Distortion compensation device and method, computer readable storage medium and communication device

Abstract

This invention relates to distortion compensation devices and methods, computer-readable storage media, and communication devices. One provided is a distortion compensation device that uses a distortion compensation model to compensate for distortion in a signal to be amplified by an amplifier, wherein internal states affecting distortion characteristics in the amplifier change, and the distortion compensation model includes: a plurality of computational models having corresponding distortion compensation characteristics for the amplifier in different internal states; and a combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal states.

Description

Technical Field

[0001] This disclosure relates to a distortion compensation device, a distortion compensation method, a non-transitory computer-readable storage medium, and a communication device. Background Technology

[0002] Amplifiers exhibit distortion characteristics. These distortion characteristics are also known as nonlinear characteristics. Distortion characteristics are represented, for example, by AM-AM or AM-PM characteristics.

[0003] In an amplifier, the internal states affecting distortion characteristics can change. These internal states can be, for example, the state in which Idq drift occurs, a phenomenon caused by changes in the idle current Idq flowing through the amplifier. The generation of Idq drift alters the amplifier's distortion characteristics.

[0004] Idq drift is a phenomenon caused by the capture of charge carriers that have entered the impurity energy levels of a semiconductor, and it is caused in devices using compound semiconductors such as GaN, particularly in high electron mobility transistors (HEMTs). Fluctuations in Idq drift alter the distortion characteristics of an amplifier.

[0005] A method for compensating for distortion characteristics caused by Idq drift is disclosed in Japanese Patent Application Publication Nos. 2017-220744 and 2014-17670. Summary of the Invention

[0006] However, in distortion compensation for amplifiers whose internal states change, affecting distortion characteristics, it is desirable to use distortion compensation characteristics corresponding to the changed internal states. Although patent documents 1 and 2 disclose methods for compensating distortion characteristics caused by Idq drift, they do not disclose methods for appropriately expressing distortion compensation characteristics corresponding to the changed internal states. Therefore, it is not possible to properly perform distortion compensation corresponding to the changed internal states.

[0007] Therefore, it is desirable to use distortion compensation characteristics that correspond to the changed internal state.

[0008] One aspect of this disclosure is a distortion compensation device. The distortion compensation device of this disclosure is a distortion compensation device that uses a distortion compensation model to compensate for the distortion of a signal to be amplified by an amplifier, wherein the internal states affecting distortion characteristics in the amplifier are changing, wherein the distortion compensation model includes: a plurality of computational models having corresponding distortion compensation characteristics for the amplifier in different internal states; and a combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal states.

[0009] Another aspect of this disclosure is a distortion compensation method. The distortion compensation method of this disclosure is for compensating for distortion in a signal to be amplified by an amplifier, wherein the internal states affecting distortion characteristics in the amplifier are changing, and the distortion compensation method includes the step of: combining a distortion compensation device with multiple computational models having corresponding distortion compensation characteristics for the amplifier in different internal states in a combination ratio corresponding to the changed internal states.

[0010] Another aspect of this disclosure is a computer program. The computer program of this disclosure is for distortion compensation of a signal to be amplified by an amplifier, wherein internal states affecting distortion characteristics in the amplifier are changed, and the computer program causes a computer to perform a process comprising: combining multiple computational models having corresponding distortion compensation characteristics for the amplifier in different internal states in a combination ratio corresponding to the changed internal states.

[0011] Another aspect of this disclosure is a communication device. The communication device of this disclosure is a communication device comprising: an amplifier that amplifies a signal for communication; and a distortion compensation device that uses a distortion compensation model to compensate for signal distortion, wherein the amplifier is configured such that internal states affecting distortion characteristics are changed, wherein the distortion compensation model comprises: a plurality of computational models having corresponding distortion compensation characteristics for the amplifier in different internal states; and a combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal states. Attached Figure Description

[0012] Figure 1 This is a block diagram of the distortion compensation processing unit in the distortion compensation device according to the first embodiment;

[0013] Figure 2 It is a configuration diagram of the communication equipment;

[0014] Figure 3 This is a hardware configuration diagram of the distortion compensation device;

[0015] Figure 4 This is an explanatory diagram of the distortion compensation model;

[0016] Figure 5 The diagram illustrates the relationship between input power and internal state parameters.

[0017] Figure 6 The diagram illustrates the relationship between internal state parameters and the computational model.

[0018] Figure 7 It is an explanatory diagram used to generate the computational model;

[0019] Figure 8 This is a flowchart of the process for generating a distortion compensation model;

[0020] Figure 9 This is an illustration of the α[n] generator;

[0021] Figure 10 This is a block diagram of the distortion compensation device according to the second embodiment;

[0022] Figure 11 This is an explanatory diagram of the distortion compensation model;

[0023] Figure 12 The diagram illustrates the relationship between internal state parameters and the computational model.

[0024] Figure 13 This is a flowchart of the process for generating the amplifier model; and

[0025] Figure 14 It is α p [n] An explanatory diagram of generators. Detailed Implementation

[0026] [Description of embodiments of this disclosure]

[0027] (1) The distortion compensation device according to the embodiment uses a distortion compensation model to compensate for the distortion of a signal to be amplified by an amplifier, in which the internal states affecting distortion characteristics are changing. Distortion characteristics are also referred to as nonlinear characteristics. Internal states are, for example, states that generate Idq drift. The distortion compensation model includes: a plurality of computational models having corresponding distortion compensation characteristics for amplifiers in different internal states; and a combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal states. The distortion compensation characteristics corresponding to the changed internal states are obtained by combining the computational models with a combination ratio corresponding to the changed internal states.

[0028] (2) The distortion compensation device may also include a generator that generates parameters indicating the internal state. In this case, the distortion compensation device is capable of generating parameters indicating the internal state. The generator may be provided in an external device of the distortion compensation device. The generator may be provided in an external device of the communication device described later. The combination ratio may be based on the parameters.

[0029] (3) Preferably, the combination ratio is determined based on a parameter indicating the internal state. Preferably, the parameter is calculated based on the signal level. In this case, the parameter can indicate the internal state that changes depending on the level of the signal to be amplified by the amplifier. Since the combination ratio is determined based on the parameter indicating the internal state, a combination ratio corresponding to the level of the signal to be amplified by the amplifier is obtained.

[0030] (4) Preferably, the parameters are calculated based on past values. Past values ​​of the parameters indicate past internal states. Therefore, the parameters can reflect past internal states. As a result, the memory effect of the amplifier is reflected in the parameters.

