DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 and 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki et al. (US 7418056 B2, hereinafter Suzuki) in view of Bai (US 8564368 B1, hereinafter Bai) and Copeland (US 8046199 B2, hereinafter Copeland) and in further view of BAI (US 8957729 B2, hereinafter BAI)
Regarding Claim 1, Suzuki teaches
A method for operating a digital pre-distorter, DPD, (100) for a non-linear electronic device (140)
(Suzuki [Col. 3, lines 22-24] a digital predistorter using a power series model to compensate for nonlinear distortion of a power amplifier is provided.)
(Suzuki (A digital pre-distorter [Fig. 3, 302], controller [Fig 5, 326] non-linear electronic device [Fig. 3,310], processing circuitry [Fig. 3, 324]))
the method being performed by a DPD controller (200), the method comprising:
receiving (S 102) an input signal destined to be input to the non-linear electronic device (140);
(Suzuki [Col. 5, lines 39-40] The digital predistorter 302 receives a digital signal to be transmitted (referred to as a "digital transmission signal"))
providing (S108) the output signal as input to the non-linear electronic device (140).
(Suzuki [Col. 7, lines 58-67 and Col. 8, lines 1-10] the actual output signal of the digital predistorter 302 contains the inphase component yi(m) and the quadrature component yq(m). The output of the predistorter 302 (containing the in phase and quadrature components) is then converted to a modulation signal y(t), which is expressed as y(t)=yi(m)cos(2.pi.ft)-yq(m)sin(2.pi.ft). (7) If the modulated signal y(t) is input to the power amplifier 310,
the output z(t) of the power amplifier 310 is expressed as
(43) .function..times..function..times..function..infin..times..times..times..- times..times..function. ##EQU00002## which is a power series of the input signal y(t). The i-th order distortion component is expressed as the i-th order term of the power series polynomial (8). The coefficient bi of the term represents the contribution of the i-th distortion component.)
But Suzuki does not teach
selecting (S 104) a basis function, BF, that represent non-linear input-output characteristics of the non-linear electronic device (140),
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms,
polynomial cross-terms,
memory cross-terms, and
common tap delays shared by all polynomial terms;
obtaining (S106) an output signal by subjecting the input signal to a linearization function defined by the basis function;
However, Bai in view of Suzuki teaches
selecting (S 104) a basis function, BF, that represent non-linear input-output characteristics of the non-linear electronic device (140),
(Bai [Col. 3, lines 40-46] One approach to modeling a distortion function, referred to herein as the polynomial approach, is to represent the distortion function as a set of less complicated basis functions and compute the output of the distortion function as the weighted sum of the basis functions. The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
obtaining (S106) an output signal by subjecting the input signal to a linearization function defined by the basis function;
(Bai [Col. 1, lines 57-59] Exemplary embodiment of the invention comprise methods of predistorting an input signal to an electronic device that operates on an input signal to generate an output signal.
[Col. 3, lines 44-46] The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki and by incorporating Bai to have a digital predistorter to have an input signal, providing the output signal to a non-linear electronic device as an input and utilizing basis function in the process.
The motivation of doing so would have enabled the linearization of the output signal defined by basis function.
But the combination of Suzuki and Bai do not teach
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms,
polynomial cross-terms,
memory cross-terms, and
common tap delays shared by all polynomial terms;
However, Copeland in view of Suzuki and Bai teaches
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms, polynomial cross-terms,
(Copeland [Col. 19, lines 22-28] Volterra filters are used in the art for modeling and analysis of non-linear systems. The use of Volterra series expansions to address polynomial non-linear systems is described in further detail in the book "Polynomial Signal Processing", by V. John Mathews and Giovanni L. Sicuranza, Copyright 2000, John Wiley and Sons, and in particular Chapter 2, entitled "Volterra Series Expansions", pages 19-63.
[Col. 10, lines 37-40] the summers each combining cross terms of the real and complex filter stages and the equalizer having programmable weights to equalize imbalances in the real and complex signals.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki and Bai and by incorporating Copeland to have utilized runed Volterra-series basis function.
