PhD dissertation presentation

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Medium Access Control for DynamicSpectrum Sharing in Cognitive Radio Networks

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

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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

Future of wireless networks

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Source: U.S. Department of Commerce

Source: Nokia

Source: Cisco

Dynamic Spectrum Access

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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

http://www.gonzalo-vazquez-vilar.eu/�






•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,


CMAC protocol with cooperative sensing

•Distributed p-persistent CSMA access and FD spectrum sensing


Asynchronous full–duplex CMAC protocol

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Four Considered Settings

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CMAC protocol design with parallel sensing CMAC protocol with sequential sensing

CMAC protocol with cooperative sensingSource: 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

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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.

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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-SynchronizationActive SUs broadcast beacon signals.

Phase 3-Data transmission* Active SUs randomly choose backofftime and decrease backoff time counter.* SU will start to transmit data when its counter reaches 0.

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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 packetCycle 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

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Proposition 1: The objective function satisfies the following properties

Initialize the parameter W = 1

Numerical results

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(a) Single channel scenario, N = 15 (b) Multi-channel scenario, N = 10, M = 5.

Basic access mechanism and m = 4.

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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.

Motivation

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Throughput maximization problem can be written as:

Challenges: This problem is an MINLP problem!

Real-worldhardware-constrained CRs

Channel assignment:Need to assign good channels to each SU

Non-overlapping assignment Overlapping assignment

Overlappingchannel

# SUs is comparable to # channels# SUs << # channels

Non-overlapping Channel Assignment

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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.

Overlapping Channel Assignment

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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.


Phase 1 - Synchronization SUs exchange beacons.

Phase 2- SensingSUs sequentially sense assigned channels

Phase 3 - Contention & TransmissionIf 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.

Overlapping Channel Assignment Algorithm

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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.

Numerical results (1)

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(a) For M = 2, (b) For M = 15


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(a) under max-min fairness, (b) under throughput maximization

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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 channelCollocated network: each vacant channel can be used by 1 SU

Distributed Cooperative Spectrum Sensing

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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

MAC Protocol Design

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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.

Throughput Analysis

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Throughput Maximization

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Challenges: This problem is an MINLP problem!

Throughput Maximization

Channel Assignment for Throughput Maximization

Channel Assignment Algorithm

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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.


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(a) ∆γ = −7, N = 10 and M = 4, (b) N = 10 and M = 4


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

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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

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We propose an adaptive MAC design and different existing designs can be achieved through suitable configuration of our protocol parameters!!!

FDC-MAC Protocol Design

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FDC-MAC consists of contention, spectrum sensing, and access functions.

Contention phasep-persistent CSMA principle

Data phase1.FD sensing stage (Psen, TS)2.Tx stage (Pdat, T-TS)

FDTx mode HDTx mode

Throughput Analysis & Maximization

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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

Algorithm

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Proposition 1: The objective function satisfies the following properties

FDC-MAC Configuration AlgorithmStep 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).


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(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.


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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

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.

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[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.

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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]

• 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 Smartgridapplication

Applications of cognitive radio networking techniques for Smartgrids

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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

PhD dissertation presentation

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