[0031] (5) The distortion compensation characteristics of each of the multiple calculation models preferably have characteristics for compensating for a first memory effect having a first response time in the amplifier. The parameter calculation model used to calculate the parameters preferably expresses a second memory effect having a second response time in the amplifier that is longer than the first response time. In this case, the amplifier's second memory effect is reflected in the parameters.

[0032] (6) Preferably, the combination ratio is calculated based on the signal level. In this case, a combination ratio corresponding to the level of the signal to be amplified is obtained.

[0033] (7) Preferably, a temperature-dependent parameter that varies depending on temperature conditions is used to calculate the combination ratio. In this case, the changes in amplifier characteristics due to temperature are supported.

[0034] (8) Multiple computational models can be two computational models. In this case, the distortion compensation model becomes simple.

[0035] (9) The distortion compensation device may also include a selector. Multiple computational models may include three or more computational models. The selector is preferably configured to select two or more computational models from the three or more computational models. In this case, a more appropriate computational model can be selected.

[0036] (10) The selector is preferably configured to select two or more computational models based on parameters indicating the internal state. In this case, the appropriate computational model corresponding to the internal state is selected.

[0037] (11) The multiple computational models may include: a first computational model having a first distortion compensation characteristic for an amplifier in a first internal state; and a second computational model having a second distortion compensation characteristic for an amplifier in a second internal state different from the first internal state. The combination ratio preferably has a value corresponding to a transitional internal state between the first and second internal states. In this case, the distortion compensation model is able to express distortion compensation characteristics for an amplifier in a transitional internal state.

[0038] (12) The multiple computational models may include: a first computational model having a first distortion compensation characteristic for an amplifier in a first internal state; a second computational model having a second distortion compensation characteristic for an amplifier in a second internal state different from the first internal state; and a third computational model having a third distortion compensation characteristic for an amplifier in a third internal state different from the first and second internal states. The second internal state is preferably a transitional internal state between the first and third internal states. In this case, computational models corresponding to at least three internal states can be used.

[0039] (13) The distortion compensation method according to the embodiment is a distortion compensation method for compensating the distortion of a signal to be amplified by an amplifier, wherein the internal state affecting the distortion characteristics of the amplifier is changed, the distortion compensation method comprising the step of combining a distortion compensation device with a combination ratio corresponding to the changed internal state, having a plurality of computational models having corresponding distortion compensation characteristics for amplifiers in different internal states.

[0040] (14) The computer program according to an embodiment is a computer program for distortion compensation of a signal to be amplified by an amplifier, wherein the internal states affecting distortion characteristics in the amplifier are changed, the computer program causing a computer to perform a process comprising: combining multiple computational models having corresponding distortion compensation characteristics for amplifiers in different internal states in a combination ratio corresponding to the changed internal states. The computer program is stored in a non-transitory computer-readable storage medium.

[0041] (15) The communication device according to the embodiment includes: an amplifier that amplifies a signal for communication; and a distortion compensation device that uses a distortion compensation model to compensate for distortion of the signal, wherein the amplifier is configured such that an internal state affecting distortion characteristics is changed, wherein the distortion compensation model includes: a plurality of computational models having corresponding distortion compensation characteristics for the amplifier in different internal states; and a combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal state.

[0042] [Details of the embodiments of this disclosure]

[0043] [First Embodiment]

[0044] Figure 1 and Figure 2 The figure shows a distortion compensation device 20 according to a first embodiment. Figure 2 The diagram also illustrates the configuration of a communication device 50, including a distortion compensation device 20. The distortion compensation device 20 compensates for the distortion of the signal to be amplified by the amplifier 10.

[0045] Amplifier 10 is, for example, a power amplifier. Amplifier 10 is, for example, a gallium nitride (GaN)-HEMT. Amplifier 10 is not limited to GaN amplifiers, and can be a HEMT device using compound semiconductors such as aluminum nitride (AIN) or indium nitride (InN), or AlGaN, InAIN, or InGaN as a mixed crystal of AIN and InN.

[0046] The amplifier 10 using a compound semiconductor such as GaN has a transient response called Idq drift. Idq drift is a phenomenon in which the free current Idq decreases and the distortion characteristics change as charge carriers are trapped in the impurity energy levels of the semiconductor.

[0047] In devices that induce Idq drift, distortion changes immediately according to fluctuations in the signal's electrical power. Signal power fluctuations are particularly likely to occur in communication systems that alternate between time-division duplex (TDD) transmission and reception. In amplifiers that generate Idq drift, the distortion is difficult to compensate for using a single distortion compensation model due to the altered distortion characteristics.

[0048] The likelihood of Idq drift occurring depends on the input power (input signal level) and the degree of drift. Drift is more likely to occur as input power increases. In the state where Idq drift has occurred, the gain decreases in the region of low input power. In contrast, as input power decreases, the reduced idle current Idq begins to recover, and the gain recovers over time.

[0049] Typically, amplifier 10 exhibits distortion known as the memory effect. The memory effect is the phenomenon where the amplifier's output signal is influenced by past input signals. Memory effects can include memory effects with short response times (first memory effect with a first response time; short-term memory effect) and memory effects with long response times (second memory effect with a second response time longer than the first response time; long-term memory effect). Here, response time is the time it takes for the output to respond to changes in the input, and response time is also referred to as the time constant.

[0050] The characteristic change caused by Idq drift represents a state with a second memory effect having a long response time. Constructing an amplifier model that only expresses a first memory effect with a short response time is relatively easy, but it is difficult to construct a single amplifier model that expresses a second memory effect with a long response time in addition to the first memory effect with a short response time. In particular, modeling becomes more difficult when the extent of the second memory effect depends on the internal state of the amplifier, such as the degree to which Idq has decreased.

[0051] Therefore, this embodiment uses a distortion compensation model 200, which can appropriately express distortion compensation characteristics based on changes in the internal state (state of the second memory effect) of the generated state, such as Idq drift.

[0052] In the following text, the signal is primarily represented by discrete values ​​*[n] processed in digital circuitry. Here, *[n] is the complex baseband IQ signal sampled at time n×T in a system with a sampling interval T [seconds].