The motivation of doing so would have enabled the system to capture most important non-linear and memory-related effects without carrying the full complexity of an unpruned model.
But the combination of Suzuki, Bai and Copeland do not teach
memory cross-terms, and common tap delays shared by all polynomial terms;
But BAI in view of Suzuki, Bai and Copeland teach
memory cross-terms, and common tap delays shared by all polynomial terms;
(BAI [Col. 7, lines 52-56] Memory effects, i.e., the dependence of an output signal on prior states of the input signal as well as on the present state, can also be incorporated into a distortion function. FIG. 7 is a block diagram of an exemplary non-linear distortion model 700 that includes memory.
[Col. 8, lines 3-5] The memory models in memory part 720 may have any of a variety of structures. One possible structure, a tapped delay line model with unit delays, is illustrated in FIG. 8.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai and Copeland and by further incorporating BAI to have memory cross terms shared tap delays in all polynomial terms.
The motivation of doing so would have enabled the system to select the key kernels that improve linearization.
Regarding Claim 2, the combination of Suzuki, Bai, Copeland and BAI teach the method of claim 1 and BAI further teaches
wherein polynomial orders of the higher- than-order-1 polynomial terms depend on statistics of the input signal
and/or the non-linear input- output characteristics of the non-linear electronic device (140).
(BAI [Col. 4, lines 36-40] As seen in the pre-distortion system 100 of FIG. 1, an input signal x(n) is input to the pre-distorter 110. The pre-distorter 110 pre-distorts the input signal x(n) to compensate for the distortion introduced by the power amplifier 120 when the power amplifier 120 is operated in its non-linear range.)
(Note: Claim language requires examiner to find reference for at least one limitation. Examiner has elaborated one and that is considered to be sufficient.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland and by incorporating BAI for the system to have higher order terms.
The motivation of doing so would have enabled the system to select the key kernels that improve linearization.
Regarding Claim 3, the combination of Suzuki, Bai, Copeland and BAI teach the method of claim 1 and BAI further teaches
wherein the tap delays are selected using a block orthogonal matching pursuit, block-OMP, algorithm.
(BAI [Col. 7, lines 25-44] function..function..function..times..times..times..function..function..t- imes. ##EQU00002## where the subscript, `ORTHO,k` of the tap function f.sub.ORTHO,k(x(n)) denotes an orthogonal basis function of the k-th order.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland and by incorporating BAI to select tap delays.
The motivation of doing so is to reduce parameter burden and simplifies implementation
Regarding Claim 4 and as applied to Claim 3, BAI further teaches
wherein the tap delays are selected from a set of available candidate tap delays.
(BAI [Col. 9, lines 43-50] Each of the distortion models in FIG. 3-7 includes a set of taps, or data samples, that are weighted and summed to form the "desired" distortion signal d(n))
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland and by incorporating BAI to select tap delays from a set of tap delays.
The motivation of doing so is to reduce parameter burden and simplifies implementation
Regarding Claim 5 and as applied to Claim 4, BAI further teaches
wherein the set of available candidate tap delays is represented by a matrix, and wherein the selected tap delays represent a submatrix extracted from the matrix.
(BAI [Col. 11, lines 22-24] Also, given a tapped delay line structure for the memory model, consecutive input data samples are directly used to create the matrix U that is used for coefficient evaluations.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland and by incorporating BAI to have tap delays in matrix.
The motivation of doing so is to reduce parameter burden and simplifies implementation
Regarding Claim 6, the combination of Suzuki, Bai, Copeland and BAI teach the method of claim 1 and Bai further teaches
wherein for a memory length M, the basis function is defined by: (n) , where:
- Ai) )x*q2 (n - A)xp2 (n -(n - Di)xP1(n - Di),
where x(n) denotes the input signal, where y(n) denotes the output signal, where Ai denotes address delay for filter tap i, where Di denotes data delay for the filter tap i, where q1, q2, P1, and P2 denote polynomial orders, where Tf(Ix(n - Ai)I) represents the f:th complex function with respect to Ix(n - Ai)|, and where x* denotes complex conjugate of x.
