ADSL Equalization Van Herck Hans Verheyden Jonas

Content Implementation of theoretical schematic Optimisation algorithm (LMS) Simulation results

QAM demod decoder S/P quadrature amplitude modulation (QAM) encoder mirror data and N -IFFT P/S D/A + transmit filter N -FFT and remove mirrored data S/P TRANSMITTER RECEIVER N/2 subchannelsN real samples N/2 subchannels receive filter + A/D Bits P/S

QAM demod decoder S/P quadrature amplitude modulation (QAM) encoder mirror data and N -IFFT add cyclic prefix P/S D/A + transmit filter N -FFT and remove mirrored data S/P remove cyclic prefix TRANSMITTER RECEIVER N/2 subchannelsN real samples N/2 subchannels receive filter + A/D Bits channel P/S

Real channel

QAM demod decoder invert channel = FEQ S/P quadrature amplitude modulation (QAM) encoder mirror data and N -IFFT add cyclic prefix P/S D/A + transmit filter N -FFT and remove mirrored data S/P remove cyclic prefix TRANSMITTER RECEIVER N/2 subchannelsN real samples N/2 subchannels receive filter + A/D Bits channel P/S

QAM demod decoder invert channel = FEQ S/P quadrature amplitude modulation (QAM) encoder mirror data and N -IFFT add cyclic prefix P/S D/A + transmit filter N -FFT and remove mirrored data S/P remove cyclic prefix TRANSMITTER RECEIVER N/2 subchannelsN real samples N/2 subchannels time domain equalizer (FIR filter) receive filter + A/D Bits channel P/S

Transmitter

Channel and TEQ

Receiver

TEQ Determine W and B through different algorithms Choose length W and B Goal: h ∗ w shorter than h z-z- h + w b - xkxk rkrk ekek nknk + TIR TEQCIR

Least Mean Squares z-z- h + w b - xkxk rkrk ekek nknk + TIR TEQCIR

Simulation results More or less ideal channel

Simulation results Real channel

Simulation results Influence CP

Optimised LMS (FSTEQ) Every iteration step: B = FFT(b) Check if B lies between boundaries Scale if needed New b = IFFT(B)

Conclusion Implementing TEQ doesn’t reduce error percentage, but does increase efficiency Still errors in received symbols: error coding is needed

Questions?