1. Designing of a fast LUT based DDA FIR
system with adaptive co-efficient for
spectrum sensing in Cognitive Radio
Presented by:
Wasim Arif
Assistant Professor,
Department of ECE,
NIT, Silchar, India
Presented by:
Wasim Arif
Assistant Professor,
Department of ECE,
NIT, Silchar, India

2. The presentation answer the following questions.
What is Cognitive Radio?
What is Spectrum Sensing?
How an FIR system helps in the process of realization ?
What is DDA Algorithm?

3. Acoustic Spectrum Radio Spectrum

4. The NTIA’s spectrum allocation chart makes available spectrum look scarce.

5. Measurements from the Berkeley Wireless Re-
search Center show the allocated spectrum is vastly
underutilized.

6. Cognitive Radio architecture showing the interactions between the
software knowledge, knowledge base and learning engines

7. More specifically, the CR technology will enable the users to :
• determine which portions of the spectrum are available and detect the
presence of licensed users when a user operates in a licensed band
(spectrum sensing)
• select the best available channel (spectrum management)
• coordinate access to this channel with other users (spectrum sharing)
• vacate the channel when a licensed user is detected (spectrum mobility)
IEEE has also endeavored to formulate a novel wireless air
interface standard based on CR. The IEEE 802.22 working group
aims to develop wireless regional area network physical (PHY) and
medium access control (MAC) layers for use by unlicensed devices in
the spectrum allocated to TV bands

8. Various aspects of spectrum sensing for cognitive radio

9. One of the most important components of CR is the ability to
measure, sense, learn, and be aware of the parameters related to the
radio channel characteristics, availability of spectrum and power,
interference and noise temperature, radio’s operating environment,
user requirements, and applications
Therefore, most existing spectrum sensing algorithms focus on the detection of
the primary transmitted signal based on the local observations of the CR.
To enhance the detection probability, many signal detection techniques can be
used in spectrum sensing
•Matched Filter Detection √
•Energy Detection
•Cyclostationary Detection
•Wavelet Detection

10. • A digital Matched Filter
Matched filter :obtained by
correlating a known signal, or
template, with an unknown
signal to detect the presence of
the template in the unknown
signal. This is equivalent to
convolving the unknown signal
with a conjugated time-
reversed version of the
template.
•The matched filter is the
optimal linear filter for
maximizing the signal to noise
ratio (SNR) in the presence of
additive stochastic noise.

11. The Dynamic PDD Matched filter
The main disadvantage of Matched Filter : with the increasing number
of filter taps and samples the number of multiplication and summation
stages is exponentially increased.
To avoid this complexity the Dynamic PDD Matched filter was proposed [9]

12. Direct form realization of FIR filter
Transposed structure realization of FIR filter

13. Distributed Arithmetic
Y=<c,x> =∑c[n].x[n]=c[0]x[0]+c[1]x[1]+…………+
c[N-1].x[N-1] ; n=0 to N
Assuming, the coefficients c[n] are known constants and x[n] is a
variable, we can represent x[n] by
X[n] =∑xb[n] ×2b
with xb[n ] [0,1]ϵ
Where xb[n] denotes the bth
bit of x[n] or the nth
sample of x

15. Shift –Adder DA Architecture
A Dynamic DA architecture

16. The Distributed arithmetic with table partitioning technique

17. The implemented FIR structure with multiple coefficient banks

18. The implemented 3-tap Fast LUT based DDA FIR block with adaptive
tap –weights

19. Test bench waveform of 3 bit Fast LUT based FIR filter
Test bench waveform of 3 bit PM based FIR Filter

20. Test bench waveform of 3 bit Fast LUT based FIR structure with adaptive
multiple co-efficient bank
step 1: scan signal status channel(Select status)
step 2: select the specific tap-coefficients from the LUT based
on
Select status
step 3: for n bit input sample and select status
step 4: generate the FIR output using DDA-Fast LUT
algorithm
Step 5: end for
step 6: scan Select status
step 7: if current Select status=previous Select status
step 8: continue from step3
step 9: end if
step 10: else continue from step2

21. Device Utilization Parameter s 3 bit PM based FIR
Filter
3 bit Fast LUT based Dynamic
DA FIR
Number of Slice Flip Flops 5% 1%
Number of 4 input LUTs 3% 1%
Number of occupied Slices 6% 1%
Total Number 4 input LUTs 3% 1%
Number of bonded IOBs 36% 10%
Resource Utilization of Various 4-Tap Digital FIR Filters
Comparisons of different Device Utilization

22. Resource Utilization of Various 4-Tap Digital FIR
Filters
Graph of above table
The implementation reduces the delay by 16.1%,
reduces the number of LUTs used by 74.6%, the
number of slices by 62.5%.

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