(Bai [ Col. 3 lines 63 to Col. 4 lines 25] In Zhu, Anding, Open-Loop Digital Predistorter for RF Power Amplifiers Using Dynamic Deviation Reduction-Based Volterra Series, IEEE Transactions on Microwave Theory and Techniques, Vol. 56, No. 7, July 2008, the V-DDR approach is applied to a digital predistorter. When the dynamic order is limited to the first order, the Volterra series model for a digital predistorter can be expressed as:
.function..times..times..times..function..times..function..times..times..- function..times..times..times..function..times..times..times..times..funct- ion..times..function. ##EQU00001## where {tilde over (x)}(n) and (n) are the original input and output of the predistorter respectively.
The V-DDR approach represented by Equation (0.1) can be modified as follows:
.function..times..times..times..function..times..function..times..times..- function..times..times..function..times..times..times..times..function..fu- nction..times..function. ##EQU00002## The modifications made to Equation (0.1) to arrive at Equation (0.2) include:
1. The order of summations is reversed
2. The coefficient {tilde over (g)}.sub.2k+1,2=0
3. Substitute
.function..function..times..function..function. ##EQU00003##)
(Note: For one of the ordinary skill in the art it would have been obvious that the claim function and the reference are citing Volterra series expansion functions and are essentially realized/rendered to similar conclusion.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Copeland, BAI and by further incorporating Bai to have the equation as stated.
The motivation of doing so would have enabled to have a model reduction strategy balancing coverage and complexity
Regarding Claim 7, the combination of Suzuki, Bai, Copeland and BAI teach the method of claim 1 and Bai further teaches
wherein the DPD (100) comprises a predistortion block (120) and an adaptation block (180), and wherein coefficients of the basis function are determined and converted into look-up tables, LUTs, in the adaptation block (180).
(Bai [Col. 3, lines 6-7] The adaptation circuit 60 may be used to adapt the digital predistorter 40.
[Col. 5, lines 60-61] It is generally desirable to implement a digital predistorter using look-up tables (LUTs))
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Copeland, BAI and by further incorporating Bai to have adaptation block and look up table.
The motivation of doing so would have enabled the system to have the capacity to simplify implementation.
Regarding Claim 8 and as applied to Claim 7, Bai further teaches
wherein the input signal is subjected to the linearization function in the predistortion block (120), and wherein the LUTs are made accessible to, and used by, the predistortion block (120) when subjecting the input signal to the linearization function.
(Bai [Col. 7, lines 9-12] FIG. 5 illustrates an exemplary method 300 for predistorting an input signal according to one embodiment of the invention. A first non-linear component function is applied input signal to generate a first component signal (block 310).
[Col. 6, lines 63-65] FIG. 4 illustrates an LUT unit 260 for the embodiment illustrated in FIG. 3. The LUT unit 260 may be used to implement the LUT units 215, 225 shown in FIG. 3.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Copeland, BAI and by further incorporating Bai to make look up table (LUT) accessible.
The motivation of doing so would have enabled the system to have the capacity to simplify implementation.
Regarding Claim 15, Suzuki teaches
A digital pre-distorter, DPD, controller (200) for operating a DPD (100) for a non-linear electronic device (140), the DPD controller (200) comprising processing circuitry (210) (Suzuki [Col. 3, lines 22-24] a digital predistorter using a power series model to compensate for nonlinear distortion of a power amplifier is provided.)
(Suzuki (A digital pre-distorter [Fig. 3, 302], controller [Fig 5, 326] non-linear electronic device [Fig. 3,310], processing circuitry [Fig. 3, 324]))
the processing circuitry being configured to cause the DPD controller (200) to:
receiving (S 102) an input signal destined to be input to the non-linear electronic device (140);
(Suzuki [Col. 5, lines 39-40] The digital predistorter 302 receives a digital signal to be transmitted (referred to as a "digital transmission signal"))
providing (S108) the output signal as input to the non-linear electronic device (140).
