Architecture and the training method of a PA DPD system with space mapping applied in the predistorter

Systems and methods are disclosed for compensating for non-linearity of a power amplifier using space mapping based predistortion. In one embodiment, a transmitter includes a power amplifier that amplifies a power amplifier input signal, a predistorter that effects predistortion of the power amplifier input signal to compensate for a non-linear characteristic of the power amplifier using a space mapping based model of an inverse of the non-linear characteristic of the power amplifier, and an adaptation sub-system that adaptively configures the space mapping based model of the non-linear characteristic of the power amplifier. In one embodiment, the adaptation sub-system adaptively configures a space mapping based model of the non-linear characteristic of the power amplifier and adaptively configures the space mapping based model of the inverse of the non-linear characteristic of the power amplifier based on the space mapping based model of the non-linear characteristic of the power amplifier.

FIELD OF THE DISCLOSURE

The present disclosure relates to digital predistortion to compensate for power amplifier non-linearity.

BACKGROUND

A radio system generally includes a transmitter that transmits information-carrying signals to a receiver. The transmitter includes a power amplifier that operates to amplify the signal to be transmitted to a power level that is sufficient to enable receipt of the signal by the receiver. Radio system transmitters are required to satisfy specifications for signal levels at frequencies other than the intended transmission frequencies. Some specifications are set by government regulatory bodies, while others are set by radio communications standards such as the Third Generation Partnership Project (3GPP) or IEEE 802.11. One specification, or requirement, is adjacent channel power, which is directly related to power amplifier linearity. Power amplifier linearity corresponds to an ability to reproduce an amplified version of the input signal. Also, power amplifiers are often described in terms of their efficiency, which is defined as some comparison between average transmit signal power and total average power required to generate the transmit signal power.

At a circuit level, power amplifier linearity may be achieved by biasing transistors in such a manner that the power amplifier operates in a linear fashion. However, doing so has a cost in terms of very low operating efficiency. As such, many modern power amplifiers are configured to operate at maximum efficiency, resulting in poor linearity, and use so-called “linearization” circuitry to correct non-linearity. Some exemplary power amplifiers that have high efficiency, but low linearity, are Class AB power amplifiers, Class B power amplifiers, Class C power amplifiers, Class F power amplifiers, Doherty power amplifiers, and Chireix power amplifiers.

Various linearization schemes have evolved having various trade-offs in terms of linearity, power dissipation, and versatility or robustness. These linearization schemes include, but are not limited to, analog predistortion, digital predistortion, feed-forward linearization, and feedback linearization. Predistortion linearization uses a predefined model of power amplifier non-linearity to generate an “opposite” nonlinear response that compensates for the non-linearity of the power amplifier. By amplifying the predistorted signal, the output of the power amplifier is as if the power amplifier were linear. The model utilized for predistortion needs to be designed to enable the predistortion to counteract the non-linear characteristics of the transistors forming the power amplifier. However, transistors are designed and fabricated using different technologies and therefore can exhibit drastically different characteristics.

Traditionally, there are two approaches to modeling the non-linear characteristic of the power amplifier, namely, a polynomial based approach and an artificial neural network model approach. The polynomial based approach includes the well-known Volterra series and its simplified versions, where the power series is the most basic form. The artificial neural network model approach uses artificial neural network modeling of the power amplifier. However, these approaches do not provide the desired performance in some situations. As such, there is a need for a high fidelity model for power amplifier predistortion.

SUMMARY

Systems and methods are disclosed for compensating for non-linearity of a power amplifier using space mapping based predistortion. In one embodiment, a transmitter includes a power amplifier that amplifies a power amplifier input signal to provide a power amplifier output signal, a predistorter that effects predistortion of the power amplifier input signal to compensate for a non-linear characteristic of the power amplifier using a space mapping based model of an inverse of the non-linear characteristic of the power amplifier, and an adaptation sub-system that adaptively configures the space mapping based model of the non-linear characteristic of the power amplifier. In one embodiment, the adaptation sub-system adaptively configures a space mapping based model of the non-linear characteristic of the power amplifier and adaptively configures the space mapping based model of the inverse of the non-linear characteristic of the power amplifier based on the space mapping based model of the non-linear characteristic of the power amplifier.