[0053] For example, x[n] is the input signal before distortion compensation, and x[n] = x I [n]+j×x Q [n] expression. Here, x I [n] is the real part (I-channel) of x[n], while x Q [n] is the imaginary part (Q-channel) of x[n]. In contrast, u[n] is the distortion-compensated input signal, and is expressed as u[n] = u I [n]+j×u Q [n] expression. Here, u I [n] is the real part (I-channel) of u[n], while u Q [n] is the imaginary part (Q-channel) of u[n]. Additionally, y[n] is the output signal, and is given by y[n] = y I [n]+j×y Q [n] expression. Here, y I [n] is the real part (I-channel) of y[n], and y Q [n] is the imaginary part (Q-channel) of y[n].

[0054] Amplifier 10 is used in, for example Figure 2 In the communication device 50 shown, amplifier 10 is, for example, a power amplifier that amplifies the signal to be transmitted from the communication device 50.

[0055] Communication equipment 50 includes distortion compensation device 20. Distortion compensation device 20 uses digital signal processing to perform distortion compensation. For example... Figure 3As shown, the distortion compensation device 20 is constructed from a computer including a processor 101 and a storage device 102. The processor 101 is coupled to the storage device 102. The processor 101 is, for example, a central processing unit (CPU). The storage device 102 includes, for example, a primary storage device and a secondary storage device. The primary storage device is, for example, random access memory (RAM). The secondary storage device is, for example, a hard disk drive (HDD) or a solid-state drive (SSD). The distortion compensation device 20 may be constructed from wired logic circuitry.

[0056] Storage device 102 stores a computer program 102A that enables the computer to operate as a distortion compensation device 20. The computer program 102A is configured to cause the processor 101 to perform distortion compensation processing 101A. The computer performing distortion compensation processing 101A acts as... Figure 2 The distortion compensation device 20 shown (distortion compensation processing unit 21 and inverse characteristic estimation unit 22) is in operation.

[0057] The processor 101 reads the computer program 102A from the storage device 102 and executes the computer program 102A. The computer program 102A enables the computer to function as... Figure 1 The code for the operation of the distortion compensation model 200 and generator 300 is shown.

[0058] The computer operating as distortion compensation device 20 includes interface 103. Interface 103 includes at least one interface selected from the group consisting of: a communication interface for communicating with other computers, an input interface for connecting to an input device such as a keyboard or mouse, and an output interface for connecting to an output device such as a display.

[0059] Data such as the temperature of amplifier 10 is input to distortion compensation device 20 via interface 103.

[0060] like Figure 2 As shown, the distortion compensation device 20 includes a distortion compensation processing unit 21 and an inverse characteristic estimation unit 22. The inverse characteristic estimation unit 22 estimates the inverse model (distortion compensation model) representing the distortion compensation characteristics. The inverse model is configured, for example, as a distortion compensation function. The inverse model (distortion compensation function) indicates the inverse characteristic G of the amplification characteristic G of the amplifier 10. -1 Indicator of inverse characteristic G -1 The inverse model can be obtained as the inverse function of, for example, the characteristic G of the indicator amplifier 10. The inverse characteristic estimation unit 22 copies the estimated inverse model to the distortion compensation processing unit 21. The inverse model to be copied is specifically the parameters expressing the inverse model, and more specifically, the distortion compensation coefficients constituting the distortion compensation function.

[0061] Distortion compensation processing unit 21 uses the inverse model copied from inverse characteristic estimation unit 22 to process the input signal x[n]=xI [n]+j×x Q [n] undergoes pre-distortion compensation processing. Pre-distortion compensation processing involves applying a distortion compensation function to the input signal x[n]. The distortion compensation processing unit 21 then outputs the signal u[n] = u generated through the pre-distortion compensation processing. I [n]+j×u Q [n]. In the following text, the signal generated by the predistortion compensation process is sometimes referred to as the "compensated signal". Here, the input signal x[n] and the compensation signal u[n] are digital signals.

[0062] The communication device 50 includes DA converters (DACs) 32A and 32B, a quadrature modulator 33, a frequency converter 34, and a driver amplifier 35.

[0063] DACs 32A and 32B convert signal u[n] from a digital signal to an analog signal. Quadrature modulator 33 outputs a modulated signal obtained by quadrature modulation of the analog compensation signal (analog IQ baseband signal) output from DACs 32A and 32B. Frequency converter 34 is an up-converter and up-converts the modulated signal output from quadrature modulator 33. Driver amplifier 35 amplifies the up-converted modulated signal.

[0064] The signal output from the driver amplifier 35 is provided to amplifier 10 as the input signal to amplifier 10. Amplifier 10 outputs an output signal y(t) obtained by amplifying the input signal u(t). Peripheral circuit 10A is disposed in the pre-stage of amplifier 10, and peripheral circuit 10B is disposed in the post-stage of amplifier 10. Each of peripheral circuits 10A and 10B includes electronic circuit elements such as resistors or capacitors.

[0065] The communication device 50 also includes a coupler 36, a variable attenuator 37, a quadrature demodulator 42, filters 41A and 41B, and analog-to-digital converters (ADCs) 40A and 40B.

[0066] Coupler 36 outputs an analog monitoring signal obtained from the output signal y(t) of monitoring amplifier 10. Frequency converter 38 is a down-converter and down-converts the analog monitoring signal provided from coupler 36 via variable attenuator 37. Quadrature demodulator 42 performs quadrature demodulation on the analog monitoring signal output from frequency converter 38.

[0067] Filters 41A and 41B are low-pass or band-pass filters. The demodulated signal output from the quadrature demodulator 42 is provided to ADCs 40A and 40B after passing through filters 41A and 41B. ADCs 40A and 40B convert the demodulated signal provided by the quadrature demodulator 42 from an analog signal to a digital signal. ADCs 40A and 40B convert the digital demodulated signal y[n] = y(t) corresponding to the output signal y(t) of amplifier 10. I [n]+j×y Q [n] is provided to the inverse characteristic estimation unit 22.

[0068] Inverse characteristic estimation unit 22 uses the estimated inverse characteristic G -1 The digital demodulated signal y[n] = y is derived from the output signal y(t) of amplifier 10. I [n]+j×y Q [n] Obtain the distortion compensation signal u[n]=u I [n]+j×u Q The replica signal u'[n] = u' I [n]+j×u' Q [n].

[0069] Inverse characteristic estimation unit 22 obtains the indication distortion compensation signal u[n] = u I [n]+j×u Q [n] and the copy signal u'[n] of the distortion compensation signal = u' I [n]+j×u' Q The error signal (u') between [n] I [n]-u I [n]、u' Q [n]-u Q [n]). The inverse characteristic estimation unit 22 continuously updates the inverse characteristic G that constitutes the distortion compensation characteristic. -1 The parameters (distortion compensation coefficients; distortion compensation model) are adjusted to reduce the error signal. The updated distortion compensation coefficients (distortion compensation model) are then copied to the distortion compensation processing unit 21.

