What s New in Digital Pre-Distortion?
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1 What s New in Digital Pre-Distortion? Dr. John Wood Outline What s Old in DPD? Multi-Protocol Signals Bandwidth Sample Rate and Memory Effects DPD methods Real-Time Coefficient Adaption Crest Factor Reduction DPD Update 1 1
2 What does a Digital Pre-Distorter do? The PredistorterG acts on the input so that the signal out of the PA is linear: v out = f PA (G(v in )) = A.v in Gain expansion DPD Update 2 The DPD System DPD Update 3 2
3 Traditional DPD Systems Look-up Tables (LUT) implemented in FPGA LUT or nonlinear filters in ASICs Limited Signal Bandwidth Single Modulation, Multi-Carrier MC-GSM is a Challenge Coefficient Adaption is relatively slow Dedicated Floating-Point DSP chip DPD Update 4 Indirect Learning FPGA DSP IC Compare Pre-Distorted signals Requires two copies of predistorter The DPD essentially creates an inverse model of the PA Forward Path (DPD2) coefficients are updated only after convergence DPD Update 5 3
4 Adaptive Control - Direct Learning FPGA DSP IC Compare Input & Output signals After convergence, these (should) contain no distortion Only one DPD calculation Coefficient Adaption Cost Function requires some element to enable adjustment of DPD coefficients - IP Forward Path (DPD) coefficients are updated only after convergence DPD Update 6 Multi-Protocol Signals LTE + WCDMA (4-8 channels) LTE + MC-GSM (up to 8 carriers) Wide Bandwidth 40 MHz occupied BW in MHz total High Sample Rates MS/s required: oversampling Crest Factor Reduction can be challenging Usually done at a reduced sampling rate DPD Update 7 4
5 Wideband MP Signal MC-CDMA LTE 65 MHz DPD needs to reduce ACP and wideband IMs Receive Band Interference problem DPD Update 8 Wide Signal Bandwidth DPD Rule of Thumb requires 5x signal bandwidth to accommodate IM/ACP distortion products 100 MHz Signal BW 500 MHz DPD bandwidth 500 MHz IF or baseband BW for zero-if systems DAC & ADC bandwidths 1 GS/s DACs at 16 bits; 500 MS/s ADCs are bits Filters and Interpolation Large Oversample rates: 64 or 80x for WCDMA DPD Update 9 5
6 Memory Effects Output of PA at time t 0 depends on input at t 0, and at previous times History of the input signal, or Memory Effect If the sampling rate of the input signal is doubled, we should expect the Memory time to remain the same, but more samples are needed The memory samples are known as taps DPD Update 10 Bandwidth, Sample Rate, MACs The higher the required bandwidth The higher the sample rate of the signal The higher the digital system clock More arithmetic calculations are needed greater DSP processor power required More memory terms are required More RAM needed for waveform capture and coefficient storage DPD Update 11 6
7 Finding the characteristic time delay Mutual Information Applies to statistical signals where I is the mutual information for a delay of τ, p 1 is the probability density of the data sample y at times t and t-τ, p 2 is the joint probability of the original & delayed samples Mutual Information exhibits a minimum at optimal time delay Can result in fewer memory taps without loss of fidelity DPD Update 12 Digital Pre-Distortion: LUT Complex LUT pre-distorter finds the inverse instantaneous gain and phase of the PA for the input signal power Need multiple LUTs for memory-capable DPD One additional LUT per tap is common DPD Update 13 7
8 Digital Pre-Distortion: Polynomial Memory Polynomial DPD More Nonlinearity More Memory DPD Update 14 Digital Pre-Distortion: Volterra Series Real-time calculation of Volterra polynomial DPD Update 15 8
9 The impact of Volterra Cross-terms on DPD correction Volterra Cross-terms add more complexity pruning is essential Dynamic Deviation Reduction techniques DPD Update 16 DPD Linearization of Behavioural Model Memory Polynomial 3M7P DDR with 2 nd -order dynamics and even-degree polynomial terms DPD Update 17 9
10 Example Polynomial Calculation Volterra polynomial: 11 th degree static poly + 3 memory taps 10 MACs + 3 shift-delays 15 Cross-terms MACs, depending on complexity of dynamics Data sample rate ~ 300 MS/s Processing required: 20 complex gigamacs or about 80 gigaflops DPD Update 18 DPD Coefficient Adaption Adaptive DPD adjusts the LUT or polynomial coefficients as the signal changes The nonlinear function is linear-in-parameters Use linear least squares techniques to find the DPD parameters a DPD Update 19 10
11 Linear Least-Squares Methods Least Squares Estimation LSE Requires Matrix Inversion Large processor power and memory required Off-line method Least Mean Squares LMS Avoids Matrix Inversion Recursive Least Squares Avoids Matrix Inversion Faster convergence than LSE/LMS techniques Can be used in Real-Time DPD Coefficient adaption Still requires large processor power, but memory requirements are significantly reduced DPD Update 20 Crest Factor Reduction Modern digital communications signals are spectrally efficient Have high Peak-to-Average Power Ratio PAPR Especially problematic with Multi-Carrier and Multi- Protocol signals DPD is an expansive function Tends to increase PAPR Power Amplifier efficiency is reduced Use Crest Factor Reduction methods to reduce PAPR DPD Update 21 11
12 CFR Techniques All CFR techniques require a form of peak detection, to find the signal peaks above some threshold Clip & Filter Leads to in-band distortion (EVM) and out of band distortion or spectral regrowth (ACL) Peak Windowing Apply a local filter around the peak to reduce it increases ACL Pulse or Tone Injection Add a pulse at the peak location to reduce it increases EVM Tone Reservation Add energy to the signal to reduce peaks: use for LTE DPD Update 22 Summary DPD Challenges Wide bandwidth, Multi-Carrier & Multi-Protocol signals Real-time DPD coefficient updates More DSP processing power required Higher data sampling rates & clock frequencies DPD Update 23 12
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