In one particular embodiment, the space mapping based model of the inverse of the non-linear characteristic of the power amplifier includes a coarse model of the inverse of the non-linear characteristic of the power amplifier and a space mapping that maps the coarse model of the inverse of the non-linear characteristic of the power amplifier into a fine model of the inverse of the non-linear characteristic of the power amplifier. Likewise, the space mapping model of the non-linear characteristic of the power amplifier includes a coarse model of the non-linear characteristic of the power amplifier and a space mapping that maps the coarse model of the non-linear characteristic of the power amplifier into a fine model of the non-linear characteristic of the power amplifier.

In one embodiment, the adaptation sub-system performs initial training of the predistorter by first training the coarse model of the non-linear characteristic of the power amplifier while the space mapping for the coarse model of the non-linear characteristic of the power amplifier and the predistorter are in by-pass mode. Second, the adaptation sub-system trains the space mapping for the coarse model of the non-linear characteristic of the power amplifier while the coarse model of the non-linear characteristic of the power amplifier is active and the predistorter is in by-pass mode. Third, the adaptation sub-system trains the coarse model of the inverse of the non-linear characteristic of the power amplifier while the coarse model of the non-linear characteristic of the power amplifier is active and the space mapping for the coarse model of the non-linear characteristic of the power amplifier and the space mapping for the coarse model of the inverse of the non-linear characteristic of the power amplifier are in by-pass mode. Fourth, the adaptation sub-system trains the space mapping for the coarse model of the inverse of the non-linear characteristic of the power amplifier while the coarse model of the inverse of the non-linear characteristic of the power amplifier, the coarse model of the non-linear characteristic of the power amplifier, and the space mapping for the coarse model of the non-linear characteristic of the power amplifier are active.

In another embodiment, the adaptation sub-system performs initial training of the predistorter by first training the coarse model of the non-linear characteristic of the power amplifier while the space mapping for the coarse model of the non-linear characteristic of the power amplifier and the predistorter are in by-pass mode. Second, the adaptation sub-system trains the coarse model of the inverse of the non-linear characteristic of the power amplifier while the coarse model of the non-linear characteristic of the power amplifier is active and the space mapping for the coarse model of the non-linear characteristic of the power amplifier and the space mapping for the coarse model of the inverse of the non-linear characteristic of the power amplifier are in by-pass mode. Third, the adaptation sub-system trains the space mapping for the coarse model of the non-linear characteristic of the power amplifier while the coarse model of the non-linear characteristic of the power amplifier is active and the predistorter is in by-pass mode. Fourth, the adaptation sub-system trains the space mapping for the coarse model of the inverse of the non-linear characteristic of the power amplifier while the coarse model of the inverse of the non-linear characteristic of the power amplifier, the coarse model of the non-linear characteristic of the power amplifier, and the space mapping for the coarse model of the non-linear characteristic of the power amplifier are active.

DETAILED DESCRIPTION

FIG. 1illustrates a transmitter10that compensates for power amplifier non-linearity using space mapping based predistortion according to one embodiment of the present disclosure. The transmitter10may be any type of transmitter. In one particular embodiment, the transmitter10is a wireless transmitter such as that used in a cellular communication network. In general, the transmitter10includes a power amplifier (PA)12that amplifies a power amplifier input signal (SPA—IN) to provide a power amplifier output signal (SPA—OUT) and a space mapping based predistorter14(hereinafter “predistorter14”) that effects predistortion of the power amplifier input signal (SPA—IN) to compensate for a non-linear characteristic of the power amplifier12using a space mapping based model of an inverse of the non-linear characteristic of the power amplifier12. The transmitter10also includes a space mapping based adaptation sub-system16(hereinafter “adaptation sub-system16”) that adaptively configures the space mapping based model of the inverse of the non-linear characteristic of the power amplifier12.

