Improved digital pre-distortion correction for radio frequency power amplifiers
By gradually increasing the transmission power of narrowband or broadband training signals, predicting and applying the correction coefficient of the RF power amplifier, the problem of low performance of RF power amplifiers at high power is solved, and the performance improvement of power amplifiers and current consumption is achieved.
Patent Information
- Application Number
- CN202410764789.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2023-06-14
- Filing Date
- 2024-06-14
- Publication Date
- 2024-12-17
AI Technical Summary
When existing RF power amplifiers weigh linearity and power efficiency, sending training signals at high power is prone to violate power spectral density regulation requirements, resulting in reduced performance and increased current consumption.
By gradually increasing the transmission power of narrowband or broadband training signals, the correction coefficients of the power amplifier at high power are predicted, using curve fitting to generate additional coefficients, stored and applied to improve power amplifier performance and reduce current consumption.
It effectively overcomes the problem of low power amplifier performance, reduces the training signal transmission time, reduces current consumption, and increases the transmission distance of RF signals.
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Figure CN119154812A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure pertains to wireless devices and, more particularly, to improving digital predistortion correction of radio frequency power amplifiers. Background Art
[0002] Wireless Local Area Network (WLAN) (e.g. networks and other networks operating under the IEEE 802.11 standard or other wireless standards) provide wireless connectivity in a variety of battery-operated applications (e.g., portable game consoles, automotive infotainment systems, etc.). BRIEF DESCRIPTION OF THE DRAWINGS
[0003] Figure 1 is a block diagram of an exemplary wireless device for pre-distortion correction of a radio frequency power amplifier in accordance with some embodiments.
[0004] Figure 2A is an illustrative example of a correction table generated by pre-distortion correction according to a certain embodiment.
[0005] Figure 2B is a graphical illustration of a correction table generated by pre-distortion correction according to an embodiment.
[0006] Figure 3 is a flow chart of an exemplary method of pre-distortion correction of a radio frequency power amplifier according to some embodiments.
[0007] Figure 4 is a block diagram of an exemplary wireless device for pre-distortion correction of a radio frequency power amplifier in accordance with some embodiments. DETAILED DESCRIPTION
[0008] Radio frequency (RF) modules (e.g., WLAN modules) typically use radio frequency power amplifiers (e.g., power amplifiers) to convert low-power radio frequency signals into higher-power radio frequency signals to drive the antenna of the transmitter. The higher-power radio frequency signals generated by the power amplifier help the low-power radio frequency signals to be sent farther. Typically, power amplifiers must make a trade-off between linearity and power efficiency. The linearity of a power amplifier refers to the ability of the power amplifier to produce an accurate replica of the input signal. The power efficiency of a power amplifier refers to the amount of input power converted to output power that affects signal quality. This trade-off becomes increasingly important when RF modules are incorporated into battery-operated devices. More specifically, because if the power amplifier has poor power efficiency, the battery of the battery-operated device will be exhausted, the power efficiency of the power amplifier becomes more important. Therefore, power amplifiers (especially those incorporated into battery-operated devices) are designed to be as energy-efficient as possible, resulting in nonlinear power amplifiers. Nonlinear power amplifiers can compress and / or amplify signals of different amplitudes by different amounts. This is in contrast to linear power amplifiers, which compress and / or amplify signals of different amplitudes by fixed amounts.
[0009] Therefore, digital baseband techniques are implemented to compensate for the trade-off between the linearity of the power amplifier and the power efficiency of the power amplifier. Digital baseband techniques, such as digital pre-distortion (DPD) or power amplifier pre-distortion (PAPD), are used to linearize the power amplifier. Typical digital baseband techniques input a training signal, such as a sinusoidal or similar type of signal, which is swept over a range of input power values to learn the nonlinearity of the power amplifier. However, these techniques may violate various regulatory requirements associated with power spectral density (PSD), where PSD refers to the amount of power over a given bandwidth. For example, sending a training signal at high power to learn the nonlinearity of the power amplifier typically results in a PSD that violates these regulatory requirements.
