CN116545815A - Method and device for enhancing gain stability of digital predistortion loop - Google Patents
Method and device for enhancing gain stability of digital predistortion loop Download PDFInfo
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Abstract
The invention provides a method and a device for enhancing the gain stability of a digital predistortion loop, which can introduce a variable step length concept into a communication system at the minimum and simplest cost, introduce a dynamic convergence step length factor, and solve the stability of loop signal gain under different power fluctuation of signals so as to enhance the stability of gain calculation and the stability of predistortion algorithm correction. And effectively improves the quality of the communication system channel. And extracting a limited depth variable step convergence factor mu gear table through early simulation analysis, taking a signal which changes in real time as a gear lookup table address, and tracking and calling a theoretically optimal mu value when the signal changes to assist the stability of the LMS algorithm. By applying the scheme, the realization cost is extremely low, the optimal flexibility is realized in a register configuration form, and the stability of the iterative LMS algorithm can be tracked rapidly in different complex communication systems.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a method and a device for enhancing gain stability of a digital predistortion loop.
Background
The application of the adaptation in signal processing is very wide, such as equalization of filtering algorithms, acoustic echo cancellation, network echo cancellation, active noise control, biomedical engineering, etc. The nonlinearity introduced by active devices is common in wireless communication systems. Digital predistortion is a very efficient solution that enables the predistortion function to be implemented before input to the nonlinear device. Least Mean Square (LMS) adaptive filters are most popular because of their simplicity and robustness. The step size parameter is critical to the performance of the LMS and determines the rate at which the algorithm converges along the error performance plane. In order to solve the problem of rapid convergence, many variable step size methods have emerged in the engineering world. According to the LMS mathematical formula, under the condition that the average power of the signal is relatively stable, the value of the theoretical deduction convergence step factor mu is fixed, and stable performance output can be formed, but in an actual communication system, signal service is changed in real time, the service is not continuous, so that the average power of the service signal is higher in a period of time, lower in a period of time and rapid in change. It is certainly not reasonable to directly apply the static μ value to control the stability of LMS algorithm convergence.
In engineering implementation, in order to solve the problem of the stability of the LMS algorithm, a plurality of measures are taken, such as a leakage mode is applied to the coefficient, so that the stability of the coefficient under the high-precision requirement is improved; for example, the iteration step length is adjusted to improve the stability of LMS convergence. Deriving a variable step LMS filter using the square of the instantaneous error can improve the convergence limit performance of the LMS algorithm, typically when the estimation error is large, the LMS uses a large step, and vice versa. To mitigate the effects of uncorrelated perturbations, a scholars have then developed a scheme that uses the squared autocorrelation of adjacent spacing errors. The step size is considered to be controlled by gradient vector weighted average by the learner, so that the convergence speed is improved, and a lower steady-state offset error is obtained. In a word, from academic world to engineering world, reasonable optimization of convergence step factor mu can solve the problem of engineering stability of LMS algorithm application. Many studies fall to the actual engineering, and cost is considered, and many typical academic studies are not adopted by the engineering community, cost of engineering implementation is considered, and difficulty in design implementation is considered.
Therefore, how to provide a method for calculating the convergence step factor μ, which can be implemented in practical engineering at low cost, is a problem to be solved at present.
Disclosure of Invention
In order to improve the above problems, the present invention provides a method and apparatus for enhancing the gain stability of a digital predistortion loop.
In a first aspect of an embodiment of the present invention, there is provided a method for enhancing digital predistortion loop gain stability, the method comprising:
a gain calculation model of the digital predistortion structure is realized at fixed points by using an LMS algorithm;
inputting simulation signal streams with different static powers into the model, and respectively calculating convergence step factors mu corresponding to the different powers;
constructing a convergence step factor table according to the power of the simulation signal and the convergence step factor mu obtained by corresponding calculation;
inputting a dynamic power simulation signal into the model, looking up a table from the convergence step size factor table according to the power of the dynamic power simulation signal to obtain a corresponding convergence step size factor mu value, and substituting the obtained convergence step size factor mu value into a gain iteration calculation formula;
after the fluctuation condition of the gain convergence curve is observed, the convergence step factor mu value in the convergence step factor table is adjusted according to the difference between the fluctuation condition of the gain convergence curve and the ideal state.
