Modern wireless systems require both wide bandwidth and high linearity in the radio power amplifiers, a difficult combination to achieve. To date, the most successful architecture to correct for the nonlinearity in the power amplifier has been feedforward linearization. For many applications, its drawbacks in power efficiency are more than made up in linearity and bandwidth.
A generic feedforward linearizer for a power amplifier is shown in FIG. 1. The relationship of the output to the input of the circuits labeled “signal adjuster” (109, 110, 111) depends on the settings of one or more control parameters of these circuits. The signal adjuster circuits do not necessarily all have the same structure, nor are they all necessarily present in an implementation. Usually, only one of signal adjusters a 110 and c 109 are present. An “adaptation controller” 114 monitors the internal signals of the signal adjuster circuits, as well as other signals in the linearizer. On the basis of the monitored signal values and the relationships among those monitored signals, the adaptation controller 114 sets the Values of the signal adjuster control parameters. In FIG. 1, a stroke on an arrow denotes a multiplicity of monitor signals or a multiplicity of control settings that set the control parameter values. As will be appreciated by those skilled in the art, the elements shown as pickoff points, adders or subtractors may be implemented by directional couplers, splitters or combiners, as appropriate.
Signal adjuster circuits form adjustable linear combinations of filters. A typical internal structure is shown in FIG. 2a for signal adjuster a 110. The input signal is split into one or more branches, M in total, each of which has a different linear filter Haj(f), j=1 . . . M (200, 202, 204). The output of each filter is weighted by a complex coefficient (i.e., magnitude and phase, or sine and cosine) in a complex gain adjuster (CGA 201, 203, 205), and the weighted outputs are summed by combiner 206 to form the output signal of the signal adjuster. In prior art signal adjuster circuits, the filters are simple delays, as shown in FIG. 2b, causing the signal adjuster to act as a finite impulse response (FIR) filter at RF, with possibly irregular spacing in time.
However, other filter choices are possible, including bandpass filters and bandstop filters. In general, the filters may be nonlinear in signal amplitude and may be frequency dependent. Examples include, without limitation, a cubic or Bessel function nonlinearity with intended or inadvertent nonlinearity, a bandpass filter with cubic dependence on signal amplitude, etc. (The mention in this Background Section of the use of these other filters in signal adjusters, however, is not intended to imply that this use is known in the prior art. Rather, the use of these other filters in signal adjusters is intended to be within the scope of the present invention.)
The CGAs themselves may have various implementation structures, two of which are shown in FIG. 3A and FIG. 3B. The implementation shown in FIG. 3A uses polar control parameters GA and GB, where GA sets the amplitude of the attenuator 301, while GB sets the phase of the phase shifter 302, which respectively attenuate and phase shift the RF input signal I to produce the RF output signal O. The implementation shown in FIG. 3B uses Cartesian control parameters, also designated GA and GB, where GA sets the real part of the complex gain, while GB sets the imaginary part of the complex gain. In this implementation, the RF input signal I is split into two signals by splitter 306, one of which is phase shifted by 90 degrees by phase shifter 303, while the other is not. After GA and GB are respectively applied by mixers or attenuators 305 and 304, the resulting signals are added by combiner 307 to produce the RF output signal O. As disclosed in U.S. Pat. No. 6,208,207, the complex gain adjusters may themselves be linearized so that any desired setting may be obtained predictably by an appropriate setting of control voltages.
The operation of a multibranch feedforward linearizer resembles that of single branch structures. With reference to FIG. 1, assume for simplicity's sake that signal adjuster c 109 is absent, that is, the RF input signal is directly input to the power amplifier 103. Within the signal cancellation circuit 101, appropriate setting of the CGA gains in signal adjuster a 110 allow it to mimic the desired linear portion of the power amplifier response, including the effects of amplifier delay and other filtering, and to compensate for linear impairments of its own internal structure. The unwanted components of the power amplifier output, such as nonlinear distortion, thermal noise and linear distortion are thereby revealed at the output of the first subtractor 106. Within the distortion cancellation circuit 102, appropriate setting of the parameters of the signal adjuster b 111 allows it to compensate for delay and other filtering effects in the amplifier output path and in its own internal structure, and to subtract a replica of the unwanted components from the amplifier output delayed by delay 112. Consequently, the output of the second subtractor 107 contains only the desired linear components of the amplifier output, and the overall feedforward circuit acts as a linear amplifier. Optional delay 104 is not used in this configuration.
It is also possible to operate with signal adjuster c, and replace signal adjuster a 110 with a delay 104 in the lower branch of the signal cancellation circuit 101, which delays the input signal prior to subtractor 106. The advantage of this configuration is that any nonlinear distortion generated in signal adjuster c 109 is cancelled along with distortion generated in the power amplifiers.
Generally, one- and two-branch signal adjusters are known in the art (see, for example, U.S. Pat. No. 5,489,875, which is incorporated herein by reference), as well as three-or-more branch signal adjusters (see, for example, U.S. Pat. No. 6,208,207, which is also incorporated by reference).