[0010] Complying with these regulatory requirements requires that the training signal not be sent at high power. Not sending the training signal at high power to learn the nonlinearity of the power amplifier degrades the performance of the power amplifier since the entire range of input power values cannot be used to learn the nonlinearity of the power amplifier. Using a wideband training signal instead of a narrowband training signal prevents the training signal from violating regulatory requirements associated with PSD when sent at higher powers. The longer these techniques are performed, the more likely it is that the wideband training signal will significantly increase current consumption.
[0011] Aspects and embodiments of the present disclosure address these and other limitations of the prior art by implementing systems and methods for predicting one or more coefficients of a power amplifier at high power. In response to starting digital pre-distortion correction of a power amplifier of an RF module, a set of correction coefficients (e.g., a set of estimated coefficients) is determined. In some embodiments, this set of estimated correction coefficients is determined by gradually increasing the transmission power of a narrowband training signal between an initial transmission power value and a specified transmission power value. With each increase, the narrowband training signal is sent to the power amplifier of the RF module. The power amplifier can provide output power as feedback to help determine the estimated coefficients at each transmission power. In some embodiments, a set of estimated correction coefficients is determined by sending a broadband training signal (which has a sample transmission power with a larger dynamic range) to the power amplifier of the RF module. The power amplifier can provide output power as feedback to help determine the estimated coefficients at each sample transmission power in the sample transmission power. This set of estimated correction coefficients is stored in a memory.
[0012] Curve fitting may be performed on a specified number of estimated coefficients in a set of estimated coefficients (e.g., a subgroup of the set of estimated coefficients). The specific number may be a predetermined number, or dynamically determined. A fitted curve associated with the curve fitting is generated for the subgroup of the set of estimated coefficients. Additional coefficients may be determined by interpolating and / or extrapolating the fitted curve. Interpolating the fitted curve generates additional coefficients within a range of transmission powers associated with the subgroup of the set of estimated coefficients. Extrapolating the fitted curve generates additional coefficients (e.g., predicted coefficients) outside a range of transmission powers associated with the subgroup of the set of estimated coefficients. The additional coefficients (e.g., predicted coefficients) are stored in a memory.
[0013] Aspects of the present disclosure overcome these and other deficiencies by predicting coefficients that violate regulatory limits at higher transmit powers, thereby improving power amplifier performance, and reducing the time to send training signals to the power amplifier for correction, thereby reducing current consumption during correction.
[0014] Figure 1 1 is a diagram of one embodiment of an exemplary network device (e.g., wireless device 110) according to some embodiments. Wireless device 110 may be implemented as an integrated circuit (IC) device (e.g., disposed on a single semiconductor die). Wireless device 110 includes various modules and components, but it should be understood that some modules and components may not be present for the sake of brevity.
[0015] The wireless device 110 may include a transmitter 140 coupled to one or more antennas 180 configured to transmit and receive radio waves. The antenna 180 may be a single antenna, a multiple-input multiple-output (MIMO) antenna, multiple antennas, multiple MIMO antennas, etc. The transmitter 140 generates a radio frequency alternating current to supply the one or more antennas 180 so that the one or more antennas 180 radiate radio waves. The transmitter 140 may include a radio frequency (RF) power amplifier 145 to increase the power of the signal, thereby increasing the range of the radio waves. The RF power amplifier 145 may be coupled between the input signal and the one or more antennas 180. The RF power amplifier 145 increases the power of the signal by converting a low-power radio frequency signal into a higher-power signal.
[0016] The wireless device 110 may also include one or more processing devices 120. In some embodiments, the processing device(s) 120 may include one or more central processing units (CPUs), finite state machines (FSMs), field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc. The processing device(s) 120 may be a single processing device that performs various operations to correct parameters of the RF power amplifier 145 of the wireless device 110. In some embodiments, the wireless device 110 may have a dedicated processor for improving digital pre-distortion correction of the RF power amplifier 145, separate from a processor that performs other operations on the wireless device 110 (e.g., processes associated with data transmission and reception).