Optionally, the gain calculation model implemented with LMS algorithm fixed point is:
where x (n) is the baseband ideal modeling signal, fb (n) is the signal of the outer feedback loop in the digital predistortion structure, y (n) is the predistortion output signal, error signal e (n) is the difference between transmit signal x (n) and feedback signal fb (n), gain (n) is the Gain compensation value.
Optionally, the step of inputting the simulated signal streams with different static powers into the model and calculating convergence step factors μ corresponding to the different powers respectively specifically includes:
dividing the simulation signal flow into a plurality of gears according to the magnitude of static power, and respectively inputting the gears into the model;
for each gear, respectively observing the convergence condition of the iterative Gain (n) changing along with time under different convergence step factors mu;
and obtaining the convergence step factor mu corresponding to the condition of stable fluctuation of the gain of each gear.
Optionally, the step of looking up a table from the convergence step factor μ table according to the power level of the dynamic power simulation signal to obtain a corresponding convergence step factor μ value specifically includes:
acquiring the instantaneous power or the instantaneous average power of the dynamic power simulation signal;
and according to the instantaneous power or the instantaneous average power, obtaining a corresponding convergence step factor mu value from the convergence step factor table in a table look-up mode.
In a second aspect of an embodiment of the present invention, there is provided an apparatus for enhancing gain stability of a digital predistortion loop, the apparatus comprising:
the model building unit is used for realizing a gain calculation model of the digital predistortion structure by using an LMS algorithm at fixed points;
the static simulation unit is used for inputting simulation signal streams with different static powers into the model and respectively calculating convergence step factors mu corresponding to the different powers;
the table establishing unit is used for establishing a convergence step factor table according to the power of the simulation signal and the convergence step factor mu obtained by corresponding calculation;
the dynamic simulation unit is used for inputting a dynamic power simulation signal into the model, acquiring a corresponding convergence step factor mu value from the convergence step factor table according to the power of the dynamic power simulation signal, and substituting the acquired convergence step factor mu value into a gain iteration calculation formula;
the table establishing unit is further configured to adjust a convergence step factor μ value in the convergence step factor table according to a difference between the gain convergence curve fluctuation condition and the ideal state after observing the gain convergence curve fluctuation condition.
Optionally, the gain calculation model realized by the model building unit by using the LMS algorithm at fixed points is:
where x (n) is the baseband ideal modeling signal, fb (n) is the signal of the outer feedback loop in the digital predistortion structure, y (n) is the predistortion output signal, error signal e (n) is the difference between transmit signal x (n) and feedback signal fb (n), gain (n) is the Gain compensation value.
Optionally, the static simulation unit is specifically configured to:
dividing the simulation signal flow into a plurality of gears according to the magnitude of static power, and respectively inputting the gears into the model;
for each gear, respectively observing the convergence condition of the iterative Gain (n) changing along with time under different convergence step factors mu;
and obtaining the convergence step factor mu corresponding to the condition of stable fluctuation of the gain of each gear.
Optionally, the dynamic simulation unit is specifically configured to:
acquiring the instantaneous power or the instantaneous average power of the dynamic power simulation signal;
and according to the instantaneous power or the instantaneous average power, obtaining a corresponding convergence step factor mu value from the convergence step factor table in a table look-up mode.
A third aspect of an embodiment of the present invention provides an electronic device, including:
one or more processors; a memory; one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of the first aspect.
A fourth aspect of an embodiment of the present invention provides a computer readable storage medium, wherein the computer readable storage medium has program code stored therein, the program code being callable by a processor to perform the method according to the first aspect.