[0070] Return to reference Figure 1 The distortion compensation processing unit 21 of the distortion compensation device 20 according to the embodiment includes a distortion compensation model 200. The distortion compensation model 200 has an inverse characteristic G to be a distortion compensation characteristic. -1 The distortion compensation model 200 includes multiple computational models G1. -1 and G2 -1 As an example, Figure 1 The distortion compensation model 200 shown includes the calculation model G1. -1 and G2 -1The number is two. That is, distortion compensation model 200 includes the first calculation model G1. -1 Second computational model G2 -1 Distortion compensation characteristics G of distortion compensation model 200 -1 Represented as computational model G1 -1 and G2 -1 The synthetic properties. Computational model G1 -1 and G2 -1 Each of these is represented by, for example, a function. The number of computational models can be three or more, and the distortion compensation characteristic G in distortion compensation model 200 can be used. -1 It is represented as a composite property of three or more computational models.

[0071] In this embodiment, the first computational model G1 -1 The calculations are performed by the first arithmetic unit 210. The first arithmetic unit 210 calculates the first computational model G1. -1 It is applied to the input signal x[n] and outputs the calculation result G1. -1 (x[·]). Second computational model G2 -1 The calculations are performed by the second arithmetic unit 220. The second arithmetic unit 220 calculates the second computational model G2. -1 It is applied to the input signal x[n] and the calculation result G2 is output. -1 (x[·]).

[0072] The distortion compensation model 200 also includes a combiner 230. The combiner 230 includes multipliers 231 and 232 and an adder 233. The combiner 230 obtains the model G1 by combining calculations with dynamically changing combination ratios. -1 and G2 -1 The resulting synthetic properties. Computational model G1 -1 and G2 -1 The synthesis characteristics are the distortion compensation characteristics G of distortion compensation model 200. -1 Distortion compensation characteristics G -1 It changes depending on the combination ratio.

[0073] In this embodiment, the combiner 230 combines the calculation result G1 of the first arithmetic unit 210 with a changed combination ratio. -1 (x[·]) and the result G2 calculated by the second arithmetic unit 220 -1 (x[·]). A computational model G1 can be constructed by combining elements in a combination ratio. -1 and G2 -1 The coefficients of the function are used to obtain the composite function, and the obtained composite function can be applied to the input signal x[n].

[0074] The distortion compensation device 20 includes a generator 300 for generating a parameter α[n] for determining a dynamically changing combination ratio. In this embodiment, the generator 300 generates the parameter α[n] based on the input signal u[n]. Here, as an example, α[n] is a real number from 0 to 1. The distortion compensation model 200 obtains the parameter α[n] from the generator 300. The generator 300 can be provided to an external device of the distortion compensation device 20. In this case, the distortion compensation device 20 obtains α[n] generated by the external generator 300 and provides the obtained α[n] to the distortion compensation model 200.

[0075] In this embodiment, the dynamically changing combination ratio is represented by α[n]:1-α[n]. Therefore, the combination ratio is automatically determined when the parameter α[n] is dynamically determined. Here, α[n] indicates the combination ratio used for the first calculation model G. 1-1 The ratio (weights), and multiplier 231 will calculate the first computational model G1. -1 Multiply by α[n] (see also) Figure 1 and Figure 4 Additionally, 1-α[n] indicates the value used for the second computational model G2. -1 The ratio (weights), and multiplier 232 will use the second computational model G2 -1 Multiply by 1-α[n] (see also) Figure 1 and Figure 4 The distortion compensation device 20 includes a constant generator 250 for generating “1” in 1-α[n].

[0076] Adder 233 adds the outputs of multipliers 231 and 232. The result of the addition by adder 233 represents the calculation result (distortion compensation signal u[n]) performed by distortion compensation model 200.

[0077] According to the embodiment, the distortion compensation model 200 is composed of... Figure 4 Equation (1) presented in the text represents this. Formula (1) is equivalent to Figure 1 The distortion compensation model 200 is presented in [the document]. Additionally, in [the document]... Figure 4 In this embodiment, the distortion compensation model 200 is represented as a calculation model G1 calculated by combining the combination ratios determined by α[n]. -1 and G2 -1 The resulting model.

[0078] Computational model G1 -1 and G2 -1 Each has distortion compensation characteristics for amplifier 10 in different internal states. More specifically, the first calculation model G1 -1 It has distortion compensation characteristics for amplifier 10 in the first internal state, while the second calculation model G2 -1It has distortion compensation characteristics for amplifier 10 in the second internal state. The first internal state is different from the second internal state.

[0079] The internal state is not particularly limited, as long as it is the state of the amplifier that affects the distortion characteristics of amplifier 10. For example, the internal state is based on the value of the idle current Idq flowing through amplifier 10. Idq varies depending on the magnitude of the input power (input signal level). In this embodiment, an internal state parameter α[n] closer to 0 indicates a larger decrease in Idq, while an internal state parameter α[n] closer to 1 indicates a smaller decrease in Idq.

[0080] Figure 5 The diagram illustrates the relationship between the input power value and the internal state parameter α[n] of the state generated based on Idq drift. Figure 5 As shown, when a large input power is applied to amplifier 10, an Idq drift is generated, and α[n] becomes closer to 0. In contrast, as the input power decreases, less Idq drift is generated, and α[n] becomes closer to 1. As seen above, the internal state parameter α[n] changes depending on the input power value.

[0081] In this embodiment, different computational models G1 corresponding to the generated states of Idq drift are prepared. -1 and G2 -1 The distortion compensation model 200 for amplifier 10 in any generated state of Idq drift is calculated by combining model G1 according to the changed states (internal states) based on Idq drift. -1 and G2 -1 This is obtained by computational model G1. -1 and G2 -1 The combination is the computational model G1 -1 and G2 -1 Linear combinations (see) Figure 4 Equation (1) in the equation.

[0082] With the first computational model G1 -1 The corresponding first internal state indicates that no Idq drift has been generated in amplifier 10. For example... Figure 6 The first internal state presented is the state where the internal state parameter α[n] = 1. For example... Figure 7 The first computational model G1 is determined based on the input and output signals of amplifier 10 in a state S1 where no Idq drift is generated. -1 The coefficient.