[0017] The processing device(s) 120 may include a pre-distortion correction logic unit 125 (e.g., correction logic unit 125) to learn the nonlinearity of the RF power amplifier 145. The correction logic unit 125 may be activated in response to a trigger event. The trigger event may include power-on of the wireless device, the temperature of the wireless device exceeding a predetermined temperature value, or the time since the last activation of the calibration logic unit 125 exceeds a predetermined time value. Once activated, the correction logic unit 125 may send a training signal to the RF power amplifier 145. The training signal may be sinusoidal.
[0018] In some embodiments, the training signal may be a narrowband signal (e.g., a narrowband training signal). The correction logic unit 125 may set the transmission power of the narrowband training signal. Initially, the transmission power is an initial transmission power value. The transmission power of the narrowband training signal is set, and the narrowband training signal is sent to the RF power amplifier 145 by the correction logic unit 125. The correction logic unit 125 may receive a loopback signal from the RF power amplifier 145, which provides the output power of the RF power amplifier 145 associated with the transmission power of the narrowband training. The correction logic unit 125 may determine an estimated correction coefficient (e.g., an estimated coefficient) based on the loopback signal (e.g., the output power). The correction logic unit 125 adds the input value to the correction table 135, and stores the estimated coefficient in the newly added input value indexed by the transmission power. The correction table 135 may be stored in the memory 130 of the wireless device 110, and the memory 130 may be (or include) a non-volatile (e.g., read-only (ROM)) memory, and / or a volatile (e.g., random access (RAM)) memory.
[0019] The correction logic unit 125 may increase the transmission power incrementally by a step value (or a predetermined adjustment value) until a specified transmission power is reached (i.e., between the initial transmission power and the specified transmission power). With each increase, the correction logic unit 125 updates the transmission power of the narrowband training signal. The correction logic unit 125 sends the narrowband training signal to the RF power amplifier 145 to receive the loopback signal. The correction logic unit 125 determines the estimation coefficient based on the loopback signal. The correction logic unit 125 adds the input value to the correction table 135 and stores the estimation coefficient in the newly added input value indexed by the updated transmission power.
[0020] In some embodiments, the specified transmission power can be a predetermined transmission power based on regulatory provisions associated with the PSD. In other embodiments, the specified transmission power can be dynamically determined based on each input value of the correction table 135. More specifically, the correction logic unit 125 can measure the steepness (e.g., transmission power metric) of a series of drawn input values (e.g., transmission power, estimated coefficients) from the correction table 135. Depending on the embodiment, the transmission power metric may include other calculations, such as accumulators and / or fractional adders. Therefore, if the transmission power metric is less than a predetermined metric threshold, the correction table 135 increases the transmission power. Otherwise, if the transmission power metric is equal to or greater than a predetermined metric threshold, the correction table 135 stops increasing the transmission power. Once the transmission power is no longer increased, the correction logic unit 125 no longer sends the narrowband training signal to the RF power amplifier 145.
[0021] In some embodiments, the training signal may be a broadband signal (e.g., a broadband training signal) rather than a narrowband signal. The broadband training signal may have a larger and more dynamic range of sample transmission powers (e.g., a set of transmission powers). The correction logic unit 125 may send the broadband training signal to the RF power amplifier 145. The correction logic unit 125 may receive a loopback signal from the RF power amplifier 145 that provides the output power of the RF power amplifier 145 associated with each transmission power in a set of transmission powers. The correction logic unit 125 may determine an estimated correction coefficient (e.g., an estimated coefficient) based on each received loopback signal. For each transmission power, the correction logic unit 125 adds the input value to the correction table 135, and stores the estimated coefficient associated with the corresponding transmission power in a set of transmission powers in the newly added input value indexed by the corresponding transmission power.