In summary, the present invention provides a method, apparatus, electronic device and storage medium for enhancing the gain stability of a digital predistortion loop, which can introduce a variable step concept into a communication system with minimum and simplest cost, and effectively improve the quality of a communication system channel. The method comprises the steps of extracting a gear table of a limited depth variable step convergence factor mu through early simulation analysis, taking a signal changing in real time as a gear lookup table address, and tracking and calling a theoretically optimal mu value when the signal changes so as to assist the stability of an LMS algorithm. By applying the scheme, the realization cost is extremely low, the optimal flexibility is realized in a register configuration form, and the stability of the iterative LMS algorithm can be tracked rapidly in different complex communication systems.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for enhancing digital predistortion loop gain stability in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a gain calculation model of a digital predistortion architecture according to an embodiment of the present invention;
FIG. 3 is a diagram showing the comparison between the calculated gain and the ideal gain under the static convergence factor μ of the dynamic power variation signal according to the embodiment of the present invention;
FIG. 4 is a diagram showing the comparison between the static convergence factor μ and the calculated gain and the ideal gain using the dynamic convergence factor μ table for the dynamic power variation signal according to the embodiment of the present invention;
FIG. 5 is a functional block diagram of an apparatus for enhancing digital predistortion loop gain stability in accordance with an embodiment of the present invention;
fig. 6 is a block diagram of an electronic device for performing a method of enhancing digital predistortion loop gain stability in accordance with an embodiment of the present invention;
fig. 7 is a block diagram of a computer-readable storage medium storing or carrying program code for implementing a method of enhancing digital predistortion loop gain stability in accordance with an embodiment of the present invention.
Reference numerals:
a model building unit 110; a static simulation unit 120; a table establishing unit 130; a dynamic simulation unit 140; an electronic device 300; a processor 310; a memory 320; a computer-readable storage medium 400; program code 410.
Detailed Description
The application of the adaptation in signal processing is very wide, such as equalization of filtering algorithms, acoustic echo cancellation, network echo cancellation, active noise control, biomedical engineering, etc. The nonlinearity introduced by active devices is common in wireless communication systems. Digital predistortion is a very efficient solution that enables the predistortion function to be implemented before input to the nonlinear device. Least Mean Square (LMS) adaptive filters are most popular because of their simplicity and robustness. The step size parameter is critical to the performance of the LMS and determines the rate at which the algorithm converges along the error performance plane. In order to solve the problem of rapid convergence, many variable step size methods have emerged in the engineering world. According to the LMS mathematical formula, under the condition that the average power of the signal is relatively stable, the value of the theoretical deduction convergence step factor mu is fixed, and stable performance output can be formed, but in an actual communication system, signal service is changed in real time, the service is not continuous, so that the average power of the service signal is higher in a period of time, lower in a period of time and rapid in change. It is certainly not reasonable to directly apply the static μ value to control the stability of LMS algorithm convergence.
In engineering implementation, in order to solve the problem of the stability of the LMS algorithm, a plurality of measures are taken, such as a leakage mode is applied to the coefficient, so that the stability of the coefficient under the high-precision requirement is improved; for example, the iteration step length is adjusted to improve the stability of LMS convergence. Deriving a variable step LMS filter using the square of the instantaneous error can improve the convergence limit performance of the LMS algorithm, typically when the estimation error is large, the LMS uses a large step, and vice versa. To mitigate the effects of uncorrelated perturbations, a scholars have then developed a scheme that uses the squared autocorrelation of adjacent spacing errors. The step size is considered to be controlled by gradient vector weighted average by the learner, so that the convergence speed is improved, and a lower steady-state offset error is obtained. In a word, from academic world to engineering world, reasonable optimization of convergence step factor mu can solve the problem of engineering stability of LMS algorithm application. Many studies fall to the actual engineering, and cost is considered, and many typical academic studies are not adopted by the engineering community, cost of engineering implementation is considered, and difficulty in design implementation is considered.
Therefore, how to provide a method for calculating the convergence step factor μ, which can be implemented in practical engineering at low cost, is a problem to be solved at present.