More specifically, as illustrated, the transmitter10includes a baseband signal source18, the predistorter14, an upconversion and modulation sub-system20, the power amplifier12, a filter22, an attenuator24, a downconversion and demodulation sub-system26, and the adaptation sub-system16connected as shown. In operation, the baseband signal source18generates and outputs a baseband signal (SBB). The predistorter14predistorts the baseband signal (SBB) using a space mapping based model of an inverse of the non-linear characteristic of the power amplifier12to thereby provide a predistorted baseband signal (SBB—PD) that is predistorted in such a manner as to compensate for the non-linear characteristic of the power amplifier12. The predistorted baseband signal (SBB—PD) is upconverted and modulated by the upconversion and modulation sub-system20to provide the power amplifier input signal (SPA—IN). The power amplifier12amplifies the power amplifier input signal (SPA—IN) to provide the power amplifier output signal (SPA—OUT). Notably, due to the predistortion, the power amplifier output signal (SPA—OUT) is as if the power amplifier12were a linear, or substantially linear, device. The power amplifier output signal (SPA—OUT) is filtered by the filter22to remove undesired frequency components to thereby provide an output signal (SOUT) of the transmitter10.

For the feedback path, the attenuator24attenuates the power amplifier output signal (SPA—OUT) by a factor 1/G, where G is a gain of the power amplifier12, to thereby provide an attenuated power amplifier output signal (SPA—OUT—A). The downconversion and demodulation sub-system26downconverts and demodulates the attenuated power amplifier output signal (SPA—OUT—A) to provide a baseband feedback signal (SBB—FB). The adaptation sub-system16adaptively configures the predistorter14based on the baseband feedback signal (SBB—FB) and a time-aligned (e.g., delayed) version of the predistorted baseband signal (SBB—PD). The adaptation sub-system16adaptively configures the predistorter14, and more specifically the space mapping based model of the inverse of the non-linear characteristic of the power amplifier12, based on the space mapping based model of the non-linear characteristic of the power amplifier12. By using the space mapping based models of the non-linear characteristic of the power amplifier12and the inverse of the non-linear characteristic of the power amplifier12, a high fidelity model of the inverse of the non-linear characteristic of the power amplifier12is obtained without the computation complexity that would otherwise be required to obtain such a high fidelity model without the use of space mapping.

FIG. 2illustrates the predistorter14and the adaptation sub-system16in more detail according to one embodiment of the present disclosure. As illustrated, the predistorter14includes a space mapping based fine model (i.e., a fine model that utilizes a space mapping technique) of the inverse of the non-linear characteristic of the power amplifier12, which is referred to herein as a predistortion (PD) fine model28. Notably, in this particular embodiment, the PD fine model28is more precisely a space mapping based model of a baseband equivalent of the inverse of the non-linear characteristic of the power amplifier12. However, for ease of discussion, the PD fine model28is referred to herein as a space mapping based model of the inverse of the non-linear characteristic of the power amplifier12. The PD fine model28includes a coarse model30of the inverse of the non-linear characteristic of the power amplifier12(hereinafter “PD coarse model30”), and a space mapping32(hereinafter “PD space mapping32”) that maps the PD coarse model30to the PD fine model28using a space mapping technique.

In general, the PD coarse model30is a less precise or lower fidelity model of the inverse of the non-linear characteristic of the power amplifier12than the PD fine model28. The PD coarse model30is generally any model of the inverse of the non-linear characteristic of the power amplifier12for which space mapping can be used to provide the PD fine model28. For example, the PD coarse model30may be, for example, a model such as those used in conventional predistorters to compensate for power amplifier non-linearity such as, for example, a polynomial based model, a look-up-table (LUT) based model, or a neural network based model.

