Opportunistic spectrum access in cognitive radio networks
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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 . Spectrum scarcity. More wireless services.

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Opportunistic Spectrum Access in Cognitive Radio Networks

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Opportunistic spectrum access in cognitive radio networks

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

(Well known) Motivations for Cognitive Radio Networks

  • 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.


Opportunistic spectrum access

Opportunistic Spectrum Access

  • Design Objectives:

  • Non-intrusiveness

  • Spectral efficiency

  • Cost efficiency

  • Decentralized


Three basic access schemes

Three basic access schemes

PU -- primary user (licensee of the channel)

SU -- secondary user (cognitive ratio)


Problem formulation

Problem Formulation

  • Assumptions:

  • Exponentially distributed idle period

  • General primary busy period distribution

  • Perfect sensing

  • Knowledge of average idle time/busy time

  • Constraint Metrics:

  • Bounded collision probability

  • Bounded overlapping time

  • Optimization problem:


Fundamental limits of opportunistic spectrum access

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)


Comparison of vx and vac

Comparison of VX and VAC


Comparison of vx and ks

Comparison of VX and KS


Observations

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

Fixed length packet wins

  • Under the collision probability constraint, the secondary user achieves the maximum throughput when it transmits fixed length packets


Overhead consideration

Overhead Consideration

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


Relation between two constraint metrics

Relation between two constraint metrics


Multi band multiple secondary systems

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

Two sensing strategies


Simulation result for multi band competitive systems

Simulation result for Multi-band competitive systems


Smart antenna technique applied in cognitive radio networks

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 antenna 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, respectively

  • Performance metric:

  • talk-able zone of CR


System model

System Model


Optimal beamforming problem with constraints

Optimal Beamforming Problem with Constraints

  • Can be solved efficiently by convex optimization method


A typical beamforming pattern of a secondary tx

A Typical Beamforming pattern of a Secondary TX


Simulation results 1

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:


Simulation results 2

Simulation Results (2)

  • PT uses Transmit beamforming

  • 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:


Integration of mac phy design in cognitive radio networks

Integration of MAC/PHY design in Cognitive Radio Networks

  • Design Objective:

  • Under the collision probability constraint, increase the capacity of secondary users

  • A cross-layer approach

  • Channel models

  • Rich scattering environment: Rayleigh fading MISO channel from CT to CR and PR

  • Rayleigh SISO fading channel from PT to PR and CR


Received signal model

Received signal model

  • Idea:

    • when overlapping happens, primary user can decode its 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;

  • Collision probability:


Simulation result

Simulation Result


Conclusions

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


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