In order to solve the problem, the invention uses the real-time signal power variation characteristic to look up a table to obtain a corresponding proper mu value to ensure the stability of the LMS algorithm, and has extremely low realization cost and high flexibility.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms such as "top", "bottom", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or those that are conventionally put in use, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like, are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
Referring to fig. 1, an embodiment of the present invention provides a method for enhancing gain stability of a digital predistortion loop, wherein in a product mainly applying digital predistortion correction, a correction algorithm applied by the product needs to perform delay and gain alignment between a signal loop and a reference signal, and the method includes:
and step S101, a gain calculation model of the digital predistortion structure is realized by using an LMS algorithm at fixed points.
A set of fixed point simulation LMS data flow environments can be built by adopting simulation software, for example, S imu l i nk is used for simulating logic behaviors, so that the simulation LMS algorithm can be more accurately applied to the calculation of a gain pair Ji Ji through power calibration and fixed point signal data flow. Preparing a typical dynamic power variation signal, enabling 16bi t to have a symbol fixed point average power to be-23 dBFs, and building an S imu l nk simulation LMS algorithm platform; the method comprises the steps of defining the fixed point and the power of a signal by considering the peak-to-average ratio of the signal and the signal-to-noise ratio of the signal, and ensuring that the signal is not saturated.
As other implementations of embodiments of the present invention, both MATLAB and python can be used for the construction of simulation environments.
Specifically, the gain calculation model realized by the LMS algorithm fixed point is:
where x (n) is the baseband ideal modeling signal, fb (n) is the signal of the outer feedback loop in the digital predistortion structure, y (n) is the predistortion output signal, error signal e (n) is the difference between transmit signal x (n) and feedback signal fb (n), gain (n) is the Gain compensation value. The acquisition of e (n) requires that x (n) be aligned with the fb (n) integer and fractional delays. The gain calculation model structure is shown in fig. 2.
Step S102, inputting simulation signal streams with different static powers into the model, and respectively calculating convergence step factors mu corresponding to the different powers.
The specific operation method is as follows:
dividing a simulation signal stream of static power into a plurality of gears according to the power, and respectively inputting the gears into the model;
for each gear, respectively observing the convergence condition of the iterative Gain (n) changing along with time under different convergence step factors mu;
and obtaining the convergence step factor mu corresponding to the condition of stable fluctuation of the gain of each gear.
It should be noted that the division of the gear may be related to the signal characteristics of the input. For example, in the simulation model Simu i nk, considering that the signal modulus value is 15bit fixed point, the power gear can be divided according to bit, and each 1bit corresponds to one power gear. For each power shift, a corresponding convergence step factor μmay be obtained.
Step S103, a convergence step factor table is constructed according to the power of the simulation signal and the convergence step factor mu obtained through corresponding calculation.
The mu value distribution of the convergence factor table is subject to small step size of high power and large step size of low power.
Step S104, inputting a dynamic power simulation signal into the model, looking up a table from the convergence step factor table according to the power of the dynamic power simulation signal to obtain a corresponding convergence step factor mu value, and substituting the obtained convergence step factor mu value into a gain iteration calculation formula.
When the table look-up is performed, determining which power gear the power of the dynamic power simulation signal belongs to may be performed by obtaining the instantaneous power of the dynamic power simulation signal for comparison or obtaining the instantaneous average power of the dynamic power simulation signal.
And then according to the instantaneous power or the instantaneous average power, looking up a table from the convergence step-length factor mu table to obtain a corresponding convergence step-length factor mu value.
And obtaining a corresponding convergence step factor mu value through table lookup, and then, adding the convergence step factor mu value into a gain iterative calculation formula of the gain calculation model to perform iterative calculation.
It should be noted that, because the signal power of the dynamic power simulation signal is in a state of dynamic change from time to time, the corresponding table lookup and value taking for the value of the convergence step factor μ are also performed in real time. Typically with each beat in the system being performed periodically.
Step S105, after observing the fluctuation condition of the gain convergence curve, adjusting the convergence step factor mu value in the convergence step factor table according to the difference between the fluctuation condition of the gain convergence curve and the ideal state.
