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US 6,603,823 B1Granted
Channel estimator
AssigneeIntel Corporation
InventorsDaniel Yellin, Doron Rainish, Rony Ashkenazi
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Abstract
The present invention is a channel estimator based on the values of received data and on a priori probabilities only of received symbols. The channel estimator includes a symbol probability generator, a noise variance estimator and a channel tap estimator. The symbol probability generator generates a priori probabilities only of transmitted symbols found in the received signal(s). The noise variance estimator estimates at least one noise variance corrupting the received signal(s). The channel tap estimator generates channel estimates from the received signal(s), the a priori probabilities and the noise variance(s).
Claims27
A channel estimator to operate on at least one received signal, the channel estimator comprising:
- a symbol probability generator to generate a priori probabilities only of transmitted symbols found in said at least one received signal;
- a noise variance estimator to estimate at least one noise variance corrupting said at least one received signal;
- a channel tap estimator to generate channel estimates from said at least one received signal, said a priori probabilities and said at least one noise variance;
- and
- wherein said channel tap estimator is able to solve the following equation:
- h ML = 1 2 T · ∑ t = 1 T y _ ( t ) · z ( t ;
- h ML ) *
- where {overscore (y)}(t) is a vector of downconverted and demodulated received signal of said at least one received signal, t is the time variable, hML is a ML channel estimator where ML denotes Maximum Likelihood, T is the sampling time, z(t;hML) is a mathematical scalar process, and (·)* denotes the conjugate of the bracketed expression.
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A channel estimator operating on at least one received signal, the channel estimator comprising:
- a symbol probability generator to generate a priori probabilities only of transmitted symbols found in said at least one received signal;
- a noise variance estimator to estimate at least one noise variance corrupting said at least one received signal;
- a channel tap estimator to generate channel estimates from said at least one received signal, said a priori probabilities and said at least one noise variance, and
- wherein said noise variance estimator is able to generate a noise variance {overscore (σ)}2 as follows:
- σk2(n)=(1−α)·σk2(n−1)+α·{tilde over (σ)}k2(n)
- where {overscore (σ)}2 is the noise variance vector, σk2 is the noise variance for the k-th element, k is the element numerator, the time index n is in units of slot, α is a user selectable exponential forgetting factor and {tilde over (σ)}k2 is defined by σ ~ k 2 ( n ) ≡ 1 N P ∑ t = 1 N P [LeftBracketingBar] y _ k ( t ) G ( t ) · ( s ( t ) ) * [RightBracketingBar] 2 - [LeftBracketingBar] 1 N P ∑ t = 1 N P y _ k ( t ) G ( t ) · ( s ( t ) ) * [RightBracketingBar] 2
- where Np is the number of pilot symbols per slot, G(t) is an automatic gain control (AGC) level and {overscore (y)}k(t) is the k-th element of the vector {overscore (y)}(t), s(t) is a sequence of independent and identically distributed random variables of data symbols with time varying, a priori symbol probabilities and (·)* denotes the conjugate of the bracketed expression.
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A method of estimating a channel using at least one received signal, the method comprising:
- generating a priori probabilities only of transmitted symbols found in said at least one received signal;
- estimating at least one noise variance corrupting said at least one received signal;
- generating channel estimates from said at least one received signal, said a priori probabilities and said at least one noise variance,
- wherein generating said channel estimates solves the following equation:
- h ML = 1 2 T · ∑ t = 1 T y _ ( t ) · z ( t ;
- h ML ) *
- where {overscore (y)}(t) is a vector of downconverted and demodulated received signal of said at least one received signal, t is the time variable, hML is a ML channel estimator where ML denotes Maximum Likelihood, T is the sampling time, z(t;hML) is a mathematical scalar process, and (·)* stands to denote the conjugate of the bracketed expression.
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A method of estimating a channel using at least one received signal, the method comprising:
- generating a priori probabilities only of transmitted symbols found in said at least one received signal;
- estimating at least one noise variance corrupting said at least one received signal;
- generating channel estimates from said at least one received signal, said a priori probabilities and said at least one noise variance, and
- wherein estimating said at least one noise variance comprises generating a noise variance {overscore (σ)}2 a as follows:
- σk2(n)=(1−α)·σk2(n−1)+α·{tilde over (σ)}k2(n)
- where {overscore (σ)}2 is the noise variance vector, {overscore (σ)}k2 is the noise variance for the k-th element, k is the element numerator, the time index n is in units of slot, α is a user selectable exponential forgetting factor and {tilde over (σ)}k2 is defined by σ ~ k 2 ( n ) ≡ 1 N P ∑ t = 1 N P [LeftBracketingBar] y _ k ( t ) G ( t ) · ( s ( t ) ) * [RightBracketingBar] 2 - [LeftBracketingBar] 1 N P ∑ t = 1 N P y _ k ( t ) G ( t ) · ( s ( t ) ) * [RightBracketingBar] 2
- where Np is the number of pilot symbols per slot, G(t) is an automatic gain control (AGC) level and {overscore (y)}k(t) is the k-th element of the vector {overscore (y)}(t), s(t) is a sequence of independent and identically distributed random variables of data symbols with time varying, a priori symbol probabilities and (·)* denotes the conjugate of the bracketed expression.
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Dependencies are inferred from the claim text.