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diff --git a/python/dpd/README.md b/python/dpd/README.md deleted file mode 100644 index 307a2f5..0000000 --- a/python/dpd/README.md +++ /dev/null @@ -1,264 +0,0 @@ -Digital Predistortion Computation Engine for ODR-DabMod -======================================================= - -This folder contains a digital predistortion prototype. -It was only tested in a laboratory system, and is not ready -for production usage. - -Concept -------- - -ODR-DabMod makes outgoing TX samples and feedback RX samples available to an -external tool. This external tool can request a buffer of samples for analysis, -can calculate coefficients for the predistorter in ODR-DabMod and load the new -coefficients using the remote control. - -The external tool is called the Digital Predistortion Computation Engine (DPDCE). -The DPDCE is written in python, and makes use of the numpy library for -efficient computation. Its sources reside in the *dpd* folder. - -The predistorter in ODR-DabMod supports two modes: polynomial and lookup table. -In the DPDCE, only the polynomial model is implemented at the moment. - -The *dpd/main.py* script is the entry point for the *DPD Computation Engine* -into which these features will be implemented. The tool uses modules from the -*dpd/src/* folder: - -- Sample transfer and time alignment with subsample accuracy is done by *Measure.py* -- Estimating the effects of the PA using some model and calculation of the updated - polynomial coefficients is done in *Model.py* and other specific *Model_XXX.py* files -- Finally, *Adapt.py* updates the ODR-DabMod predistortion setting and digital gain - -These modules themselves use additional helper scripts in the *dpd/src/* folder. - -Requirements ------------- - -- USRP B200. -- Power amplifier. -- A feedback connection from the power amplifier output, such that the average power level at - the USRP RX port is at -45dBm or lower. - Usually this is done with a directional coupler and additional attenuators. -- ODR-DabMod with enabled *dpd_port*, and with a samplerate of 8192000 samples per second. -- Synchronous=1 so that the USRP has the timestamping set properly, internal refclk and pps - are sufficient (not GPSDO necessary). -- A live mux source with TIST enabled. - -See dpd/dpd.ini for an example. - -The DPD server port can be tested with the *dpd/show_spectrum.py* helper tool, which can also display -a constellation diagram. - -Hardware Setup --------------- - - - - -Our setup is depicted in the Figure above. We used components with the following properties: - 1. USRP TX (max +20dBm) - 2. Band III Filter (Mini-Circuits RBP-220W+, 190-250MHz, -3.5dB) - 3. Power amplifier (Mini-Circuits, max +15dBm output, +10 dB gain at 200MHz) - 4. Directional coupler (approx. -25dB @ 223MHz) - 5. Attenuator (-20 dB) - 6. Attenuator (-30 dB) - 7. USRP RX (max -15dBm input allowed, max -45dBm desired) - 8. Spectrum analyzer (max +30dBm allowed) - -It is important to make sure that the USRP RX port does not receive too much -power. Otherwise the USRP will break. Here is an example of how we calculated -the maximal USRP RX input power for our case. As this is only a rough -calculation to protect the port, the predistortion software will later -automatically apply a normalization for the RX input by adapting the USRP RX -gain. - - TX Power + PA Gain - Coupling Factor - Attenuation = 20dBm + 10dB -25dB -50dB = -45dBm - -Thus we have a margin of about 30dB for the input power of the USRP RX port. -Keep in mind we need to calculate using peak power, not average power, and it is -essential that there is no nonlinearity in the RX path! - -Software Setup --------------- - -We assume that you already installed *ODR-DabMux* and *ODR-DabMod*. -You should install the required python dependencies for the DPDCE using -distribution packages. You will need at least scipy, matplotlib and -python-zeromq. - -Use the predistortion ----------------------- - -Make sure you have a ODR-DabMux running with a TCP output on port 9200. - -Then run the modulator, with the example dpd configuration file. - -``` -./odr-dabmod dpd/dpd.ini -``` - -This configuration file is different from usual defaults in several respects: - - * logging to /tmp/dabmod.log - * 4x oversampling: 8192000 sample rate - * a very small digital gain, which will be overridden by the DPDCE - * predistorter enabled - -The TX gain should be chosen so that you can drive your amplifier into -saturation with a digital gain of 0.1, so that there is margin for the DPD to -operate. - -You should *not modify txgain, rxgain, digital gain or coefficient settings manually!* -When the DPDCE is used, it controls these settings, and there are command line -options for you to define initial values. - -To verify that the communication between the DPDCE and ODR-DabMod is ok, -you can use the status and reset options: - -``` -cd dpd -python main.py --status -python main.py --reset -``` - -The reset option sets all DPD-related settings to the defaults (as shown in the -`--help` usage screen) and stops. - -When neither `--status` nor `--reset` is given, the DPDCE will run the predistortion -algorithm. As a first test you should run the DPDCE with the `--plot` -parameter. It preserves the output power and generates all available -visualisation plots in the newly created logging directory -`/tmp/dpd_<time_stamp>`. As the predistortion should increase the peak to -shoulder ratio, you should select a *txgain* in the ODR-DabMod configuration -file such that the initial peak-to-soulder ratio visible on your spectrum -analyser. This way, you will be able to see a the -change. - -``` -cd dpd -python main.py --plot -``` - -The DPDCE now does 10 iterations, and tries to improve the