The PD space mapping32is implemented using any suitable technique that is suitable for mapping the PD coarse model30to the PD fine model28. In one particular embodiment, the PD space mapping32is implemented using a neural network. Notably, as will be appreciated by one of ordinary skill in the art, space mapping is a term of art that refers to advanced modeling techniques that enable efficient, high fidelity modeling using a coarse, or approximate, surrogate model. A space mapping, P, is a mapping of parameters, xc, for a coarse model (e.g., the PD coarse model30) into parameters, xf, for a fine model (e.g., the PD fine model28) such that:
Rc(P(xf))≈Rf(xf),
where Rcis a response vector of the coarse model and Rfis a response vector of the fine model. While not essential or critical for understanding the concepts described herein, for a detailed discussion of some exemplary space mapping techniques, the interested reader is directed to J. W. Bandler, Q. S. Cheng, S. Dakroury, A. S. Mohamed, M. H. Bakr, K. Madsen, and J. Sondergaard, “Space mapping: The state of the art,”IEEE Trans. Microw. Theory Tech., vol. 52, no. 1, pp. 337-361, January 2004 and Lei Zhang, Jianjun Xu, Mustapha C. E. Yagoub, Runtao Ding, and Qi-Jun Zhang, “Efficient Analytical Formulation and Sensitivity Analysis of Neuro-Space Mapping for Nonlinear Microwave Device Modeling,”IEEE Trans. Microw. Theory Tech., vol. 53, no. 9, pp. 2752-2767, September 2005.

In operation, the PD fine model28receives the baseband signal (SBB) from the baseband signal source18. The PD space mapping32converts the baseband signal (SBB) into a coarse model input signal (SC—IN—PD). The PD coarse model30processes the coarse model input signal (SC—IN—PD) to provide a coarse model output signal (SC—OUT—PD). The PD space mapping32then converts the coarse model output signal (SC—OUT—PD) into the predistorted baseband signal (SBB—PD), which is also an output signal of the PD fine model28.

Similarly, the adaptation sub-system16includes a space mapping based fine model (i.e., a fine model that utilizes a space mapping technique) of the non-linear characteristic of the power amplifier12, which is referred to herein as a PA fine model34. Notably, in this particular embodiment, the PA fine model34is more precisely a space mapping based model of a baseband equivalent of the non-linear characteristic of the power amplifier12. However, for ease of discussion, the PA fine model34is referred to herein as a space mapping based model of the inverse of the non-linear characteristic of the power amplifier12. The PA fine model34includes a coarse model36(hereinafter “PA coarse model36”) of the non-linear characteristic of the power amplifier12, and a space mapping38(hereinafter “PA space mapping38”) that maps the PA coarse model36to the PA fine model34using a space mapping technique.

In general, the PA coarse model36is a less precise or lower fidelity model of the non-linear characteristic of the power amplifier12than the PA fine model34. The PA coarse model36is generally any model of the non-linear characteristic of the power amplifier12for which space mapping can be used to provide the PA fine model34. For example, the PA coarse model36may be, for example, a polynomial based model, a LUT based model, or a neural network based model. Notably, the PA coarse model36is a counter-part of the PD coarse model30and preferably has the same structure as the PD coarse model30. Also, the PA coarse model36preferably models short-term memory effects of the power amplifier12.

The PA space mapping38is implemented using any suitable technique that is suitable for mapping the PA coarse model36to the PA fine model34. In one particular embodiment, the PA space mapping38is implemented using a neural network. The PA space mapping38is a counter-part of the PD space mapping32and preferably has the same structure as the PD space mapping32. Also, the PA space mapping38preferably models long-term memory effects of the power amplifier12. In operation, the PA fine model34receives the predistorted baseband signal (SBB—PD), and the PA space mapping38converts the predistorted baseband signal (SBB—PD) into a coarse model input signal (SC—IN—PA). The PA coarse model36processes the coarse model input signal (SC—IN—PA) to provide a coarse model output signal (SC—OUT—PA). The PA space mapping38then converts the coarse model output signal (SC—OUT—PA) into a PA fine model output signal (SPA—MODEL—OUT).