Other types of linearizers use only a predistortion adjuster circuit c. As will be appreciated by those skilled in the art, in this linearizer the signal adjuster circuit a is merely a delay line ideally matching the total delay of the adjuster circuit c and the power amplifier. In this case, the distortion cancellation circuit, comprising the distortion adjuster circuit b, the error amplifier and the delay circuit, is not used—the output of the linearizer is the simply the output of the signal power amplifier. The goal of the adjuster circuit c is to predistort the power amplifier input signal so that the power amplifier output signal is proportional to the input signal of the linearizer. That is, the predistorter acts as a filter having a transfer characteristic which is the inverse of that of the power amplifier, except for a complex constant (i.e., a constant gain and phase). Because of their serial configuration, the resultant transfer characteristic of the predistorter and the power amplifier is, ideally, a constant gain and phase that depends on neither frequency nor signal level. Consequently, the output signal will be the input signal amplified by the constant gain and out of phase by a constant amount, that is, linear. Therefore, to implement such predistortion linearizers, the transfer characteristic of the power amplifier is computed and a predistortion filter having the inverse of that transfer characteristic is constructed. Preferably, the predistortion filter should also compensate for changes in the transfer function of the power amplifier, such as those caused by degraded power amplifier components.
For example, a three-branch adaptive polynomial predistortion adjuster circuit c 109 is shown in FIG. 8. The upper branch 800 is linear, while the middle branch has a nonlinear cubic polynomial filter 801 and the lower branch has a nonlinear quintic polynomial filter 802, the implementation of which nonlinear filters is well known to those skilled in the art. Each branch also has a CGA, respectively 803, 804, and 805, to adjust the amplitude and phase of the signal as it passes therethrough. By setting the parameters (GA, GB) of each of the CGAs, a polynomial relationship between the input and output of the adjuster circuit can be established to compensate for a memoryless nonlinearity in the power amplifier. The adaptation controller, via a known adaptation algorithm, uses the input signal, the output of the nonlinear cubic polynomial filter, the output of the nonlinear quintic polynomial filter, and the error signal (the power amplifier output signal minus an appropriately delayed version of input signal) to generate the parameters (GA, GB) for the three CGAs.
Generally, the adaptation algorithm, whether to generate the control parameters for the CGAs of an analog predistorter linearizer or a feedforward linearizer, is selected to minimize a certain parameter related to the error signal (for example, its power over a predetermined time interval). Examples of such adaptation algorithms are known in the art, such as the stochastic gradient, partial gradient, and power minimization methods described in U.S. Pat. No. 5,489,875.
For example, FIG. 6a shows an adaptation controller using the stochastic gradient algorithm. For generating the control signals (GA, GB) for the CGAs of adjuster circuit a 110, the bandpass correlator 606 correlates the error signal at the output of subtractor 106 with each of the monitor signals output from the adjuster circuit a 110. The controller integrates the result using integrator 608, via loop gain amplifier 607, to generate CGA control signals (GA, GB). The internal structure of a bandpass correlator 606 that estimates the correlation between the complex envelopes of two bandpass signals is shown in FIG. 6b. The bandpass correlator includes a phase shifter 601, mixers 602 and 603, and bandpass filters (or integrators) 604 and 605. The operation of this bandpass correlator is described in U.S. Pat. No. 5,489,875 in FIG. 3 thereof and its corresponding text. By use of a controllable RF switch at its inputs, a hardware implementation of a bandpass correlator can be connected to different points in the circuit, thereby allowing bandpass correlations on various pairs of signals to be measured by a single bandpass correlator.
U.S. Pat. No. 5,489,875 also discloses an adaptation controller using a “partial gradient” adaptation algorithm by which the correlation between two bandpass signals is approximated as a sum of partial correlations taken over limited bandwidths at selected frequencies. This provides two distinct benefits: first, the use of a limited bandwidth allows the use of a digital signal processor (DSP) to perform the correlation, thereby eliminating the DC offset that appears in the output of a correlation implemented by directly mixing two bandpass signals; and second, making the frequencies selectable allows calculation of correlations at frequencies that do, or do not, contain strong signals, as desired, so that the masking effect of strong signals on weak correlations can be avoided. FIG. 7, adapted from FIG. 9 of U.S. Pat. No. 5,489,875, illustrates a partial correlator, in which local oscillators 701 and 702 select the frequency of the partial correlation. Frequency shifting and bandpass filtering are performed by the mixer/bandpass filter combinations 703/707, 704/708, 705/709, 706/710. The signals output by the bandpass filters 709 and 710 are digitally converted, respectively, by analog-to-digital converters (ADCs) 711 and 712. Those digital signals are bandpass correlated by DSP 713 to produce the real and imaginary components of the partial correlation. The partial correlator is illustrated for two stages of analog downconversion, but more or fewer stages may be required, depending on the application. (See, for example, FIG. 9 of U.S. Pat. No. 5,489,875 and its accompanying text for a description of the operation of such partial correlators.)
