About Spectrum sensing. Detection of signal at low SNR and classifying the signal and noise using machine learning algorithm.

1. Project Student
Rock Feller Singh
Russells P
PRK20EC2009
II M.Tech, Communication
systems
IMPLEMENTATION OF WIDE-BAND SPECTRUM
SENSING IN LOW SNR ENVIRONMENT
Project Guide
Dr S.Merlin Gilbert
Raj
Assistant Professor

2. Outline
 Problem Scenario
 Challenges
 Methodology
 Results
 Conclusions
 References

3. Problem Scenario
 Why Spectrum Scarcity?
 Increase in wireless devices
 Shortage in spectrum
 Under utilization of spectrum
 Cognitive Radio
 Spectrum Sensing
 Wide-band spectrum sensing
 Low SNR
 Signal fading.
 Practically receiver SNR is very
low.

4. Challenges
In reality signal is always
not ideal and combined
with unwanted signal that
is considered as noise.
A clean signal will have a
high SNR and a noisy
signal will have a low
SNR.

5. Goals
 Classify signal and noise
 Achieve high accuracy in
low SNR condition
 Feature extraction

6. Literature survey
S.NO [REF] AUTHOUR,
YEAR
PAPER TITLE IMPLEMENTAT
ION
ALGORITHM DATASET FREQUENC
Y BAND
PERFORMANCE
METRICS
1 Rhana , Adley -
2019
Implementation
of multi channel
energy detection
SS techniques in
CRnetworks
using LABVIEW
on USRP
NI USRP 2924R
, LABVIEW
ED in
frequency
domain
1000
samples/sec
400MHz -
4.4GHz
Pfa = 0.1
2 Elena, Dobre ,
Alexander -2016
USRP-based
experimental
platform for ED
in CR systems
USRP 2932,
MATLAB
Energy
detection
470MHz-
870MHz
SNR = -9.15dB
Pd = 0.82
Pfa = 0.05

7. S.NO [REF] AUTHOUR,
YEAR
PAPER TITLE IMPLEMENTA
TION
ALGORITHM DATASET FREQUENCY
BAND
PERFORMANCE
METRICS
3 Mohamoud, Ali
Beydoun, Oussama
Bazzi - 2020
Experimental study
of spectrum sensing
based Energy
detection using
USRP
NI USRP 2901,
MATLAB
Calculation of
energy in time
and frequency
domain
10000
iteration,
1000 samples
915MHz -
925MHz
SNR = -20dB -
0dB
Pfa = 0.1
Pd: 0.690, 0.812,
0.946, 0.950
4 Jayashree, Ishwarya -
2020
Spectrum sensing
based on cascade
approach on
cognitive radio
NI USRP 2920,
MATLAB
Cyclic prefix
autocorrelation
detection
(CPED)
N=1000,
5000
815MHz -
950MHz SNR: -20dB to
-10dB = Pd: 0
SNR: -10dB to
-0dB =Pd:0-0.3
Pfa = 0.2
5 Authu Avinish,
Ramesh Babu - 2019
Enhanced dynamics
noise variance
based energy
sensing for
cognitive radio
using USRP at WiFi
bands
NI USRP 2932 Dynamic noise
variance based
energy sensing
2000/4000/60
00 samples
More than
25MHz
SNR = -20dB
Pd: 80.91
Pfa: 2.69

8. S.NO [REF] AUTHOUR,
YEAR
PAPER TITLE IMPLEMENTATI
ON
ALGORITHM DATASET FREQUENCY
BAND
PERFORMANCE
METRICS
6 Jacob, Benjamin, Evaluation of real USRP X310 with cognitive 410 test interval 10MHz - 6GHz 45MHz baseband
Anthony - 2020 time Predictive UBX-160 perception-actio to -45MHZ
spectrum sharing n cycle baseband at
for cognitive radio 10MHz interval
Test set dwell
times of 410us,
2.05ms, 4.1ms
7 Chris prema, Covariance and NI USRP, Python Experimental 1000 Monte 93.5MHz SNR: -20dB
Muhammad Eigen value based evaluation of carlo simulation Pfa: 0.1
SS using USRP in covariance and Pd = 0.3
real environment eigen value
based method
8 Daval K. Patel, Artificial neural USRP-N210, Hybrid Training 94MHz SNR:-20dB
Angel network design for MATLAB spectrum samples 100 in Pfa = 0.0440
improved spectrum sensing low SNR Pd = 1
sensing in cognitive technique
radio

