Iterative interference cancellation using mixed feedback weights and stabilizing step sizes

• US 20070110132A1
• Filed: 06/13/2006
• Published: 05/17/2007
• Est. Priority Date: 11/15/2005
• Status: Active Grant

• Current Assignee: III Holdings 1, LLC
• Original Assignee: Rambus Inc.
• Inventors: McCloud, Michael, Guess, Tommy, Nagarajan, Vijay, Lamba, Gagandeep
• Granted Patent: US 7,702,048 B2
• Time in Patent Office: Days
• US Class Current: 375/148
• CPC Class Codes: H04B 1/7107 Subtractive interference cancellation
• CPC Class Codes: H04B 1/71072 Successive interference cancellation
• CPC Class Codes: H04B 1/71075 Parallel interference cancellation
• CPC Class Codes: H04B 1/7115 Constructive combining of multi-path signals, i.e. RAKE receivers
• CPC Class Codes: H04L 2025/03375 Passband transmission
• CPC Class Codes: H04L 25/03006 Arrangements for removing intersymbol interference

Abstract

A receiver is configured for canceling intra-cell and inter-cell interference in coded, multiple-access, spread-spectrum transmissions that propagate through frequency-selective communication channels. The receiver employs iterative symbol-estimate weighting, subtractive cancellation with a stabilizing step-size, and mixed-decision symbol estimates. Receiver embodiments may be implemented explicitly in software or programmed hardware, or implicitly in standard Rake-based hardware either within the Rake (i.e., at the finger level) or outside the Rake (i.e., at the user or subchannel symbol level).

Claims

1. An iterative interference canceller configured for performing a plurality of iterations for each of a plurality of input symbol decisions for producing updated interference-cancelled symbol decisions, the canceller comprising a soft weighting module, a mixed-decision module, and a stabilizing step size module, the soft weighting module configured to apply at least one symbol weight to the plurality of input symbol decisions, the at least one symbol weight comprising a function of an input symbol decision merit.

2. The interference canceller recited in claim 1, wherein the canceller is configured to measure the input symbol decision merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the plurality of input symbol decisions and proximity of at least one of the plurality of input symbol decisions to a nearby constellation point.

3. The interference canceller recited in claim 2, wherein the interference canceller is configured to process a pilot signal for measuring the average ratio of signal power to interference-plus-noise power.

4. The interference canceller recited in claim 2, wherein the interference canceller is configured to employ at least one statistical signal processing technique for estimating the average ratio of signal power to interference-plus-noise power.

5. The interference canceller recited in claim 2, wherein the function of the average ratio of signal power to interference-plus-noise power is characterized by

${\gamma }^{}$

[ i ] = max ⁢

{ C , 1 1 + 1 / SINR [ i ] } ,

where γ

^[i] is a symbol weight after an i^th iteration of the interference canceller, max { } is a function for selecting a maximum value from a set of quantities within brackets { }, SINR^[i] denotes an average ratio of signal power to interference-plus-noise power (SINR) of the symbol after the i^th iteration of the interference canceller, and C is a non-negative real constant for enforcing a minimum symbol weight.

6. The interference canceller recited in claim 2, wherein the canceller is configured to employ time-series averaging for calculating the proximity as a statistical average.

7. The interference canceller recited in claim 1, wherein the at least one symbol weight is characterized by:

${\mathrm{\gamma <br><br>}}^{\left[i\right]}=\frac{\mathrm{Re}<br><br>\left\{E<br><br>\left[\mathrm{slice}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\left({\hat{b}}^{\left[i\right]}\right)}^{*}<br><br>{\hat{b}}^{\left[i\right]}\right]\right\}}{E\left[{<br><br>{\hat{b}}^{\left[i\right]}<br><br>}^2\right]},$

where γ

^[i] is a symbol weight after an i^th iteration of the interference canceller, {circumflex over (b)}^[i] is a symbol decision after an i^th iteration of the interference canceller, slice ({circumflex over (b)}^[i]) represents the quantization of {circumflex over (b)}^[i] to a nearest constellation point, Re{ } returns a real part of an argument, E[ ] represents a statistical expectation or its estimate with a time average, ∥

represents the magnitude of a complex quantity, and * denotes the conjugate of a complex quantity.

8. The interference canceller recited in claim 1, wherein the at least one symbol weight comprises a plurality of symbol weights, the soft weighting module configured to set a subset of the plurality of symbol weights to zero.

9. The interference canceller recited in claim 8, wherein non-zero symbol weights of the plurality of symbol weights correspond to at least one of a set of symbols, the set comprising desired symbols and symbols corresponding to a predetermined group of strongest signals.

10. An interference cancellation method employing a plurality of iterations for each of a plurality of input symbol decisions for converting the plurality of input symbol decisions into updated interference-cancelled symbol decisions, wherein each of the plurality of iterations comprises applying at least one symbol weight to the plurality of input symbol decisions, applying a stabilizing step size to an error signal, and performing mixed-decision processing, the method comprising:

providing for calculating the at least one symbol weight from a function of an input symbol decision merit.

11. The method recited in claim 10, wherein providing for calculating the at least one symbol weight comprises measuring the input symbol decision merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the plurality of input symbol decisions and proximity of at least one of the plurality of input symbol decisions to a nearby constellation point.

12. The method recited in claim 11, comprising providing for processing a pilot signal for measuring the average ratio of signal power to interference-plus-noise power.