[0083] With the second computational model G2 -1The corresponding second internal state indicates a state in amplifier 10 that generates more Idq drift than when it is in the first internal state. For example... Figure 6 As shown, the second internal state is the state where the internal state parameter α[n] = 0. For example... Figure 7 As shown, the second computational model G2 is determined based on the input and output signals of amplifier 10 in state S2 where Idq drift has occurred. -1 The coefficient.

[0084] Figure 8 The diagram illustrates the computational model G1 that constitutes the distortion compensation model 200. -1 and G2 -1 The processing involves first providing the input signal to amplifier 10 and measuring the output signal (step S11). The input signal is, for example, a communication signal transmitted by communication device 50. Through the measurement in step S11, a data pair of the input and output signals of amplifier 10 is obtained.

[0085] In step S12, the internal state parameter α[n] is calculated from the input signal level provided to amplifier 10. The internal state parameter α[n] is calculated by α[n] generator 300 based on the level of input signal x[n].

[0086] In step S13, the time T1 when the value of α[n] becomes 1 is determined (see...). Figure 7 Time T1 is the time when the speculative amplifier 10 is in its first internal state without generating Idq drift.

[0087] In step S14, the data identifiers of the input and output signals within a predetermined past time period starting from time T1 are used to construct the first computational model G1. -1 coefficient h m,l,k Through this process, the first computational model G1 was obtained. -1 .

[0088] In step S15, the time T2 when the value of α[n] becomes 0 is determined (see...). Figure 7 Time T2 is the time when the speculative amplifier 10 is in the second internal state that generates the Idq drift.

[0089] In step S16, the second calculation model G2 is identified from the data of the input signal and the output signal during a predetermined past time period starting from time T2. -1 coefficient g m,l,k Through this process, the second computational model G2 was obtained. -1 .

[0090] Computational model G1 -1 and G2 -1Each of these equations represents the distortion compensation characteristic used to compensate for the nonlinear characteristics (distortion characteristics) of amplifier 10. The typical expression used to model the nonlinear characteristics can be used as the computational model G1. -1 and G2 -1 The computational model can be expressed in forms such as generalized memory polynomials, Winer-Hammerstein models, Sarah models, or Volterra series. Figure 4 The computational model G1 presented in -1 and G2 -1 It is expressed by generalized memory polynomials. Conventional models such as generalized memory polynomials, Winer-Hammerstein models, Sarah models, or Volterra series can express the distortion compensation characteristics of an amplifier in a certain internal state, but they cannot properly express the distortion compensation characteristics when the nonlinear characteristics (distortion characteristics) change according to the changes in the internal state of the amplifier.

[0091] exist Figure 4 The first computational model G1 presented in the text -1 (See equation (2), h) m,l,k It expresses the first computational model G1 -1 The coefficients, and multiply the input signal x[·] by h. m,l,k The values ​​of M1 and M2 within the range of m and the value of L within the range of l are defined based on the length of the first response time (short response time) of the first memory effect (short-term memory effect). 1,m and L 2,m The value of . That is, the first computational model G1 -1 Compensation has a first memory effect with a short first response time.

[0092] exist Figure 4 The second computational model G2 presented in -1 (See equation (3)) where g m,l,k It is an expression of the second computational model G2 -1 The coefficients, and multiply the input signal x[·] by g m,l,k The values ​​of M3 and M4, defining the range of m, and L, defining the range of l, are determined based on the length of the first response time (short response time) of the first memory effect (short-term memory effect). 3,m and L 4,m The value of . That is, the value of the second computational model G2. -1 Compensation is achieved by addressing the first memory effect, which has a short first response time. First computational model G1 -1 The first response time in the second computational model G2 is not necessarily the same as the second computational model G2. -1 The first response time is the same in both cases.

[0093] First computational model G1 -1 This represents the distortion compensation characteristics of amplifier 10 in the first internal state (α[n] = 1), however, the second calculation model G2 -1 This represents the distortion compensation characteristics of amplifier 10 in its second internal state (α[n] = 0). However, the calculation model G1 -1 and G2 -1 Neither of these adequately represents the distortion compensation characteristics of amplifier 10 for any transitional internal state (0 < α[n] < 1) between the first and second internal states.

[0094] Therefore, in this embodiment, the computational model G1 is calculated based on the combination ratio corresponding to any transitional internal state. -1 and G2 -1 The distortion compensation model 200 obtained through combination can represent the distortion compensation characteristics of amplifier 10 in a transitional internal state. An extrapolation method can be used to obtain the combination ratio corresponding to internal states outside the range between the first and second internal states. In this embodiment, the combination ratio is calculated based on the level of the input signal x[n] (see...). Figure 1 ).

[0095] Computational model G1 -1 and G2 -1 The combination ratio can have a value corresponding to any transitional internal state (0 < α[n] < 1) between the first internal state and the second internal state. In the transitional internal state, the state in which the Idq drift is generated is between the state in which the Idq drift is generated in the first internal state and the state in which the Idq drift is generated in the second internal state.

[0096] Figure 9 The diagram illustrates the α[n] generator 300. The α[n] generator 300 calculates the first parameter R[n] and the second parameter α[n], which indicate the internal state, and outputs the second parameter α[n] used to determine the combination ratio. The first parameter R[n], which indicates the internal state, represents the state of the Idq drift.

[0097] according to Figure 9 Equation (4) in the equation calculates the first parameter R[n]. Equation (4) constitutes the parameter calculation model for calculating the parameter R[m]. As presented in equation (4), the first parameter R[n+1] at n+1 is calculated based on the level of the input signal x[n] at n. More specifically, the first parameter R[n+1] is calculated based on the past value (previous value) R[n] of the parameter and the input signal x[n].

[0098] In equation (4), R[n] and R[n+1] are m×1 scalar matrices. I is an m×m identity matrix. A is an m×m scalar matrix. B is an m×m scalar matrix and the coefficient matrix to be multiplied by the input signal x[n]. Here, m is a positive integer, and the same applies to the case described below. As m increases, the generated state of Idq drift is expressed more precisely.

[0099] Equation (4) shows that as the level of the input signal x[n] increases, Idq drift is more likely to occur, and the first parameter R[n+1] increases. Additionally, Equation (4) shows that as the level of the input signal x[n] decreases, Idq drift is less likely to occur, and the first parameter R[n+1] decreases.