9. S.NO [REF] AUTHOUR,
YEAR
PAPER TITLE IMPLEMENTAT
ION
ALGORITHM DATASET FREQUENCY
BAND
PERFORMANCE
METRICS
9 Anirudh, Ranjan Hardware USRP B210, K-means Testing data/ 70MHz to SNR = -8dB
implementation of MATLAB clustering training data 6GHz Pd = 0.5
K-means clustering approach = 1000x1000 Pfa = 0.1
based spectrum
sensing using USRP
in a cognitive radio
systems
10 Jayesh Patil, PSF-Based Spectrum NI USRP N2922, Pattern-Sequen 400Ms/s 935MHz -
Neeraj Bokde, Occupancy Prediction LABVIEW ce-Based 960MHz
Sudhir Kumar in Cognitive Radio Forecasting
Mishra and Method
Kishore Kulat -
2020
11 Ashwini Kumar Statistical NI USRP, SVM-based SCRSS SVM 810MHz - 815 MHz and 825
Varma and Feature-Based SVM LABVIEW blind sensing Training data 830MHz MHz were used as
Debjani Mitra - Wideband Sensing model = 0.36272 PU signals with 20
2020 Testing data = dB and −10 dB of
0.00196 SNR
VMMA SVM
training data=
0.36390,
testing data=
0.00199

10. Inference of Literature Survey
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
Statistical…
Machine…
Spatio-…
Hardware…
Generative…
A
Machine…
When
Machine…
IMPLEMENTA…
Practical
Radio…
Spectrum…
Non-…
Spectrum…
Spectrum…
Deep
Learning…
Accuracy
Dataset

11. SDR Work Bench
 NI USRP-2922
 Transceiver
NI USRP-2922

12. Methodology
RF DATASET MODELLING
DATA PROCESSING
FEATURE EXTRACTION
(Cross Correlation, Moving
Average)
MACHINE LEARNING
MODELING
SPECTRAL DETECTION
(Pd vs Pfa)
Pd= Pr(Y>𝛌th|H1)
Pfa= Pr(Y>𝛌th| H0)

13. System Model
 Let the received wideband signal be defines as,
𝑥 𝑚 = 𝑠 𝑚 + 𝑤 𝑚
 𝑋(𝑚) is observed signal, 𝑠(𝑚) is primary wideband
signal and 𝑤(𝑚) is noise

14. Dataset Modeling
 Machine learning methods learn
from examples.
Dataset Generation
 over-the-air using
 NI USRP-2922 interfaced with
LabVIEW software.
 Using AWGN channel
 Various SNR
 Propagation path
 LoS and NLoS conditions
S.N
o
Parameters Tx(815 MHz) Rx(810MHz-830
MHz)
1. IQ Sampling Rate 1 M 1 M
2. Gain 15 dB 20 dB
3. Active Antenna TX1 RX2
4. PN-Sequence 10 10
5. Frame size 4096 10,000
6. PSK Filter parameter(𝛼) 0.50 0.50
7. PSK Filter length 6 6
8. Samples/ Symbol 8 8
9. Acquisition duration (sec) - 10 m
Experimental
Setup(AWGN Channel)
Specifications

15. Dataset Modeling
 Machine learning methods learn
from examples.
Wide-band sensed signal at 815MHz
S.N
o
Parameters Tx(815 MHz) Rx(810MHz-830
MHz)
1. IQ Sampling Rate 1 M 1 M
2. Gain 15 dB 20 dB
3. Active Antenna TX1 RX2
4. PN-Sequence 10 10
5. Frame size 4096 10,000
6. PSK Filter parameter(𝛼) 0.50 0.50
7. PSK Filter length 6 6
8. Samples/ Symbol 8 8
9. Acquisition duration (sec) - 10 m
Experimental
Setup(AWGN Channel)
Specifications

16. Dataset Modeling
S.N
o
Parameters Tx(815 MHz) Rx(810 MHz-
830 MHz)
1. IQ Sampling Rate 1 M 1 M
2. Gain 15 dB 20 dB
3. Active Antenna TX1 RX2
4. PN-Sequence 10 10
5. Frame size 4096 10,000
6. PSK Filter parameter(𝛼) 0.50 0.50
7. PSK Filter length 6 6
8. Samples/ Symbol 8 8
9. Acquisition duration (sec) - 10 m
Experimental Setup
Specifications
Captured data at different receiver location

17. Features of SVM
 To develop and validate a real-world data efficiently SVM
based blind sensing model uses two statistical features,
 Correlation based feature and
 Moving average based feature