(Suzuki [Col. 7, lines 58-67 and Col. 8, lines 1-10] the actual output signal of the digital predistorter 302 contains the inphase component yi(m) and the quadrature component yq(m). The output of the predistorter 302 (containing the in phase and quadrature components) is then converted to a modulation signal y(t), which is expressed as y(t)=yi(m)cos(2.pi.ft)-yq(m)sin(2.pi.ft). (7) If the modulated signal y(t) is input to the power amplifier 310,
the output z(t) of the power amplifier 310 is expressed as
(43) .function..times..function..times..function..infin..times..times..times..- times..times..function. ##EQU00002## which is a power series of the input signal y(t). The i-th order distortion component is expressed as the i-th order term of the power series polynomial (8). The coefficient bi of the term represents the contribution of the i-th distortion component.)
But Suzuki does not teach
selecting (S 104) a basis function, BF, that represent non-linear input-output characteristics of the non-linear electronic device (140),
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms,
polynomial cross-terms,
memory cross-terms, and
common tap delays shared by all polynomial terms;
obtaining (S106) an output signal by subjecting the input signal to a linearization function defined by the basis function;
However, Bai in view of Suzuki teaches
selecting (S 104) a basis function, BF, that represent non-linear input-output characteristics of the non-linear electronic device (140),
(Bai [Col. 3, lines 40-46] One approach to modeling a distortion function, referred to herein as the polynomial approach, is to represent the distortion function as a set of less complicated basis functions and compute the output of the distortion function as the weighted sum of the basis functions. The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
obtaining (S106) an output signal by subjecting the input signal to a linearization function defined by the basis function;
(Bai [Col. 1, lines 57-59] Exemplary embodiment of the invention comprise methods of predistorting an input signal to an electronic device that operates on an input signal to generate an output signal.
[Col. 3, lines 44-46] The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki and by incorporating Bai to have a digital predistorter to have an input signal, providing the output signal to a non-linear electronic device as an input and utilizing basis function in the process.
The motivation of doing so would have enabled the linearization of the output signal defined by basis function.
But the combination of Suzuki and Bai do not teach
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms,
polynomial cross-terms,
memory cross-terms, and
common tap delays shared by all polynomial terms;
However, Copeland in view of Suzuki and Bai teaches
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms, polynomial cross-terms,
(Copeland [Col. 19, lines 22-28] Volterra filters are used in the art for modeling and analysis of non-linear systems. The use of Volterra series expansions to address polynomial non-linear systems is described in further detail in the book "Polynomial Signal Processing", by V. John Mathews and Giovanni L. Sicuranza, Copyright 2000, John Wiley and Sons, and in particular Chapter 2, entitled "Volterra Series Expansions", pages 19-63.
[Col. 10, lines 37-40] the summers each combining cross terms of the real and complex filter stages and the equalizer having programmable weights to equalize imbalances in the real and complex signals.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki and Bai and by incorporating Copeland to have utilized runed Volterra-series basis function.
The motivation of doing so would have enabled the system to capture most important non-linear and memory-related effects without carrying the full complexity of an unpruned model.
But the combination of Suzuki, Bai and Copeland do not teach
memory cross-terms, and common tap delays shared by all polynomial terms;
But BAI in view of Suzuki, Bai and Copeland teach
memory cross-terms, and common tap delays shared by all polynomial terms;
(BAI [Col. 7, lines 52-56] Memory effects, i.e., the dependence of an output signal on prior states of the input signal as well as on the present state, can also be incorporated into a distortion function. FIG. 7 is a block diagram of an exemplary non-linear distortion model 700 that includes memory.
[Col. 8, lines 3-5] The memory models in memory part 720 may have any of a variety of structures. One possible structure, a tapped delay line model with unit delays, is illustrated in FIG. 8.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai and Copeland and by further incorporating BAI to have memory cross terms shared tap delays in all polynomial terms.