[0100] In equation (4), when the input signal x[n] is assumed to be zero, R[n+1] = (IA)R[n]. In equation (4), -A represents the time constant for the recovery of the reduced idle current Idq, and expresses the recovery of the idle current Idq at certain intervals (sampling interval T[seconds]) when the input signal x[n] is zero. The value of each element of matrix A is sufficiently small (e.g., approximately 1 / 1000 to 1 / 1000000). The value of each element in matrix A is set sufficiently small so that (IA) to be multiplied by R[n] is almost identical to the identity matrix, and the degree of reduction from R[n] to R[n+1] is reduced. That is, the value of each element in matrix A is set sufficiently small so that the change in the first parameter R[n] becomes gradual. As seen above, the parameter calculation model indicated by equation (4) expresses a second memory effect with a long response time.

[0101] In this embodiment, the magnitude of each element in matrix A is set small enough that the second memory effect expressed by equation (4) has a longer response time than the first memory effect expressed by equations (2) and (3).

[0102] Here, A and B in equation (4) are set according to the physical properties and characteristics of amplifier 10. When the physical properties and characteristics of amplifier 10 are temperature-dependent, A and B are temperature-dependent parameters that change depending on the temperature conditions of amplifier 10. For example, the ease with which Idq drift is generated depends on the temperature of amplifier 10. Drift is more likely to occur at lower temperatures, but less likely to occur at higher temperatures.

[0103] In equation (4), temperature-related parameters A and B are used to calculate parameter R[n]. Temperature-related parameters A and B are variable, and their values ​​are adjusted by temperature-related parameter adjuster 310. Temperature-related parameter adjuster 310 adjusts temperature-related parameters A and B based on the temperature conditions provided as simulation data. In this embodiment, temperature-related parameters A and B are used to calculate the combination ratio. Therefore, the combination ratio is affected by the temperature conditions.

[0104] exist Figure 9 In equation (5), the first parameter R[n], which indicates the state of Idq drift, is transformed into a second parameter α[n], whose value is in the range of 0 and below 1. In equation (5), F(·) is a normalization function that normalizes the first parameter R[n] to the range of 0 and below 1. The second parameter α[n] is also a parameter indicating the internal state.

[0105] The normalization function F(·) is appropriately set such that as the value of the first parameter R[n] increases (as more Idq drifts are generated), α[n] becomes closer to 0, and as the value of the first parameter R[n] decreases (as fewer Idq drifts are generated), α[n] becomes closer to 1.

[0106] The generation state of Idq drift can be expressed using internal states as follows. The initial internal state is defined as the state without Idq drift obtained when the input signal level to the amplifier is zero for a sufficiently long time. As the input signal level increases, the internal state changes more significantly from the initial internal state. That is, the difference between the internal state when the input signal level is a first level and the initial internal state is greater than the difference between the internal state when the input signal level is a second level lower than the first level and the initial internal state. As the input signal level decreases, the internal state returns to the initial internal state over time. That is, when the input signal level changes from the first level to a second level lower than the first level (e.g., 0V), the internal state returns to the initial internal state over time.

[0107] like Figure 4 Presented in the computational model G2 -1 The corresponding change from the initial internal state in the second internal state is greater than that in the computational model G1. -1 The corresponding change from the initial internal state in the first internal state. The combination ratio α is used to calculate model G1. -1 Compared to computational model G1 -1 and G2 -1 The ratio of combinations of sums. In Figure 9In equation (4), n corresponds to a certain time (first time), and n+1 corresponds to a time after the first time (second time). In this case, in equations (4) and (5), according to the first term of equation (4), when the input signal level at n is zero, the combination ratio α (second combination ratio) at n+1 is less than the combination ratio α (first combination ratio) at n. Additionally, according to the second term of equation (4), when the input signal level at n is the first level, the combination ratio α at n+1 is greater than the combination ratio α at n+1 when the input signal level at n is the second level, which is smaller than the first level. Therefore, it becomes possible to model the distortion compensation characteristics for compensating the nonlinear characteristics (distortion characteristics) of an amplifier with Idq drift.

[0108] Furthermore, as presented in equation (4), the combination ratio α at n+1 is the sum of the first and second terms, the first term being smaller than the combination ratio α at n, and the second term increasing with the input signal level. Therefore, it becomes possible to model a distortion compensation model for compensating the nonlinear characteristics (distortion characteristics) of an amplifier with Idq drift.

[0109] [Second Embodiment]

[0110] Figure 10 The figure shows the distortion compensation processing unit 21 of the distortion compensation device 20 according to the second embodiment. The configuration in the second embodiment, unless otherwise specified, is the same as that in the first embodiment.

[0111] There may be situations where using two models does not yield sufficient representational accuracy. Therefore, depending on the generation state of the Idq drift, using three or more computational models allows for a more appropriate representation of the characteristics of amplifier 10. Thus, in the second embodiment, as an example, a coupled model obtained by combining two adjacent computational models among the three computational models corresponding to the three internal states is used as the distortion compensation model 200.

[0112] The distortion compensation device 20 according to the second embodiment includes a selector 260 for selecting a computational model to be selected. The distortion compensation device 20 according to the second embodiment includes three computational models G1. -1 G2 -1 and G3 -1 In other words, the distortion compensation model 200 has the first calculation model G1. -1 Second computational model G2 -1 and the third computational model G3 -1 The distortion compensation device 20 may include four or more computational models. In the second embodiment, the distortion compensation characteristic G in the distortion compensation model 200... -1 Represented as from computational model G1 -1 G2-1 and G3 -1 The synthetic properties of the selected computational model.

[0113] In the second embodiment, selector 260 selects from multiple computational models G1 -1 G2 -1 and G3 -1 Select two computational models Gp to be combined -1 and G p+1 -1 The number of computational models to be selected is not limited to two, and three or more computational models can be selected from four or more computational models.

[0114] The selected computational model G p -1 The calculations are performed by the first arithmetic unit 210. The first arithmetic unit 210 calculates the model G. p -1 It is applied to the input signal x[n] and outputs the calculation result G. p -1 (x[·]). The selected computational model G p+1 -1 The calculations are performed by the second arithmetic unit 220. The second arithmetic unit 220 calculates the model G. p+1 -1 It is applied to the input signal x[n] and outputs the calculation result G. p+1 -1 (x[·]).

