1. Paper ID:88
Analysis of OFDM Parameters using Cyclostationary Spectrum Sensing in Cognitive Radio
INMIC2011

2. Overview Spectrum sensing in Cognitive Radio Proposed Solution (SCF)
Design Approach
Test
Comparative Analysis
Conclusion
Problem Statement/Previous Work
Results/Limitations

3. Cognitive Radio A cognitive radio is a Software defined radio that:
CDMA
800MHZ
OFDM
2.4GHZ
QPSK
400MHZ
THE INTERFERENCE WAVE
Sensing the radio environment
Communicating in the most appropriate way according to the environment
Selecting the appropriate frequency and type of communications and switching the function of the device
A cognitive radio is a
Software defined radio that:
Detects
Retains
Adapts
To its environment

4. Spectrum Management
Spectrum pooling : is a spectrum management strategy in which multiple radio spectrum users can coexist within a single allocation of radio spectrum space.
Spectrum Leasing: utilizes the available spectrum and leases it to unlicensed or rental users in case of free slots.
Opportunistic Spectrum Usage: utilizes the dynamic spectrum access of available spectrum amongst the participating users.

5. Problem Statement
Previous work mainly incorporated cyclic prefix across the spectrum head and tail that enabled:
Enhanced bandwidth as the guard-band intervals were removed
Easy signal detection as one tail corresponded to the head of the next carrier
The system has many constraints and they were:
Increased noise levels that had adverse effects on SNR at the RF receiving end
Lesser probability of detection as the number of carriers increased

6. Our Proposed Model We focused on: Generating larger number of carriers
Optimized cyclic prefix generator in order to reduce the guard- band intervals
Incorporating standard noise functions to make our spectrum more like a real world challenge
Performed Spectral Correlation Function (SCF) that overlapped the spectrum in frequency domain
Time shifting and normalization to reduce the noise-bed

7. Proposed Model
We focused on a DVB-T system that already has cyclic prefixes added and supports OFDM
We simulated 1705 carriers and added sample noise on the above spectrum
The spectrum was then correlated and normalized

8. Proposed Model 1705 4-QAM symbols 4096 IFFT samples G(t) T/2
F(p) = 1/T
Normalization
SCF
S(t) Fc

9. Proposed System Parameters

10. Proposed System- Area of Focus
Antenna
A/D
RF Filter
LNA
AGC
Digital
Processing
PLL
VCO
IF/BB Filter
Low Noise
Amplifier
Analog-to-Digital Converter
Automatic Gain Control
Mixer
Effective SNR
Better the control over SNR, better the detection ; thus better the received signal strength

11. Design Approach – a Mathematical Model
A DVB-T OFDM Signal
A time deterministic auto-correlation function
SCF calculation over carrier frequency

12. Test Results – Comparative Analysis
SCF calculated over 1024
4-QAM modulated carriers.
The primary users are detected around centre frequency returning the maximum SCF value
Notice that the noise levels remain almost uniform that could be eliminated using intermediate noise cancellation filters

13. Test Results – Comparative Analysis
SCF calculated over 1705
4-QAM modulated carriers.
The primary users are detected around centre frequency returning the maximum SCF value with a better spectrum utilization but at the cost of increased noise levels which makes the signal reception another daunting problem

14. Test Results – Primary User Detection
Primary user (PU)
Frequencies over the respective spectrum around carrier frequency for 1024 carriers.

15. Test Results – Primary User Detection
Primary user (PU)
Frequencies over the respective spectrum around carrier frequency for 1705 carriers.
Notice the increased noise-bed

16. Conclusive Results
Green with 1024 carriers, Blue with 1705 carriers

17. Conclusion With our research work we were able to demonstrate:
The efficiency of spectrum by eliminating guard-bands and utilizing cyclic prefixes
We utilized SCF function that helped us to better manage the spectrum and detect the primary user
Our simulation results clearly explained that our parameters were spectrum efficient and we needed less number of carriers to occupy the same spectrum efficiently and it was more appropriate for spectrum detection and filtering at the RF receiving end

18. Future Work
We are currently focused towards our same SCF and normalization algorithm towards:
Primary user detection for Relay Networks in LTE-A environment specially :
Node centric
Edge centric (where the signal strengths degrade and noise-levels are further increased)
We are also designing an OFDM simulator with SCF, Noise incorporation, Normalization functionality to better understand the spectrum detection and Dynamic spectrum Management technologies of future coming wireless standards

19. References
“IEEE 802 LAN/MAN standards Committee WG on WRANs (Wireless Regional Area Networks)”, ‘ Retrieved: 12 OCT, 2011.
Ekram Hossain, Dusit Niyato and Zhu Han, “Dynamic Spectrum Access and Management in Cognitive Networks”, 1st Edition, Cambridge University Press 2009, and ISBN:
Huseyin Arslan, “Cognitive Radio, Software Defined Radio and Adaptive Wireless Systems” 1st Edition, Springer Press 2007, and ISBN:
M. Schwartz, Mobile Wireless Communications. Cambridge University Press,
Linda E. Doyle, “Essentials of Cognitive Radio”, 1st ed., Cambridge University Press 2009, ISBN:
S. Enserink and D. Cochran, “A Cyclostationary feature Detector”, IEEE signals, systems and computers, 1994, ISBN:
W A Gardner, “Introduction to Random Processes with applications to Signals and Systems”, New York Mc Millan 1985.

20. References
“DV Video Broadcasting (DVB); DVB specification for Data Broadcasting”, ETSI EN:301192
G. Bansal, M. Hossain, P. Kaligineedi, H. Merceir, C. Nicola, U. Phuyal, M. Mamunur Rashid, K.C. Wavegedara, Z. Hasan, M. Khabbazian, and V.K. Bhargava, “Some Research Issues in Cognitive Radio Networks”, pp AFRICON conference proceedings 2007, ISBN:
J. Chen, A. Gibson, and J. Zafara, “Cyclostationary Spectrum Detection in Cognitive Radios” IET seminar on Cognitive Radio and Software Defined Radio, pp. 1-5, September (2002).
Mary Ann, “OFDM Simulations using Matlab” [Online].