The motivation of doing so would have enabled the system to select the key kernels that improve linearization.
Regarding Claim 16 and as applied to Claim 15, Suzuki teaches
a receive module (210a) configured to perform the step of receiving the receive an input signal destined to be input to the non-linear electronic device (140);
(Suzuki [Col. 5, lines 39-40] The digital predistorter 302 receives a digital signal to be transmitted (referred to as a "digital transmission signal"))
(Suzuki (a receive module [Fig. 3, 324])
a provide module (210d) configured to perform the step of providing provide the output signal as input to the non-linear electronic device (140).
(Suzuki [Col. 7, lines 58-67 and Col. 8, lines 1-10 and Fig. 3] the actual output signal of the digital predistorter 302 contains the inphase component yi(m) and the quadrature component yq(m). The output of the predistorter 302 (containing the in phase and quadrature components) is then converted to a modulation signal y(t), which is expressed as y(t)=yi(m)cos(2.pi.ft)-yq(m)sin(2.pi.ft). (7) If the modulated signal y(t) is input to the power amplifier 310,
the output z(t) of the power amplifier 310 is expressed as
(43) .function..times..function..times..function..infin..times..times..times..- times..times..function. ##EQU00002## which is a power series of the input signal y(t). The i-th order distortion component is expressed as the i-th order term of the power series polynomial (8). The coefficient bi of the term represents the contribution of the i-th distortion component.)
But Suzuki does not teach
a select module (210b) configured to perform the step of selecting the BF
an obtain module (210c) configured to perform the step of obtaining the obtain an output signal by subjecting the input signal to a linearization function defined by the basis function;
However, Bai in view of Suzuki teaches
a select module (210b) configured to perform the step of selecting the BF
(Bai [Col. 3, lines 40-46 and Fig.4 and Fig. 5] One approach to modeling a distortion function, referred to herein as the polynomial approach, is to represent the distortion function as a set of less complicated basis functions and compute the output of the distortion function as the weighted sum of the basis functions. The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
an obtain module (210c) configured to perform the step of obtaining the
(Bai [Col. 1, lines 57-59] Exemplary embodiment of the invention comprise methods of predistorting an input signal to an electronic device that operates on an input signal to generate an output signal.
[Col. 3, lines 44-46] The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki and by incorporating Bai to have a digital predistorter to have an input signal, providing the output signal to a non-linear electronic device as an input and utilizing basis function in the process.
The motivation of doing so would have enabled the linearization of the output signal defined by basis function.
Regarding Claim 17, the combination of Suzuki, Bai, Copeland and BAI teach the method of claim 15 and BAI further teaches
wherein polynomial orders of the higher- than-order-1 polynomial terms depend on statistics of the input signal
and/or the non-linear input- output characteristics of the non-linear electronic device (140).
(BAI [Col. 4, lines 36-40] As seen in the pre-distortion system 100 of FIG. 1, an input signal x(n) is input to the pre-distorter 110. The pre-distorter 110 pre-distorts the input signal x(n) to compensate for the distortion introduced by the power amplifier 120 when the power amplifier 120 is operated in its non-linear range.)
(Note: Claim language requires examiner to find reference for at least one limitation. Examiner has elaborated one and that is considered to be sufficient.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland and by incorporating BAI for the system to have higher order terms.
The motivation of doing so would have enabled the system to select the key kernels that improve linearization.
Regarding Claim 18 and as applied to Claim 15, Suzuki teaches
A computer program (1020) for operating a digital pre-distorter, DPD, (100) for a non-linear electronic device (140), the computer program comprising computer code which, when run on processing circuitry (210) of a DPD controller (200),
(Suzuki [Col. 3, lines 22-24] a digital predistorter using a power series model to compensate for nonlinear distortion of a power amplifier is provided.)
