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Energy Efficient Source Coding and Modulation for Wireless Applications

Energy Efficient Source Coding and Modulation for Wireless Applications. Yashwanth Prakash Sandeep.K.S.Gupta Arizona State University Tempe, AZ 85287. Overview. Introduction Minimum energy codes Error correction Performance comparison Conclusion. Introduction. Wireless sensors.

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Energy Efficient Source Coding and Modulation for Wireless Applications

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  1. Energy Efficient Source Coding and Modulation for Wireless Applications Yashwanth Prakash Sandeep.K.S.Gupta Arizona State University Tempe, AZ 85287

  2. Overview • Introduction • Minimum energy codes • Error correction • Performance comparison • Conclusion

  3. Introduction • Wireless sensors. • Military surveillance. • Industrial monitoring. • Civilian home RF. • Medical implants (biosensors).

  4. Wireless sensors • Operate in the ISM band. • Low data rate. • Short range of operation. • Demands low power and low complexity at both circuit and system level.

  5. On-Off Keying Modulation • Energy consumption proportional to signals transmitted. 1 0 1 0 0 1

  6. Energy efficiency in OOK • Reduce number of bit-1s to be transmitted. • No control over the information sequence. • Map source bits to codes with less number of bit-1s.

  7. Minimum Energy (ME)Coding • C.Erin & H.Asada (coding optimality and code book optimality). C1 C2 … … Cn P1 P2 … … Pn • Sources with known statistics.

  8. Our Approach of ME Codes • Sources with unknown statistics. • Minimum energy codes considered. ‘k’ Bits ‘n’ Bits M Symbols = 2k • More energy efficient. - Only one bit-1 per code.

  9. System Model Info Source ME coding Modulator RF Transmitter 1 0 0 1 0 1 …… 0 0 0 1 0 0 0 0 0 1 0 0 0 0 …… k -source bits n - code bits

  10. ME Code Example • k = 3 n = 7 ME(n,k) = ME(7,3) 000 001 010 011 100 101 110 111 0000000 1000…0 0100…0 0010…0 …… ….. …… 0000…1 k- Bits n-Bits

  11. ME codes • Our approach achieves • Lesser number of bit-1 in the transmitted code • Safely assign to source symbols of any probability of occurrence. • Code Rate = (k / n) = (k / 2k-1)

  12. Error Detection(Bit-by-bit hard decision) Transmitted Codeword Bits 1 0 0 0 1 0 0 …… AWGN channel Received Codeword bits 0.8554 0.5059 0.01 0.9229 0.2122 0.5 Threshold Detector 1 1 0 0 1 0 0 ….. Codeword in error

  13. Performance without error correction

  14. Bandwidth / Power Vs ‘n’

  15. Error Correction Transmitted Codeword Corrected Codeword Demodulator output 0 1 0 0 0 0 0 0.0635 1.2360 0.0120 0.0010 0.5640 -0.021 0.640 0 1 0 0 0 0 0 Select Largest instead of bit-by-bit AWGN Channel Error Correction with soft-decision

  16. Optimal Detection • Transmit: Cm = [ c1m c2m ……… cnm] • Receive: R = [ r1 r2 ……… rn ] • Argmax m = 1,2,… {Pr(Cm/R)} • = MAX [ Correlation Metric] = MAX[ C(R,Cm) m = 1,2,… ] = MAX[ r1C1m +r2C2m+……+ rnCnm ]

  17. Performance with error correction

  18. Retinal Prosthesis Applicationat ASU • BS: Base Station • C: Biosensor chip B S C

  19. Block Diagram C H A N N E L Camera Image Processor DSP Coding/ Modulation Tx Rx Decode/ Demod Processor Sensor Power Recovery

  20. Thank you !!! Questions ?

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