System and method for time-domain equalization in discrete multi-tone system

• US 20030227967A1
• Filed: 06/06/2002
• Published: 12/11/2003
• Est. Priority Date: 06/06/2002
• Status: Abandoned Application

• Current Assignee: Proscend Communications, Inc.
• Original Assignee: Proscend Communications, Inc.
• Inventors: Chang, Yuan-Shuo, Hsu, Chih-Ming, Wang, Chin-Liang
• Application Number:
  US10/162,914
• Publication Number:
  US 20030227967A1
• Time in Patent Office: Days
• US Class Current: 375/229
• CPC Class Codes: H04L 2025/03617 Time recursive algorithms H04L2025/03643 takes precedence
• CPC Class Codes: H04L 25/03057 with a recursive structure H04L25/03031 takes precedence
• CPC Class Codes: H04L 27/2647 Arrangements specific to the receiver only equalisation H04L27/01

Abstract

A novel structure for the TEQ in a DMT system receiver to shorten the length of the effective channel impulse response is provided. A time-domain equalizer, based on the decision-feedback filter structure, along with a training method is disclosed. In accordance with the DFE-based TEQ in the DMT system, the data symbols that transmitted through the effective shortened channel would be more reliable.

Claims

1. A time-domain equalizer system, comprising;

QAM silicers for converting the output of FEQ to the corresponding signal in the QAM constellation for each subcarrier;

an IFFT for inverse fast Fourier transforming data generated by QAM slicers;

a Paraller/Serial converter (P/S) for converting said IFFT output data into a serial form;

a feedforward filter (FF) for whitening the received noises and producing an overall effective channel response such that the output only has causal components;

a feedback filter (FB) for reconstructing the residual causal ISI by using the past decisions;

a delay line for buffering the signals to the input of the feedback filter; and

a switch for connecting the input end of said delay line to a first node.

2. The time-domain equalizer system according to claim 1, wherein said switch further can be connected the input end of said delay line to a second node.

3. The time-domain equalizer system according to claim 1, wherein said feedforward filter continues processing the incoming digital samples at the ADC output and meantime the input end of delay line should be connected to said second node for feeding the last demodulated DMT symbol already here back to the input of said feedback filter during a predetermined time.

4. The time-domain equalizer system according to claim 1, wherein said input end of delay line should be switched to said first node for importing said feedback filter directly from said input of said serial/parallel converter until another DMT symbol be collected at said input of said FEQ.

5. A time-domain equalizer system, comprising

a feedforward filter(FF) for whitening the received noises and producing an overall effective channel response such that the output only has causal components;

a feedback filter(FB) for reconstructing the residual causal ISI by using the past decisions; and

a delay ling for buffering the signals to the input of the feedback filter.

6. A training method of TEQ, comprising the steps

a) fixing the feedforward and feedback filters, and updating a TIR filter in a frequency domain;

b) performing a windowing operation on said TIR in a time domain to limit the taps outside the window of length v+1 to be zero;

c) fixing said TIR and updating said feedforward and feedback filter in said frequency domain; and

d) performing said windowing operations on said feedforward and feedback filters in said time domain to limit them to only N_a and N_b consecutive non-zero taps, respectively.

7. The training method of TEQ according to claim 6, wherein the step of fixing the feedforward and feedback filters and updating a TIR filter in a frequency domain further comprises the step of transforming the coefficients of feedforward(FF), feedback(FB) and TIR filters into their corresponding sets of frequency domain samples.

8. The training method of TEQ according to claim 6, wherein the step of fixing the feedforward and feedback filters and updating a TIR filter in a frequency domain further comprises the step of transforming the training data, the received data, and the input data of FB into frequency samples.

9. The training method of TEQ according to claim 6, wherein the step of fixing the feedforward and feedback filters and updating a TIR filter in a frequency domain further comprises the step of computing the output frequency samples of FF, FB, and TIR.