(Suzuki (A digital pre-distorter [Fig. 3, 302], controller [Fig 5, 326] non-linear electronic device [Fig. 3,310], processing circuitry [Fig. 3, 324]))
causes the DPD controller (200) to:receive (S 102) an input signal destined to be input to the non-linear electronic device (140);
(Suzuki [Col. 5, lines 39-40] The digital predistorter 302 receives a digital signal to be transmitted (referred to as a "digital transmission signal"))
prove(S108) the output signal as input to the non-linear electronic device (140).
(Suzuki [Col. 7, lines 58-67 and Col. 8, lines 1-10] the actual output signal of the digital predistorter 302 contains the inphase component yi(m) and the quadrature component yq(m). The output of the predistorter 302 (containing the in phase and quadrature components) is then converted to a modulation signal y(t), which is expressed as y(t)=yi(m)cos(2.pi.ft)-yq(m)sin(2.pi.ft). (7) If the modulated signal y(t) is input to the power amplifier 310,
the output z(t) of the power amplifier 310 is expressed as
(43) .function..times..function..times..function..infin..times..times..times..- times..times..function. ##EQU00002## which is a power series of the input signal y(t). The i-th order distortion component is expressed as the i-th order term of the power series polynomial (8). The coefficient bi of the term represents the contribution of the i-th distortion component.)
But Suzuki does not teach
A computer program (1020)
the computer program comprising computer code
select (S 104) a basis function, BF, that represent non-linear input-output characteristics of the non-linear electronic device (140),
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms,
polynomial cross-terms,
memory cross-terms, and
common tap delays shared by all polynomial terms;
obtain (S106) an output signal by subjecting the input signal to a linearization function defined by the basis function;
However, Bai in view of Suzuki teaches
select (S 104) a basis function, BF, that represent non-linear input-output characteristics of the non-linear electronic device (140),
(Bai [Col. 3, lines 40-46] One approach to modeling a distortion function, referred to herein as the polynomial approach, is to represent the distortion function as a set of less complicated basis functions and compute the output of the distortion function as the weighted sum of the basis functions. The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
obtain (S106) an output signal by subjecting the input signal to a linearization function defined by the basis function;
(Bai [Col. 1, lines 57-59] Exemplary embodiment of the invention comprise methods of predistorting an input signal to an electronic device that operates on an input signal to generate an output signal.
[Col. 3, lines 44-46] The set of basis functions used to model the distortion function is referred to herein as the basis function set.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki and by incorporating Bai to have a digital predistorter to have an input signal, providing the output signal to a non-linear electronic device as an input and utilizing basis function in the process.
The motivation of doing so would have enabled the linearization of the output signal defined by basis function.
But the combination of Suzuki and Bai do not teach
A computer program (1020)
the computer program comprising computer code
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms,
polynomial cross-terms,
memory cross-terms, and
common tap delays shared by all polynomial terms;
However, Copeland in view of Suzuki and Bai teaches
wherein the basis function is defined by kernels of a pruned Volterra series, VS, and
comprises higher-than-order-1 polynomial terms, polynomial cross-terms,
(Copeland [Col. 19, lines 22-28] Volterra filters are used in the art for modeling and analysis of non-linear systems. The use of Volterra series expansions to address polynomial non-linear systems is described in further detail in the book "Polynomial Signal Processing", by V. John Mathews and Giovanni L. Sicuranza, Copyright 2000, John Wiley and Sons, and in particular Chapter 2, entitled "Volterra Series Expansions", pages 19-63.
[Col. 10, lines 37-40] the summers each combining cross terms of the real and complex filter stages and the equalizer having programmable weights to equalize imbalances in the real and complex signals.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki and Bai and by incorporating Copeland to have utilized runed Volterra-series basis function.
The motivation of doing so would have enabled the system to capture most important non-linear and memory-related effects without carrying the full complexity of an unpruned model.
