Patent Grant
10992326
The present disclosure relates to approaches for regular/continuous adaptation of parameterized digital predistorter (compensator).
Power amplifiers, especially those used to transmit radio frequency communications, generally have nonlinear characteristics. For example, as a power amplifier's output power approaches its maximum rated output, nonlinear distortion of the output occurs. One way of compensating for the nonlinear characteristics of power amplifiers is to ‘predistort’ an input signal (e.g., by adding an ‘inverse distortion’ to the input signal) to negate the nonlinearity of the power amplifier before providing the input signal to the power amplifier. The resulting output of the power amplifier is a linear amplification of the input signal with reduced nonlinear distortion. Digital predistorted devices (comprising, for example, power amplifiers) are relatively inexpensive and power efficient. These properties make digital predistorted power amplifiers attractive for use in telecommunication systems where amplifiers are required to inexpensively, efficiently, and accurately reproduce the signal present at their input. However, the computation effort associated with conventional digital predistortion (DPD) adaptation processes is substantial, which adversely effects the predistortion accuracy and robustness due to the limits the computational efforts may place on the number and speed at which DPD coefficients can be derived.
Adaptive optimization of digital predistortion (DPD) parameters (e.g., coefficients applied to a set of predetermined basis functions) can result in a significant computational undertaking that can tax the resources of a computing system, slow down processes, and affect the quality of optimization. In some embodiments described herein, processes to approximate DPD parameters in an efficient way that relies on efficient management of a buffer holding samples used in DPD parameter determination processes are disclosed. The buffer management operations do not require that input/output samples (needed for the DPD parameter determination processes) be stored based on some known schedule. Rather, the buffer (or a portion thereof) may be populated with input and output samples according to a process that is independent of the processes used to perform the actual computation of DPD parameters/coefficients (e.g., replacing content of the buffer(s) may be performed at time instances determined independently of a process to select the samples that are used to compute the DPD parameters/coefficients). As such, the buffer may be updated according to any type of scheduling process (e.g., when new samples become available, according to a pseudo-random schedule, etc.)
The DPD parameter determination processes of some of the embodiments described herein also rely on a stochastic gradient approach in which samples in the buffer (populated according to whichever scheduling approach is used) are selected according a pseudo-random process that selects buffer locations (storing corresponding input and output samples) independently of when the buffer's content gets replaced (in accordance with the buffer replacement scheduling process implemented). The realized buffer management and control approaches (a buffer content replacement process that is independent of the buffer location selection process) used in conjunction with the stochastic gradient approaches of some of the described embodiments result in a computationally efficient approach for computing DPD coefficient approximations.
In a general aspect, an adaptation module of a linearization system performs regular updates of DPD coefficients based on a stochastic subgradient approach that uses approximation of gradient error function values, with such approximations computed based on pseudo-randomly selection of observed output samples of the linearization system (and their corresponding input samples) stored in a buffer, and based on an independent replacement process to replace at least some of samples stored in the buffer.
In some variations, a method for digital compensation is provided that includes selecting at least one location of a selection buffer, the selection buffer storing a plurality of observed output samples obtained from one or more of output batches produced by a signal chain, according to a pseudo-random buffer selection process, and accessing at least one observed output sample, with the at least one observed output sample being generated in response to corresponding one or more input samples provided to a digital compensator applying to one or more input batches a plurality of basis functions, weighed by compensator coefficients, to produce intermediary samples, and with the one or more intermediary samples provided to the signal chain. The method further includes computing an approximation of a sub-gradient value computed according to a stochastic gradient process to derive a sub-gradient of an error function representing a relationship between the accessed at least one observed output sample stored in the pseudo randomly selected at least one location of the selection buffer, and at least one of, for example, the corresponding one or more or more input samples, and/or one or more intermediate samples, produced by the digital compensator, corresponding to the pseudo randomly selected at least one location of the selection buffer storing the at least one observed output sample. The method additionally includes computing updated values for the compensator coefficients based on current values of the compensator coefficients and the computed approximation of the sub-gradient value computed according to the sub-gradient of the error function, and replacing, at a time instance determined independently of the pseudo-random buffer selection process, content of at least a portion of the selection buffer with different one or more observed output samples responsive to one or more other input samples.
Embodiments of the method include at least some of the features described in the present disclosure, as well as one or more of the following features.
Computing the updated values for the compensator coefficients may include computing the updated values for the compensator coefficients based on a sum of the current values of the compensator coefficients and a product of a pre-determined multiple, representative of a step size, and the approximation of the sub-gradient value computed according to the sub-gradient of the error function.
Computing the updated values for the compensator coefficients may include deriving the updated values for the compensator coefficients according to the relationship
{tilde over (x)}k+1={tilde over (x)}k+αa[τk]′(e[τk]−a[τk]{tilde over (x)}k)−αρ{tilde over (x)}k, with {tilde over (x)}=0,
where zk are the current values of the compensator coefficients, {tilde over (x)}k+1 are the updated compensator coefficients, τ1, τ2, . . . , τn are independent random variables uniformly distributed on T={t}, a[τ] represents a matrix of values resulting from application of the basis functions at time τ to the one or more input samples corresponding to the pseudo randomly selected observed output sample, e[τ] is input/output mismatch in the observed data between an output of an observer receiver and the output of the digital compensator, ρ>0 is a regularization constant, and α>0 is a value representative of the step size, such that α(ρ+|a[t]|2)<A for every t∈T, where A is some pre-specified constant value.
The method may further include adjusting the value of a representative of the step size based on a measure of progress of the stochastic gradient process, with the measure of progress being determined by comparing an average values of |e[τk]| and |e[τk]−a[τk]{tilde over (x)}k|.
The method may further include repeating the pseudo-random buffer selection process to select a subsequent at least one buffer location to access a different at least one observed output sample from the selection buffer, and computing updated values for the compensator coefficients based, at least in part, on the different at least one observed output sample.
Replacing, at the time instance determined independently of the pseudo-random buffer selection process, the content of the at least the portion of the selection buffer may include replacing the content of the at least the portion of the selection buffer based on one or more of, for example, i) receipt of a replacement signal indicating availability of the different one or more replacement observed output samples responsive to the one or more other input samples, ii) scheduled arrival of the different one or more replacement observed output samples responsive to the one or more other input samples, and/or iii) unscheduled arrival at an arbitrary time instance of the different one or more replacement observed output samples responsive to the one or more other input samples.
Replacing the at least the portion of the selection buffer may include flushing the at least the portion of the selection buffer storing the plurality of observed output samples, and filling the at least the portion of the flushed selection buffer with the different one or more replacement observed output samples responsive to the one or more other input samples.
Replacing the at least the portion of the selection buffer may include replacing according to the pseudo-random replacement process the entire selection buffer storing the plurality of observed output samples, with a different set of observed output samples, corresponding to a different set of input samples provided to the digital compensator.
The method may further include one or more of, for example, communicating the output signal of the signal chain to a remote device via one or more of, for example, a wired communications interface and/or a wireless communication interface, and/or receiving the input signal via one or more of, for example, the wired communications interface and/or the wireless communication interface.
In some variations, a linearization system is provided that includes a signal chain comprising at least one analog electronic amplification element, with the signal chain producing output signals including non-linear distortions, a digital compensator to compensate for non-linear behavior of the signal chain by applying a plurality of basis functions weighed by compensator coefficients to input signals to the compensator resulting in intermediary samples provided as input to the signal chain, an observation receiver to produce observed output samples of the output signals produced by the signal chain, a selection buffer to store a plurality of the observed output samples, and an adaptation module. The adaptation module is configured to select at least one location of the selection buffer, according to a pseudo-random buffer selection process, and access at least one observed output sample. The adaptation module is further configured to compute an approximation of a sub-gradient value computed according to a stochastic gradient process to derive a sub-gradient of an error function representing a relationship between the accessed at least one observed output sample stored in the pseudo randomly selected at least one location of the selection buffer, and at least one of, for example, corresponding one or more or more input samples, and/or corresponding one or more intermediate samples produced by the digital compensator in response to the one or more input samples. The adaptation module is additionally configured to compute updated values for the compensator coefficients based on current values of the compensator coefficients and the computed approximation of the sub-gradient value computed according to the sub-gradient of the error function, and replace, at a time instance determined independently of the pseudo-random buffer selection process, content of at least a portion of the selection buffer with different one or more observed output samples responsive to one or more other input samples.