[0022] Sending a training signal (eg, a narrowband signal or a wideband signal) to the RF power amplifier 145 to obtain estimated coefficients may be referred to as online calibration by the calibration logic unit 125 .
[0023] The correction logic unit 125 may determine a specified number of input values from the correction table 135 to perform curve fitting. The specified number of input values may be a predetermined number of input values. For example, the correction logic unit 125 may select a last predetermined number of input values from the correction table 135. In other embodiments, the correction logic unit 125 may dynamically determine the specified number of input values.
[0024] The correction logic unit 125 may perform a curve fit on a specified number of input values from the correction table 135. The correction logic unit 125 may perform the curve fit using least squares estimation, weighted least squares estimation, or any other suitable estimation technique. The curve fit produces a fitted curve along a set of input values confirmed by the correction logic unit 125 using the specified number of input values. In some embodiments, the correction logic unit 125 may perform an interpolation that uses the fitted curve to confirm additional coefficients (e.g., interpolation coefficients) along the range of transmission powers covered by a set of input values. In response, for each interpolation coefficient, the correction logic unit 125 adds the input value to the correction table 135 and stores the interpolation coefficient in the newly added input value indexed by the transmission power associated with the interpolation coefficient. The correction logic unit 125 may insert the newly added input value into the position of the correction table 135 where the numerical ordering of the transmission powers is maintained.
[0025] The correction logic unit 125 may perform an extrapolation that uses the fitted curve to project, extend, or expand the fitted curve to identify additional coefficients (e.g., extrapolated coefficients or predicted coefficients) beyond the range of transmission powers covered by a set of input values. In some embodiments, the fitted curve may be projected, extended, or expanded to a predetermined maximum transmission power. In one example, the predetermined maximum transmission power may be a transmission power value necessary to fully understand the nonlinearity of the RF power amplifier 145 beyond a specified power index. In another example, the predetermined maximum transmission power may be a maximum transmission power of a signal that the RF power amplifier 145 is capable of transmitting.
[0026] In response, for each extrapolation coefficient, the correction logic unit 125 adds an input value to the correction table 135 and stores the extrapolation coefficient in the newly added input value indexed by the transmission power associated with the extrapolation coefficient. The correction logic unit 125 may insert the newly added input value into the position of the correction table 135 in which the numerical ordering of the transmission powers is maintained.
[0027] Curve fitting is performed on one or more estimated coefficients to interpolate and / or extrapolate from the fitted curve associated with the performance of the curve fitting, and additional coefficients (eg, interpolated and / or extrapolated coefficients) may be referred to as an offline correction by the correction logic unit 125 .
[0028] Figure 2A is an illustrative example of a correction table 200 (eg, table 200) generated by pre-distortion correction. Correction table 200 is similar to Figure 1 Table 200 may include a plurality of rows (or input values), each row being identified by a transmission power. Each row stores coefficient values corresponding to a transmission power. A first subset of the stored coefficient values may be obtained by sending a training signal to a power amplifier (e.g., Figure 1 The first subset of stored coefficient values are typically coefficients between the initial transmission power and the specified transmission power.
[0029] The second subset of stored coefficient values may be obtained by performing curve fitting on one or more stored coefficient values in the first subset of stored coefficient values, and interpolating the second subset of stored coefficient values from the fitted curve generated by the curve fitting. The second subset of stored coefficient values may be one or more additional coefficient values between the initial transmission power and the specified transmission power. The additional coefficient values are not included in the one or more coefficient values in the first subset of stored coefficient values. The third subset of stored coefficient values may be obtained by performing extrapolation from the fitted curve on one or more additional coefficient values beyond the initial transmission power and the specified transmission power (e.g., from the specified transmission power to the maximum transmission power). Therefore, the additional coefficient values are separate and unique from the first and second subsets of stored coefficient values.
