Electronics Guide

Power Amplifier Nonlinearity

Power amplifier nonlinearity arises from the fundamental physics of transistor operation. As signal amplitude increases, the transistor's gain compresses, its phase shift changes, and eventually the output saturates. These effects depend not only on the instantaneous signal level but also on the signal's history, a phenomenon called memory effects caused by thermal dynamics and bias circuit time constants.

The nonlinearity manifests in the frequency domain as spectral regrowth, where energy spreads from the intended transmission band into adjacent channels. For modulated signals with high peak-to-average power ratios, common in modern wireless standards, even modest nonlinearity produces significant adjacent channel interference. Regulatory bodies specify adjacent channel power ratios (ACPR) that limit this interference, often requiring linearity better than minus forty to minus fifty decibels relative to the main signal.

Traditional approaches to achieving this linearity include operating the amplifier well below its maximum capability (backing off), using feedforward linearization with auxiliary amplifiers, or employing analog predistortion circuits. Each approach has significant drawbacks: back-off wastes expensive amplifier capability and power supply capacity, feedforward adds complexity and power consumption, and analog predistortion has limited correction capability. Digital predistortion avoids these compromises by performing precise correction in the digital domain.

Memoryless Predistortion Models

The simplest predistortion approach assumes the amplifier's output depends only on the current input, with no memory of past inputs. This memoryless model characterizes the amplifier's AM-AM (amplitude-to-amplitude) and AM-PM (amplitude-to-phase) distortion curves, then applies inverse functions to the input signal.

Polynomial models represent the amplifier transfer function as a power series:

y(t) = a1 times x(t) + a2 times x(t) squared + a3 times x(t) cubed + higher order terms

For bandpass signals, only odd-order terms produce in-band distortion, so the model often includes only odd powers. The predistorter applies the inverse polynomial, chosen so the cascade of predistorter and amplifier produces a linear overall response.

Look-up table (LUT) approaches store the inverse transfer function directly, indexed by input amplitude. The table values are determined during a calibration phase that measures the amplifier's actual response. LUT methods can represent arbitrary nonlinearities without the approximation error inherent in polynomial truncation, though they require sufficient table resolution to avoid quantization effects.

Memoryless models work well for narrowband signals but become inadequate as signal bandwidth increases. Wideband signals exercise the amplifier's memory effects, causing distortion that depends on the signal's time evolution, not just its instantaneous amplitude.

Memory Polynomial Models

Memory polynomial models extend memoryless approaches to capture amplifier dynamics. The output depends on current and past input values, with polynomial nonlinearity applied to each tap:

y(t) = sum over memory depth M of sum over polynomial order K of coefficients a(k,m) times |x(t-m)|^(k-1) times x(t-m)

This structure captures the interaction between amplitude-dependent nonlinearity and linear memory effects. The memory depth M and polynomial order K are chosen based on the amplifier's behavior and the signal bandwidth. Typical implementations use memory depths of five to fifteen samples and polynomial orders up to nine or eleven.

Generalized memory polynomial (GMP) models add cross-terms that capture interactions between samples at different delays, improving accuracy for amplifiers with complex memory behavior. The additional terms increase computational cost but provide better correction for challenging amplifiers.

Model coefficient estimation uses least-squares fitting to measured amplifier input-output data. The digital predistorter captures samples of the transmitted signal and corresponding feedback from the amplifier output, then solves for coefficients that minimize the error between the desired linear output and the actual amplifier output. This estimation runs continuously or periodically to track amplifier changes with temperature and aging.

Adaptive DPD Implementation

Production DPD systems must adapt continuously to track amplifier variations. Temperature changes alter transistor characteristics, aging shifts bias points, and even signal statistics can affect the optimal predistortion. Adaptive algorithms monitor the amplifier output and adjust predistortion parameters to maintain performance.

The adaptation architecture includes a feedback path that samples the amplifier output, a comparison function that measures the residual distortion, and an update algorithm that modifies the predistorter coefficients. The feedback path typically uses a dedicated receiver that captures a portion of the transmitted signal, downconverts it, and digitizes it for comparison with the original input.