In some variations, a non-transitory machine-readable medium is provided that stores a design structure comprising elements that, when processed in a computer-aided design system, generate a machine-executable representation of a linearization system that is used to fabricate hardware comprising a selecting circuit to select at least one location of a selection buffer, the selection buffer storing a plurality of observed output samples obtained from one or more of output batches produced by a signal chain, according to a pseudo-random buffer selection process, and access at least one observed output sample, with the at least one observed output sample being generated in response to corresponding one or more input samples provided to a digital compensator applying to one or more input batches a plurality of basis functions, weighed by compensator coefficients, to produce intermediary samples, and with the one or more intermediary samples provided to the signal chain. The linearization system further includes a computing circuit to compute an approximation of a sub-gradient value computed according to a stochastic gradient process to derive a sub-gradient of an error function representing a relationship between the accessed at least one observed output sample stored in the pseudo randomly selected at least one location of the selection buffer, and at least one of, for example, the corresponding one or more or more input samples, and/or one or more intermediate samples, produced by the digital compensator, corresponding to the pseudo randomly selected at least one location of the selection buffer storing the at least one observed output sample. The linearization system additionally includes a second computing circuit to compute updated values for the compensator coefficients based on current values of the compensator coefficients and the computed approximation of the sub-gradient value computed according to the sub-gradient of the error function, and a replacing circuit to replace, at a time instance determined independently of the pseudo-random buffer selection process, content of at least a portion of the selection buffer with different one or more observed output samples responsive to one or more other input samples.
In some variations, a non-transitory computer readable media is provided, that is programmed with instructions, executable on a processor, to select at least one location of a selection buffer, the selection buffer storing a plurality of observed output samples obtained from one or more of output batches produced by a signal chain, according to a pseudo-random buffer selection process, and access at least one observed output sample, with the at least one observed output sample being generated in response to corresponding one or more input samples provided to a digital compensator applying to one or more input batches a plurality of basis functions, weighed by compensator coefficients, to produce intermediary samples, and with the one or more intermediary samples provided to the signal chain. The instructions include one or more instructions to compute an approximation of a sub-gradient value computed according to a stochastic gradient process to derive a sub-gradient of an error function representing a relationship between the accessed at least one observed output sample stored in the pseudo randomly selected at least one location of the selection buffer, and at least one of, for example, the corresponding one or more or more input samples, and/or one or more intermediate samples, produced by the digital compensator, corresponding to the pseudo randomly selected at least one location of the selection buffer storing the at least one observed output sample, The instructions include additional one or more instructions to compute updated values for the compensator coefficients based on current values of the compensator coefficients and the computed approximation of the sub-gradient value computed according to the sub-gradient of the error function, and replace, at a time instance determined independently of the pseudo-random buffer selection process, content of at least a portion of the selection buffer with different one or more observed output samples responsive to one or more other input samples.
Embodiments of the linearization system, the design structure, and/or the computer readable media may include at least some of the features described in the present disclosure, including any of the above method features.
In some variations, another method for digital compensation of a signal chain comprising at least one analog electronic amplification element, is provided that includes storing, at a selection buffer, a plurality of observed output samples of an output signal produced by the signal chain in response to a received input signal, the output signal comprising a plurality of output batches of the output signal, each batch of the output signal corresponding to a different one or more batches of the input signal, with the output signal produced by the signal chain including non-linear distortions. The method also includes deriving, using a stochastic gradient process, a plurality of sets of coefficients, used by a digital compensator for respectively weighing a plurality of basis functions applied by the digital compensator to input samples of the input signal, with deriving the plurality of sets of coefficients using the stochastic gradient process including selecting, according to a pseudo-random buffer selection process, at least one location of the selection buffer to access at least one of the plurality of observed output samples stored in the selection buffer, and computing updated values for the plurality of sets of coefficients based on current intermediate values for the plurality of sets of coefficients and a sub-gradient value computed according to a sub-gradient of an error function representing a relationship between the at least one of the plurality of observed output samples at the pseudo-randomly selected location of the selection buffer, and at least one or more input data samples, or intermediary samples produced by the digital compensator, corresponding to the at least one of the plurality of observed output samples stored at the pseudo randomly selected location of the selection buffer. The deriving additionally includes repeating the selecting to select a subsequent at least one location of the selection buffer, according to the buffer selection pseudo-random process, to access a subsequent at least one observed output sample, and computing updated values for the plurality of the sets of coefficients based, at least in part, on the subsequent at least one observed output sample, and replacing, at a time instance determined independently of the pseudo-random buffer selection process, at least a portion of the plurality of observed output samples stored in the selection buffer with a different set of observed output samples, corresponding to a different set of input samples provided to the digital compensator.
Embodiments of the additional method may include at least some of the features described in the present disclosure, including any of the above method, linearization system, structure design, and computer readable medium features, as well as one or more of the following features.
Replacing, at the time instance determined independently of the pseudo-random buffer selection process, the at least the portion of observed output samples may include replacing the at least the portion of observed output samples based on one or more of, for example, i) receipt of a replacement signal indicating availability of the different set of observed output samples corresponding to the different set of input samples, ii) scheduled arrival of the different set of observed output samples corresponding to the different set of input samples, and/or iii) unscheduled at an arbitrary time instance arrival of the different set of observed output samples corresponding to the different set of input samples.
Computing the updated values for the plurality of sets of coefficients based on the current intermediate values for the plurality of the sets of coefficients and the sub-gradient value computed according to the sub-gradient of the error function may include computing the updated values for the plurality of sets of coefficients based on the current intermediate values for the plurality of sets of coefficients and a pre-determined multiple, representative of a step size, of the sub-gradient value computed according to the sub-gradient of the error function using the accessed at least one of the plurality observed output sample and the at least one or more input data samples corresponding to the at least one observed output sample.
Computing the updated values for the plurality of sets of coefficients may include deriving the updated values for the plurality of sets of coefficients according to the relationship
{tilde over (x)}k+1={tilde over (x)}k+αa[τk]′(e[τk]−a[τk]{tilde over (x)}k)−αρ{tilde over (x)}k, with {tilde over (x)}0=0,
where τ1, τ2, . . . , τn are independent random variables uniformly distributed on T={t}, a[τ] represents a matrix of values resulting from application of the basis functions at time τ to the input samples corresponding to the pseudo randomly selected observed output sample, e[τ] is input/output mismatch in the observed data between an output of an observer receiver and the output of the digital compensator, ρ>0 is a regularization constant, and α>0 is a value representative of the step size, such that α(ρ+|a[t]|2)<A for every t∈T, where A is some pre-specified constant value.
The method may further include adjusting the value of α representative of the step size based on a measure of progress of the stochastic gradient process, wherein the measure of progress is determined by comparing an average values of |e[τk]| and |e[τk]−a[τk]{tilde over (x)}k|.
The method may further include receiving the input signal via one or more of, for example, a wired communications interface and/or a wireless communication interface.
The method may further include communicating the output signal of the signal chain to a remote device via one or more of, for example, a wired communications interface, and/or a wireless communication interface.
In some variations, an additional linearization system is provided that includes a signal chain comprising at least one analog electronic amplification element, the signal chain producing output signals including non-linear distortions, a digital compensator to compensate for non-linear behavior of the signal chain by applying a plurality of basis functions weighed by compensator coefficients to input signals to the compensator resulting in intermediary samples provided as input to the signal chain, an observation receiver to produce a plurality of observed output samples of an output signal produced by the signal chain in response to a received input signal, the output signal comprising a plurality of output batches of the output signal, each batch of the output signal corresponding to a different one or more batches of the input signal, with the output signal produced by the signal chain includes non-linear distortions, a selection buffer to store the plurality of the observed output samples, and an adaptation module. The adaptation module is configured to derive, using a stochastic gradient process, the compensator coefficients, including to select, according to a pseudo-random buffer selection process, at least one location of the selection buffer to access at least one of the plurality of observed output samples stored in the selection buffer, compute updated values for the compensator coefficients based on current intermediate values for the compensator coefficients and a sub-gradient value computed according to a sub-gradient of an error function representing a relationship between the at least one of the plurality of observed output samples at the pseudo-randomly selected location of the selection buffer, and at least one or more input data samples, or one or more intermediary samples produced by the digital compensator, corresponding to the at least one of the plurality of observed output samples stored at the pseudo randomly selected location of the selection buffer. The adaptation module configured to derive the compensator coefficients is further configured to repeat the selecting to select a subsequent at least one location of the selection buffer, according to the buffer selection pseudo-random process, to access a subsequent at least one of the plurality of observed output samples, and compute updated values for the plurality of the sets of coefficients based, at least in part, on the subsequent at least one of the plurality observed output samples. The adaptation module configured to derive the compensator coefficients is additionally configured to replace, at a time instance determined independently of the pseudo-random buffer selection process, at least a portion of the plurality of observed output samples stored in the selection buffer with a different set of observed output samples, corresponding to a different set of input samples provided to the digital compensator.