[0030] Figure 2B is a correction table generated by pre-distortion correction (e.g., Figure 2A 200 in FIG. 25 ). A first subset of stored coefficient values within a first range 240 of transmission power represents all coefficient values (e.g., estimated coefficients) obtained by online calibration. The stored coefficient values within a second range 250 of transmission power represent a subset of the first subset of stored coefficient values used for curve fitting. The curve fitting produces a fitted curve 255. The fitted curve 255 can be used to interpolate additional coefficient values (e.g., interpolated coefficients) that will typically fall within the second range 250 of transmission power. The fitted curve 255 can be extended to extrapolate additional coefficient values (e.g., extrapolated or predicted coefficients). The fitted curve 255 can be extended according to a third range 265 of transmission power, which is outside the first range 240 of transmission power (and inherently, the second range 250 of transmission power). The third range 265 of transmission power may include all possible extrapolation coefficients that may fall within the third range 265 of transmission power (eg, the third range 265 of transmission power).
[0031] Figure 3 is a flow chart of an exemplary method 300 for improving digital predistortion correction of an RF power amplifier according to some embodiments. The method 300 may be performed by a processing logic unit of a wireless device. The processing logic unit that performs the method 300 may include hardware (e.g., circuitry, dedicated logic units, programmable logic units, microcode, etc.), firmware and / or software, or any combination thereof. In some embodiments, the method 300 may be performed by Figure 1The method 300 may be performed by the processing device 120 (or any other processing logic unit) of the wireless device 110 in the embodiment. The processing device 120 executing the method 300 may execute instructions of the pre-distortion correction logic unit 125. In some embodiments, the method 300 may be performed by a single processing thread. Alternatively, the method 300 may be performed by two or more processing threads, each thread performing one or more separate functions, routines, subroutines, or operations of the method. The processing threads implementing the method 300 may be synchronized (e.g., using semaphores, critical sections, and / or other thread synchronization mechanisms). Alternatively, the processing threads implementing the method 300 may be executed asynchronously with respect to each other. The various operations of the method 300 may be performed in a manner similar to Figure 3 300. Some operations of method 300 may be performed simultaneously with other operations. Some operations may be optional.
[0032] At block 310, the processing logic unit may initiate digital predistortion correction of a power amplifier of a radio frequency (RF) module. As previously described, the digital predistortion correction may be initiated in response to a triggering event, such as power-on of the wireless device, the temperature of the wireless device exceeding a predetermined temperature value, or the time since the last initiation of the digital predistortion correction exceeding a predetermined time value.
[0033] At block 320, the processing logic unit may determine a set of estimation coefficients based on a transmission power of a training signal sent to the power amplifier between a first transmission power value and a second transmission power value. The first transmission power value may be an initial transmission power value. The second transmission power value may be dynamically determined by calculating a transmission power metric and comparing it to a predetermined metric threshold. The transmission power metric corresponds to the steepness of all previous estimation coefficients. Thus, the transmission power is gradually increased until the transmission power metric exceeds the predetermined metric threshold. In some embodiments, the second transmission power value is a predetermined transmission power value based on regulatory provisions. Each estimation coefficient in the set of estimation coefficients is determined by incrementing the transmission power of the training signal by a predetermined step value between the first transmission power value and the second transmission power value.
[0034] As previously described, the training signal may be a narrowband signal or a broadband signal. In some embodiments, if the training signal is a broadband signal, the broadband signal includes a set of sample transmission powers associated with the broadband signal. Therefore, method 300 iterates each sample transmission power in a set of sample transmission powers to obtain a set of estimation coefficients.
[0035] At block 330, the processing logic may generate a set of prediction coefficients based on a subset of the set of estimated coefficients. The set of prediction coefficients is derived from a fitted curve applied to the subset of the set of estimated coefficients. The fitted curve may be based on least squares estimation or weighted least squares estimation. As previously described, the subset is selected from a set of estimated coefficients. In some embodiments, the selection of the subset may be based on a transmission power metric or a predetermined number of estimated coefficients (e.g., a predetermined number of estimated coefficients from a previous time). A set of prediction coefficients is generated by extending a fitted curve associated with the curve fit from a second transmission power value to a maximum power of a power amplifier of the RF module. Thus, a set of prediction coefficients is derived from an extended fitted curve between the second transmission power value and the maximum power of the power amplifier of the RF module.