13. The method recited in claim 11, comprising providing for employing at least one statistical signal processing technique for estimating the average ratio of signal power to interference-plus-noise power.

14. The method recited in claim 11, wherein the function of the average ratio of signal power to interference-plus-noise power is characterized by

${\gamma }^{}$

[ i ] = max ⁢

{ C , 1 1 + 1 / SINR [ i ] } ,

where ≢

5^[i] is a symbol weight after an i^th iteration of the interference canceller, max { } is a function for selecting a maximum value from a set of quantities within brackets { }, SINR^[i] denotes an average ratio of signal power to interference-plus-noise power (SINR) of the symbol after the i^th iteration of the interference canceller, and C is a non-negative real constant for enforcing a minimum symbol weight.

15. The method recited in claim 11, comprising providing for time-series averaging for calculating the proximity as a statistical average.

16. The method recited in claim 10, wherein the at least one symbol weight is characterized by:

${\mathrm{\gamma <br><br>}}^{\left[i\right]}=\frac{\mathrm{Re}<br><br>\left\{E<br><br>\left[\mathrm{slice}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\left({\hat{b}}^{\left[i\right]}\right)}^{*}<br><br>{\hat{b}}^{\left[i\right]}\right]\right\}}{E\left[{<br><br>{\hat{b}}^{\left[i\right]}<br><br>}^2\right]},$

where γ

^[i] is a symbol weight after an i^th iteration of the interference canceller, {circumflex over (b)}^[i] is a symbol decision after an i^th iteration of the interference canceller, slice ({circumflex over (b)}^[i]) represents the quantization of {circumflex over (b)}^[i] to a nearest constellation point, Re{ } returns a real part of an argument, E[ ] represents a statistical expectation or its estimate with a time average, ∥

represents the magnitude of a complex quantity, and * denotes the conjugate of a complex quantity.

17. The method recited in claim 10, wherein the at least one symbol weight comprises a plurality of symbol weights, and providing for calculating the at least one symbol weight comprises setting a subset of the plurality of symbol weights to zero.

18. The method recited in claim 17, wherein non-zero symbol weights of the plurality of symbol weights correspond to at least one of a set of symbols, the set comprising desired symbols and symbols corresponding to a predetermined group of strongest signals.

19. An iterative interference cancellation system configured for converting input symbol decisions into updated interference-cancelled symbol decisions, wherein signal processing in each of a plurality of iterations for each of the input symbol decisions is performed by a weighting means configured for applying at least one symbol weight to the input symbol decisions, a stabilizing step size means configured for applying a stabilizing step size to an error signal, and a mixed-decision processing means configured for performing mixed-decision processing on an interference-cancelled signal, the system comprising:

a weight-calculation means configured for calculating the at least one symbol weight from a function of a merit of an input symbol decision.

20. The system recited in claim 19, wherein the weight-calculation means is configured to measure the merit as at least one of a set of functions, the set comprising a function of an average ratio of signal power to interference-plus-noise power, and a function of at least one of the input symbol decisions and proximity of at least one of the input symbol decisions to a nearby constellation point.

21. The system recited in claim 20, wherein the weight-calculation means is configured to process a pilot signal for measuring the average ratio of signal power to interference-plus-noise power.

22. The system recited in claim 20, wherein the weight-calculation means is configured to employ at least one statistical signal processing technique for estimating the average ratio of signal power to interference-plus-noise power.

23. The system recited in claim 20, wherein the function of the average ratio of signal power to interference-plus-noise power is characterized by

${\gamma }^{}$

[ i ] = max ⁢

{ C , 1 1 + 1 / SINR [ i ] } ,

where γ

^[i] is a symbol weight after an i^th iteration of the interference canceller, max { } is a function for selecting a maximum value from a set of quantities within brackets { }, SINR^[i] denotes an average ratio of signal power to interference-plus-noise power (SINR) of the symbol after the i^th iteration of the interference canceller, and C is a non-negative real constant for enforcing a minimum symbol weight.

24. The system recited in claim 20, wherein the weight-calculation means is configured to employ time-series averaging for calculating the proximity as a statistical average.

25. The system recited in claim 19, wherein the at least one symbol weight is characterized by:

${\mathrm{\gamma <br><br>}}^{\left[i\right]}=\frac{\mathrm{Re}<br><br>\left\{E<br><br>\left[\mathrm{slice}<br><br>\text{\hspace{1em}<br><br>}<br><br>{\left({\hat{b}}^{\left[i\right]}\right)}^{*}<br><br>{\hat{b}}^{\left[i\right]}\right]\right\}}{E\left[{<br><br>{\hat{b}}^{\left[i\right]}<br><br>}^2\right]},$

where γ

^[i] is a symbol weight after an i^th iteration of the interference canceller, {circumflex over (b)}^[i] is a symbol decision after an i^th iteration of the interference canceller, slice ({circumflex over (b)}^[i]) represents the quantization of {circumflex over (b)}^[i] to a nearest constellation point, Re{ } returns a real part of an argument, E[ ] represents a statistical expectation or its estimate with a time average, ∥

represents the magnitude of a complex quantity, and * denotes the conjugate of a complex quantity.

26. The system recited in claim 19, wherein the at least one symbol weight comprises a plurality of symbol weights, the soft weighting module configured to set a subset of the plurality of symbol weights to zero.

27. The system recited in claim 26, wherein non-zero symbol weights of the plurality of symbol weights correspond to at least one of a set of symbols, the set comprising desired symbols and symbols corresponding to a predetermined group of strongest signals.