Embodiments of the additional linearization system may include at least some of the features described in the present disclosure, including any of the features of the above methods, the first linearization system, the design structures, and the computer readable medium.
In some variations, a design structure is provided that is encoded on a non-transitory machine-readable medium, with the design structure including elements that, when processed in a computer-aided design system, generate a machine-executable representation to fabricate hardware of one or more of the system modules and circuits described above.
In some variations, an integrated circuit definition dataset that, when processed in an integrated circuit manufacturing system, configures the integrated circuit manufacturing system to manufacture one or more of the system modules and/or circuits described above.
In some variations, a non-transitory computer readable media is provided that is programmed with a set of computer instructions executable on a processor that, when executed, cause the operations comprising the various method steps described above.
Embodiments of the design structures, the integrated circuit definition datasets, and the computer-readable media may include at least some of the features described in the present disclosure, including at least some of the features described above in relation to the methods, the linearization systems, the media, and/or the design structures.
Other features and advantages of the invention are apparent from the following description, and from the claims.
These and other aspects will now be described in detail with reference to the following drawings.
Like reference symbols in the various drawings indicate like elements.
Disclosed are systems, devices, methods, media, design structures, IC's, and other implementations for a buffer management approach that facilitates a stochastic gradient methodology to minimize a regularized mean square error to compute DPD coefficients applied to basis functions (also referred to as “transformation functions”) used in digital predistortion implementations. In some examples, a method is provided that includes selecting at least one location of a selection buffer, storing a plurality of observed output samples obtained from one or more of output batches produced by a signal chain, according to a pseudo-random buffer selection process, and accessing at least one observed output sample. The at least one observed output sample is generated in response to corresponding one or more input samples provided to a digital compensator applying to one or more input batches a plurality of basis functions, weighed by compensator coefficients, to produce intermediary samples, with the one or more intermediary samples provided to the signal chain. The method further includes computing an approximation of a sub-gradient value computed according to a stochastic gradient process to derive a sub-gradient of an error function representing a relationship between the accessed at least one observed output sample stored in the pseudo randomly selected at least one location of the selection buffer, and at least one of, for example, the corresponding one or more or more input samples, or one or more intermediate samples, produced by the digital compensator, corresponding to the pseudo randomly selected at least one location of the selection buffer storing the at least one observed output sample. The method further includes computing updated values for the compensator coefficients based on current values of the compensator coefficients and the computed approximation of the sub-gradient value computed according to the sub-gradient of the error function, and replacing, at a time instance determined independently of the pseudo-random buffer selection process, content of at least a portion of the selection buffer with different one or more observed output samples responsive to one or more other input samples.
In some additional examples, a linearization system is disclosed that includes a signal chain comprising at least one analog electronic amplification element, with the signal chain producing output signals including non-linear distortions, a digital compensator to compensate for non-linear behavior of the signal chain by applying a plurality of basis functions weighed by compensator coefficients to input signals to the compensator resulting in intermediary samples provided as input to the signal chain, an observation receiver to produce observed output samples of the output signals produced by the signal chain, a selection buffer to store a plurality of the observed output samples, and an adaptation module (also referred to as an adaptation unit or adaptor). The adaptation module is configured to select at least one location of the selection buffer, according to a pseudo-random buffer selection process, and access at least one observed output sample. The adaptation module is also configured to compute an approximation of a sub-gradient value computed according to a stochastic gradient process to derive a sub-gradient of an error function representing a relationship between the accessed at least one observed output sample stored in the pseudo randomly selected at least one location of the selection buffer, and at least one of, for example, corresponding one or more or more input samples, and/or corresponding one or more intermediate samples produced by the digital compensator in response to the one or more input samples. The adaptation module is further configured to compute updated values for the compensator coefficients based on current values of the compensator coefficients and the computed approximation of the sub-gradient value computed according to the sub-gradient of the error function, and replace, at a time instance determined independently of the pseudo-random buffer selection process, content of at least a portion of the selection buffer with different one or more observed output samples responsive to one or more other input samples.
In some embodiments, the implementations described herein may be configured to fill in the data buffers, and a DPD estimation (adaptation) unit can processes the data in a random fashion while updating the DPD coefficients. In some such embodiments, the implementations are configured to, at an arbitrary time, flush out old data and refill the buffers with new data (sequentially, to preserve the order of the new samples) to be processed by the estimation unit. This is repeated during the operation of the system. The estimation unit can either stay agnostic to the data refresh process, or use a trigger or flag to indicate that existing data has been processed and the estimation unit is ready to accept new data.
Thus, with reference to
As further illustrated in
As depicted in
The system 100 illustrated in
In some embodiments, at least some functionality of the linearization system 100 (including the updating of DPD coefficients based on the stochastic subgradient approach) may be implemented using a controller (e.g., a processor-based controller) included with one or more of the modules of the system 100 (the compensator 110, the adaptation module 140, or any of the other modules of the system 100). The controller may be operatively coupled to the various modules or units of the system 100, and be configured to compute approximations of the gradient of the error function (as will be discusses in greater detail below), update DPD coefficients based on those approximations, and perform digital predistortion using the continually updated DPD coefficients. The controller may include one or more microprocessors, microcontrollers, and/or digital signal processors that provide processing functionality, as well as other computation and control functionality. The controller may also include special purpose logic circuitry, e.g., an FPGA (field programmable gate array), an ASIC (application-specific integrated circuit), a DSP processor, a graphics processing unit (GPU), an accelerated processing unit (APU), an application processor, customized dedicated circuitry, etc., to implement, at least in part, the processes and functionality for the system 100. The controller may also include memory for storing data and software instructions for executing programmed functionality within the device. Generally speaking, a computer accessible storage medium may include any non-transitory storage media accessible by a computer during use to provide instructions and/or data to the computer. For example, a computer accessible storage medium may include storage media such as magnetic or optical disks and semiconductor (solid-state) memories, DRAM, SRAM, etc.
As noted, the coefficients derived by the block A are used to weigh the basis functions (transformation functions) used by block C to predistort the input u. Predistorting the signal u, by the digital predistorter 110 requires, in some embodiments, making the difference between y and u as small as possible.
A common representation of the compensator C (the subsystem 110 of
where
is the stack of recent (around t) baseband input samples, and, in some embodiments, p[t] is d-dimensional real vector of environmental parameters at time t (e.g., VDD and temperature, in which case d=2), xk (k=1, . . . , n) are the elements of a complex n-dimensional vector x∈Cn (the coefficients of the compensator), and Bk(q,p) are carefully selected “basis functions” of τ-dimensional complex vector q and d-dimensional real vector p (a “careful” selection means that the operation of computing a single sample v[t] of the corrected signal takes significantly less than n scalar multiplications). An example of a compensator, such as the compensator implemented in block C, operating according to an inverse model of the nonlinear distortion of the transmit chain (e.g., realized in the block F) and using basis functions weighed by regularly updated DPD coefficients, includes a compensator such that providing the input signal, u, causes the compensator to generate the intermediate input signal, v as follows:
where fi(∩) is the ith basis function of n basis functions and xi is the ith parameter (e.g., the ith weight) corresponding to the ith basis function. Each basis function, in this example, is a linear function (e.g., u(t−1)) or a non-linear function (e.g., |u(t)|2) of the input, u, which may include memory (e.g., u(t)*u(t−1)).
In some implementations, the adaptation process adjusts the coefficients set of coefficients, xk (where k represents a time instance or interval during which that set xk is being used) of the predistorter by observing the measurement samples y[t] and trying to minimize the regularized mean square error according to:
where
In the above expression, e[t]=v[t]−y[t] is the input/output mismatch in the observed data, ρ>0 is the regularization factor (used to prevent poor conditioning of the optimization problem, which tends to result in unstable interaction between the adaptation process and the compensator application, as well as unreasonably large entries of the optimal x*=argmin E(x)), T={t} is a set of time samples, N is the number of elements in T. Equivalently, the function E=E(x) to be minimized can be expressed in the form:
where a[t]=[B1(qy[t],p[t]), B2(qy[t],p[t]), . . . , Bn(qy[t],p[t])] are the complex 1-by-n matrices formed by the basis function samples, and
are the components of the Gramian matrix computed from the data.