[0115] Combiner 230 obtains the selected computational model G by combining it with dynamically changing combination ratios. p -1 and G p+1 -1 The synthesized properties obtained. The selected computational model G. p -1 and G p+1 -1 The synthesis characteristics are the distortion compensation characteristics G of distortion compensation model 200. -1 The distortion compensation model 200 in the second embodiment is composed of... Figure 11 Equation (7) in the equation represents this. The combination ratio α in equation (7) will be described later. p [n]:(1-α p [n]).

[0116] In this embodiment, the combiner 230 combines the calculation result G of the first arithmetic unit 210 with a changed combination ratio. p -1 (x[·]) and the calculation result G of the second arithmetic unit 220 p+1-1 (x[·]). The selected computational model G can be constructed in advance based on the combination ratio. p -1 and G p+1 -1 The coefficients of the function are used to obtain the composite function, and the obtained composite function can be applied to the input signal x[n].

[0117] Computational model G1 -1 G2 -1 and G3 -1 Each has distortion compensation characteristics for amplifier 10 in different internal states. More specifically, the first calculation model G1 -1 It has a first distortion compensation characteristic for amplifier 10 in the first internal state. Second calculation model G2 -1 It has a second distortion compensation characteristic for amplifier 10 in the second internal state. The third calculation model G3 -1 It has a third distortion compensation feature for amplifier 10 in the third internal state.

[0118] like Figure 12 As shown, the first internal state, the second internal state, and the third internal state are different states. This is consistent with the first computational model G1. -1 The corresponding first internal state is the state in amplifier 10 that generates at least Idq drift. The first internal state corresponds to the internal state parameter α[n] = 1. This is consistent with the third computational model G3. -1 The corresponding third internal state is the state in amplifier 10 that generates at most Idq drift. The third internal state corresponds to the internal state parameter α[n] = 0.

[0119] With the second computational model G2 -1 The corresponding second internal state is an intermediate internal state between the first and third internal states. Figure 12 In this context, the second internal state corresponds to the internal state parameter α[n] = 0.5. Under the second internal state, the state in which the Idq drift is generated lies between the state generated under the first internal state and the state generated under the third internal state. The model can be expressed more precisely by increasing the number of internal states corresponding to the computational model to three or more.

[0120] Selector 260 selects the computational model G to be combined based on the internal state of amplifier 10. p -1 and G p+1 -1 More specifically, such as Figure 11 and Figure 12As shown, in the case of 0.5 (=β2) < α[n] ≤ 1 (=β1) (in the case of p=1), the first computational model G1 is selected. -1 Second computational model G2 -1 As the computational model to be combined. In this case, the distortion compensation model 200 has a first computational model G1. -1 Second computational model G2 -1 The synthetic properties, and these synthetic properties are determined by Figure 11 Equation (7-1) in the equation represents this. Additionally, it is used for the first computational model G1. -1 The combination ratio is α1[n], while the second calculation model G2 is used. -1 The combination ratio is (1-α1[n]). Here, according to Figure 11 Equation (8-1) in the equation is used to determine α1[n].

[0121] The synthesis characteristics expressed by equation (7-1) can represent the distortion compensation characteristics of amplifier 10 for any first transition internal state (0.5 < α[n] < 1) between the first internal state and the second internal state. In the second embodiment, the distortion compensation characteristics of amplifier 10 for the first transition internal state (0.5 < α[n] < 1) are expressed more appropriately than in the first embodiment.

[0122] In the case that 0 (=β3)≤α[n]≤0.5 (=β2) (in the case of p=2), the second computational model G2 is chosen. -1 and the third computational model G3 -1 As the computational model to be combined. In this case, distortion compensation model 200 has a second computational model G2. -1 and the third computational model G3 -1 The synthetic properties, and these synthetic properties are determined by Figure 11 Equation (7-2) in the equation represents this. Additionally, it is used for the second computational model G2. -1 The combination ratio is α²[n], while the third calculation model G3 is used. -1 The combination ratio is (1-α²[n]). Here, α²[n] is determined according to equation (8-2).

[0123] The synthesis characteristics expressed by equation (7-2) can represent the distortion compensation characteristics of amplifier 10 in the second transition internal state (0 < α[n] < 0.5) between the second and third internal states. In the second embodiment, the distortion compensation characteristics of amplifier 10 in the second transition internal state (0 < α[n] < 0.5) can be appropriately expressed compared to the first embodiment.

[0124] Figure 13 The diagram illustrates the computational model G1.-1 G2 -1 and G3 -1 The processing involves first providing the input signal to amplifier 10 and then measuring the output signal (step S21).

[0125] In step S22, the internal state parameter α[n] is calculated from the input signal level provided to amplifier 10. The internal state parameter α[n] is calculated by α[n] generator 300 based on the level of input signal u[n].

[0126] In step S23, the time T1 when the value of α[n] becomes 1 is determined. Time T1 is the time when the speculative amplifier 10 is in the first internal state with the minimum generation of Idq drift.

[0127] In step S24, the first calculation model G1 is identified from the data of the input signal and the output signal within a predetermined past time period starting from time T1. -1 The coefficients. Through this process, the first computational model G1 was obtained. -1 .

[0128] In step S25, the time T2 when the value of α[n] becomes 0.5 is determined. Time T2 is the time when the speculative amplifier 10 is in the second internal state as an intermediate internal state.

[0129] In step S26, the second calculation model G2 is identified from the data of the input signal and the output signal during a predetermined past time period starting from time T2. -1 The coefficients. Through this process, the second computational model G2 was obtained. -1 .

[0130] In step S27, the time T3 when the value of α[n] becomes 0 is determined. Time T3 is the time when the speculative amplifier 10 is in the third internal state that generates at most Idq drift.

[0131] In step S28, the data identifiers of the input and output signals within a predetermined past time period starting from time T3 are used to construct the third calculation model G3. -1 The coefficients. Through this process, the third computational model G3 was obtained. -1 .

[0132] like Figure 10 As shown, the distortion compensation device 20 of the second embodiment includes a generator 301 that generates a parameter α[n] for determining the combination ratio from a parameter α[n] indicating the internal state. p [n]. Generator 301 obtains α[n] from generator 300 and outputs α. p [n].

[0133] like Figure 14 As shown, generator 301 according to Figure 14 Equation (8) in the equation calculates the parameter α from the parameter α[n]. p [n]. Generator 301 will set the value in β. p ≥α[n]≥β p+1 Normalize α[n] within the range to 0≤α p Values ​​within the range [n]≤1. Equation (8) becomes as follows when 0.5 (=β2)<α[n]≤1 (=β1) (when p=1): Figure 11 Equation (8-1) in the equation becomes, in the case of 0 (=β3)≤α[n]≤0.5 (=β2) (in the case of p=2), that is... Figure 11 Equation (8-2) in the text.