Least mean squares (LMS) and recursive least squares (RLS) algorithms are common choices for coefficient adaptation. LMS offers simplicity and low computational cost but converges slowly. RLS converges faster but requires more computation and can become unstable if not carefully implemented. Hybrid approaches use RLS for initial convergence, then switch to LMS for tracking.

The adaptation bandwidth, the rate at which coefficients can change, must be fast enough to track relevant amplifier variations but slow enough to provide stable, accurate estimates. Typical systems update coefficients on millisecond timescales, much faster than thermal time constants but much slower than the signal bandwidth.

DPD System Architecture

A complete DPD system comprises several functional blocks working together. The predistortion function itself operates on complex baseband samples at a rate several times the signal bandwidth to accommodate spectral regrowth. Digital-to-analog converters transform the predistorted signal for upconversion and amplification. The feedback path includes RF sampling or downconversion, analog-to-digital conversion, and digital signal alignment.

Sample alignment between forward and feedback paths presents a practical challenge. The feedback signal must be precisely aligned with the original input for accurate error computation. Correlation-based alignment algorithms find the delay that maximizes the similarity between signals, compensating for variable delays in the analog paths.

Crest factor reduction (CFR) often precedes DPD, reducing the signal's peak-to-average power ratio to allow higher average power while staying within the amplifier's linear range. CFR and DPD work together, with CFR reducing the peaks that would stress the amplifier and DPD correcting the residual nonlinearity.

Modern DPD implementations achieve adjacent channel power ratios of minus fifty decibels or better while enabling amplifier efficiency improvements of ten to twenty percentage points compared to back-off approaches. This efficiency gain translates directly to reduced power consumption, heat dissipation, and operating costs, making DPD essential for base stations and other high-power wireless infrastructure.

Echo Sources and Characteristics

In wireline communications, echo arises from impedance mismatches at the interface between four-wire and two-wire telephone circuits, at cable splices, and at equipment connections. The hybrid transformer that couples the four-wire long-distance network to two-wire local loops cannot provide perfect isolation because the local loop impedance varies unpredictably with line length, wire gauge, and loading coils.

The echo path can be modeled as a linear filter whose impulse response extends over tens or hundreds of milliseconds, depending on the physical system. The response includes multiple reflections that decay over time, potentially with significant energy at long delays. Echo return loss, the ratio of transmitted power to echo power, typically ranges from ten to twenty-five decibels, meaning a substantial portion of the transmitted signal appears at the receiver.

Acoustic echo in hands-free communication devices presents additional challenges. The acoustic coupling between loudspeaker and microphone varies with room geometry, which changes as people move or doors open. The path includes reflections from walls and furniture with delays up to hundreds of milliseconds. Non-linear effects from loudspeaker distortion complicate the modeling.

Adaptive Filter Structure

Echo cancellers use adaptive filters to model the echo path and subtract the estimated echo from the received signal. The filter input is the transmitted signal, and the filter output is subtracted from the receiver input to produce the clean received signal. The adaptation algorithm adjusts filter coefficients to minimize the residual error.

Finite impulse response (FIR) structures dominate echo cancellation because they are inherently stable and their linear-in-parameters form simplifies adaptation. The filter length must span the entire echo path duration, potentially requiring thousands of taps for long acoustic paths. Efficient implementations use block processing and frequency-domain algorithms to manage the computational load.

The LMS algorithm updates each filter coefficient in proportion to the correlation between the error signal and the corresponding delayed input sample. The update step size controls the trade-off between adaptation speed and steady-state error. Normalized LMS (NLMS) divides the step size by the input signal power, providing more consistent adaptation across varying signal levels.

The basic LMS update equation is: coefficient w(n+1) equals w(n) plus mu times error e(n) times input x(n), where mu is the step size. Convergence requires the step size to be less than the reciprocal of the filter length times the input power, though practical implementations use smaller values for stability margin.