[0036] At block 340, the processing logic unit may store a set of estimated coefficients and a set of predicted coefficients. As previously described, after each training signal is sent at a transmission power value and a loopback signal is received, the set of estimated coefficients is stored. The set of predicted coefficients is stored once it is generated. The set of estimated coefficients and the set of predicted coefficients together represent a set of coefficients between an initial transmission power value and a maximum transmission power value of the power amplifier.
[0037] Figure 4 is a diagram of one embodiment of a wireless device 400 according to some embodiments. The wireless device 400 includes various modules and components, but it should be understood that some modules and components may not be present for the sake of brevity.
[0038] The wireless device 400 may include a processing device 410. The processing device 410 may include a processing core 412 and a memory 416. In some embodiments, the memory 416 may be (or include) a non-volatile (e.g., read-only (ROM)) memory, and / or a volatile (e.g., random access (RAM)) memory. In some embodiments, the memory 416 may be one or more data registers. Similar to Figure 1 The processing core 412 may execute instructions to perform operations in the pre-distortion correction logic unit 125 to learn the nonlinearity of the power amplifier.
[0039] The wireless device may include a wireless local area network (WLAN) module 450. The WLAN module 450 may include an RF power amplifier 455. In response to a trigger event (e.g., power-on of the wireless device, the WLAN module exceeding a predetermined temperature value, or the time since the last nonlinearity was learned exceeding a predetermined time value), the processing core 412 begins sending a training signal to the RF power amplifier 455. The training signal may be sinusoidal.
[0040] If the training signal is a narrowband signal, the processing core begins sending the training signal at an initial transmission power value to a specified transmission power value. After each transmission of the training signal at the transmission power value, the processing core 412 determines an estimated correction coefficient and stores the estimated correction coefficient and the corresponding transmission power value in a memory 416 (e.g., a register, a non-volatile memory, or a volatile memory). In addition, the processing core 412 determines whether the specified transmission power value (e.g., a predetermined transmission power value) has been reached. In some embodiments, the processing core 412 can dynamically determine whether to continue to increase the transmission power value and obtain additional estimated correction coefficients based on a transmission power metric (based on the steepness of a plot of previously estimated coefficients). The processing core 412 compares the transmission power metric with a predetermined metric threshold. If the transmission power metric exceeds the predetermined metric threshold, the processing core 412 ends sending the training signal. If the transmission power metric does not exceed the predetermined metric threshold, the processing core 412 continues to increase the transmission of the training signal at an increased transmission power value.
[0041] If the training signal is a wideband signal, the processing core sends the training signal with a set of sample transmit power values. After each transmission of the training signal with a sample transmit power value in the set of sample transmit power values, the processing core 412 determines an estimated correction coefficient and stores the estimated correction coefficient and the corresponding sample transmit power value in a memory 416 (e.g., a register, a non-volatile memory, or a volatile memory). The processing core 412 can decide to end further transmission of the training signal by determining whether a transmit power metric (based on the steepness of a plot of previously estimated coefficients) exceeds a predetermined metric threshold.
[0042] Processing core 412 may confirm a specified number of estimated coefficients from memory 416 to perform curve fitting. In some embodiments, the specified number of estimated coefficients may be a predetermined number of input values (e.g., a predetermined number of estimated coefficients from a previous time). Processing core 412 may perform curve fitting on the specified number of estimated coefficients using least squares estimation, weighted least squares estimation, or any other suitable estimation technique. Processing core 412 may perform interpolation to confirm additional coefficients (e.g., interpolation coefficients) along the range of transmission power values covered by the specified number of estimated coefficients for curve fitting. Each interpolation coefficient and its corresponding transmission power value are stored in memory 416.