Multibranch signal adjusters allow for the amplification of much wider bandwidth signals than could be achieved with single branch adjusters, since the former provides for adaptive delay matching. Further, multibranch signal adjusters can provide intermodulation (IM) suppression with multiple nulls, instead of the single null obtainable with single-branch adjusters. FIG. 4, for example, shows two nulls produced with a two-branch signal adjuster circuit. This property of multibranch signal adjusters further supports wide signal bandwidth capability. The two- and three-branch FIR signal adjusters respectively disclosed by U.S. Pat. Nos. 5,489,875 and 6,208,207 can also compensate for frequency dependence of their own components, as well as delay mismatch. However, despite the above features, there is still a need for techniques to improve the reliability of the adaptation of multibranch feedforward linearizers.
One such desirable technique is to decorrelate the branch signals monitored by the adaptation controller. This can be appreciated from consideration of a two-branch FIR signal adjuster, as depicted in FIG. 2B (M=2). The difference in delays between the two branches is relatively small compared with the time scale of the modulation of the RF carrier. Consequently, the two signals are very similar, tending to vary almost in unison. Adaptive adjustment of the CGA gains by known stochastic gradient or power minimization techniques will cause the two gains also to vary almost in unison. However, it is the difference between the gains that produces the required two nulls, instead of one, in the frequency response; and because the difference between the signals is so small, the difference of CGA gains is unacceptably slow to adapt.
It is known in the art that decorrelation of equal power branch signals of a two-branch FIR signal adjuster has the potential to greatly speed adaptation. Specifically, U.S. Pat. No. 5,489,875 discloses a circuit structure that decorrelates the branch signals of a two-branch FIR signal adjuster to the sum and the difference of the two complex envelopes (“common mode” and “differential mode”, respectively) for separate adaptation. This circuit takes advantage of the special property that when there is equal power in the branches of the two-branch FIR signal adjuster, the common mode and the differential mode correspond to the eigenvectors of the correlation matrix of the two complex envelopes. Consequently, the common mode and differential mode are uncorrelated, irrespective of the degree of correlation of the original branch signals. Accordingly, use of the sum and difference signals, instead of the original signals, separates the common and differential modes, thereby allowing, for example, adaptation by the stochastic gradient method to give more emphasis, or gain, to the weak differential mode. This in turn allows the signal adjuster to converge, and form the dual frequency nulls, as quickly as the common mode.
In all other linearizers, however, the linear combinations of branch signals which comprise the uncorrelated modes are not readily determinable in advance. The coefficients for such combinations depend on the relative delays (or filter frequency responses) of the branches and on the input signal statistics (autocorrelation function or power spectrum). Accordingly, for these other linearizers, the adaptation controller must determine the uncorrelated modes and adjust their relative speeds of convergence.
Another technique desired to improve the reliable operation of multibranch feedforward linearizers is self-calibration. The need for it can be understood from the fact that the monitored signals, as measured by the adaptation controller 114 in FIG. 1, are not necessarily equal to their counterpart internal signals within the signal adjuster blocks and elsewhere. The reason is that the cables and other components of the signal paths that convey the internal signals of the adjuster blocks to the adaptation controller introduce inadvertent phase and amplitude changes. The true situation for an M-branch signal adjuster is represented in FIG. 5, where these changes are represented by “observation filters” Ham1(f) to HamM(f) (501, 502, 503) that transform the internal signals νa1 . . . νaM before they appear as monitor signals νam1 . . . νamM at the adaptation controller. In the simplest case, the observation filters and filter networks consist of frequency-independent amplitude and phase changes on each of the signal paths. The responses of the observation filters are initially unknown. Observation filters have been omitted for signals νin and νe because, without loss of generality, their effects can be included in the illustrated branch filters and observation filters. Although FIG. 5 illustrates only signal adjuster a 110, a similar problem is associated with signal adjusters b 111 and c 109.
The presence of unknown observation filters causes two related problems. First, although adaptation methods based on correlations, such as stochastic gradient, attempt to make changes to CGA gains in directions and amounts that maximally reduce the power in the error signal, the observation filters introduce phase and amplitude shifts. In the worst case of a 180-degree shift, the adaptation adjustments maximally increase the error signal power—that is, they cause instability and divergence. Phase shifts in the range of −90 degrees to +90 degrees do not necessarily cause instability, but they substantially slow the convergence if they are not close to zero. The second problem is that it is difficult to transform the branch signals to uncorrelated modes if their monitored counterparts do not have a known relationship to them.
Determination of the observation filter responses, and subsequent adjustment of the monitor signals in accordance therewith, is termed calibration. Procedures for calibration (i.e., self-calibration) remove the need for manual calibration during production runs and remove concerns that subsequent aging and temperature changes may cause the calibration to be in error and the adaptation to be jeopardized.