But the combination of Suzuki, Bai and Copeland do not teach
memory cross-terms, and common tap delays shared by all polynomial terms;
But BAI in view of Suzuki, Bai and Copeland teach
A computer program (1020)
(BAI [Col. 14, lines 35-36] The present invention can also be embedded in a computer program product.)
the computer program comprising computer code
(BAI [Col. 14, lines 41-44] Computer program or application in the present context means any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function
memory cross-terms, and common tap delays shared by all polynomial terms;
(BAI [Col. 7, lines 52-56] Memory effects, i.e., the dependence of an output signal on prior states of the input signal as well as on the present state, can also be incorporated into a distortion function. FIG. 7 is a block diagram of an exemplary non-linear distortion model 700 that includes memory.
[Col. 8, lines 3-5] The memory models in memory part 720 may have any of a variety of structures. One possible structure, a tapped delay line model with unit delays, is illustrated in FIG. 8.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai and Copeland and by further incorporating BAI to have memory cross terms shared tap delays in all polynomial terms.
The motivation of doing so would have enabled the system to select the key kernels that improve linearization.
Regarding Claim 19, the combination of Suzuki, Bai, Copeland and BAI teach Claim 18 and BAI further teaches
A computer program product (1010) comprising a computer program (1020) according to claim 18, and a computer readable storage medium (1030) on which the computer program is stored.
(BAI [Col. 14, lines 30-36] A typical combination of hardware and software could be a specialized computer system, having one or more processing elements and a computer program stored on a storage medium that, when loaded and executed, controls the computer system such that it carries out the methods described herein. The present invention can also be embedded in a computer program product.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai and Copeland and by further incorporating BAI for the system to have computer readable storage medium on which the computer program is stored.
The motivation of doing so would have enabled the system to carry out the methods described in the invention with accuracy and with efficiency.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Suzuki et al. (US 7418056 B2, hereinafter Suzuki) in view of Bai (US 8564368 B1, hereinafter Bai), Copeland (US 8046199 B2, hereinafter Copeland) and BAI (US 8957729 B2, hereinafter BAI) and in further view of McCormick (US 11283666 B1, hereinafter McCormick et al.)
Regarding Claim 9, the combination of Suzuki, Bai, Copeland and BAI teach the method of claim 7, but does not teach
wherein the value of bin 1 in the LUT for tap delay i and nonlinear function f is given by:LUTi,f(l)=Tf(Ix(n - Ai)1) where 1 is given by a linear mapping function, l= floor(Ix(n - Ai)|/R), wherein R denotes resolution of each bin.
However, in a similar endeavor, McCormick teaches
(McCormick [Col. 23, Lines 60-67] After companding, compander 704 outputs the companded sample to each of a delay tap 706, a LUT A 708, and a LUT B 710. Delay tap 706 is configured to “delay” providing an input sample to a LUT C 712 by a certain time amount, in effect causing a previous input sample (e.g., x.sub.n-1) to be provided to LUT C 712 relative to the current input samples (e.g., x.sub.n) provided to LUT A 708 and LUT B 710. Delay tap 722 is similar to delay tap 706.
[Col. 32, lines 24-33]At receiver system 430, a sample selection technique can employ binning the received data by amplitude. For each bin, a median-amplitude exemplar sample is selected, and its value and time-index are sent on the data link to feedback data link receiver 416.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland, BAI and by further incorporating McCormic to have the binning capacity in the LUT.
The motivation of doing so would have enabled the system to have the capacity to simplify implementation.
Claims 10-11 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Suzuki et al. (US 7418056 B2, hereinafter Suzuki) in view of Bai (US 8564368 B1, hereinafter Bai), Copeland (US 8046199 B2, hereinafter Copeland) and BAI (US 8957729 B2, hereinafter BAI) and in further view of Yun (US 20220053268 A1, hereinafter Yun)
Regarding Claim 10, the combination of Suzuki, Bai, Copeland and BAI teach the method of claim 1, but does not teach
wherein coefficients of the basis function are determined using a block recursive least squares, block-RLS, algorithm.
However, in a similar endeavor, Yun teaches
wherein coefficients of the basis function are determined using a block recursive least squares, block-RLS, algorithm.
