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BriefPPT

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BriefPPT

  1. 1. What is AMR? Receive Random Signal Identify what modulation scheme it uses Send it to appropriate demodulator
  2. 2. Where is it used? Military Applications Intercepting Jamming Civilian Applications Interference identification Spectrum management
  3. 3. Drawbacks of previously established methods • Depends of accuracy of operator • Very slow to detect Operator Monitored • Lot of simultaneously active H/W • Again dependent on operators inference Bank of demodulators
  4. 4. Implementation of Proposed work
  5. 5. Methods for modulation recognition Decision Theory based • Decision tree flowchart Likelihood based • Maximum Likelihood- based Machine Learning • Artificial Neural Network
  6. 6. Methodology Preprocessing • Signal Isolation • Signal Segmentation Key Feature Extraction • γmax , σap , σdp , P, σaa , σaf , σa , µa 4,2 , µf 4,2 Modulation classification • Decision tree • ANN
  7. 7. Key feature extraction model :
  8. 8. Flowchart for modulation recognition
  9. 9. Summary of features and typical thresholds Feature Distinction Thresholds for (30dB SNR)Subset 1 (High) Subset 2 (Low) Ratio P USB, LSB Others 0.75 Sigma dp MFSK, DSB, SSB AM, MASK 0.5 Sigma ap FM, MFSK, DSB MASK, MPSK 4 Gamma Max MASK , AM , DSB MFSK, FM ,MPSK 10 Mue a AM MASK 1.526 Mue f FM MFSK 1.6 Sigma aa 4ASK 2ASK 0.2 Sigma a 4PSK 2PSK 0.04 Sigma af 4FSK 2FSK 0.8
  10. 10. Demerits of feature extraction based approach: • Threshold values is dependent on fc/fm ratio, signal to noise ratio, modulation index. • Frequent operator intervention for recalibrating thresholds. • Holds good for high values of SNR
  11. 11. Introduction to Artificial Neural Networks (ANN) • Artificial neural network (ANN) is a machine learning approach that models human brain and consists of a number of artificial neurons • An Artificial Neural Network is specified by: − neuron model: the information processing unit of the NN − an architecture: a set of neurons and weighted links connecting neurons along with biases − a learning algorithm: used for training the NN by modifying the weights in order to model a particular learning task correctly on the training examples.
  12. 12. Need for Neural network approach Speed • Does not contain complex real-time processing Programming • No need to manually program thresholds, it learns from examples Hardware • Post-training, it is just a combinational circuit
  13. 13. A Typical Neuron
  14. 14. Training of Neural Network
  15. 15. No. Modulation Type Lowest SNR for successful detection 1 AM 1 dB 2 DSBSC 0 dB 3 USB 2 dB 4 LSB -3 dB 5 FM 1 dB 6 BASK 11 dB 7 BFSK -2 dB 8 BPSK -1 dB 9 4 – ASK -6 dB 10 4 – FSK 5 dB 11 4 - PSK -11 dB
  16. 16. HARDWARE IMPLEMENTATION
  17. 17. General Workflow for VHDL Implementation MATLAB Algorithm Fixed-point design VHDL Coding Simulation Synthesis, Place & Route Test
  18. 18. Simulation screen
  19. 19. Decision Modulation Type 1 DSB 2 AM 3 FM 4 LSB 5 USB C Corrupted (or None)

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