In addition, the adaptation sub-system16includes adaptors40and42that operate to adaptively configure the PA fine model34and the PD fine model28, respectively, arranged as shown. More specifically, the adaptor40receives the baseband feedback signal (SBB—FB) and the PA fine model output signal (SPA—MODEL—OUT). As discussed below in detail, the adaptor40configures the PA fine model34, and more specifically the PA coarse model36and the PA space mapping38, such that a difference between, or error between, the baseband feedback signal (SBB—FB) and the PA fine model output signal (SPA—MODEL—OUT) is minimized (e.g., zero or substantially zero).

The adaptor42generally operates to configure the PD fine model28based on the PA fine model34. More specifically, the adaptor42configures the PD fine model28based on a first adaptor input signal (SPA) from the PA fine model34and a second adaptor input signal (SPD) from the PD fine model28. The first adaptor input signal (SPA) is output by a multiplexor44. The multiplexor44outputs either the predistorted baseband signal (SBB—PD), the coarse model input signal (SC—IN—PA), the coarse model output signal (SC—OUT—PA), or the PA fine model output signal (SPA—MODEL—OUT). The multiplexor44is controlled by, for example, the adaptor42or a controller associated with the adaptation sub-system16. The second adaptor input signal (SPD) is either the baseband signal (SBB), the coarse model input signal (SSBB—PD), the coarse model output signal (SC—OUT—D), or the predistorted baseband signal (SBB—PD) output by a multiplexor46. The multiplexor46is controlled by, for example, the adaptor42or a controller associated with the adaptation sub-system16.

FIG. 3is a flow chart illustrating an initial training process performed by the adaptation sub-system16ofFIGS. 1 and 2according to one embodiment of the present disclosure. The process ofFIG. 3is graphically illustrated inFIGS. 4A through 4D. While discussing the process ofFIG. 3, references toFIGS. 4A through 4Dwill be made where applicable. First, the adaptation sub-system16trains the PA coarse model36with the PA space mapping38and the predistorter14in a by-pass mode or otherwise in an inactive state (step100). The predistorter14is in the by-pass or inactive mode when the PD coarse model30and the PD space mapping32are in a by-pass or inactive mode.

As illustrated inFIG. 4A, when training the PA coarse model36, the predistorter14and the PA space mapping38are by-passed, or inactive, such that the baseband signal (SBB) is input to the upconversion and modulation sub-system20and the PA coarse model36. The upconversion and modulation sub-system20upconverts and modulates the baseband signal (SBB) to provide the power amplifier input signal (SPA—IN), which is amplified by the power amplifier12to provide the power amplifier output signal (SPA—OUT). The power amplifier output signal (SPA—OUT) is attenuated by the attenuator24, and the resulting attenuated power amplifier output signal (SPA—OUT—A) is downconverted and demodulated by the downconversion and demodulation sub-system26to provide the baseband feedback signal (SBB—FB).

The PA coarse model36processes the baseband signal (SBB) to provide the PA coarse model output signal (SC—OUT—PA). Then, because the PA space mapping38is by-passed, the PA coarse model output signal (SC—OUT—PA) is output to the adaptor40. Using a conventional adaptive filtering technique, the adaptor40configures the PA coarse model36to minimize a difference, or error, between the PA coarse model output signal (SC—OUT—PA) and the baseband feedback signal (SBB—FB). In this manner, the PA coarse model36is trained as a coarse model of the non-linearity of the power amplifier12.

Second, after the PA coarse model36is trained, the adaptation sub-system16trains the PA space mapping38with the PA coarse model36active and the predistorter14in by-pass, or inactive, mode (step102). As illustrated inFIG. 4B, when training the PA space mapping38, the predistorter14is by-passed, or inactive, such that the baseband signal (SBB) is input to the upconversion and modulation sub-system20and the PA fine model34. The upconversion and modulation sub-system20upconverts and modulates the baseband signal (SBB) to provide the power amplifier input signal (SPA—IN), which is amplified by the power amplifier12to provide the power amplifier output signal (SPA—OUT). The power amplifier output signal (SPA—OUT) is attenuated by the attenuator24, and the resulting attenuated power amplifier output signal (SPA—OUT—A) is downconverted and demodulated by the downconversion and demodulation sub-system26to provide the baseband feedback signal (SBB—FB).