As time progresses, the data represented by the samples (qy[t],p[t],e[t]), t∈T, gets refreshed with the outcomes of new measurements. This is usually done by either re-computing the Gramian, or by averaging new and old Gramians, or by replacing the old (qy[t],p[t],e[t]) samples with the new ones. As noted, in some embodiments, the process to replace or refresh a buffer holding sample data is performed independently of any of the processes that access the buffer (e.g., including the processes to compute DPD coefficients). In some example implementations, the adaptation process used for a DPD implementation attempts to find the vector x of compensator coefficients as an approximation of the optimal x*=argmin E(x).
An approach to finding x*=argmin E(x) is by computing it as the (unique) solution x* (ρIn+G)−1L of the linear equation (ρIn+G)x=L, where In denotes the n-by-n identity matrix. This approach requires computation (and storage) of the n-by-n Hermitian matrix G, which makes it impractical even for very simple compensators (for example, a compensator with n=100 complex coefficients will require computing and storing n2=10000 real coefficients of G). In addition, solving large systems of linear equations on FPGA or ASIC is a challenge on its own, and does not resolve the challenge of large storage required for Gramians of all but very simple DPD compensators. Additional details describing DPD adaptation processes (including use of Gramians to derive DPD coefficients) is discussed in U.S. patent application Ser. No. 16/004,594 entitled “Linearization System,” Ser. No. 16/408,979 entitled “Digital Compensator for a Non-Linear System,” Ser. No. 16/422,448 entitled “Digital Predistortion in Varying Operating Conditions,” and Ser. No. 16/656,686 entitled “Multi-Band Digital Compensator for a Non-Linear System,” the contents of all of which are incorporated herein by reference in their entireties.
The gradient of E=E(x) at a point x∈Cn is given by:
and can be computed and stored as an n-by-1 complex matrix, which reduces the storage requirements, compared to those of the Gramian.
Once computed, the gradient can be used to adjust the current guess {tilde over (x)} at the optimal x, according to {tilde over (x)}+={tilde over (x)}−γ∇E(z), making it slightly better (as long as the step size parameter y is sufficiently small). With the updated guess {tilde over (x)}={tilde over (x)}+ the optimal x, the gradient can be re-computed, and another adjustment can be made. Repeating the process sufficiently many times leads to an arbitrarily accurate approximation {tilde over (x)} of x*. Thus, in approaches based on a gradient of the error function E(x), a new/subsequent set of coefficients to weigh the basis functions applied by the compensator (e.g., the compensator realized by the block C) can be estimated/approximated as a change of the current coefficients by a pre-determined multiple (i.e., a step-size γ) of the value of the gradient of E(x).
Re-computing the gradient requires a significant number of operations each time. Since the number of steps of gradient search, before it reaches a satisfactory level of optimality, tends to be significant, compensator adaptation based on gradient search may be slow.
Accordingly, to reduce the amount of computation required to derive a current value of the gradient of E(x) (based on which updated coefficients are to be computed), in some embodiments, a sub-gradient approach to minimize E(x) may be used to approximate the DPD coefficients x. Particularly, a stochastic gradient approach to minimize the function E=E(x) is implemented based on the fact that the gradient ∇E(x) is the expected value of the vector random variable,
ξ=2[ρx+a[τ]′(a[τ]x−e[τ])],
where τ is the random variable uniformly distributed on the finite set T={t}, and that random samples of ξ which are much cheaper, from a computational effort, to compute than the true gradient ∇E(x)) can replace ∇E(x) in the gradient search iterations without a significant loss of convergence rate. In the above expression, a[τ] represents a matrix of values resulting from application of the basis functions, as weighed by the DPD coefficients x, to the input sample(s) at a randomly selected instant corresponding to the random variable τ, and e[τ] is the input/output mismatch in the observed data between an output of an observer receiver and the output of the digital compensator (e.g., the output y of the block S, and the output v of the block C depicted in
Thus, a modified version of the stochastic gradient approach, involves constructing a sequence of random values {tilde over (x)}0, {tilde over (x)}1, . . . (taking values in Cn), and updating the current coefficients at an instance k (namely, {tilde over (x)}k) as follows:
{tilde over (x)}k+1={tilde over (x)}k+αa[τk]′(e[τk]−a[τk]{tilde over (x)}k)−αρ{tilde over (x)}k, {tilde over (x)}0=0,
where τ1, τ2, . . . , τn are independent random variables uniformly distributed on T={t}, ρ>0 is the regularization constant from the definition of E=E(x), and α>0 is a pre-determined multiple (which may be a constant value that can be adjusted at regular or irregular periods) such that α(ρ+|a[t]|2)<2 for every t∈T. The pre-determined value a represents the step size that is applied to a value based on the expected value of the vector random variable used to approximate the gradient ∇E(x). It is to be noted that, in some embodiments, the random sequence τ1, τ2, . . . , τn needs to navigate through all available samples in the set T.
The expected value
{tilde over (y)}k+1={tilde over (y)}k+ϵ({tilde over (x)}k−{tilde over (y)}k), with ϵ∈(0,1].
The difference between {tilde over (y)}k and x* is small for large k, as long as ϵ>0 is small enough. This approach to minimize E(x) is referred to as a “projection” method, since the map,
x→x+|a[t]|−2a[t]′(e[t]−a[t]x[t])
projects x onto the hyperplane a[t]x=e[t].
In practical implementations of the processes to compute approximation of the gradient of E(x), and to thus update the current coefficients, τk are generated as a pseudo-random sequence of samples, and the calculations of {tilde over (y)}k can be eliminated (which formally corresponds to ϵ=1, i.e., {tilde over (y)}k={tilde over (x)}k+1). As a rule, this requires using a value of a with smaller minimal upper bound for α(ρ+|a[t]|2) (for example, α(ρ+|a[t]|2)<1, or α(ρ+|a[t]|2)<0.5).
In some embodiments, the values of a is sometime adjusted, depending on the progress made by the stochastic gradient optimization process, where the progress is measured by comparing the average values of |e[τk]| and |e[τk]−a[τk]{tilde over (x)}k|. In some embodiments, a regular update of the set of the optimization problem parameters a[t], e[t] may be performed to replace the data samples a[t], e[t] observed in the past with new observations.
Thus, in the implementations described herein, a batch of observed outputs (i.e., outputs of the observation receiver 130 implemented in the block S) are stored in a buffer (such as the buffer 150 of the system 100 of
As discussed herein, because the DPD coefficients are dynamically updated (reflecting the fact that the behavior of the system 100 changes over time), the expected values for the vector random value ξ represents a good approximation or estimate for the gradient ∇E(x), thus allowing for cheaper computation (from a computational effort perspective) of the values used to update the DPD coefficient x used by the compensator of the system 100.
As noted, in some embodiments, the value of the step size (that multiplies the approximation determined for the gradient ∇E(x)) may be adjusted based on a measure of progress of the stochastic gradient process, with the measure of progress being determined by, for example, comparing an average values of |e[τk]| and |e[τk]−a[τk]{tilde over (x)}k|. In some examples the process starts off with a relatively larger step size and then gradually reduces the step size.
With reference now to
With continued reference to
Having computed the sub-gradient value approximation, the procedure 200 next includes computing 230 updated values for the current compensator coefficients based on current values of the compensator coefficients and the computed approximation of the sub-gradient value computed according to the sub-gradient of the error function.
In some embodiments, computing the updated values for the compensator coefficients may include computing the updated values for the current values of the compensator coefficients based on a sum of the current values of the compensator coefficients and a product of a pre-determined multiple, representative of a step size, and the approximation of the sub-gradient value computed according to the sub-gradient of the error function. In some such examples, computing the updated values for the compensator coefficients may include deriving the updated values for the compensator coefficients according to the relationship:
{tilde over (x)}k+1={tilde over (x)}k+αa[τk]′(e[τk]−a[τk]{tilde over (x)}k)−αρ{tilde over (x)}k,with {tilde over (x)}0=0,
where {tilde over (x)}k are the current values of the compensator coefficients, {tilde over (x)}k+1 are the updated compensator coefficients, τ1, τ2, . . . , τn are independent random variables uniformly distributed on T={t}, a[τ] represents a matrix of values resulting from application of the basis functions at time τ to the one or more input samples corresponding to the pseudo randomly selected observed output sample, e[τ] is input/output mismatch in the observed data between an output of an observer receiver and the output of the digital compensator, ρ>0 is a regularization constant, and α>0 is a value representative of the step size, such that α(ρ+|a[t]|2)<A for every t∈T, where A is some pre-specified constant value.