[0043] The processing core 412 may perform extrapolation to identify additional coefficients (e.g., extrapolated coefficients or predicted coefficients) that exceed the range of transmission power values covered by the specified number of estimated coefficients for curve fitting. The range of transmission power values may be from a transmission power value associated with a previous estimated coefficient in the specified number of estimated coefficients for curve fitting to a predetermined maximum transmission power (e.g., a maximum transmission power of a signal that the RF power amplifier 145 can receive). Each extrapolated coefficient and its corresponding transmission power value are stored in the memory 416.
[0044] It should be understood that the above description is intended to be illustrative rather than restrictive. For those skilled in the art, many other implementation examples will be apparent after reading and understanding the above description. Although the present disclosure describes specific examples, it will be appreciated that the systems and methods of the present invention are not limited to the examples described herein, but can be implemented with modifications within the scope of the attached claims. Therefore, the description and drawings should be considered in an illustrative sense rather than a restrictive sense. Therefore, the scope of the present disclosure should be determined with reference to the attached claims together with the full scope of equivalents claimed by such claims.
[0045] The methods, hardware, software, firmware or code embodiments described above may be implemented via instructions or code stored on a machine-accessible, machine-readable, computer-accessible or computer-readable medium that can be executed by a processing element. "Memory" includes any mechanism that provides (i.e., stores and / or transmits) information in a form readable by a machine (e.g., a computer or electronic system). For example, "memory" includes random access memory (RAM) (e.g., static RAM (SRAM) or dynamic RAM (DRAM)); ROM, magnetic or optical storage media; flash memory devices; electrical storage devices; optical storage devices; acoustic storage devices, and any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).
[0046] References throughout this specification to "one embodiment" or "an embodiment" indicate that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present disclosure. Thus, multiple occurrences of the phrase "in one embodiment" or "in an embodiment" throughout this specification do not necessarily all refer to the same embodiment. Furthermore, particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0047] In the foregoing description, specific embodiments have been given with reference to specific exemplary embodiments. However, it is apparent that various modifications and changes may be made thereto without departing from the broader spirit and scope of the present disclosure as set forth in the appended claims. Therefore, the description and drawings should be considered in an illustrative rather than a restrictive sense. In addition, the foregoing use of embodiments, examples, and / or other exemplary language does not necessarily refer to the same embodiment or the same example, but may refer to different and distinct embodiments, as well as possibly the same embodiments.
[0048] The words "example" or "exemplary" are used herein to represent serving as an example, instance or illustration. Any aspect or design described herein as "example" or "exemplary" is not necessarily to be interpreted as being preferred or advantageous over other aspects or designs. On the contrary, the use of the words "example" or "exemplary" is intended to present concepts in a concrete form. As used in this application, the term "or" is intended to represent an inclusive "or", not an exclusive "or". That is, unless otherwise specified or clearly derived from the context, "X includes A or B" is intended to represent any natural inclusive arrangement in a natural inclusive arrangement. That is, if X includes A; X includes B; or X includes both A and B, then in any of the aforementioned examples, "X includes A or B" is satisfied. In addition, unless otherwise specified or clearly indicated from the context as being directed to a singular form, the article "one" used in this application and the appended claims should generally be interpreted as meaning "one or more". In addition, unless so described, the term "embodiment" or "one embodiment" or "example" or "one example" used throughout the text is not intended to represent the same embodiment or example. Also, the terms “first,” “second,” “third,” “fourth,” etc. as used herein refer to marks for distinguishing different elements and may not necessarily have ordinal meanings according to their numerical references.