In the PA fine model34, the baseband signal (SBB) is processed by the PA space mapping38and the PA coarse model36to provide the PA fine model output signal (SPA—MODEL—OUT). Using a space mapping training technique, the adaptor40configures the PA space mapping38to minimize a difference, or error, between the PA fine model output signal (SPA—MODEL—OUT) and the baseband feedback signal (SBB—FB). In this manner, the PA space mapping38is trained to map the PA coarse model36into a fine model of the non-linear characteristic of the power amplifier12. The space mapping training technique may be, for example, the basic back propagation (BP) algorithm, or other first order gradient based algorithms or other standard optimization algorithms involving second order derivatives or their variations. While not essential for understanding the concepts disclosed here, for an exemplary space mapping training technique, the interested reader is directed to Rumelhart, D. E., Hinton, G. E., and Williams, R. J.,Learning representations by back-propagating errors, Nature, 323, 533-536, October 1986.

Third, after the PA space mapping38is trained, the adaptation sub-system16trains the PD coarse model30with the PA coarse model36active and the PA space mapping38and the PD space mapping32in by-pass, or inactive, mode (step104). As illustrated inFIG. 4C, when training the PD coarse model30, the PD space mapping32is by-passed such that the baseband signal (SBB) is provided to the PD coarse model30and the output of the PD coarse model30is provided as the predistorted baseband signal (SBB—PD) (i.e., the output of the PD fine model28). Notably, while training the PD coarse model30, the upconversion and modulation sub-system20and the power amplifier12can be disabled.

In addition, when training the PD coarse model30, the PA space mapping38is by-passed such that the predistorted baseband signal (SBB—PD) is input into the PA coarse model36. The resulting PA coarse model output signal (SC—OUT—PA) is then provided to the adaptor42via the multiplexor44. Notably, while training the PD coarse model30, the adaptor40is disabled such that the PA coarse model36remains constant while the PD coarse model30is trained. In addition, the attenuator24and the downconversion and demodulation sub-system26can be disabled. The adaptor42then trains the PD coarse model30to minimize a difference, or error, between the PA coarse model output signal (SC—OUT—PA) and the baseband signal (SBB), thereby training the PD coarse model30as the inverse of the PA coarse model36.

Lastly, after the PD coarse model30is trained, the adaptation sub-system16trains the PD space mapping32with the PA coarse model36, the PA space mapping38, and the PD coarse model30active (step106). More specifically, as illustrated inFIG. 4D, the baseband signal (SBB) is provided to the PD fine model28and processed by both the PD space mapping32and the PD coarse model30to provide the predistorted baseband signal (SBB—PD). Notably, while training the PD space mapping32, the upconversion and modulation sub-system20and the power amplifier12can be disabled.

When training the PD space mapping32, the predistorted baseband signal (SBB—PD) is input into the PA fine model34and processed by both the PA space mapping38and the PA coarse model36to provide the PA fine model output signal (SPA—MODEL—OUT). The PA fine model output signal (SPA—MODEL—OUT) is then provided to the adaptor42via the multiplexor44. Notably, while training the PD space mapping32, the adaptor40is disabled such that the PA fine model34remains constant while the PD space mapping32is trained. In addition, the attenuator24and the downconversion and demodulation sub-system26can be disabled. The adaptor42then trains the PD space mapping32using a space mapping technique to minimize a difference, or error, between the PA fine model output signal (SPA—MODEL—OUT) and the baseband signal (SBB), thereby training the PD fine model28as the inverse of the PA fine model34. In this manner, the PD space mapping32is trained.

