Iterative interference cancellation using mixed feedback weights and stabilizing step sizes

• US 7,711,075 B2
• Filed: 06/13/2006
• Issued: 05/04/2010
• Est. Priority Date: 11/15/2005
• Status: Active Grant

• Current Assignee: III Holdings 1, LLC (Intellectual Ventures LLC)
• Original Assignee: TensorComm, Inc. (Rambus Inc.)
• Inventors: Guess, Tommy, Nagarajan, Vijay, McCloud, Michael L, Lamba, Gagandeep Singh
• Primary Examiner(s): KIM, KEVIN
• Application Number:
  US11/451,932
• Publication Number:
  US 20070110133A1
• Time in Patent Office: 1,421 Days
• Field of Search: 375/346
• US Class Current: 375/346
• 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: H04B 1/712 Weighting of fingers for combining, e.g. amplitude control or phase rotation using an inner loop
• 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 interference canceller configured for computing interference-cancelled symbol estimates, and performing processing during each of at least one iteration, the canceller comprising a decision module, the decision module configured for processing each of one or more input symbol estimates for producing one of a hard decision or a soft decision, and a de-biasing module configured for scaling the input symbol estimates with a scale factor to remove bias computed on the input symbol estimates for producing de-biased input symbol estimates.

2. The interference canceller recited in claim 1, wherein the de-biasing module is configured to set the scale factor equal to estimates of an expected ratio of symbol amplitudes to amplitudes of the input symbol estimates for each of the input symbol estimates having a known constellation.

3. The interference canceller recited in claim 2, wherein the de-biasing module is configured to produce de-biased symbol estimates characterized by

$d^{\left[i\right]}$

b ~ [ i ] ⁢

⁢

with ⁢

⁢

d [ i ] = E ⁡

[ 

b 

] E [ 

b ~ [ i ] 

]

where d^[i] is a de-bias scale factor after an i^th iteration of the interference canceller, {tilde over (b)}^[i] is an input symbol estimate after an i^th iteration of the interference canceller, b is a symbol from a known constellation, | | represents the magnitude of a complex quantity, E[ ] represents a statistical expectation or its estimate with a time average.

4. The interference canceller recited in claim 2, wherein the de-biasing module is configured to produce de-biased symbol estimates characterized by

$d^{\left[i\right]}$

b ~ [ i ] ⁢

⁢

with ⁢

⁢

d [ i ] = E ⁡

[ 

b 

2 ] E [ 

b ~ [ i ] 

2 ]

where d^[i] is a de-biased scale factor after an i^th iteration of the interference canceller, {tilde over (b)}^[i] is an input symbol estimate after an i^th iteration of the interference canceller, b is a symbol from a known constellation, | | represents the magnitude of a complex quantity, E[ ] represents a statistical expectation or its estimate with a time average.

5. The interference canceller recited in claim 1, wherein the de-biasing module is configured to set the de-bias scale factor of at least one symbol having a known constellation to unity.

22. The interference canceller in claim 1, wherein the de-biasing module is configured to set the scale factor to unity for input symbol estimates having constellations that are unknown to the receiver.

6. An interference canceller configured for computing interference-cancelled symbol estimates, and performing processing during each of at least one iteration, the canceller comprising a decision module, the decision module configured for processing each of one or more input symbol estimates for producing one of a hard decision or a soft decision, wherein the decision module is configured for processing symbols with known constellations by partitioning a constellation space into hard- and soft-decision regions based on a function of proximity to nearby constellation points.

7. The interference canceller recited in claim 6, wherein the decision regions for at least one symbol are configured such that the hard-decision regions are collectively exhaustive so that only a hard decision is produced or the soft-decision regions are collectively exhaustive so that only a soft symbol decision is produced.

8. An interference cancellation method configured for computing interference-cancelled symbol estimates and providing for decisions, wherein providing for decisions comprises providing for processing each of one or more input symbol estimates for producing one of a hard decision or a soft decision, further comprising providing for de-biasing which is configured for de-biasing the input symbol estimates with a scale factor to remove bias computed on the input symbol estimates for producing de-biased input symbol estimates.

9. The method recited in claim 8, wherein providing for de-biasing is configured to set the scale factor equal to estimates of an expected ratio of symbol amplitudes to amplitudes of the input symbol estimates for each of the input symbol estimates having a known constellation.