10. The training method of TEQ according to claim 6, wherein the desired signals are obtained by the following equation;

D_k=A_w,kR_k−

B_w,kX^d_k;

where A_w,k and B_w,k are corresponding sets of frequency domain samples of feedforward, feedback, and X^d_k and R_k are the frequency samples.

11. The training method of TEQ according to claim 6, wherein the error signals are further obtained as the following equation;

12. The training method of TEQ according to claim 6, wherein the step of fixing the feedforward and feedback filters and updating a TIR filter in a frequency domain further comprises the step of updating the TIR coefficients by the following equation;

13. The training method of TEQ according to claim 6, wherein the step of performing a windowing operation on said TIR in a time domain to limit the taps outside the window of length v+1 to be zero further comprises the step of transforming the coefficients of updated TIR filter into said time domain taps by IFFT.

14. The training method of TEQ according to claim 6, wherein the step of performing a windowing operation on said TIR in a time domain to limit the taps outside the window of length v+1 to be zero further comprises the step of limiting the taps of TIR filters to v+1 consecutive samples by placing a fixed size window on it.

15. The training method of TEQ according to claim 6, wherein the step of performing a windowing operation on said TIR in a time domain to limit the taps outside the window of length v+1 to be zero further comprises the step of normalizing the energy of windowed TIR filter.

16. The training method of TEQ according to claim 6, wherein the step of fixing said TIR and updating said feedforward and feedback filter in said frequency domain further comprises the step of transforming the coefficients of feedforward(FF), feedback(FB) and TIR filters into their corresponding sets of frequency domain samples.

17. The training method of TEQ according to claim 6, wherein the step of fixing said TIR and updating said feedforward and feedback filter in said frequency domain further comprises the step of transforming the training data, the received data, and the input data of FB into frequency samples.

18. The training method of TEQ according to claim 6, wherein the step of fixing said TIR and updating said feedforward and feedback filter in said frequency domain further comprises the step of computing the output frequency samples of FF, FB, and TIR.

19. The training method of TEQ according to claim 6, wherein the desired signals are obtained by the following equation;

D_k=T_w,kX_k;

where T_w,k is corresponding sets of frequency sample of TIR, and X_k is the input data of feedback filter.

20. The training method of TEQ according to claim 6, wherein the error signals are further obtained as the following equation;

E_k=D_k−

Z_k;

where Z_k is the difference between the output frequency samples of feedforward filter and feedback filter.

21. The training method of TEQ according to claim 6, wherein the step of fixing said TIR and updating said feedforward and feedback filter in said frequency domain further comprises the step of updating the FF coefficients by the following equation;

A_w,k=A_w,k+β

E_kR*_k;

where the parameter of β

is the step size, and R*_k is the complex-conjugate values of R_k and X^d_k.

22. The training method of TEQ according to claim 6, wherein the step of fixing said TIR and updating said feedforward and feedback filter in said frequency domain further comprises the step of updating the FB coefficients by the following equation;

B_u,k=B_w,k+γ

E_k(X^d_k)*;

where the parameter of γ

is the step size.

23. The training method of TEQ according to claim 6, wherein the step of performing said windowing operations on said feedforward and feedback filters in said time domain to limit them to only N_a and N_b consecutive non-zero taps respectively further comprises the step of transforming the coefficients of updated FF and FB filters into the time domain taps by IFFT.

24. The training method of TEQ according to claim 6, wherein the step of performing said windowing operations on said feedforward and feedback filters in said time domain to limit them to only N_a and N_b consecutive non-zero taps respectively further comprises the step of limiting the taps FF and FB filters to have N_a and N_b consecutive taps.

25. The training method of TEQ according to claim 6, wherein the step of performing said windowing operations on said feedforward and feedback filters in said time domain to limit them to only N_a and N_b consecutive non-zero taps respectively further comprises the step of normalizing the energy of windowed FF and FB filters.