Analysis Of Ofdm Parameters Using Cyclostationary Spectrum Sensing

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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

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  • Hi can you provide full matlab code for this simmulation ? I am interested in CR and ofdm
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Analysis Of Ofdm Parameters Using Cyclostationary Spectrum Sensing

  1. 1. Analysis of OFDM parameters usingcyclostationary spectrum sensing in Cognitive Radio Presented by :Omer Ali
  2. 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. 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. 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. 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. 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. 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. 8. Spectrum Utilization ? Spectral Adaptation WaveformsT I M E Frequency
  9. 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. 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. 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. 12. How did we proceed ?1. QAM modulation2. OFDM signal generation3. Cyclic Prefix addition at the guard-band locations4. Incorporating AWGN channel5. Symbol Transmission through AWGN6. Signal Detection using DSS techniques7. Spectral Correlation Function of the received function for better PSD and noise removal
  13. 13. OFDM Signal Generation Up conversionbitstream 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. 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 becomesWhere T is the period of sampling frequency.• This can be represented in complex vector as
  15. 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. 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. 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. 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. 19. Cyclostationary Feature - Continued• So, if that is True, the PSD should appear as: f f -fo fo Sy f f -2fo 2fo
  20. 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. 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 filterWhere B is modeled as the bandwidth of the function for filtering
  22. 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 uX(t) + ἀ/2 BPF v(t) v j2πἀt e -ἀ/2
  23. 23. Coding behind the project Signal Generation Serial Conversion
  24. 24. Coding - Continued Cyclic Prefix addition Up-sampling for carrier
  25. 25. Coding Continued SCF Function The Plots
  26. 26. The OutcomesThe PSD of generic symbol received
  27. 27. Outcomes - Continued PSD while utilizing SCFPSD without SCF
  28. 28. Outcomes -Continued Detected primary and secondary users around centre frequency in the absence of SCFReduced noise-bed and detected primary andsecondary users aroundcenter frequency in the presence of SCF

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