18. Metrics for various SNR

19. Detection Probability
 The Pd and Pfa were calculated by
counting the number of TP, TN, FP,
and FN instances.
 𝑃𝑑 =
𝑇𝑃
𝑇𝑃+𝐹𝑁
 𝑃𝑓𝑎 = 1 −
𝑇𝑁
𝑇𝑁+𝐹𝑃
Pd vs SNR using (AWGN Channel)
Pfa vs SNR(AWGN
channel)

20. Detection Probability
 The Pd and Pfa were calculated by
counting the number of TP, TN, FP,
and FN instances.
 𝑃𝑑 =
𝑇𝑃
𝑇𝑃+𝐹𝑁
 𝑃𝑓𝑎 = 1 −
𝑇𝑁
𝑇𝑁+𝐹𝑃
Pd vs LoS & NLoS condition
Pfa vs LoS & NLoS condition
Measure data location X-axis label
NLoS-1 1
NLoS-2 2
NLoS-3 3
NLoS-4 4
NLoS-5 5
NLoS-6 6
NLoS-7 7
NLoS-8 8
NLoS-9 9

21. References
[1]Rhana M. Elshishtawy§1 , Adly S. Tag Eldien§2 , Mostafa M. Fouda§3 , and Ahmed H. Eldeib “
Implementation of Multi-Channel Energy Detection Spectrum Sensing Technique in Cognitive Radio
Networks Using LabVIEW on USRP-2942R” 978-1-7281-5146-5/19/$31.00 ©2019 IEEE
[2]Elena Iulia Dobre, Alexandru Martain, Cailin vadeanue “ USRP-based experimental platform for ED in
CR systems “ 971-1-7281-5146-5/19/$31.00 ©2016 IEEE
[3]Dhaval K. Patel1 • Miguel Lo´pez-Benı´tez2,3 • Brijesh Soni1 • A´ ngel F. Garcı´a-Ferna´ndez “ Artificial
neural network design for improved spectrum sensing in cognitive radio” Springer Science+Business
Media, LLC, part of Springer Nature 2020
[4]Jayesh Patil, Neeraj Bokde, Sudhir Kumar Mishra and Kishore Kulat “ PSF-Based Spectrum Occupancy
Prediction in Cognitive Radio” Springer Nature Singapore Pte Ltd. 2020
[5]Mahmoud H. Shehady, Ali Beydoun, and Oussama Bazzi “ Experimental Study of Spectrum Sensing
Based on Energy Detection Using USRP” International Journal of Electronics and Electrical
Engineering Vol. 8, No. 3, September 2020
[6]Avuthu Avinash Reddy, Ramesh Babu Battula, Dinesh Gopalani, Kurra Chaithanya “ DISCERN:
Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi-Fi
bands” Int J Commun Syst. 2020;e4550. wileyonlinelibrary.com/journal/dac © 2020 John Wiley & Sons,
Ltd.
[7]Ashwini Kumar Varma and Debjani Mitra “ Statistical Feature-Based SVM Wideband Sensing” IEEE
COMMUNICATIONS LETTERS, VOL. 24, NO. 3, MARCH 2020
[8]Muhammed Althaf C, S Chris Prema “ Covariance and Eigenvalue Based Spectrum Sensing Using

22. Acknowledgements
Scheme : AICTE
MODROB RF LABORATORY

23. Thank You

24. Raw IQ samples for various SNR

25. SCRSS vi

26. Feature Vector Z
SNR Z1 Z2 Z3 Z4 Z5 … Z2000
-30dB -0.128 -0.321 -0.404 0.012818 0.013415 … 0.011567
-25dB 0.029375 -0.027233 -0.027016 0.026768 0.026226 … -0.042703
-20dB 0.044988 0.042367 -0.041231 -0.039848 0.040202 … -0.064327
-15dB -0.059949 -0.057947 0.056373 0.053999 0.053237 … 0.070341
-10dB -0.076146 0.07292 -0.071927 -0.069139 0.067452 … -0.083796
0dB -0.093201 -0.089085 0.086845 -0.084683 -0.082549 … 0.097743
5dB -0.109029 0.106073 0.103064 0.099603 0.098101 … -0.111204
10dB 0.126583 0.121899 -0.120048 0.115713 -0.112996 … -0.127064
15dB 0.143136 -0.139441 0.135764 -0.132717 0.129136 … 0.143283
20dB -0.161466 -0.155986 0.153363 -0.148483 -0.146049 … 0.158482

27. VMMA vi
No. of
samples 1 2 3 4 5 6 7 8 … 10,000
Z 10.92774 0.233207 0.246639 0.144085 0.144016 0.146527 0.143137 0.145822 … 0.132545

28. SVM_Train

29. SVM_Test

30. Project outcome
Conference certificate Badge from NI