For the gain convergence curve fluctuation condition of iterative calculation by using a table lookup to obtain the convergence step factor mu value, continuous observation is needed, and the gain convergence curve fluctuation condition is compared with an ideal state. As a preferred embodiment, the gain value calculated in real time at the static power corresponding to the gear power and its stability may be used as a reference convergence curve and convergence value of the ideal state for comparison, as shown in fig. 3. Based on the difference between the two, the convergence step factor mu value in the convergence step factor table is adjusted, and the fluctuation of the gain convergence curve is ensured to be similar to the fluctuation in the static power scene.
It should be noted that the above process may be repeated continuously through multiple simulation experiments to determine an optimal convergence step factor table for use in subsequent practical engineering applications.
On the basis of the scheme, in order to verify the effectiveness of the scheme provided by the invention, when a dynamic power simulation signal is input into the model, a static convergence step factor mu can be synchronously adopted to be substituted into a gain iterative calculation formula for calculation, and in the method, the signal power is unchanged in size, convergence step and convergence speed, and larger fluctuation of performance can occur theoretically, as shown in fig. 4. The corresponding fluctuation condition of the gain convergence curve is compared with the fluctuation condition of the gain convergence curve in a table look-up mode, so that the effectiveness of the invention is verified.
The following is a specific example:
in this embodiment, the test signal is NR100M, the transmission mode is tm2.0a, the downlink frequency point of the test is 2.6G, the carrier frequency point is 2.6GHz,Transce iver chip is Bai Ze, the test signal and the feedback signal are collected in a large amount in Bai Ze chips after the test signal is looped back to 20 chips through an external power amplifier, the average power of the digital signal is calibrated to-23 dBFs, and the fixed point has a symbol of 16 bits.
The implementation method of the invention comprises the following steps of S1: setting up a fixed point LMS algorithm to realize a loop gain calculation project of a link, and setting up an external loop-back simulation link; s2: transmitting 10ms cell service data, performing effective test, and collecting about 6.7ms data; s3: firstly, calculating loop gain by using a static convergence factor mu, and recording a real-time output value; s4: and then using 4 gear dynamic convergence factor tables, actually calling [15,13,11,9] bit, confirming the address of the lookup table by the real-time signal power gear, taking the input signal as a modulus value in actual measurement, saturating the high bit by 2bit, cutting the low bit by 11bit, and only reserving the effective lookup table address of 2 bit.
In summary, the invention provides a method for enhancing the gain stability of a digital predistortion loop, which can introduce a variable step length concept into a communication system at the minimum and simplest cost, introduce a dynamic convergence step length factor, and solve the stability of loop signal gain under different power fluctuation of signals so as to improve the stability of gain calculation and the stability of predistortion algorithm correction. And effectively improves the quality of the communication system channel. And extracting a limited depth variable step convergence factor mu gear table through early simulation analysis, taking a signal which changes in real time as a gear lookup table address, and tracking and calling a theoretically optimal mu value when the signal changes to assist the stability of the LMS algorithm. By applying the scheme, the realization cost is extremely low, the optimal flexibility is realized in a register configuration form, and the stability of the iterative LMS algorithm can be tracked rapidly in different complex communication systems.
As shown in fig. 5, the apparatus for enhancing gain stability of a digital predistortion loop provided by the embodiment of the present invention includes:
a model building unit 110, configured to implement a gain calculation model of the digital predistortion structure at fixed points by using an LMS algorithm;
the static simulation unit 120 is configured to input simulated signal streams with different static powers into the model, and calculate convergence step factors μ corresponding to the different powers respectively;
a table establishing unit 130, configured to construct a convergence step factor table μ according to the power of the simulation signal and the convergence step factor μ obtained by the corresponding calculation;
the dynamic simulation unit 140 is configured to input a dynamic power simulation signal into the model, and obtain a corresponding convergence step factor μ value from the convergence step factor μ table by looking up a table according to the power level of the dynamic power simulation signal; and calculating a gain compensation value of the digital predistortion structure according to the acquired convergence step size factor mu value.
As a preferred implementation manner of this embodiment, the model building unit 110 uses the LMS algorithm to implement the gain calculation model at fixed points as follows:
where x (n) is the baseband ideal modeling signal, fb (n) is the signal of the outer feedback loop in the digital predistortion structure, y (n) is the predistortion output signal, error signal e (n) is the difference between transmit signal x (n) and feedback signal fb (n), gain (n) is the Gain compensation value.
