Opportunistic Spectrum Access in Cognitive Radio

Networks

Project Team: Z. Ding and X. Liu (co-PIs)

S. Huang and E. Jung (GSR)University of California, Davis

(Well known) Motivations for Cognitive Radio Networks

WiMAX Base StationCellular tower

Smart Car

Public Safety Station tower

TV tower

AP

AP

AP

Wireless Sensor Network

Wireless Sensor Network

Wireless Sensor Network

Wireless Sensor Network

Smart House

Smart House

Smart House

Smart House

• Spectrum scarcity.• More wireless services.• Inefficient static spectrum

allocation. • Existence of a large

amount of under-utilized spectrum.

• Advantage of flexible and cognitive spectrum access scheme needed: cognitive radio.

Traditional Static Spectrum Allocation

100MHz 10GHz

Opportunistic Spectrum Access

Radio tower

AP

House

Primary User

Secondary User

• Design Objectives: Non-intrusiveness Spectral efficiency Cost efficiency Decentralized

Three basic access schemes

Collision! Success

PU Xmit

SU Xmit

Virtual Xmit

Vacation

Sensing Point

Overlapping time

Collision! Success

PU:

SU: VX

SU: KS

Collision! SuccessSU: VAC

PU -- primary user (licensee of the channel)SU -- secondary user (cognitive ratio)

Problem Formulation• Assumptions: Exponentially distributed idle period General primary busy period distribution Perfect sensing Knowledge of average idle time/busy time

r

c

P

P

tsC

1

1

2

or, ,

.. max

• Constraint Metrics:Bounded collision probabilityBounded overlapping time

• Optimization problem:

Fundamental limits of opportunistic spectrum access

• Primary channel with exponentially distributed idle period • Bounded collision probability constraints• Maximum achievable throughput of a secondary user

--- collision probability bound --- percentage of idle time (by primary users)

2C

Comparison of VX and VAC

Comparison of VX and KS

Observations• VX, VAC and KS schemes have indistinguishable

throughput performance, under collision probability constraint;

• The smaller the packet length, the larger the throughput.

• The result can be extended to systems with multiple primary users and multiple secondary users (treat all secondary users as a “super” secondary user)

Fixed length packet wins• Under the collision probability constraint, the secondary

user achieves the maximum throughput when it transmits fixed length packets

Overhead Consideration

• Optimal packet length achieves trade-off between overhead and collision probability

Relation between two constraint metrics

Multi-band multiple secondary systems

• No synchronization between secondary users and primary users

• No control channel for secondary users• Collision probability constraint• Perfect sensing

Two sensing strategies

Vacation

Randomly choose a

Channel to sense

Busy?

Transmit a packet

Virtual Transmit

N

Y

Vacation

Sensing All channel

All channel busy

Randomly choose an

idle channel

Virtual Transmit

N

Y

Transmit a packet

Random-Sensing All-Channel-Sensing

Simulation result for Multi-band competitive systems

Smart Antenna Technique Applied in Cognitive Radio Networks

• Design Objective: Maximize the QoS of SUs while protecting PUs Design MAC Protocols to take advantages of smart ante

nna technologies• System Setup: One primary Tx (PT), one primary Rx (PR) One cognitive Tx (CT) , one cognitive Rx (CR) PT and CT transmit simultaneously to PR and CR, respe

ctively• Performance metric: talk-able zone of CR

System Model

pp

pc

cc

cp

cpdpcd

ccd

ppd

Cognitive Tx

Cognitive Rx

Primary Tx

Primary Rx

cppccccs

pccppppp

nshshy

nshshy

cpjidh iHiijij ,, ),( vw

Optimal Beamforming Problem with Constraints

• Can be solved efficiently by convex optimization method

],[,2/1|)(|1|)(|

..

|)(|maxmin

cccccjcjc

ccc

cic

GGts

Gcpcicpcw

)()( vwHccG

manifoldarray :)(v

A Typical Beamforming pattern of a Secondary TX

0 50 100 150 200 250 300 350-80

-70

-60

-50

-40

-30

-20

-10

0

2i

|Gs(

2i)|

in d

BBeamforming Pattern of Cognitive Tx

Cognitive Rx Primary Rx

Simulation Results (1)

• PT uses omni-directional antenna

• PRs are evenly distributed over the area centered at PT

• Interference to PR is less than 0.1 of the received signal power

• Spectrum efficiency increased at least by:

0 500 1000 1500 2000 2500 3000 3500 4000 45000

500

1000

1500

2000

2500

3000

3500

4000

4500

c = 15dB

T = 6dBp = Pr[SINRc

T]

PT(omni-directional Ant.)

CT

p = 0.9

p = 0.7

p = 0.5

%64.40Area TotalArea Shaded

p

Simulation Results (2)• PT uses Transmit beamformi

ng• PRs are evenly distributed ov

er the area centered at PT• Interference to PR is less tha

n 0.1 of the received signal power

• Spectrum efficiency increased at least:

0 500 1000 1500 2000 2500 3000 3500 4000 45000

500

1000

1500

2000

2500

3000

3500

4000

4500

PT (TXBF)

p = 0.9

p = 0.7

p = 0.5

CT

c = 15dB

T = 6dBp = Pr[SINRc T]%15.45

Area TotalArea Shaded

p

Integration of MAC/PHY design in Cognitive Radio Networks

• Design Objective:Under the collision probability constraint,

increase the capacity of secondary usersA cross-layer approach• Channel modelsRich scattering environment: Rayleigh fading

MISO channel from CT to CR and PRRayleigh SISO fading channel from PT to PR

and CR

Received signal model

• Idea: – when overlapping happens, primary user can decode i

ts signal as long as the interference power from secondary user is very small.

– Transmit beamforming helps in this scenario, since it can mitigate the interference to primary users;

]Pr[ 01*1

22

121

IIPP

vlvPP

cpcc

cc

• Collision probability:

d thresholceInterferen:

PR toCT from ceInterferen:

0I

Icp

Simulation Result

Conclusions• Opportunistic spectrum access of secondary

users can increase the spectrum efficiency of system

• Smart antenna technique enables concurrent transmission of primary users and secondary users, and reduces interference to primary user

• Integration of PHY/MAC layer can improve system’s spectrum efficiency