[0134] Although embodiments of the invention have been described in detail, it should be understood that various changes, substitutions and modifications can be made thereto without departing from the spirit and scope of the invention.

Claims

1. A distortion compensation device, wherein the distortion compensation device uses a distortion compensation model to compensate for the distortion of a signal to be amplified by an amplifier, wherein the internal state of the amplifier's distortion characteristics is altered, wherein... The distortion compensation model includes: Multiple computational models, each having corresponding distortion compensation characteristics for the amplifier in different internal states; and A combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal state, wherein the combination ratio changes dynamically. Wherein, the combination ratio is based on the parameters of the internal state. The distortion compensation characteristics of each of the plurality of computational models have characteristics for compensating for a first memory effect having a first response time in the amplifier, and The parameter calculation model used to calculate the internal state is expressed as a second memory effect in the amplifier having a second response time that is longer than the first response time.

2. The distortion compensation device according to claim 1, further comprising a generator for generating the parameters indicating the internal state.

3. The distortion compensation device according to claim 2, wherein: The combination ratio is determined based on the parameters indicating the internal state, and The parameters of the internal state are determined based on the level of the signal.

4. The distortion compensation device according to claim 3, wherein, The parameters of the internal state are also determined based on past values ​​of the parameters of the internal state.

5. The distortion compensation device according to any one of claims 1 to 4, wherein, The combination ratio is calculated based on the signal level.

6. The distortion compensation device according to any one of claims 1 to 4, wherein, The combination ratio is calculated using temperature-dependent parameters that vary depending on temperature conditions.

7. The distortion compensation device according to any one of claims 1 to 4, wherein, The multiple computational models are two computational models.

8. The distortion compensation device according to any one of claims 1 to 4, further comprising a selector, wherein: The plurality of computational models includes three or more computational models, and The selector is configured to select two or more computational models from the three or more computational models to be combined by the combiner.

9. The distortion compensation device according to claim 8, wherein, The selector is configured to select two or more computational models based on parameters indicating the internal state.

10. The distortion compensation device according to any one of claims 1 to 4, wherein The plurality of computational models include: A first computational model, the first computational model having a first distortion compensation characteristic for the amplifier in a first internal state. A second computational model has a second distortion compensation characteristic for the amplifier in a second internal state different from the first internal state, and The combination ratio has a value corresponding to the transitional internal state between the first internal state and the second internal state.

11. The distortion compensation device according to any one of claims 1 to 4, wherein: The plurality of computational models include: A first computational model, the first computational model having a first distortion compensation characteristic for the amplifier in a first internal state. A second computational model, the second computational model having a second distortion compensation characteristic for the amplifier in a second internal state different from the first internal state, and A third computational model has a third distortion compensation characteristic for the amplifier in a third internal state different from the first and second internal states. The second internal state is an intermediate internal state between the first internal state and the third internal state.

12. The distortion compensation device according to any one of claims 1 to 4, wherein: As the level of the input signal to the amplifier increases, the internal state changes more significantly from its initial internal state, and as the level of the input signal decreases, the internal state returns to its initial internal state over time. The plurality of computational models include: A first computational model having a first distortion compensation feature for the amplifier in a first internal state; A second computational model has a second distortion compensation characteristic for the amplifier in a second internal state different from the first internal state. The change from the initial internal state when in the second internal state is greater than the change from the initial internal state when in the first internal state. The first combination ratio is the combination ratio of the first calculation model relative to the sum of the first calculation model and the second calculation model at the first time point. The second combination ratio is the combination ratio of the first calculation model relative to the sum of the first calculation model and the second calculation model at a second time after the first time. When the level of the input signal is zero at the first time point, the second combination ratio is less than the first combination ratio, and The second combination ratio when the level of the input signal at the first time is a first level is greater than the second combination ratio when the level of the input signal at the first time is a second level that is lower than the first level.

13. The distortion compensation device according to claim 12, wherein, The second combination ratio is the sum of the first term and the second term, wherein the first term is less than the first combination ratio, and the second term increases as the level of the input signal increases at the first time.

14. A distortion compensation method for compensating the distortion of a signal to be amplified by an amplifier, wherein the internal state of the amplifier's distortion characteristics is altered, the distortion compensation method comprising the following steps: The distortion compensation device combines multiple computational models having corresponding distortion compensation characteristics for the amplifier in different internal states at a combination ratio corresponding to the changed internal states, wherein the combination ratio changes dynamically. Wherein, the combination ratio is based on the parameters of the internal state. The distortion compensation characteristics of each of the plurality of computational models have characteristics for compensating for a first memory effect having a first response time in the amplifier, and The parameter calculation model used to calculate the internal state is expressed as a second memory effect in the amplifier having a second response time that is longer than the first response time.

15. A non-transitory computer-readable storage medium storing a computer program for compensating for distortion of a signal to be amplified by an amplifier, wherein the internal state affecting the distortion characteristics of the amplifier is changed, the computer program causing a computer to perform processing, the processing comprising: Multiple computational models with corresponding distortion compensation characteristics for the amplifier in different internal states are combined using combination ratios corresponding to the changed internal states, wherein the combination ratios change dynamically. Wherein, the combination ratio is based on the parameters of the internal state. The distortion compensation characteristics of each of the plurality of computational models have characteristics for compensating for a first memory effect having a first response time in the amplifier, and The parameter calculation model used to calculate the internal state is expressed as a second memory effect in the amplifier having a second response time that is longer than the first response time.

16. A communication device, comprising: An amplifier that amplifies signals used for communication; as well as A distortion compensation device that uses a distortion compensation model to compensate for distortion in the signal. The amplifier is configured such that the internal states affecting distortion characteristics are altered. The distortion compensation model includes: Multiple computational models, each having corresponding distortion compensation characteristics for the amplifier in different internal states, and A combiner that combines the plurality of computational models with a combination ratio corresponding to the changed internal state, wherein the combination ratio changes dynamically. Wherein, the combination ratio is based on the parameters of the internal state. The distortion compensation characteristics of each of the plurality of computational models have characteristics for compensating for a first memory effect having a first response time in the amplifier, and The parameter calculation model used to calculate the internal state is expressed as a second memory effect in the amplifier having a second response time that is longer than the first response time.

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