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

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

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

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.

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

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

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

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

Proposed System Parameters

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

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

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

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

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

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

Conclusive Results Green with 1024 carriers, Blue with 1705 carriers

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

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

References “IEEE 802 LAN/MAN standards Committee 802.22 WG on WRANs (Wireless Regional Area Networks)”, ‘http://www.ieee802.org/22’, 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: 978-0-511-58032-1. Huseyin Arslan, “Cognitive Radio, Software Defined Radio and Adaptive Wireless Systems” 1st Edition, Springer Press 2007, and ISBN: 978-1-4020- 5541-6. M. Schwartz, Mobile Wireless Communications. Cambridge University Press, 2005. Linda E. Doyle, “Essentials of Cognitive Radio”, 1st ed., Cambridge University Press 2009, ISBN:978-0-511-83367-9 S. Enserink and D. Cochran, “A Cyclostationary feature Detector”, IEEE signals, systems and computers, 1994, ISBN: 0-8186-6405-3. W A Gardner, “Introduction to Random Processes with applications to Signals and Systems”, New York Mc Millan 1985.

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