Double-Talk Detection

When both parties speak simultaneously (double-talk), the received signal contains both the desired far-end speech and the near-end speech mixed with echo. If the adaptive filter continues to adapt during double-talk, it will treat the far-end speech as echo and diverge from the true echo path model. Double-talk detection identifies these periods and freezes adaptation to prevent divergence.

The Geigel algorithm, a simple and widely used detector, compares the received signal level to the transmitted signal level. If the received signal significantly exceeds what the echo alone could produce, double-talk is declared. The threshold must balance sensitivity (detecting all double-talk) against false triggers (halting adaptation unnecessarily).

More sophisticated detectors use correlation-based measures or monitor the adaptation itself for signs of divergence. The normalized cross-correlation between the error signal and the transmitted signal provides a robust indication: high correlation suggests the error is mainly echo, while low correlation suggests far-end speech is present.

Some modern systems use separate adaptation step sizes for double-talk and single-talk conditions rather than completely freezing adaptation. This approach maintains tracking of slowly varying echo paths even during double-talk while preventing rapid divergence.

Non-Linear Echo Cancellation

When the echo path includes non-linear elements, linear adaptive filters cannot fully cancel the echo. Loudspeakers driven at high levels exhibit harmonic distortion and intermodulation that creates echo components not present in the transmitted signal's linear transformation. Overdriven amplifiers in the echo path similarly create non-linear echo.

Volterra filters extend linear filtering to include polynomial combinations of delayed inputs, capturing memoryless and memory nonlinearities. A second-order Volterra filter includes both linear terms and products of input samples at different delays. The exponential growth of terms with polynomial order and memory depth limits practical implementations to low-order approximations.

Hammerstein models separate the nonlinearity from the linear dynamics: a memoryless nonlinearity followed by a linear filter. This structure reduces complexity while capturing the dominant characteristics of loudspeaker distortion. The nonlinearity is typically represented as a polynomial or look-up table, and both the nonlinearity parameters and the linear filter coefficients adapt to match the actual echo path.

Practical acoustic echo cancellers often accept some residual non-linear echo and rely on post-processing, such as comfort noise injection or residual echo suppression, to mask it perceptually. The combination of a linear canceller, non-linear extension, and post-processing provides acceptable quality for hands-free communication.

Sources of DC Offset

Direct-conversion receivers translate the RF signal directly to baseband, making them susceptible to multiple DC offset mechanisms. Local oscillator leakage to the mixer RF port creates a self-mixing product at DC. Device mismatches in differential circuits produce static offsets. Temperature variations and aging change these offsets over time.

The offset magnitude can be large compared to the desired signal, particularly for weak received signals. A direct-conversion receiver might have DC offset equivalent to a signal several tens of decibels above the sensitivity level. Without cancellation, this offset would consume most of the analog-to-digital converter's dynamic range and dominate the baseband signal.

In high-resolution data converters, comparator and amplifier offsets limit accuracy. A sixteen-bit converter must control offsets to approximately fifteen microvolts at one-volt full scale to maintain full resolution. Achieving this accuracy with analog trimming alone requires expensive calibration procedures and does not track variations over temperature and time.

Highpass Filtering Approaches

The simplest digital DC offset removal uses a highpass filter to block the zero-frequency component while passing the desired signal. A first-order highpass filter with cutoff frequency fc passes signal frequencies above fc while attenuating DC and low frequencies. The filter is typically implemented as a recursive structure:

y(n) = alpha times y(n-1) + x(n) - x(n-1)

The coefficient alpha, slightly less than one, determines the cutoff frequency. Values near one provide very low cutoff frequencies but slow settling after transients.

This approach works well when the desired signal has no significant energy near DC. However, many modulation formats, including amplitude shift keying and some pulse amplitude modulation schemes, have spectral energy extending to DC. Highpass filtering these signals causes intersymbol interference and degrades performance.

For signals with DC content, the highpass filter cutoff must be lower than the signal bandwidth, potentially much lower. A filter with cutoff at one hertz has a settling time of hundreds of milliseconds, unacceptable for burst-mode communications where each received packet needs rapid DC acquisition.

