Defining Software Defined Radios, Cognitive Radios, the need for spectrum sensing and an insight on the Cyclostationary parameters that better help in feature detection in Cognitive Radios

1. Analysis of OFDM parameters using
cyclostationary spectrum sensing in
Cognitive Radio
Presented by :Omer Ali

2. What is a Cognitive Radio ?
• Cognitive Radio is built on the basis of a
Software-defined Radios SDR
• Cognitive Radio can provide the spectral
awareness technology to support FCC
initiatives in Spectral Use

3. Is Cognitive Radio SMART ?
• It knows where it is
• It knows what services are
available, for example, it
can identify then use
empty spectrum to
communicate more
efficiently
• It knows what services
interest the user, and
knows how to find them
• It knows the current
degree of needs and future
likelihood of needs of its
user
• Learns and recognizes
usage patterns from the
user
• Applies “Model Based
Reasoning” about user
needs, local content,
environmental context

4. Why Spectrum Sensing ?
• Spectrum awareness or spectrum sensing
makes a radio environment cognitive i.e. to
memorize the spectrum holes or voids that
could be utilized

5. Why OFDM ?
• OFDM symbols are used in this research because it
supports broader bandwidth and is normally utilized
in current MIMO technologies.
• The modulation scheme can be varied and the
corresponding spectrum efficiency and spectrum
utilization varies per modulation scheme.
• Limitations – OFDM power leakages to adjacent
channels

6. OFDM – Advantages / Disadvantages ?
• Advantages
– Simple implementation by means of FFT
– High spectral efficiency considering (no. of sub-
carriers)
– Anti ICI and ISI makes OFDM receiver less complex, as
almost no equalizer is needed.
• Disadvantages
– Requires highly linear amplifiers
– Sensitive to Doppler Effect
– Guard-time introduces overhead

7. Why to sense Spectrum holes ?
• As FCC agrees on utilizing the spectrum holes for DVB-T for unlicensed
users; it is vital to lease this unused spectrum to users in the vicinity.
• Finding spectrum holes ? That means the spectrum should be dispersed ?
• The answer is somewhat YES. Think about utilizing the primary spectrum
for DVB-T applications and the secondary spectrum for unlicensed users.

8. Spectrum Utilization ?
Spectral Adaptation Waveforms
T
I
M
E
Frequency

9. How to Sense the Spectrum?
• Spectrum sensing is currently achieved
dynamically using DSS
• Are there any trade-offs in terms of different
sensing techniques ?
• The Answer is YES .
– One might sense a empty spectrum easily but it
might be the one with very power SNR.
– So, the goal is to sense the proper spectrum for
unlicensed users

10. Research Goal ?
• Using OFDM for DVB-T applications calculate the
primary and secondary users
• Improve bandwidth by removing guard-band ,
BUT , will it have any impact on ICI?
• If ICI increases, then we should come up with
something for better utilization . Cyclic prefix
maybe ….
• What to do with the received signal with lots of
noise ? Maybe normalize the whole received
spectrum and pick-up the most healthy
spectrum ….

11. How to generate signals that matches
close to DVB-T Application ?
• DVB-T systems can be used in either 2K or 8K
mode. We choose 2K mode having :
– 1705 sub-carriers are used to transmit the data
out of total 2048 sub-carriers
– Inverse Fourier Transform (IFFT) of the QAM of the
data is taken and guard-band intervals are added
at the start of OFDM frame for DVB-T applications

12. How did we proceed ?
1. QAM modulation
2. OFDM signal generation
3. Cyclic Prefix addition at the guard-band
locations
4. Incorporating AWGN channel
5. Symbol Transmission through AWGN
6. Signal Detection using DSS techniques
7. Spectral Correlation Function of the received
function for better PSD and noise removal

13. OFDM Signal Generation
Up conversion
bitstream QAM Pilot S-> P IFFT P -> S Cyclic
Mapping Insertions Extension Analog
signal
QAM mapping is a block that groups these bits together as per modulation schemes:
N=1 for BPSK, N=2 for QPSK and n-QAM for higher orders

14. Some Maths behind OFDM signals
• For a single carrier, the complex signal can be:
• If we consider N samples, OFDM signal appears to be summation of these N
symbols
• During the symbol length, the amplitude and phase remains constant
• These carriers are centered around fo , the time domain representation becomes
Where T is the period of sampling frequency.
• This can be represented in complex vector as

15. Maths behind OFDM - continued
• In last equation is the representation of complex components in
frequency domain
• If we follow the IFFT transform, we can see that it is the summation of
orthogonal components in frequency domain
• The simplified complex form follows , where an and bn follows
the modulation scheme, hence making:
• After complex vector multiplication, real signal part can be estimated as:

16. Cyclic Extension
• Last serial samples are added to next OFDM frame by cyclic
extension
• How its done ? Lets see some basics and maths behind cyclic
extension and Spectral correlation function to see its significance

17. Cyclostationary Features
• A very simple periodic signal
• In terms of Fourier coefficients
• After modulation with a sine-wave
• Considering a is of random wide-sense spectrum nature, we can auto-correlate
and can compute the power spectral density
• Auto-correlation of a
• Power spectral density of a can be found by
• Keeping that in mind the Power Spectral Density of x(t) can be found by :
• Problem with the above equation ? No sine wave components presents

18. Cyclostationary Feature - continued
• Lets use trigonometric identities in order to have:
1. Some DC components
2. Some higher order periodic components
3. Simple depiction of modulated periodic symbol
A simple quadratic function
Which can be reduced to
Furthermore b(t) has a DC component that should appear at f=0
Also, the higher order components should also appear at

19. Cyclostationary Feature - Continued
• So, if that is True, the PSD should appear as:
f
f
-fo fo
Sy
f
f
-2fo 2fo

20. Cyclostationary Feature - Continued
• Problem with previous depiction?
– Not every symbol appears as a DC with some known higher order
components
– In order to add random delays, we should come up with some pulse
modulation in order to have varying magnitudes.
– So, we can only have a DC magnitude appearing at nth order but with no
varying magnitudes.
• Speculating that into consideration, the basic function becomes:
• Where spectral lines should appear at m.fo , where m is integer multiplier
• If we equate m.fo as ἀ , we can define our approximation equation:
• ἀt = m.fo for periodic
Time intervals

21. Cyclostationary Feature - Continued
• Now with the assumptions we can say that the function is periodic if the delay
product contains spectral lines; which can roughly be modeled as:
• The cyclic auto-correlation function can then proceed with the complex vector:
• Now the basic idea of Spectral Correlation function is to find average power in
frequency domain
• The last approximations were to concentrate on the received signals at the center
frequency as if they were passed through a narrowband filter
Where B is modeled as the bandwidth of the function for filtering

22. Spectral Correlation Density
• The spectral correlation density was computed by the Fourier
Transform of the cyclic autocorrelation
f
x
-j2πἀt
e
U(t)
f + ἀ/2 f + ἀ/2
BPF u
X(t)
+ ἀ/2
BPF
v(t) v
j2πἀt
e -ἀ/2

23. Coding behind the project
Signal Generation
Serial
Conversion

24. Coding - Continued
Cyclic Prefix
addition
Up-sampling
for carrier

25. Coding Continued
SCF Function
The Plots

26. The Outcomes
The PSD of generic symbol received

27. Outcomes - Continued
PSD while utilizing
SCF
PSD without SCF

28. Outcomes -Continued
Detected primary and
secondary users around
centre frequency in the
absence of SCF
Reduced noise-bed and
detected primary and
secondary users around
center frequency in the
presence of SCF