Designing of a fast LUT based DDA FIR system with adaptive co-efficient for spectrum sensing in Cognitive Radio

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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
AIML-11 Conference, DUBAI
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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?
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Radio Spectrum
Acoustic Spectrum
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The NTIAs spectrum allocation chart makes
available spectrum look scarce.
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Measurements from the Berkeley Wireless Re-search
Center show the allocated spectrum is vastly
underutilized.
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Cognitive Radio architecture showing the
interactions between the software knowledge,
knowledge base and learning engines
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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
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Various aspects of spectrum sensing for cognitive
radio
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• 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,
  radios 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 v
• Energy Detection
• Cyclostationary Detection
• Wavelet Detection

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• 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.

• A digital Matched Filter

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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
The Dynamic PDD Matched filter
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Direct form realization of FIR filter
Transposed structure realization of FIR filter
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Distributed Arithmetic Yltc,xgt
?cn.xnc0x0c1x1
cN-1.xN-1 n0 to N Assuming, the
coefficients cn are known constants and xn is
a variable, we can represent xn by   Xn
?xbn 2b with xbn ? 0,1 Where
xbn denotes the bth bit of xn or the nth
sample of x
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Shift Adder DA Architecture
A Dynamic DA architecture
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The Distributed arithmetic with table
partitioning technique
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The implemented FIR structure with multiple
coefficient banks
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The implemented 3-tap Fast LUT based DDA FIR
block with adaptive tap weights
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Test bench waveform of 3 bit Fast LUT based FIR
filter
Test bench waveform of 3 bit PM based FIR Filter
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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 statusprevious Select status step
8 continue from step3 step 9 end
if step 10 else continue from step2
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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
Comparisons of different Device Utilization
Method/ Parameter Direct Braun Wallace Array D D A Adaptive Fast LUTDDA
Slices 51 74 41 69 32 21
LUT 24 126 62 118 59 23
Delay (ns) 14.8 17.2 16.8 19.7 13.2 13.2
Flip-Flops 16 16 14 16 4 16
IOB 29 29 67 29 9 13
Adder/ Subtractor 6 6 5 6 35 15
Resource Utilization of Various 4-Tap Digital FIR
Filters
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Method/ Parameter Direct Braun Wallace Array D D A Adaptive Fast LUTDDA
Slices 51 74 41 69 32 21
LUT 24 126 62 118 59 23
Delay (ns) 14.8 17.2 16.8 19.7 13.2 13.2
Flip-Flops 16 16 14 16 4 16
IOB 29 29 67 29 9 13
Adder/ Subtractor 6 6 5 6 35 15
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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Thank you
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