(Yun [0136, line 17-21] a method for sharing required calculation values when calculating filter coefficients between sub-filters may also be applied to the following Recursive Least Square (RLS) algorithm, in addition to the Wiener filter. This will be described below with reference to FIG. 5B.
[0138, lines 1-5] when the filter coefficients of respective sub-filters are calculated using the Recursive Least Square (RLS) algorithm, a formula for updating the RLS algorithm for the j-th sub-filter is represented by the following Equation 19.
[0141, lines 7-11] by means of the structure illustrated in the drawing, calculation of the Kalman gain vector and the inverse matrix of the correlation matrix is performed only once, rather than M times, for the M sub-filters, thus reducing a computational load.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland, BAI and by further incorporating Yun to utilize recursive least square algorithm.
The motivation of doing so would have enabled efficient coefficient learning and tracking
Regarding Claim 11 and as applied to Claim 10, Yun further teaches
wherein the coefficients are a function of a gain matrix
(Yun [0141, lines 7-11] by means of the structure illustrated in the drawing, calculation of the Kalman gain vector.)
wherein determining the gain matrix involves performing a matrix inversion of a matrix V'.
(Yun [0139] Here, k.sub.j(n) is the Kalman gain vector of the j-th sub-filter, and R.sub.j.sup.−1(n) may be the inverse matrix of the correlation matrix for the j-th sub-filter.
[0141, lines 9-11] the inverse matrix of the correlation matrix is performed only once.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland, BAI and by further incorporating Yun to allocate the criteria of gain matrix.
The motivation of doing so would have converted the model into a practical low-complexity implementation
Regarding Claim 13 and as applied to Claim 10, Yun further teaches
wherein the matrix inversion is recursively performed.
(Yun [0141, lines 7-11] by means of the structure illustrated in the drawing, calculation of the Kalman gain vector and the inverse matrix of the correlation matrix is performed only once, rather than M times, for the M sub-filters, thus reducing a computational load.)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland, BAI and by further incorporating Yun for the matrix inversion to be performed recursively.
The motivation of doing so would have converted the model into a practical low-complexity implementation
Regarding Claim 14 and as applied to Claim 10, Suzuki further teaches
wherein all but the diagonal entries of the matrix V' are set to zero when the matrix inversion is performed.
(Suzuki [Col. 13, lines 52-55] the initial value may be set to zero at the beginning, using an algorithm that can learn to find the appropriate initial value through the running of the algorithm.)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Suzuki et al. (US 7418056 B2, hereinafter Suzuki) in view of Bai (US 8564368 B1, hereinafter Bai), Copeland (US 8046199 B2, hereinafter Copeland), BAI (US 8957729 B2, hereinafter BAI) and Yun (US 20220053268 A1, hereinafter Yun) in further view of McCormick (US 11283666 B1, hereinafter McCormick et al.)
Regarding Claim 12, combination of the combination of Suzuki, Bai, Copeland, BAI and Yun teach the method of claim 1, but do not teach
wherein a regularization term is added to all diagonal entries of the matrix V' before the matrix inversion is performed.
However, in a similar endeavor, McCormic teaches,
wherein a regularization term is added to all diagonal entries of the matrix V' before the matrix inversion is performed.
(McCormik [Col. 32, lines 52-55] In some embodiments, due to possibly very noisy output measurements, modelling robustness for forward modelling can be improved by solving a generalized Tikhonov regularization problem, at a block 1212:
McCormik [Col. 32, lines 66 to Col. 33, lines 2] The following tri-diagonal Q matrix (referred to as a “second-difference matrix”) which penalizes change between adjacent entries in the look-up table solutions)
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the examined application to have modified Suzuki, Bai, Copeland, BAI, Yun and by further incorporating McCormic to add a regularization term to all diagonal matrix.
The motivation of doing so would have converted the model into a practical low-complexity implementation.
Conclusion
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/RANA H MAHMUD/Examiner, Art Unit 2644
/KATHY W WANG-HURST/Supervisory Patent Examiner, Art Unit 2644
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