Feedback-Based Cancellation

Feedback-based methods estimate the DC offset and subtract it from the signal, providing faster convergence than highpass filtering while accommodating signals with DC content. The basic structure integrates the output signal and feeds back the integral to subtract from the input:

y(n) = x(n) - offset(n)

offset(n) = offset(n-1) + mu times y(n)

The adaptation parameter mu controls convergence speed versus steady-state noise. Small mu provides slow but accurate convergence; large mu converges quickly but with more residual fluctuation.

For signals with zero mean (after removing the DC offset), this structure converges to the true offset without affecting the signal. However, signals with non-zero mean, such as certain data patterns, cause the estimated offset to track the signal mean, creating distortion. Signal-dependent adaptation, where mu is reduced when the signal is likely to have non-zero mean, addresses this issue.

Training-Based Methods

Communication systems that include training sequences or pilots can estimate DC offset during known-signal periods. The expected value of the training sequence is known, so any deviation from this value represents DC offset plus noise. Averaging over the training period reduces noise and provides an accurate offset estimate.

The estimated offset is then held constant during data transmission, eliminating any distortion of the data signal. Periodic training sequences update the estimate to track slow offset variations. This approach provides fast acquisition (limited only by training sequence length) and zero distortion of data symbols.

Blind estimation techniques extract DC offset information from data signals without explicit training. For constant-envelope modulations like FSK, the signal mean should equal the DC offset since the data itself has zero mean. For QAM constellations with symmetric symbol distributions, the received constellation centroid indicates the DC offset.

Decision-directed methods use demodulated symbols to reconstruct the expected received signal and estimate offset from the difference. These methods require sufficiently high signal quality for reliable symbol decisions but provide continuous tracking during data reception.

Types of IQ Imbalance

Gain imbalance occurs when the I and Q paths have different amplitudes, typically due to component tolerances in mixers, filters, and amplifiers. A few percent gain difference is common without calibration. In the received signal, gain imbalance causes the constellation to appear stretched along one diagonal.

Phase imbalance arises when the quadrature local oscillator signals are not exactly ninety degrees apart. Typical RF quadrature generators achieve phase accuracy of one to three degrees. Phase imbalance skews the received constellation, rotating one axis relative to the other.

Frequency-dependent imbalance results from filter mismatches between I and Q paths. Different cutoff frequencies or group delay variations cause imbalance that changes across the signal bandwidth. This type of imbalance is particularly problematic for wideband signals where the imbalance varies significantly across the occupied spectrum.

The combined effect of gain and phase imbalance creates image frequency interference. A desired signal at positive frequency offset from the carrier produces an unwanted image at the corresponding negative offset. The image rejection ratio (IRR), the power ratio between desired and image signals, is limited to approximately twenty-five to thirty-five decibels for typical uncorrected receivers, insufficient for demanding applications.

Image Rejection Analysis

Mathematically, IQ imbalance causes cross-coupling between positive and negative frequency components. The received baseband signal can be expressed as:

r(t) = K1 times s(t) + K2 times conjugate of s(t)

where s(t) is the desired signal, conjugate of s(t) is its complex conjugate (the image), and K1 and K2 are complex constants determined by the imbalance. Perfect quadrature yields K1 = 1 and K2 = 0. With imbalance, K2 becomes non-zero, introducing the image.

The image rejection ratio in decibels equals minus twenty times log base ten of |K2/K1|. For small imbalances, IRR is approximately minus twenty times log base ten of the square root of (g squared plus phi squared), where g is the fractional gain error and phi is the phase error in radians. One degree phase error and one percent gain error yield roughly forty decibel IRR.

OFDM systems are particularly sensitive to IQ imbalance because each subcarrier's image falls on another subcarrier, creating interference that cannot be removed by carrier spacing. Image rejection of sixty decibels or better is often required for modern wireless standards, far beyond what analog circuits alone achieve.

Blind Estimation Methods

Blind IQ imbalance estimation extracts imbalance parameters from the received signal without requiring special training sequences. These methods rely on statistical properties of the transmitted signal that are known a priori.

