Iterative interference cancellation using mixed feedback weights and stabilizing step sizes
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).
1. An interference canceller configured for performing at least one iteration for each of a plurality of input symbol estimates for producing updated interference-cancelled symbol estimates, the canceller comprising a weighting module, the weighting module configured to apply at least one symbol weight to the plurality of input symbol estimates, the at least one symbol weight comprising a function of an input symbol merit, wherein the canceller is configured to measure the input symbol 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 estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and wherein the function of the average ratio of signal power to interference-plus-noise power is substantially characterized by
γ
[
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.
2. An interference canceller configured for performing at least one iteration for each of a plurality of input symbol estimates for producing updated interference-cancelled symbol estimates, the canceller comprising a weighting module, the weighting module configured to apply at least one symbol weight to the plurality of input symbol estimates, the at least one symbol weight comprising a function of an input symbol merit, wherein the canceller is configured to measure the input symbol 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 estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and wherein the canceller is configured to employ time-series averaging for calculating the proximity as a statistical average.
3. An interference canceller configured for performing at least one iteration for each of a plurality of input symbol estimates for producing updated interference-cancelled symbol estimates, the canceller comprising a weighting module, the weighting module configured to apply at least one symbol weight to the plurality of input symbol estimates, the at least one symbol weight comprising a function of an input symbol merit wherein the at least one symbol weight is substantially characterized by:
γ
[
i
]
=
Re
{
E
[
slice
(
b
^
[
i
]
)
*
b
^
[
i
]
]
}
E
[
b
^
[
i
]
2
]
,
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 the 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.
4. An interference cancellation method employing at least one iteration for each of a plurality of input symbol estimates for converting the plurality of input symbol estimates into updated interference-cancelled symbol estimates, wherein each of the at least one iteration comprises applying at least one symbol weight to the plurality of input symbol estimates, the method further comprising:
providing for calculating the at least one symbol weight from a function of an input symbol merit,
wherein providing for calculating the at least one symbol weight comprises measuring the input symbol 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 estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and
wherein the function of the average ratio of signal power to interference-plus-noise power is substantially characterized by
γ
[
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.
5. An interference cancellation method employing at least one iteration for each of a plurality of input symbol estimates for converting the plurality of input symbol estimates into updated interference-cancelled symbol estimates, wherein each of the at least one iteration comprises applying at least one symbol weight to the plurality of input symbol estimates, the method further comprising:
providing for calculating the at least one symbol weight from a function of an input symbol merit,
wherein providing for calculating the at least one symbol weight comprises measuring the input symbol 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 estimates and proximity of at least one of the plurality of input symbol estimates to a nearby constellation point, and
comprising providing for time-series averaging for calculating the proximity as a statistical average.
6. An interference cancellation method employing at least one iteration for each of a plurality of input symbol estimates for converting the plurality of input symbol estimates into updated interference-cancelled symbol estimates, wherein each of the at least one iteration comprises applying at least one symbol weight to the plurality of input symbol estimates, the method further comprising:
providing for calculating the at least one symbol weight from a function of an input symbol merit,
wherein the at least one symbol weight is substantially characterized by:
γ
[
i
]
=
Re
{
E
[
slice
(
b
^
[
i
]
)
*
b
^
[
i
]
]
}
E
[
b
^
[
i
]
2
]
,
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 the 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.
7. An interference cancellation system configured for converting input symbol estimates into updated interference-cancelled symbol estimates, wherein signal processing in each of at least one iteration 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 estimates, the system further comprising:
a weight-calculation means configured for calculating the at least one symbol weight from a function of a merit of an input symbol,
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 estimates and proximity of at least one of the input symbol estimates to a nearby constellation point, and
wherein the function of the average ratio of signal power to interference-plus-noise power is substantially characterized by
γ
[
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.
8. An interference cancellation system configured for converting input symbol estimates into updated interference-cancelled symbol estimates, wherein signal processing in each of at least one iteration 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 estimates, the system further comprising:
a weight-calculation means configured for calculating the at least one symbol weight from a function of a merit of an input symbol,
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 estimates and proximity of at least one of the input symbol estimates to a nearby constellation point, and
wherein the weight-calculation means is configured to employ time-series averaging for calculating the proximity as a statistical average.
9. An interference cancellation system configured for converting input symbol estimates into updated interference-cancelled symbol estimates, wherein signal processing in each of at least one iteration 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 estimates, the system further comprising:
a weight-calculation means configured for calculating the at least one symbol weight from a function of a merit of an input symbol,
wherein the at least one symbol weight is substantially characterized by:
γ
[
i
]
=
Re
{
E
[
slice
(
b
^
[
i
]
)
*
b
^
[
i
]
]
}
E
[
b
^
[
i
]
2
]
,
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 the 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.