As a preferred implementation manner of this embodiment, the static simulation unit 120 is specifically configured to:
dividing the simulation signal flow into a plurality of gears according to the magnitude of static power, and respectively inputting the gears into the model;
for each gear, respectively observing the convergence condition of the iterative Gain (n) changing along with time under different convergence step factors mu;
and obtaining the convergence step factor mu corresponding to the condition of stable fluctuation of the gain of each gear.
As a preferred implementation manner of this embodiment, the dynamic simulation unit 140 is specifically configured to:
acquiring the instantaneous power or the instantaneous average power of the dynamic power simulation signal;
and according to the instantaneous power or the instantaneous average power, looking up a table from the convergence step-length factor mu table to obtain a corresponding convergence step-length factor mu value.
The device for enhancing the gain stability of the digital predistortion loop provided by the embodiment of the invention is used for realizing the method for enhancing the gain stability of the digital predistortion loop, so that the specific implementation manner is the same as the method and is not repeated here.
As shown in fig. 6, an embodiment of the present invention provides a block diagram of an electronic device 300. The electronic device 300 may be a smart phone, tablet, electronic book, etc. capable of running an application program of the electronic device 300. The electronic device 300 in this application may include one or more of the following components: a processor 310, a memory 320, and one or more applications, wherein the one or more applications may be stored in the memory 320 and configured to be executed by the one or more processors 310, the one or more applications configured to perform the method as described in the foregoing method embodiments.
Processor 310 may include one or more processing cores. The processor 310 utilizes various interfaces and lines to connect various portions of the overall electronic device 300, perform various functions of the electronic device 300, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 320, and invoking data stored in the memory 320. Alternatively, the processor 310 may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 310 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for being responsible for rendering and drawing of display content; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 310 and may be implemented solely by a single communication chip.
The Memory 320 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Memory 320 may be used to store instructions, programs, code sets, or instruction sets. The memory 320 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described below, etc. The storage data area may also store data created by the terminal in use (such as phonebook, audio-video data, chat-record data), etc.
As shown in fig. 7, a block diagram of a computer-readable storage medium 400 is provided according to an embodiment of the present invention. The computer readable medium has stored therein a program code 410, said program code 410 being callable by a processor for performing the method described in the above method embodiments.
The computer readable storage medium 400 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium 400 comprises a non-volatile computer readable medium (non-transitory computer-readable storage medium). The computer readable storage medium 400 has storage space for program code 410 that performs any of the method steps described above. These program code 410 can be read from or written to one or more computer program products. Program code 410 may be compressed, for example, in a suitable form.
In summary, the present invention provides a method, an apparatus, an electronic device, and a storage medium for enhancing gain stability of a digital predistortion loop, which can introduce a variable step concept into a communication system at the least simple cost, introduce a dynamic convergence step factor, and solve the stability of loop signal gain under different power fluctuations of signals, so as to improve the stability of gain calculation and the stability of predistortion algorithm correction. And effectively improves the quality of the communication system channel. And extracting a limited depth variable step convergence factor mu gear table through early simulation analysis, taking a signal which changes in real time as a gear lookup table address, and tracking and calling a theoretically optimal mu value when the signal changes to assist the stability of the LMS algorithm. By applying the scheme, the realization cost is extremely low, the optimal flexibility is realized in a register configuration form, and the stability of the iterative LMS algorithm can be tracked rapidly in different complex communication systems.
In several embodiments disclosed in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Claims (10)
1. A method of enhancing digital predistortion loop gain stability, the method comprising:
a gain calculation model of the digital predistortion structure is realized at fixed points by using an LMS algorithm;
inputting the simulation signal flow of the fixed-point static power into the model, and respectively calculating convergence step factors mu corresponding to different powers;
constructing a convergence step factor table according to the power of the simulation signal and the convergence step factor mu obtained by corresponding calculation;
inputting a dynamic power simulation signal into the model, looking up a table from the convergence step size factor table according to the power of the dynamic power simulation signal to obtain a corresponding convergence step size factor mu value, and substituting the obtained convergence step size factor mu value into a gain iteration calculation formula;
after the fluctuation condition of the gain convergence curve is observed, the convergence step factor mu value in the convergence step factor table is adjusted according to the difference between the fluctuation condition of the gain convergence curve and the ideal state.