For signals with circularly symmetric statistics, such as OFDM with random data, the expectation of the signal squared should be zero: E[s squared] = 0. IQ imbalance creates a non-zero component that can be measured from the received signal and used to estimate K2:

E[r squared] is proportional to K1 times conjugate of K2

Combined with the measured signal power, both K1 and K2 can be estimated. The correction filter then inverts the imbalance:

s_hat(t) = A times r(t) + B times conjugate of r(t)

where A and B are chosen to cancel the image and restore unity gain on the desired signal.

Adaptive implementations update the correction coefficients continuously, tracking slow variations in analog circuit characteristics. The adaptation uses gradient descent on the correlation measure, converging to coefficients that minimize the residual image.

Frequency-Dependent Correction

When IQ imbalance varies across frequency, a single complex gain correction is insufficient. Frequency-dependent correction requires filtering rather than simple multiplication, with separate I and Q path filters that collectively compensate the frequency-varying imbalance.

The correction structure applies FIR filters to both the direct signal and its conjugate:

s_hat(t) = sum of a(k) times r(t-k) plus sum of b(k) times conjugate of r(t-k)

The filter coefficients a(k) and b(k) are determined to provide flat frequency response for the desired signal while canceling the image across the bandwidth. Estimation requires frequency-specific measurements, either from training signals with known spectral content or from statistical analysis across frequency bins.

OFDM systems can estimate imbalance separately for each subcarrier using pilot symbols. The collection of per-subcarrier estimates defines the frequency-dependent correction, which is then applied in the frequency domain after the FFT. This approach naturally accommodates arbitrary frequency variation without requiring long time-domain filters.

Transmitter IQ Imbalance

Transmitter IQ imbalance creates similar image problems but requires different correction approaches. The transmitter cannot observe its own output directly, so imbalance estimation relies on either factory calibration or feedback through an observation receiver.

Predistortion for transmitter IQ imbalance applies the inverse imbalance before the analog modulator:

x_predist(t) = C times x(t) + D times conjugate of x(t)

The coefficients C and D are chosen so the cascade of predistortion and impaired modulator produces the desired output. Factory calibration measures the transmitter imbalance and programs fixed correction coefficients. Adaptive systems use feedback from a demodulator to estimate imbalance and update coefficients.

Joint transmitter-receiver IQ calibration is possible when both ends of a communication link cooperate. The receiver can measure the combined transmitter and receiver imbalance and report this to the transmitter, which adjusts its predistortion accordingly. After several iterations, the overall system achieves the desired image rejection with the correction distributed between transmitter and receiver.

Channel Distortion Mechanisms

Wireline channels suffer frequency-dependent attenuation and phase shift that disperse transmitted pulses in time. Copper cables exhibit increasing loss at higher frequencies, rounding pulse edges and spreading energy into adjacent symbol periods. Impedance discontinuities create reflections that arrive at the receiver delayed from the main signal, adding further ISI.

Wireless channels experience multipath propagation where signals travel multiple paths between transmitter and receiver, arriving at different times and combining at the receiver. The channel impulse response shows multiple peaks corresponding to different propagation paths, each causing ISI. Moving transmitters or receivers cause the channel to vary with time, requiring continuous equalizer adaptation.

Optical fiber channels suffer from chromatic dispersion, where different wavelength components travel at different speeds, and polarization mode dispersion in single-mode fiber. At high data rates, these effects spread pulses significantly, requiring equalization to achieve acceptable error rates.

The channel's frequency response determines the severity of ISI. Channels with relatively flat frequency response within the signal bandwidth cause mild ISI that is easily equalized. Channels with deep nulls or severe roll-off create ISI patterns that require more sophisticated equalization approaches.

Linear Equalization

Linear equalizers apply a filter whose frequency response approximates the inverse of the channel response, flattening the overall cascade and removing ISI. The filter can be implemented as a transversal (FIR) structure with adjustable tap weights that adapt to match the current channel.

The zero-forcing equalizer sets tap weights to completely eliminate ISI at the sampling instants, forcing the cascade of channel and equalizer to satisfy the Nyquist criterion. This approach perfectly removes ISI but may amplify noise at frequencies where the channel has severe attenuation. For channels with deep nulls, zero-forcing equalization can be noise-dominated.