FIG. 5is a flow chart illustrating an initial training process performed by the space mapping based predistortion sub-system ofFIGS. 1 and 2according to another embodiment of the present disclosure. This embodiment is substantially the same as that ofFIG. 3but with the ordering of steps102and104reversed. Since only the ordering of the steps has changed, the details of the individual steps are not repeated. First, the adaptation sub-system16trains the PA coarse model36with the PA space mapping38and the predistorter14in a by-pass mode or otherwise in an inactive state (step200). Second, after the PA coarse model36is trained, the adaptation sub-system16trains the PD coarse model30with the PA coarse model36active and the PA space mapping38and the PD space mapping32in by-pass, or inactive, mode (step202). Third, after the PD coarse model30is trained, the adaptation sub-system16trains the PA space mapping38with the PA coarse model36active and the predistorter14in by-pass, or inactive, mode (step204). Lastly, after the PA space mapping38is trained, the adaptation sub-system16trains the PD space mapping32with the PA coarse model36, the PA space mapping38, and the PD coarse model30active (step206).

FIGS. 6 and 7are flow charts that illustrate training of the predistorter14subsequent to the initial training process of eitherFIG. 3orFIG. 5according to one embodiment of the present disclosure. It should be noted thatFIGS. 6 and 7are only examples. Numerous variations will be apparent to one of ordinary skill in the art upon reading this disclosure. More specifically,FIG. 6illustrates a process for subsequent training of the PA fine model34. First, the adaptor40determines whether it is time to train the PA coarse model36(step300). In one particular embodiment, the PA coarse model36is trained at a predefined adaptation rate for the PA coarse model36. If it is not yet time to train the PA coarse model36, the process proceeds to step304. Otherwise, the adaptor40trains the PA coarse model36in the manner described above (step302). Note that unlike in the initial training, the predistorter14and the PA space mapping38may be active while performing subsequent training of the PA coarse model36.

Next, the adaptor40determines whether it is time to train the PA space mapping38(step304). In one particular embodiment, this subsequent training of the PA space mapping38is performed at a predefined adaptation rate for training the PA space mapping38. While the adaptation rates for the PA coarse model36and the PA space mapping38may be different, in the preferred embodiment, the PA space mapping38is trained immediately following the training of the PA coarse model36. If it is not time to train the PA space mapping38, the process returns to step300. Otherwise, the adaptor40trains the PA space mapping38in the manner described above (step306). Note that unlike in the initial training, the predistorter14may be active while performing subsequent training of the PA space mapping38. Before proceeding, it should be noted that, if the predistorter14is active during training of the PA coarse model36and the PA space mapping38in steps302and306, the adaptor42is disabled or is otherwise configured such that the PD fine model28remains constant while training the PA coarse model36and the PA space mapping38.

FIG. 7illustrates a process for subsequent training of the PA fine model34. First, the adaptor42determines whether it is time to train the PD coarse model30(step400). In one particular embodiment, the PD coarse model30is trained at a predefined adaptation rate for the PD coarse model30. If it is not yet time to train the PD coarse model30, the process proceeds to step404. Otherwise, the adaptor42trains the PD coarse model30in the manner described above (step402). Note that unlike in the initial training, the PD space mapping32, the PA coarse model36, and the PA space mapping38may be active while performing subsequent training of the PD coarse model30.

Next, the adaptor42determines whether it is time to train the PD space mapping32(step404). In one particular embodiment, this subsequent training of the PD space mapping32is performed at a predefined adaptation rate for training the PD space mapping32. While the adaptation rates for the PD coarse model30and the PD space mapping32may be different, in the preferred embodiment, the PD space mapping32is trained immediately following the training of the PD coarse model30. If it is not time to train the PD space mapping32, the process returns to step400. Otherwise, the adaptor42trains the PD space mapping32in the manner described above (step406). Before proceeding, it should be noted that the adaptor40is disabled or is otherwise configured such that the PA fine model34remains constant while training the PD coarse model30and the PD space mapping32.

The following acronyms are used throughout this disclosure.3GPP Third Generation Partnership ProjectBP Back PropagationLUT Look-Up-TablePA Power AmplifierPD Predistortion