2. The method for enhancing gain stability of a digital predistortion loop according to claim 1, wherein the gain calculation model implemented with LMS algorithm fixed point is:
where x (n) is the baseband ideal modeling signal, fb (n) is the signal of the outer feedback loop in the digital predistortion structure, y (n) is the predistortion output signal, error signal e (n) is the difference between transmit signal x (n) and feedback signal fb (n), gain (n) is the Gain compensation value.
3. The method for enhancing gain stability of a digital predistortion loop according to claim 2, wherein said step of inputting simulated signal streams of different static powers into said model and calculating convergence step factors μ corresponding to the different powers respectively comprises:
dividing the simulation signal flow into a plurality of gears according to the magnitude of static power, and respectively inputting the gears into the model;
for each gear, respectively observing the convergence condition of the iterative Gain (n) changing along with time under different convergence step factors mu;
and obtaining the convergence step factor mu corresponding to the condition of stable fluctuation of the gain of each gear.
4. A method for enhancing gain stability of a digital predistortion loop according to claim 3, wherein said step of looking up a table from said convergence step factor table according to the power level of said dynamic power simulation signal to obtain a corresponding convergence step factor μ value comprises:
acquiring the instantaneous power or the instantaneous average power of the dynamic power simulation signal;
and according to the instantaneous power or the instantaneous average power, obtaining a corresponding convergence step factor mu value from the convergence step factor table in a table look-up mode.
5. An apparatus for enhancing digital predistortion loop gain stability, said apparatus comprising:
the model building unit is used for realizing a gain calculation model of the digital predistortion structure by using an LMS algorithm at fixed points;
the static simulation unit is used for inputting simulation signal streams with different static powers into the model and respectively calculating convergence step factors mu corresponding to the different powers;
the table establishing unit is used for establishing a convergence step factor table according to the power of the simulation signal and the convergence step factor mu obtained by corresponding calculation;
the dynamic simulation unit is used for inputting a dynamic power simulation signal into the model, acquiring a corresponding convergence step factor mu value from the convergence step factor table according to the power of the dynamic power simulation signal, and substituting the acquired convergence step factor mu value into a gain iteration calculation formula;
the table establishing unit is further configured to adjust the convergence step factor μ value in the convergence step factor μ table according to a difference between the gain convergence curve fluctuation condition and the ideal state after observing the gain convergence curve fluctuation condition.
6. The apparatus for enhancing gain stability of a digital predistortion loop according to claim 5, wherein said model building unit uses LMS algorithm to implement a gain calculation model at fixed point as:
where x (n) is the baseband ideal modeling signal, fb (n) is the signal of the outer feedback loop in the digital predistortion structure, y (n) is the predistortion output signal, error signal e (n) is the difference between transmit signal x (n) and feedback signal fb (n), gain (n) is the Gain compensation value.
7. The apparatus for enhancing gain stability of a digital predistortion loop according to claim 6, wherein said static simulation unit is specifically configured to:
dividing the simulation signal flow into a plurality of gears according to the magnitude of static power, and respectively inputting the gears into the model;
for each gear, respectively observing the convergence condition of the iterative Gain (n) changing along with time under different convergence step factors mu;
and obtaining the convergence step factor mu corresponding to the condition of stable fluctuation of the gain of each gear.
8. The apparatus for enhancing gain stability of a digital predistortion loop according to claim 7, wherein said dynamic simulation unit is specifically configured to:
acquiring the instantaneous power or the instantaneous average power of the dynamic power simulation signal;
and according to the instantaneous power or the instantaneous average power, obtaining a corresponding convergence step factor mu value from the convergence step factor table in a table look-up mode.
9. An electronic device, comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications configured to perform the method of any of claims 1-4.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program code, which is callable by a processor for executing the method according to any one of claims 1-4.
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