are exchanged by SUs.
 Each SU decides status for channel using a-
out-of-b aggregation rule.
Benefits: Improve sensing performance
channel is busy
channel is idle

21. MAC Protocol Design
21
Each fixed-size cycle T is divided into 4 phases
Phase 1 - Synchronization:
SUs exchange beacons.
Phase 2- Sensing:
SUs perform sensing on
assigned channels
Phase 4 - Contention & Transmission:
 Each SU randomly chooses 1 available channel to contend.
 First SUs sense channel to check no other SU's transmission.
 Then SU transmits with prob. p.
Phase 3 - Reporting:
SUs exchange sensing results.

22. Throughput Analysis
22

23. Throughput Maximization
23
Challenges: This problem is an MINLP problem!
Throughput Maximization
Channel Assignment for Throughput Maximization

24. Channel Assignment Algorithm
24
Step 1: Assign 1 channel for each SU to minimize total cost
(τij
*).
Step 2: Find optimization of sensing and access parameters
for current assignment.
Step 3: Calculate increase-in-throughput for potential
channel assignment.
Step 4: Make 1 best channel assignment with maximum
increase-in-throughput.
Step 5: Return to step 2 until there is no potential channel
assignment.

25. Numerical results (1)
25

26. Numerical results (2)
26
(a) ∆γ = −7, N = 10 and M = 4, (b) N = 10 and M = 4

27. Numerical results (3)
27
(a) N = 10 and M = 4 (b) N = 4 and M = 3 for reporting errors.
for optimized and RR channel assignments

28. 28
Conventional research
 HD CMAC design with the two-stage sensing/access procedure.
 FD CMAC design with throughput analysis.
 FD CMAC design with throughput optimization in different design options.
Our contributions
 Joint distributed p-persistent CSMA access & FD sensing.
 Throughput analysis.
 Optimization of sensing time and transmit power parameters.
 Asynchronous FDC-MAC protocol.
System model
 n0 pairs of SUs opportunistically exploit white spaces on one frequency band.
 SUs are equipped with a FD transceiver.
 Self-interference from SU’s transmission is I(P) = ϛ (P)ξ
.
We propose an adaptive MAC design and different existing designs can be
achieved through suitable configuration of our protocol parameters!!!

29. FDC-MAC Protocol Design
29
FDC-MAC consists of contention, spectrum sensing, and access functions.
Contention phase
p-persistent CSMA principle
Data phase
1.FD sensing stage (Psen, TS)
2.Tx stage (Pdat, T-TS)
FDTx mode HDTx mode

30. Throughput Analysis & Maximization
30
Average number of transmitted bits in one contention and access cycle
per one unit of system bandwidth is
Tove is the average time overhead for one successful channel reservation.
Maximize throughput
Constraint to protect PUs
Constraints of sensing time
Throughput Maximization: For a given value of p

31. Algorithm
31
Proposition 1: The objective function satisfies the following properties
FDC-MAC Configuration Algorithm
Step 1: For a given Psen, find optimal T’’S(Psen).
Step 2: Increase Psen by ∆Psen, return step 1.
Step 3: Find global optimal (P*sen, T*S).

32. Numerical results (1)
32
(a) ϛ = 0.7 (b) ϛ = 0.08
p = 0.0022, τid = 500 ms, τac = 50 ms, n0 = 40, ξ = 1, and FDTx with Pdat = 15 dB.

33. Numerical results (2)
33
p = 0.0022, n0 = 40, ξ = 0.95, ϛ = 0.08 and FDTx with Pdat = 15 dB
(a) τid = 150 ms, τac = 50 ms (b) TS = 2.2 ms, τid = 1000 ms, τac = 50 ms

34. No. Papers
[J1] L. T. Tan, and L. B. Le, “Asynchronous Full-Duplex MAC Protocol for
Cognitive Radio Networks,” submitted.
[J2] L. T. Tan, and L. B. Le, “Joint Data Compression and MAC Protocol Design
for Smartgrids with Renewable Energy,” submitted.
[J3] L. T. Tan, and L. B. Le, “Joint Cooperative Spectrum Sensing and MAC
Protocol Design for Multi-channel Cognitive Radio Networks,” EURASIP
Journal on Wireless Communications and Networking, 2014 (101), June
2014.
[J4] L. T. Tan, and L. B. Le, “Channel assignment with access contention
resolution for cognitive radio networks,” IEEE Transactions on Vehicular
Technology, vol. 61, no. 6, pp. 2808-2823, July 2012.
[J5] L. T. Tan, and L. B. Le, “Distributed MAC Protocol for Cognitive Radio
Networks: Design, Analysis, and Optimization,” IEEE Transactions on
Vehicular Technology, vol. 60, no. 8, pp. 3990–4003, Oct. 2011.
34

35. [C1] L. T. Tan and L. B. Le, “Distributed MAC Protocol Design for Full-Duplex
Cognitive Radio Networks,” in 2015 IEEE Global Communications
Conference (IEEE GLOBECOM 2015), San Diego, CA, USA, December,
2015.
[C2] L. T. Tan and L. B. Le, “Compressed Sensing Based Data Processing and
MAC Protocol Design for Smartgrids,” in 2015 IEEE Wireless
Communication and Networking Conference (IEEE WCNC 2015), New
Orleans, LA USA, 9 - 12 March 2015.
[C3] L. T. Tan, and L. B. Le, “General Analytical Framework for Cooperative
Sensing and Access Trade-off Optimization,” in 2013 IEEE Wireless
Communication and Networking Conference (IEEE WCNC 2013), Shanghai,
China, April 2013.
[C4] L. T. Tan, and L. B. Le, “Fair Channel Allocation and Access Design for
Cognitive Ad Hoc Networks,” in 2012 IEEE Global Communications
Conference (IEEE GLOBECOM 2012), Anaheim, California, USA, pp. 1162
- 1167, December, 2012.
[C5] L. T. Tan, and L. B. Le, “Channel Assignment for Throughput Maximization
in Cognitive Radio Networks,” in 2012 IEEE Wireless Communications and
Networking Conference (IEEE WCNC 2012), Paris, France, pp. 1427-1431,
April, 2012.
35

36. 36
Positioning our contributions within the broad CMAC landscape
[J4, J5, C1, C3, C4, C5] [J1, J3]
[J3, J4, J5, C3, C4, C5]
[J1, C1]
[J1, J4, J5, C1, C4, C5]
[J3, C3]
[J5] [J3, J4, C3, C4, C5]
[J1, J5]

37. • Multi-channel FD CMAC design, channel assignment problem.
Multi-channel MAC protocol design for FD CRNs
• Cross-layer design for CMAC and routing.
CMAC and routing design for multi-hop HD and FD CRNs
• Joint cognitive protocol and data processing design for the Smartgrid
application
Applications of cognitive radio networking techniques for Smartgrids
37

38. 1 Motivations and objectives of my Ph.D. research.
2 Past research: results on HD CMAC protocols
with parallel sensing, sequential sensing,
cooperative sensing and FDC-MAC protocols with
full-duplex sensing.
3 Future Research Directions: Potential direction
on multichannel FDCRNs; multi-hop HD and FD
CRNs; and applications of CRN techniques for
Smartgrids.
38

39. LOGO