In some embodiments, the procedure 200 may further include adjusting the value of α representative of the step size based on a measure of progress of the stochastic gradient process, with the measure of progress being determined by comparing an average values of |e[τk]| and |e[τk]−a[τk]{tilde over (x)}x|.
Turning back to
The delivery of new content of observed output sample for storage in the selection buffer may be done according to some local or remote scheduling, according to discrete individual decisions, deterministic or non-deterministic decision-making processes, or based on any criterion that is generally unrelated to, and is independent of, the buffer location selection process used for selecting the output and input samples in the buffer. Thus, in some embodiments, replacing, at the time instance determined independently of the pseudo-random buffer selection process, the content of the at least the portion of the selection buffer may include replacing the content of the at least the portion of the selection buffer based on one or more of, for example: i) receipt of a replacement signal (e.g., a local signal generated locally at the device performing the digital compensation processing, or a remote signal received from a remote device providing new data) indicating availability of the different one or more replacement observed output samples responsive to the one or more other input samples, ii) scheduled arrival of the different one or more replacement observed output samples responsive to the one or more other input samples (e.g., the arrival of new data may signal to a local controller that the content of the buffer needs to be, at least partly, replaced), and/or iii) unscheduled arrival at an arbitrary time instance of the different one or more replacement observed output samples responsive to the one or more other input samples. In some examples, the provisioning of new samples may be provided due to changing conditions (environmental conditions, operating points, etc.) As noted, in some implementations, the content replacement procedure may require that every current sample in the buffer needs to be accessed at least once before the buffer (or a part thereof) can be replaced. In some embodiments, replacing the content of the at least the portion of the selection buffer may include replacing the at least the portion of the selection buffer according to a pseudo-random replacement process, independent of the pseudo-random buffer selection process, to populate the at least the portion of the selection buffer with the different one or more replacement observed output samples and corresponding one or more other input samples.
In some examples, replacing the at least the portion of the selection buffer may include flushing the at least the portion of the selection buffer storing the plurality of observed output samples, and filling the at least the portion of the flushed selection buffer with the different one or more replacement observed output samples responsive to the one or more other input samples. In some variations, replacing the at least the portion of the selection buffer may include replacing according to the pseudo-random replacement process the entire selection buffer storing the plurality of observed output samples, with a different set of observed output samples, corresponding to a different set of input samples provided to the digital compensator.
As noted, the process of selection buffer locations whose content is then used for computing sub-gradient value approximations is repeated to pseudo-randomly select subsequent buffer location. The repeating may be performed at regular (e.g., periodic) or irregular (e.g., aperiodic) intervals, and the length of interval between the repeating of the buffer location process may itself be based on some pseudo-random factor or process that is independent of any other process used in the implementations of the procedures illustrated in
In some embodiments, the procedure 200 of
The procedure 300 further includes deriving 320, using a stochastic gradient process, a plurality of sets of coefficients used by a digital compensator for respectively weighing a plurality of basis functions applied by the digital compensator to input samples of the input signal. Deriving the plurality of sets of coefficients using the stochastic gradient process includes selecting 322, according to a pseudo-random buffer selection process, at least one location of the selection buffer to access at least one of the plurality of observed output samples stored in the selection buffer.
The derivation of the plurality of sets of coefficients also includes computing 324 updated values for the plurality of sets of coefficients based on current intermediate values for the plurality of sets of coefficients and a sub-gradient value computed according to a sub-gradient of an error function representing a relationship between the at least one of the plurality of observed output samples at the pseudo-randomly selected location of the selection buffer, and at least one or more input data samples, and/or intermediary values produced by the digital compensator, corresponding to the at least one of the plurality of observed output samples stored at the pseudo randomly selected location of the selection buffer. In some embodiments, computing the updated values for the plurality of sets of coefficients may include computing the updated values for the plurality of sets of coefficients based on the current intermediate values for the plurality of sets of coefficients and a pre-determined multiple, representative of a step size, of the sub-gradient value computed according to the sub-gradient of the error function using the accessed at least one observed output sample and the at least one or more input data samples corresponding to the at least one observed output sample. For example, in some such embodiments, computing the updated values for the plurality of sets of coefficients may include deriving the updated values for the plurality of sets of coefficients according to the relationship:
{tilde over (x)}k+1={tilde over (x)}k+αa[τk]′(e[τk]−a[τk]{tilde over (x)}k)−αρ{tilde over (x)}k,with {tilde over (x)}0=0,
where τ1, τ2, . . . , τn are independent random variables uniformly distributed on T={t}, a[τ] represents a matrix of values resulting from application of the basis functions at time τ to the input samples corresponding to the pseudo randomly selected observed output sample, e[τ] is input/output mismatch in the observed data between an output of an observer receiver and the output of the digital compensator, ρ>0 is a regularization constant, and α>0 is a value representative of the step size, such that α(ρ+|a[t]|2)<A for every t∈T, where A is some pre-specified constant value.
In some embodiments, the procedure 300 may further include adjusting the value of α representative of the step size based on a measure of progress of the stochastic gradient process, with the measure of progress being determined by comparing an average values of |e[τk]| and |e[τk]−[τk]{tilde over (x)}k|.
With continued reference to
The procedure 300 also includes replacing 328, at a time instance determined independently of the pseudo-random buffer selection process, at least a portion of the plurality of observed output samples stored in the selection buffer with a different set of observed output samples, corresponding to a different set of input samples provided to the digital compensator.
In some embodiments, replacing, at the time instance determined independently of the pseudo-random buffer selection process, the at least the portion of observed output samples may include replacing the at least the portion of observed output samples based on one or more of, for example: i) receipt of a replacement signal indicating availability of the different set of observed output samples corresponding to the different set of input samples, ii) scheduled arrival of the different set of observed output samples corresponding to the different set of input samples, and/or iii) unscheduled at an arbitrary time instance arrival of the different set of observed output samples corresponding to the different set of input samples. In some examples, replacing the at least the portion of the plurality of observed output samples may include replacing the selection buffer according to a pseudo-random replacement process, independent of the pseudo-random buffer selection process, to populate at least a portion of the selection buffer with the different set of observed output samples and corresponding replacement input samples.
As noted, in some embodiments, the input signal (marked u in
The above implementations, as illustrated in
In some implementations, a computer accessible non-transitory storage medium includes a database (also referred to a “design structure” or “integrated circuit definition dataset”) representative of a system including, in part, some or all of the components of a digital predistortion, DPD adaptation implementations, and/or buffer control processes described herein. Generally speaking, a computer accessible storage medium may include any non-transitory storage media accessible by a computer during use to provide instructions and/or data to the computer. For example, a computer accessible storage medium may include storage media such as magnetic or optical disks and semiconductor memories. Generally, the database representative of the system may be a database or other data structure which can be read by a program and used, directly or indirectly, to fabricate the hardware comprising the system. For example, the database may be a behavioral-level description or register-transfer level (RTL) description of the hardware functionality in a high-level design language (HDL) such as Verilog or VHDL. The description may be read by a synthesis tool which may synthesize the description to produce a netlist comprising a list of gates from a synthesis library. The netlist comprises a set of gates which also represents the functionality of the hardware comprising the system. The netlist may then be placed and routed to produce a data set describing geometric shapes to be applied to masks. The masks may then be used in various semiconductor fabrication steps to produce a semiconductor circuit or circuits corresponding to the system. In other examples, the database may itself be the netlist (with or without the synthesis library) or the data set.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly or conventionally understood. As used herein, the articles “a” and “an” refer to one or to more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one element or more than one element. “About” and/or “approximately” as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, encompasses variations of ±20% or ±10%, ±5%, or +0.1% from the specified value, as such variations are appropriate in the context of the systems, devices, circuits, methods, and other implementations described herein. “Substantially” as used herein when referring to a measurable value such as an amount, a temporal duration, a physical attribute (such as frequency), and the like, also encompasses variations of ±20% or ±10%, ±5%, or +0.1% from the specified value, as such variations are appropriate in the context of the systems, devices, circuits, methods, and other implementations described herein.