predistortion effectiveness. -In each step the learning rate is decreased. The learning rate is the factor -with which new coefficients are weighted in a weighted mean with the old -coefficients. Moreover the nuber of measurements increases in each iteration. -You find more information about that in *Heuristic.py*. - -Each plot is stored to the logging directory under a filename containing its -time stamp and its label. Following plots are generated chronologically: - - - ExtractStatistic: Extracted information from one or multiple measurements. - - Model\_AM: Fitted function for the amplitudes of the power amplifier against the TX amplitude. - - Model\_PM: Fitted function for the phase difference of the power amplifier against the TX amplitude. - - adapt.pkl: Contains all settings for the predistortion. - You can load them again without doing measurements with the `apply_adapt_dumps.py` script. - - MER: Constellation diagram used to calculate the modulation error rate. - -After the run you should be able to observe that the peak-to-shoulder -difference has increased on your spectrum analyzer, similar to the figure below. - -Without digital predistortion: - - - -With digital predistortion, computed by the DPDCE: - - - -Now see what happens if you apply the predistortions for different TX gains. -You can either set the TX gain before you start the predistortion or using the -command line option `--txgain gain`. You can also try to adjust other -parameters. To see their documentation run `python main.py --help`. - -File format for coefficients ----------------------------- -The coef file contains the polynomial coefficients used in the predistorter. -The file format is very similar to the filtertaps file used in the FIR filter. -It is a text-based format that can easily be inspected and edited in a text -editor. - -The first line contains an integer that defines the predistorter to be used: -1 for polynomial, 2 for lookup table. - -For the polynomial, the subsequent line contains the number of coefficients -as an integer. The second and third lines contain the real, respectively the -imaginary parts of the first coefficient. Fourth and fifth lines give the -second coefficient, and so on. The file therefore contains 1 + 1 + 2xN lines if -it contains N coefficients. - -For the lookup table, the subsequent line contains a float scalefactor that is -applied to the samples in order to bring them into the range of 32-bit unsigned -integer. Then, the next pair of lines contains real and imaginary part of the first -lookup-table entry, which is multiplied to samples in first range. Then it's -followed by 31 other pairs. The entries are complex values close to 1 + 0j. -The file therefore contains 1 + 1 + 2xN lines if it contains N coefficients. - -TODO ----- - - - Understand and fix occasional ODR-DabMod crashes when using DPDCE. - - Make the predistortion more robust. At the moment the shoulders sometimes - increase instead of decrease after applying newly calculated predistortion - parameters. Can this behaviour be predicted from the measurement? This would - make it possible to filter out bad predistortion settings. - - Find a better measurement for the quality of the predistortion. The USRP - might not be good enough to measure large peak-to-shoulder ratios, because - the ADC has 12 bits and DAB signals have a large crest factor. - - Implement a Volterra polynomial to model the PA. Compared to the current - model this would also capture the time dependent behaviour of the PA (memory - effects). - - Continuously observe DAB signal in frequency domain and make sure the power - stays the same. At the moment only the power in the time domain is kept the - same. - - At the moment we assume that the USRP RX gain has to be larger than 30dB and - the received signal should have a median absolute value of 0.05 in order to - have a high quality quantization. Do measurements to support or improve - this heuristic. - - Check if we need to measure MER differently (average over more symbols?) - - Is -45dBm the best RX feedback power level? - -REFERENCES ----------- - -Some papers: - -The paper Raich, Qian, Zhou, "Orthogonal Polynomials for Power Amplifier -Modeling and Predistorter Design" proposes other base polynomials that have -less numerical instability. - -AladreĢn, Garcia, Carro, de Mingo, and Sanchez-Perez, "Digital Predistortion -Based on Zernike Polynomial Functions for RF Nonlinear Power Amplifiers". - -Jiang and Wilford, "Digital predistortion for power amplifiers using separable functions" - -Changsoo Eun and Edward J. Powers, "A New Volterra Predistorter Based on the Indirect Learning Architecture" - -Raviv Raich, Hua Qian, and G. Tong Zhou, "Orthogonal Polynomials for Power Amplifier Modeling and Predistorter Design" - - -Models without memory: - -Complex polynomial: y[i] = a1 x[i] + a2 x[i]^2 + a3 x[i]^3 + ... - -The complex polynomial corresponds to the input/output relationship that -applies to the PA in passband (real-valued signal). According to several -sources, this gets transformed to another representation if we consider complex -baseband instead. In the following, all variables are complex. - -Odd-order baseband: y[i] = (b1 + b2 abs(x[i])^2 + b3 abs(x[i])^4) + ...) x[i] - -Complete baseband: y[i] = (b1 + b2 abs(x[i]) + b3 abs(x[i])^2) + ...) x[i] - -with - b_k = 2^{1-k} \binom{k}{(k-1)/2} a_k - - -Models with memory: - - - Hammerstein model: Nonlinearity followed by LTI filter - - Wiener model: LTI filter followed by NL - - Parallel Wiener: input goes to N delays, each delay goes to a NL, all NL outputs summed. - -Taken from slide 36 of [ECE218C Lecture 15](http://www.ece.ucsb.edu/Faculty/rodwell/Classes/ece218c/notes/Lecture15_Digital%20Predistortion_and_Future%20Challenges.pdf) - |