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Improved CR Spectrum Sensing Performance with Lower ... - QoSMOS

Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis

chair

Improved CR Spectrum Sensing Performance

with Lower ACLR GFDM Signals

Rohit Datta

Vodafone Chair, TU Dresden, Germany

17th October, 2012.

QMCC’12, IEICE 2012, Fukuoka, Japan


GFDM in CR

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Motivation

Power Level

Carriers

TV Signal

• Fragmented White Space (WS)

. . .

TV Bands

Frequency

• Protection of Legacy Channels

• Low out of band leakage

• Flexible, spectrum aggregation

PSD

• Dynamic Spectrum Access

Frequency

TU Dresden

Dirty RF

Slide 2


Sensing Cognitive Radio Signals

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• What are the sensing performance of

GFDM signals?

• Does the sharper fall of the GFDM

signal affect ROC curves?

• How does asynchronousity affect the

ROC curves

• Can the GFDM receiver also be used

as a sensor?

Normalized Power (dB)

10

0

-10

-20

-30

-40

-50

-60

Sub-Carrier Index

OFDM

GFDM

TU Dresden Dirty RF Slide 3


System Model

The Data Matrix, D, in both frequency and time

K-th subcarrier data vector after circular

convolution and subcarrier up-conversion

Up-sampled Data vector in k-th subcarrier

Transmitted vector

The transmission data matrix, in

frequency and time

TU Dresden Dirty RF Slide 4


System Model

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The received data vectors are

• down-converted

• circularly convoluted with the matched filter

• Down-sampled

The Energy Detector block processes the

detected data vectors and determines whether

the channel is free or empty

TU Dresden Dirty RF Slide 5


Principles of Energy Detection

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Binary Hypothesis Testing Problem

Prob. of Detection and False Alarm

Neyman-Pearson Test Criterion

• Standard Binary Hypothesis testing problem

is valid for our aim to detect secondary

opportunistic user signal

• From the works of Urkowitz and Sousa, the

probability of detection and probability of false

alarm are given.

TU Dresden Dirty RF Slide 6


Simulation Setup

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• The Energy Detector squares and sums the

detected data, followed by the comparator that

determines whether the spectrum is occupied or

empty.

• Out of 128 subcarriers subcarriers 1-32 and 97-

128 are used to transmit OFDM/GFDM signals

• Now we are checking whether the WS

subcarriers are falsly detected to be occupied or

whether the occupied subcarriers are detected

correctly

TU Dresden Dirty RF Slide 7


Synchronous Receiver

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• When both the transmitter and receiver

are synchronous, then OFDM and

GFDM complementary ROC plots

match theoretical plots

• This shows that the sensing

performance with a GFDM sensor is

comparable to ROC curves obtained

from traditional OFDM sensors in

synchronous systems

• The SNR is varied from 0 dB to 4 dB in

steps of 1dB

TU Dresden Dirty RF Slide 8


Asynchronous Receiver

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• A more realistic scenario is where we

consider a frequency offset at the

receiver. The worst case setup with an

offset of half the subcarrier spacing is

considered.

• The OFDM and GFDM signals are

sensed by their respective OFDM and

GFDM sensors and the complementary

ROC is obtained

• The SNR is varied from 0 dB to 4 dB in

steps of 1dB

• This implies that GFDM signals can be

better detected as compared to an

OFDM signal in asynchronous systems

as OFDM is more prone to frequency

offset

TU Dresden Dirty RF Slide 9


Sensing with GFDM Receiver

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• All combinations of transmitting OFDM

as well as GFDM and using an OFDM

or GFDM receiver for sensing are

considered. Based on this we have

compared ROC curves for OFDM and

GFDM receivers

• the sensing ROC performance is best

when a GFDM transmission is sensed

by a GFDM receiver

• OFDM transmission is sensed by

GFDM sensor, then the ROC is better

than that of an OFDM based sensor.

The steep spectral shape of the GFDM

filters improve the sensing performance

of a OFDM opportunistic transmission

with higher SNR, sensing with GFDM

receiver performance improves.

TU Dresden Dirty RF Slide 10


Asynchronous Receiver with sharper

GFDM pulse

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• Adding tails to the GFDM block,

decreases the out of band leakage of the

GFDM pulse.

• This improves the sensing performance,

as shown in the adjacent figure.

TU Dresden Dirty RF Slide 11


Sensing with GFDM Receiver

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• When using the sharper filter, both

in the transmitter and also in the

receiver, the sensing performance

of the system improves.

• OFDM signal sensing performance

improves when it is sensed by

GFDM+ system

TU Dresden Dirty RF Slide 12


Conclusion and Future Work

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• GFDM is an extremely attractive multicarrier modulation scheme suitable for

cognitive radio PHY as it has a low out-of-band radiation into the adjacent

frequency bands

• Traditional OFDM signal detection techniques can be applied to GFDM as well.

• in an asynchronous cognitive radio system, complementary ROC curves for GFDM

are better than OFDM GFDM can be better sensed than OFDM

• It is also evident that using a GFDM receiver as a sensor also improves the ROC

characteristics of a traditional OFDM system.

• These simulation studies show that compared to conventional OFDM, GFDM is

more suitable for cognitive radio PHY, not only because of better spectral

shaping, but also because of better sensing characteristics

• Deriving the theoretical performance in case of asynchronous detection is a work in

progress and is kept as an outlook of the simulation study done here.

TU Dresden Dirty RF Slide 13


Thank You Slide

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Thank You!

Any Questions to: rohit.datta@ifn.et.tu-dresden.de

The research leading to these results was derived from the European Community’s Seventh

Framework Program (FP7) under Grant Agreement number 248454 (QoSMOS).

TU Dresden Dirty RF Slide 14


Welcome to Dresden, Germany!

2-5 June 2013

Dresden,

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