As used herein, including in the claims, “or” as used in a list of items prefaced by “at least one of” or “one or more of” indicates a disjunctive list such that, for example, a list of “at least one of A, B, or C” means A or B or C or AB or AC or BC or ABC (i.e., A and B and C), or combinations with more than one feature (e.g., AA, AAB, ABBC, etc.). Also, as used herein, unless otherwise stated, a statement that a function or operation is “based on” an item or condition means that the function or operation is based on the stated item or condition and may be based on one or more items and/or conditions in addition to the stated item or condition.
Although particular embodiments have been disclosed herein in detail, this has been done by way of example for purposes of illustration only, and is not intended to be limit the scope of the invention, which is defined by the scope of the appended claims. Features of the disclosed embodiments can be combined, rearranged, etc., within the scope of the invention to produce more embodiments. Some other aspects, advantages, and modifications are considered to be within the scope of the claims provided below. The claims presented are representative of at least some of the embodiments and features disclosed herein. Other unclaimed embodiments and features are also contemplated.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4979126 | Pao et al. | Dec 1990 | A |
| 5819165 | Hulkko et al. | Oct 1998 | A |
| 5980457 | Averkiou | Nov 1999 | A |
| 6052412 | Ruether et al. | Apr 2000 | A |
| 6240278 | Midya et al. | May 2001 | B1 |
| 6288610 | Miyashita | Sep 2001 | B1 |
| 6549067 | Kenington | Apr 2003 | B1 |
| 7289773 | Braithwaite | Oct 2007 | B2 |
| 7295815 | Wright et al. | Nov 2007 | B1 |
| 7321264 | Kokkeler | Jan 2008 | B2 |
| 7333557 | Rashev et al. | Feb 2008 | B2 |
| 7469491 | McCallister et al. | Dec 2008 | B2 |
| 7529652 | Gahinet et al. | May 2009 | B1 |
| 7577211 | Braithwaite | Aug 2009 | B2 |
| 7599431 | Anderson et al. | Oct 2009 | B1 |
| 7634238 | Suzuki et al. | Dec 2009 | B2 |
| 7796960 | Rashev et al. | Sep 2010 | B1 |
| 7904033 | Wright et al. | Mar 2011 | B1 |
| 8121560 | Anaraki | Feb 2012 | B1 |
| 8185066 | Camp, Jr. et al. | May 2012 | B2 |
| 8306149 | Mujica et al. | Nov 2012 | B2 |
| 8391809 | Fuller | Mar 2013 | B1 |
| 8411730 | Maeda | Apr 2013 | B2 |
| 8446979 | Yee | May 2013 | B1 |
| 8498590 | Rashev et al. | Jul 2013 | B1 |
| 8519789 | Hawkes | Aug 2013 | B2 |
| 8576941 | Bai | Nov 2013 | B2 |
| 8644437 | Kim et al. | Feb 2014 | B2 |
| 8666336 | Dick | Mar 2014 | B1 |
| 8711976 | Chandrasekaran | Apr 2014 | B2 |
| 8731005 | Schlee | May 2014 | B2 |
| 8731105 | Bai | May 2014 | B2 |
| 9071207 | Bai | Jun 2015 | B2 |
| 9130628 | Mittal et al. | Sep 2015 | B1 |
| 9173173 | Wei et al. | Oct 2015 | B2 |
| 9184710 | Braithwaite | Nov 2015 | B2 |
| 9226189 | Kularatna et al. | Dec 2015 | B1 |
| 9252712 | Li et al. | Feb 2016 | B2 |
| 9331882 | Fehri et al. | May 2016 | B2 |
| 9337782 | Mauer et al. | May 2016 | B1 |
| 9564876 | Kim et al. | Feb 2017 | B2 |
| 9590668 | Kim et al. | Mar 2017 | B1 |
| 9595920 | Li et al. | Mar 2017 | B2 |
| 9614557 | Mayer et al. | Apr 2017 | B1 |
| 9621236 | Ghannouchi et al. | Apr 2017 | B2 |
| 9628119 | Gal et al. | Apr 2017 | B2 |
| 9722646 | Matthews et al. | Aug 2017 | B1 |
| 9749161 | Gal et al. | Aug 2017 | B1 |
| 9935810 | Hammler et al. | Apr 2018 | B1 |
| 9973370 | Langer et al. | May 2018 | B1 |
| 10033413 | Pratt | Jul 2018 | B2 |
| 10033414 | Ota | Jul 2018 | B2 |
| 10079699 | Li et al. | Sep 2018 | B1 |
| 10080178 | Stapleton et al. | Sep 2018 | B2 |
| 10141896 | Huang | Nov 2018 | B2 |
| 10141961 | Huang et al. | Nov 2018 | B1 |
| 10181914 | Li et al. | Jan 2019 | B2 |
| 10404296 | Kim et al. | Sep 2019 | B2 |
| 10447511 | Xie et al. | Oct 2019 | B2 |
| 10469109 | Gutman et al. | Nov 2019 | B2 |
| 10523159 | Megretski et al. | Dec 2019 | B2 |
| 10581470 | Megretski et al. | Mar 2020 | B2 |
| 10644657 | Megretski et al. | May 2020 | B1 |
| 20010050592 | Wright et al. | Dec 2001 | A1 |
| 20020080891 | Ahn et al. | Jun 2002 | A1 |
| 20030058960 | Lee | Mar 2003 | A1 |
| 20030184374 | Huang et al. | Oct 2003 | A1 |
| 20030207680 | Yang et al. | Nov 2003 | A1 |
| 20040076247 | Barak et al. | Apr 2004 | A1 |
| 20040116083 | Suzuki et al. | Jun 2004 | A1 |
| 20040121741 | Rashev et al. | Jun 2004 | A1 |
| 20040142667 | Lochhead et al. | Jul 2004 | A1 |
| 20040196922 | Leffel | Oct 2004 | A1 |
| 20050001684 | Braithwaite | Jan 2005 | A1 |
| 20050163251 | McCallister | Jul 2005 | A1 |
| 20050163252 | McCallister et al. | Jul 2005 | A1 |
| 20050180527 | Suzuki et al. | Aug 2005 | A1 |
| 20050190857 | Braithwaite | Sep 2005 | A1 |
| 20060022751 | Fuller et al. | Feb 2006 | A1 |
| 20060154622 | Piirainen | Jul 2006 | A1 |
| 20060229036 | Muller et al. | Oct 2006 | A1 |
| 20060276147 | Suzuki | Dec 2006 | A1 |
| 20070091992 | Dowling | Apr 2007 | A1 |
| 20070230557 | Balasubramonian et al. | Oct 2007 | A1 |
| 20070241812 | Yang et al. | Oct 2007 | A1 |
| 20080003945 | Wenham | Jan 2008 | A1 |
| 20080019453 | Zhao et al. | Jan 2008 | A1 |
| 20080039045 | Filipovic et al. | Feb 2008 | A1 |
| 20080057882 | Singerl | Mar 2008 | A1 |
| 20080101502 | Navidpour et al. | May 2008 | A1 |
| 20080247487 | Cai et al. | Oct 2008 | A1 |
| 20080260066 | Cai et al. | Oct 2008 | A1 |
| 20080268794 | Mege | Oct 2008 | A1 |
| 20080268795 | Saed | Oct 2008 | A1 |
| 20080285640 | McCallister | Nov 2008 | A1 |
| 20090201084 | See et al. | Aug 2009 | A1 |
| 20100026354 | Utsunomiya et al. | Feb 2010 | A1 |
| 20100048149 | Tang et al. | Feb 2010 | A1 |
| 20100225390 | Brown et al. | Sep 2010 | A1 |
| 20100297966 | Row et al. | Nov 2010 | A1 |
| 20110044158 | Tao et al. | Feb 2011 | A1 |
| 20110085490 | Schlee | Apr 2011 | A1 |
| 20110098011 | Camp, Jr. et al. | Apr 2011 | A1 |
| 20110128992 | Maeda et al. | Jun 2011 | A1 |
| 20110135035 | Bose et al. | Jun 2011 | A1 |
| 20110150130 | Kenington | Jun 2011 | A1 |
| 20110163806 | Hongo | Jul 2011 | A1 |
| 20110187437 | Perreault et al. | Aug 2011 | A1 |
| 20110235734 | Kenington | Sep 2011 | A1 |
| 20110255627 | Gotman et al. | Oct 2011 | A1 |
| 20110273234 | Van der Heijen et al. | Nov 2011 | A1 |
