open-process algorithm design

25 November 2013 DSP-P-027 v1.1w

Open-process Algorithm Design

Dr. Desmond Phillips

Blog presentation

A case study: Digital Predistortion

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Normalised FrequencyP

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Linear PA

Non-linear PA

25 November 2013 DSP-P-027 v1.1w2

Standard versus open-process algorithm design

Open-process algorithm design may give you better signal processing

knowledge transfer

“textbook” top-down approach open-process approach

Platform capabilities help shape algorithm choice


Standard versus open-process algorithm design

Data analysis algorithms are a natural fit for open-process algorithm design

Sample-oriented Digits

Frame-oriented DSP software

Draw observations from data

Evaluate observations

Algorithm

Platform domains

• Lower bandwidth• Numerically sophisticated• Non-deterministic

• High bandwidth• Numerically simple• Deterministic

Input Data Output Information

naturalmapping


Case Study: Digital Predistortion

Why RF Power Amplifier Linearisation makes a good case study

Power Amp

x(t)

u(t) y(t)

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0.5

1

1.5

V(in)

V(o

ut)

Vin/Vout

Scaled PDF input

Closed-loop data analysis Complex maths at high data bandwidths (e.g. 10’s Msamples/second)

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Normalised Frequency

PS

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Linear PA

Non-linear PA“Spectral regrowth” contaminates adjacent channels

Occasional high amplitude samples are compressed00



Starting Point: the Non-linearity and Linearisation Model

Predistorter PAx(t)u(t)

y(t)

(v)

r(v)

input magnitude v

v

Predistortion with frame-oriented h(v) computation

AM/AM distortion

AM/PM distortion

Magnitude and phase distortion functions



A textbook predistortion algorithm – Indirect Learning Architecture

Predistorter DAC PA

Estimate delay &

compensate

ADC

@+fc

@-fc

x(t) baseband signal

TX signal to antennau(t)

y(t)u(t-t)

Estimate

r-1(v),(v)

Periodic Copy

Compute h(v)

Default “shoehorning” into digits



Alternative algorithm framework from open-process design

Predistorter DAC PA

Cross correlation

Estimate

r(v), (v)

Adaptation

• Fit models

• generate h(v)

• Estimatedelay

ADC

@+fc

@-fc

x(t) baseband signal

TX signal to antenna

u(t)

Elective mapping to digits

Elective mapping to DSPs/w

y(t)


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PSD of input (X) and output (Y)

Y() linearised

Y() unlinearised

X()


Simulation Results for x8 oversampled OFDM

Spectral Regrowth Linearised output

Y(w) within ~1dB of PSD of input X(w)


Conclusions

So what lessons have we learned in this case study?

We have achieved an effective split of functionality in the algorithm framework by thinking which domain does what, best.

– Digits– Maximum Likelihood estimation technique for r(v), (v)– Small footprint in digits (uses 50% of a Spartan XC6SLX4: $10 part)

– DSP software– Adaptation algorithm is software defined– Tuneable for statistically optimal maths and arbitrary PA models.

The open-process approach has given a more desirable, software defined, flexible framework which we can optimise.

25 November 2013 DSP-P-027 v1.1w

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