Claims
1. A method comprising: enabling digital pre-distortion correction of a power amplifier of a radio frequency (RF) module; determining a set of estimation coefficients based on a transmission power of a training signal sent to the power amplifier between a first transmission power value and a second transmission power value; generating a set of prediction coefficients based on a subset of the set of estimated coefficients, wherein the set of prediction coefficients is derived from a fitted curve applied to the subset of the set of estimated coefficients; as well as The set of estimated coefficients and the set of predicted coefficients are stored.
2. The method according to claim 1, wherein: The second transmit power value is based on a transmit power metric and a predetermined metric threshold.
3. The method according to claim 1, wherein: The subset is selected from the set of estimation coefficients based on a transmit power metric.
4. The method according to claim 1, wherein: The fitted curve is based on at least one of least squares estimation or weighted least squares estimation.
5. The method according to claim 1, wherein: The set of prediction coefficients is between the second transmission power value and the maximum power of the power amplifier of the RF module.
6. The method according to claim 1, wherein: Each estimation coefficient of the set of estimation coefficients is determined by incrementing the transmission power of the training signal by a predetermined step value between the first transmission power value and the second transmission power value.
7. The method according to claim 1, wherein: The training signal is one of a narrowband signal or a wideband signal.
8. The method according to claim 1, wherein: Generating the set of prediction coefficients includes extending a fitted curve associated with the curve fit from the second transmission power value to a maximum power of the power amplifier of the RF module.
9. A radio frequency (RF) module, comprising: Power amplifier; as well as a processing device coupled to the power amplifier, wherein the processing device is configured to: enabling digital pre-distortion correction of a power amplifier of a radio frequency (RF) module; determining a set of estimation coefficients based on a transmission power of a training signal sent to the power amplifier between a first transmission power value and a second transmission power value; generating a set of prediction coefficients based on a subset of the set of estimated coefficients, wherein the set of prediction coefficients is derived from a fitted curve applied to the subset of the set of estimated coefficients; and The set of estimated coefficients and the set of predicted coefficients are stored.
10. The radio frequency (RF) module according to claim 9, wherein: The second transmit power value is based on a transmit power metric and a predetermined metric threshold.
11. The radio frequency (RF) module according to claim 9, wherein: The subset is selected from the set of estimation coefficients based on a transmit power metric.
12. The radio frequency (RF) module according to claim 9, wherein: The fitted curve is based on at least one of least squares estimation or weighted least squares estimation.
13. The radio frequency (RF) module according to claim 9, wherein: The set of prediction coefficients is between the second transmission power value and the maximum power of the power amplifier of the RF module.
14. The radio frequency (RF) module according to claim 9, wherein: Generating the set of prediction coefficients includes extending a fitted curve associated with the curve fit from the second transmission power value to a maximum power of the power amplifier of the RF module.
15. A processing device comprising: A memory, the memory being used to store a plurality of coefficients and a set of instructions; as well as A processing core, the processing core is used to execute the instructions to perform operations, the operations comprising: enabling digital pre-distortion correction of a power amplifier of a radio frequency (RF) module; determining a set of estimation coefficients based on a transmission power of a training signal sent to the power amplifier between a first transmission power value and a second transmission power value; generating a set of prediction coefficients based on a subset of the set of estimated coefficients, wherein the set of prediction coefficients is derived from a fitted curve applied to the subset of the set of estimated coefficients; and The set of estimated coefficients and the set of predicted coefficients are stored in the memory.
16. The processing device according to claim 15, wherein: The second transmit power value is based on a transmit power metric and a predetermined metric threshold.
17. The processing device according to claim 15, wherein: The subset is selected from the set of estimation coefficients based on a transmit power metric.
18. The processing device according to claim 15, wherein: The fitted curve is based on at least one of least squares estimation or weighted least squares estimation.
19. The processing device according to claim 15, wherein: The set of prediction coefficients is between the second transmission power value and the maximum power of the power amplifier of the RF module.
20. The processing device according to claim 15, wherein: Generating the set of prediction coefficients includes extending a fitted curve associated with the curve fit from the second transmission power value to a maximum power of the power amplifier of the RF module.