| 20110273236 | Heijden et al. | Nov 2011 | A1 |
| 20120093210 | Schmidt et al. | Apr 2012 | A1 |
| 20120108189 | McCallister et al. | May 2012 | A1 |
| 20120119810 | Bai | May 2012 | A1 |
| 20120119811 | Bai et al. | May 2012 | A1 |
| 20120119831 | Bai | May 2012 | A1 |
| 20120154033 | Lozhkin | Jun 2012 | A1 |
| 20120154430 | Matsushima et al. | Jun 2012 | A1 |
| 20120176195 | Dawson et al. | Jul 2012 | A1 |
| 20120194271 | Yamamoto | Aug 2012 | A1 |
| 20120200355 | Braithwaite | Aug 2012 | A1 |
| 20120219048 | Camuffo et al. | Aug 2012 | A1 |
| 20120286865 | Chandrasekaran | Nov 2012 | A1 |
| 20120286985 | Chandrasekaran et al. | Nov 2012 | A1 |
| 20120293252 | Sorrells et al. | Nov 2012 | A1 |
| 20120295558 | Wang et al. | Nov 2012 | A1 |
| 20130033317 | Hawkes | Feb 2013 | A1 |
| 20130034188 | Rashev et al. | Feb 2013 | A1 |
| 20130044791 | Rimini et al. | Feb 2013 | A1 |
| 20130064325 | Kilambi et al. | Mar 2013 | A1 |
| 20130094610 | Ghannouchi et al. | Apr 2013 | A1 |
| 20130094612 | Kim et al. | Apr 2013 | A1 |
| 20130163512 | Rexberg et al. | Jun 2013 | A1 |
| 20130243121 | Bai | Sep 2013 | A1 |
| 20130251065 | Bai | Sep 2013 | A1 |
| 20130259159 | McCallister et al. | Oct 2013 | A1 |
| 20130329833 | Bai | Dec 2013 | A1 |
| 20140009225 | Laporte | Jan 2014 | A1 |
| 20140038659 | Wei et al. | Feb 2014 | A1 |
| 20140161159 | Black et al. | Jun 2014 | A1 |
| 20140161207 | Teterwak | Jun 2014 | A1 |
| 20140177695 | Cha et al. | Jun 2014 | A1 |
| 20140187182 | Yan et al. | Jul 2014 | A1 |
| 20140254716 | Zhou et al. | Sep 2014 | A1 |
| 20140274105 | Wang | Sep 2014 | A1 |
| 20140292579 | Oh et al. | Oct 2014 | A1 |
| 20140347126 | Laporte et al. | Nov 2014 | A1 |
| 20150043313 | Stranczl et al. | Feb 2015 | A1 |
| 20150043323 | Choi et al. | Feb 2015 | A1 |
| 20150043678 | Hammi | Feb 2015 | A1 |
| 20150049841 | Laporte et al. | Feb 2015 | A1 |
| 20150061761 | Wills et al. | Mar 2015 | A1 |
| 20150103952 | Wang et al. | Apr 2015 | A1 |
| 20150123735 | Wimpenny | May 2015 | A1 |
| 20150124907 | Li et al. | May 2015 | A1 |
| 20150171768 | Perreault | Jun 2015 | A1 |
| 20150325913 | Vagman | Nov 2015 | A1 |
| 20150326349 | Yang et al. | Nov 2015 | A1 |
| 20150333781 | Alon et al. | Nov 2015 | A1 |
| 20150357975 | Avniel et al. | Dec 2015 | A1 |
| 20150358039 | Xiong et al. | Dec 2015 | A1 |
| 20150381216 | Shor et al. | Dec 2015 | A1 |
| 20150381220 | Gal et al. | Dec 2015 | A1 |
| 20160013820 | Yamanouchi | Jan 2016 | A1 |
| 20160028433 | Ding et al. | Jan 2016 | A1 |
| 20160065147 | Pratt et al. | Mar 2016 | A1 |
| 20160087604 | Kim et al. | Mar 2016 | A1 |
| 20160094253 | Weber et al. | Mar 2016 | A1 |
| 20160095110 | Li et al. | Mar 2016 | A1 |
| 20160100180 | Oh | Apr 2016 | A1 |
| 20160112222 | Pashay-Kojouri et al. | Apr 2016 | A1 |
| 20160174118 | Duan et al. | Jun 2016 | A1 |
| 20160191020 | Velazquez | Jun 2016 | A1 |
| 20160211577 | Miller et al. | Jul 2016 | A1 |
| 20160241277 | Rexberg et al. | Aug 2016 | A1 |
| 20160249300 | Tsai et al. | Aug 2016 | A1 |
| 20160285485 | Fehri et al. | Sep 2016 | A1 |
| 20160308577 | Molina et al. | Oct 2016 | A1 |
| 20160336906 | Lee et al. | Nov 2016 | A1 |
| 20160373072 | Magesacher et al. | Dec 2016 | A1 |
| 20170005627 | Zhao et al. | Jan 2017 | A1 |
| 20170033969 | Yang et al. | Feb 2017 | A1 |
| 20170041124 | Khandani | Feb 2017 | A1 |
| 20170047899 | Abdelrahman et al. | Feb 2017 | A1 |
| 20170077981 | Tobisu et al. | Mar 2017 | A1 |
| 20170176507 | O'Keeffe et al. | Jun 2017 | A1 |
| 20170214438 | Ghannouchi et al. | Jul 2017 | A1 |
| 20170237455 | Ye et al. | Aug 2017 | A1 |
| 20170244582 | Gal et al. | Aug 2017 | A1 |
| 20170302233 | Huang | Oct 2017 | A1 |
| 20170338841 | Pratt | Nov 2017 | A1 |
| 20180097530 | Yang et al. | Apr 2018 | A1 |
| 20180159568 | Matsuura et al. | Jun 2018 | A1 |
| 20180167092 | Hausmair et al. | Jun 2018 | A1 |
| 20180287569 | Xu et al. | Oct 2018 | A1 |
| 20180337700 | Huang et al. | Nov 2018 | A1 |
| 20190007075 | Kim et al. | Jan 2019 | A1 |
| 20190104000 | Xie et al. | Apr 2019 | A1 |
| 20190238204 | Kim et al. | Aug 2019 | A1 |
| 20190260401 | Megretski et al. | Aug 2019 | A1 |
| 20190260402 | Chuang et al. | Aug 2019 | A1 |
| 20190348956 | Megretski et al. | Nov 2019 | A1 |
| 20190363676 | Megretski et al. | Nov 2019 | A1 |
| 20190363742 | Megretski et al. | Nov 2019 | A1 |
| 20200028476 | Kim et al. | Jan 2020 | A1 |
| 20200067543 | Kim et al. | Feb 2020 | A1 |
| Number | Date | Country |
|---|---|---|
| 101379718 | Mar 2009 | CN |
| 101971507 | Feb 2011 | CN |
| 104796170 | Jul 2015 | CN |
| 104871433 | Aug 2015 | CN |
| 105634539 | Jun 2016 | CN |
| 106464280 | Feb 2017 | CN |
| 0916967 | May 1999 | EP |
| 1560329 | Aug 2005 | EP |
| 1732208 | Dec 2006 | EP |
| 2991221 | Mar 2016 | EP |
| 2005-065211 | Mar 2005 | JP |
| 2010-136123 | Jun 2010 | JP |
| 2013-542696 | Nov 2013 | JP |
| 2014-533017 | Dec 2014 | JP |
| 20120154430 | Nov 2012 | WO |
| 2015107392 | Jul 2015 | WO |
| 2018156932 | Aug 2018 | WO |
| 2018227093 | Dec 2018 | WO |
| 2018227111 | Dec 2018 | WO |
| 2019014422 | Jan 2019 | WO |
| 2019031714 | Feb 2019 | WO |
| 2019070573 | Apr 2019 | WO |
| 2019094713 | May 2019 | WO |
| 2019094720 | May 2019 | WO |
| Entry |
|---|
| Cheng, Sheng-Lung, Wen-Rong Wu, Chun-Hsien Peng, Chen-Jui Hsu, and Paul Liang. “Digital predistortion for concurrent dual-band transmitter using a 2D LUT based method.” in 2015 IEEE 16th Annual Wireless and Microwave Technology Conference (WAMICON), pp. 1-4. IEEE, 2015. |
| Aguirre, et al., “On the Interpretation and Practice of Dynamical Differences Between Hammerstein and Wiener Models”, IEEE Proceedings on Control TheoryAppl; vol. 152, No. 4, Jul. 2005, pp. 349-356. |
| Barradas, et al. “Polynomials and LUTs in PA Behavioral Modeling: A Fair Theoretical Comparison”, IEEE Transactions on Microwave Theory and Techniques; vol. 62, No. 12, Dec. 2014, pp. 3274-3285. |
| Bosch et al. “Measurement and Simulation of Memory Effects in Predistortion Linearizers,” IEEE Transactions on Mircrowave Theory and Techniques; vol. 37.No. 12; Dec. 1989, pp. 1885-1890. |
| Braithwaite, et al. “Closed-Loop Digital Predistortion (DPD) Using an Observation Path with Limited Bandwidth” IEEE Transactions on Microwave Theory and Techniques; vol. 63, No. 2; Feb. 2015, pp. 726-736. |