10. The method recited in claim 9, wherein providing for de-biasing is configured to produce de-biased symbol estimates characterized by

$d^{\left[i\right]}$

b ~ [ i ] ⁢

⁢

with ⁢

⁢

d [ i ] = E ⁡

[ 

b 

] E [ 

b ~ [ i ] 

]

where d^[i] is a de-bias scale factor after an i^th iteration of the interference cancellation method, {tilde over (b)}^[i] is an input symbol estimate after an i^th iteration of the interference cancellation method, b is a symbol from a known constellation, | | represents the magnitude of a complex quantity, E[ ] represents a statistical expectation or its estimate with a time average.

11. The method recited in claim 9, wherein providing for de-biasing is configured to produce de-biased symbol estimates characterized by

$d^{\left[i\right]}$

b ~ [ i ] ⁢

⁢

with ⁢

⁢

d [ i ] = E ⁡

[ 

b 

2 ] E [ 

b ~ [ i ] 

2 ]

where d^[i] is a de-biased scale factor after an i^th iteration of the interference cancellation method, {tilde over (b)}^[i] is an input symbol estimate after an i^th iteration of the interference cancellation method, b is a symbol from a known constellation, | | represents the magnitude of a complex quantity, E[ ] represents a statistical expectation or its estimate with a time average.

12. The method recited in claim 8, wherein providing for de-biasing is configured to set the de-bias scale factor of at least one symbol having a known constellation to unity.

23. The method recited in claim 8, wherein providing for de-biasing is configured to set the scale factor to unity for input symbol estimates having constellations that are unknown to the receiver.

13. An interference cancellation method configured for computing interference-cancelled symbol estimates and providing for decisions, wherein providing for decisions comprises providing for processing each of one or more input symbol estimates for producing one of a hard decision or a soft decision, wherein providing for decisions is configured for processing symbols with known constellations by partitioning a constellation space into hard- and soft-decision regions based on a function of proximity to nearby constellation points.

14. The method recited in claim 13, wherein the decision regions for at least one symbol are configured such that the hard-decision regions are collectively exhaustive so that only a hard decision is produced or the soft-decision regions are collectively exhaustive so that only a soft symbol decision is produced.

15. An interference cancellation system configured for computing interference-cancelled symbol estimates and comprising a decision means operative during each of at least one iteration for each of one or more input symbol estimates, the decision means comprising a processing means configured for processing each of the one or more input symbol estimates for producing a hard decision or a soft decision, further comprising a de-biasing means configured for de-biasing the input symbol estimates with a scale factor to remove bias computed on the input symbol estimates for producing de-biased input symbol estimates.

16. The system recited in claim 15, wherein the de-biasing means is configured to set the scale factor equal to estimates of an expected ratio of symbol amplitudes to amplitudes of the input symbol estimates for each of the input symbol estimates having a known constellation.

17. The system recited in claim 16, wherein the de-biasing means is configured to produce de-biased symbol estimates characterized by

$d^{\left[i\right]}$

b ~ [ i ] ⁢

⁢

with ⁢

⁢

d [ i ] = E ⁢

⌊



b 

⌋

E ⁡

[ 

b ~ [ i ] 

]

where d^[i] is a de-bias scale factor after an i^th iteration of the interference cancellation system, {tilde over (b)}^[i] is an input symbol estimate after an i^th iteration of the interference cancellation system, b is a symbol from a known constellation, | | represents the magnitude of a complex quantity, E[ ] represents a statistical expectation or its estimate with a time average.

18. The system recited in claim 16, wherein the de-biasing means is configured to produce de-biased symbol estimates characterized by

$d^{\left[i\right]}$

b ~ [ i ] ⁢

⁢

with ⁢

⁢

d [ i ] = E ⁡

[ 

b 

2 ] E [ 

b ~ [ i ] 

2 ]

where d^[i] is a de-biased scale factor after an i^th iteration of the interference cancellation system, {tilde over (b)}^[i] is an input symbol estimate after an i^th iteration of the interference cancellation system, b is a symbol from a known constellation, | | represents the magnitude of a complex quantity, E[ ] represents a statistical expectation or its estimate with a time average.

19. The system recited in claim 15, wherein the de-biasing means is configured to set the de-bias scale factor of at least one symbol having a known constellation to unity.

20. The system recited in claim 15, wherein the decision means is configured for processing symbols with known constellations by partitioning a constellation space into hard- and soft-decision regions based on a function of proximity to nearby constellation points.

21. The system recited in claim 20, wherein the decision regions for at least one symbol are configured such that the hard-decision regions are collectively exhaustive so that only a hard decision is produced or the soft-decision regions are collectively exhaustive so that only a soft symbol decision is produced.