The minimum mean-square error (MMSE) equalizer balances ISI removal against noise enhancement, minimizing the total error including both residual ISI and amplified noise. The MMSE solution depends on both the channel response and the signal-to-noise ratio, adapting its behavior as conditions change. At high SNR, MMSE approaches zero-forcing; at low SNR, it accepts some ISI to avoid noise amplification.

Practical implementations use adaptive algorithms to track channel variations without requiring explicit channel estimation. The LMS algorithm adjusts tap weights based on the error between the equalizer output and known or detected symbols. Convergence requires that the input signal be sufficiently rich to excite all channel modes, which training sequences or normal data traffic provide.

Decision Feedback Equalization

Decision feedback equalization (DFE) improves on linear equalization by using past symbol decisions to cancel ISI from trailing channel taps. The structure includes a feedforward filter that processes the received signal and a feedback filter that subtracts ISI based on previously decided symbols.

The feedback filter can perfectly cancel trailing ISI without noise amplification because it operates on noise-free symbol decisions rather than noisy received samples. This advantage is particularly significant for channels with long trailing impulse responses, where a linear equalizer would require many taps and substantial noise enhancement.

The feedforward filter handles leading ISI (from symbols not yet decided) and shapes the overall response. Its design follows MMSE principles, balancing ISI cancellation and noise. The combination of MMSE feedforward filter and zero-forcing feedback filter provides better performance than linear MMSE equalization for most practical channels.

Error propagation is the primary DFE vulnerability. When a symbol decision is incorrect, the feedback filter subtracts the wrong value, increasing the error on subsequent symbols. This error can propagate through multiple symbol periods before the equalizer recovers. Techniques to mitigate propagation include using soft decisions with reduced confidence, periodic retraining with known symbols, and parallel DFE structures that explore multiple decision paths.

Fractionally-Spaced Equalization

Fractionally-spaced equalizers sample the received signal faster than the symbol rate, typically at two samples per symbol. This oversampling provides several advantages over symbol-rate (baud-rate) equalization.

First, fractionally-spaced equalizers can perform matched filtering and equalization jointly, eliminating the need for a separate receive filter whose characteristics must match the channel. The equalizer adapts to provide optimal filtering regardless of the analog front-end characteristics.

Second, the faster sampling makes equalization insensitive to sampling phase. Baud-rate equalizers require precise symbol timing; sampling at the wrong phase degrades performance. Fractionally-spaced equalizers effectively interpolate to the optimal sampling instant as part of the equalization process.

Third, more taps are available within a given time span, providing finer frequency resolution for channel inversion. This is particularly beneficial for channels with sharp spectral features that require precise equalization.

The cost is additional computational complexity, as twice as many taps must be adapted and applied per symbol. Modern implementations readily accommodate this overhead, making fractionally-spaced equalization the default choice for demanding applications.

Blind Equalization

Blind equalizers adapt without requiring known training symbols, using statistical properties of the transmitted signal to drive adaptation. This capability is valuable when training opportunities are limited or when the equalizer must acquire a new channel without interrupting data transmission.

The constant modulus algorithm (CMA) exploits the known envelope of many modulation formats. For PSK signals, all transmitted symbols have the same magnitude, so the equalizer adjusts to minimize variation in the output envelope. The cost function penalizes deviations from the expected constant modulus:

J = E[(|y|^2 - R2)^2]

where R2 is chosen to match the expected output power. CMA converges to a solution that restores the constant envelope, removing ISI in the process.

For QAM signals with multiple amplitude levels, modifications like the reduced constellation algorithm (RCA) or the multi-modulus algorithm (MMA) provide better performance. These approaches incorporate knowledge of the QAM constellation structure into the cost function.

Blind algorithms converge more slowly than trained algorithms and may converge to local minima or incorrect solutions for severely distorted channels. Practical systems often use a hybrid approach: blind acquisition until the eye opens sufficiently for reliable decisions, then switching to decision-directed adaptation for faster convergence and better tracking.