| Cavers, “Amplifier Linearization Using a Digital Predistorter with Fast Adaption and Low Memory Requirements;” IEEE Transactions on Vehicular Technology; vol. 39; No. 4; Nov. 1990, pp. 374-382. |
| D'Andrea et al., “Nonlinear Predistortion of OFDM Signals over Frequency-Selective Fading Channels,” IEEE Transactions on Communications; vol. 49; No. 5, May 2001; pp. 837-843. |
| Guan, et al. “Optimized Low-Complexity Implementation of Least Squares Based Model Extraction of Digital Predistortion of RF Power Amplifiers”, IEEE Transactions on Microwave Theory and Techniques; vol. 60, No. 3, Mar. 2012; pp. 594-603. |
| Henrie, et al., “Cancellation of Passive Intermodulation Distortion in Microwave Networks”, Proceedings of the 38.sup.th European Microwave Conference, Oct. 2008, Amsterdam, The Netherlands, pp. 1153-1156. |
| Hong et al., “Weighted Polynomial Digital Predistortion for Low Memory Effect Doherty Power Amplifier,” IEEE Transactions on Microwave Theory and Techniques; vol. 55; No. 5, May 2007, pp. 925-931. |
| Kwan, et al., “Concurrent Multi-Band Envelope Modulated Power Amplifier Linearized Using Extended Phase-Aligned DPD”, IEEE Transactions on Microwave Theory and Techniques; vol. 62, No. 12, Dec. 2014, pp. 3298-3308. |
| Lajoinie et al. Efficient Simulation of NPR for the Optimum Design of Satellite Transponders SSPAs, EEE MIT-S International; vol. 2; Jun. 1998; pp. 741-744. |
| Li et al. “High-Throughput Signal Component Separator for Asymmetric Multi-Level Outphasing Power Amplifiers,” IEEE Journal of Solid-State Circuits; vol. 48; No. 2; Feb. 2013; pp. 369-380. |
| Liang, et al. “A Quadratic-Interpolated Lut-Based Digital Predistortion Techniques for Cellular Power Amplifiers”, IEEE Transactions on Circuits and Systems; II: Express Briefs, vol. 61, No. 3, Mar. 2014; pp. 133-137. |
| Liu, et al. “Digital Predistortion for Concurrent Dual-Band Transmitters Using 2-D Modified Memory Polynomials”, IEEE Transactions on Microwave Theory and Techniques, vol. 61, No. 1, Jan. 2013, pp. 281-290. |
| Molina, et al. “Digital Predistortion Using Lookup Tables with Linear Interpolation and Extrapolation: Direct Least Squares Coefficient Adaptation”, IEEE Transactions on Microwave Theory and Techniques, vol. 65, No. 3, Mar. 2017; pp. 980-987. |
| Morgan, et al. “A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers,” IEEE Transactions of Signal Processing; vol. 54; No. 10; Oct. 2006; pp. 3852-3860. |
| Muta et al., “Adaptive predistortion linearization based on orthogonal polynomial expansion for nonlinear power amplifiers in OFDM systems”, Communications and Signal Processing (ICCP), International Conference on, IEEE, pp. 512-516, 2011. |
| Naraharisetti, et a., “2D Cubic Spline Implementation for Concurrent Dual-Band System”, IEEE, 2013, pp. 1-4. |
| Naraharisetti, et al. “Efficient Least-Squares 2-D-Cubic Spline for Concurrent Dual-Band Systems”, IEEE Transactions on Microwave Theory and Techniques, vol. 63; No. 7, Jul. 2015; pp. 2199-2210. |
| Panigada, et al. “A 130 mW 100 MS/s Pipelined ADC with 69 SNDR Enabled by Digital Harmonic Distortion Correction,” IEEE Journal of Solid-State Circuits; vol. 44; No. 12; Dec. 2009, pp. 3314-3328. |
| Peng, et al. “Digital Predistortion for Power Amplifier Based on Sparse Bayesian Learning”, IEEE Transactions on Circuits and Systems, II: Express Briefs; 2015, pp. 1-5. |
| Quindroit et al. “FPGA Implementation of Orthogonal 2D Digital Predistortion System for Concurrent Dual-Band Power Amplifiers Based on Time-Division Multiplexing”, IEEE Transactions on Microwave Theory and Techniques; vol. 61; No. 12, Dec. 2013, pp. 4591-4599. |
| Rawat, et al. “Adaptive Digital Predistortion of Wireless Power Amplifiers/Transmitters Using Dynamic Real-Valued Focused Time-Delay Line Neural Networks”, IEEE Transactions on Microwave Theory and Techniques; vol. 58, No. 1; Jan. 2010; pp. 95-104. |
| Safari, et al.“Spline-Based Model for Digital Predistortion of Wide-Band Signals for High Power Amplifier Linearization”, IEEE; 2007, pp. 1441-1444. |
| Sevic, et al. “A Novel Envelope-Termination Load-Pull Method of ACPR Optimization of RF/Microwave Power Amplifiers,” IEEE MTT-S International; vol. 2, Jun. 1998; pp. 723-726. |
| Tai, “Efficient Watt-Level Power Amplifiers in Deeply Scaled CMOS,” Ph.D. Dissertation; Carnegie Mellon University; May 2011; 129 pages. |
| Tehran, et al. “Modeling of Long Term Memory Effects in RF Power Amplifiers with Dynamic Parameters”, IEEE; 2012, pp. 1-3. |
| Yu et al. “A Generalized Model Based on Canonical Piecewise Linear Functions for Digital Predistortion”, Proceedings of the Asia-Pacific Microwave Conference; 2016, pp. 1-4. |
| Yu, et al. “Band-Limited Volterra Series-Based Digital Predistortion for Wideband RF Power Amplifiers,” IEEE Transactions of Microwave Theory and Techniques; vol. 60; No. 12; Dec. 2012, pp. 4198-4208. |
| Yu, et al.“Digital Predistortion Using Adaptive Basis Functions”, IEEE Transations on Circuits and Systems—I. Regular Papers; vol. 60, No. 12; Dec. 2013, pp. 3317-3327. |
| Zhang et al. “Linearity Performance of Outphasing Power Amplifier Systems,” Design of Linear Outphasing Power Amplifiers; Google e-book; 2003; Retrieved on Jun. 13, 2014; Retrieved from Internet <URL:http:www.artechhouse.com/uploads/public/documents/chapters/Zhang-LarsonCH- 2.pdf; pp. 35-85. |
| Zhu et al. “Digital Predistortion for Envelope-Tracking Power Amplifiers Using Decomposed Piecewise Volterra Sereis,” IEEE Transactions on Microwave Theory and Techniques; vol. 56; No. 10; Oct. 2008; pp. 2237-2247. |
| Cidronali, A., I. Magrini, R. Fagotti, and G. Manes. “A new approach for concurrent dual-band IF digital predistortion: System design and analysis.” In 2008 Workshop on Integrated Nonlinear Microwave and Millimetre-Wave Circuits, pp. 127-130. IEEE, 2008. |
| Henrie, Justin, Andrew Christianson, and William J. Chappell. “Cancellation of passive intermodulation distortion in microwave networks.” In Microwave Conference, 2008. EuMC 2008. 38th European, pp. 1153-1156. IEEE, 2008. |
| Riihonen et al., “Mitigation of Loopback Self-Interference in Full-Duplex Mimo Relays” IEEE Transactions on Signal Processing, 59(12), 5983-5993, Dec. 2011. |
