Update README

README.md

@@ -1,4 +1,4 @@
-# DPD Using Indirect Learning Architecture.
+# GMP DPD Using Indirect Learning Architecture Matlab Library
 [![DOI](https://zenodo.org/badge/142376314.svg)](https://zenodo.org/badge/latestdoi/142376314)
 
 ## How to Cite
@@ -7,7 +7,7 @@ Please cite the repo if you use it in your projects. An example bibtex entry is
 ```
 @misc{TarverILADPD,
   author       = {Tarver, Chance},
-  title        = {DPD Using Indirect Learning Architecture},
+  title        = {GMP DPD Using Indirect Learning Architecture Matlab Library},
   month        = apr,
   year         = 2019,
   doi          = {10.5281/zenodo.2648687},
@@ -42,7 +42,8 @@ Initialize with:
 The 'params' stuct needs to contain the following:
  - `params.order`: Nonlinear order of the DPD model. Must be an odd integer.
  - `params.memory_depth`: Memory depth used on each branch of the parllel hammerstein DPD model. Can be any positive integer.
- - `params.nIterateions`: Number of times to go through the indirect learning iterations. Can be any positive integer. Usually doesn't need to be higher than 5.
+ - `params.lag_depth`: Memory lag/lead depth used on each cross term branch of the GMP DPD model. Can be any positive integer. If 0, then this becomes a parallel Hammerstein memory polynomial.
+ - `params.nIterations`: Number of times to go through the indirect learning iterations. Can be any positive integer. Usually doesn't need to be higher than 4.
 
 The DPD model can be trained by `dpd.perform_learning(tx_data, board);`. The `tx_data` is a column vector of IQ data to send through the PA for generating a model. The `board` is a PA class that needs to have a `transmit` method. This works with my [PA model](https://github.com/ctarver/Power-Amplifier-Model) or [WARPLab Wrapper](https://github.com/ctarver/WARPLab-Matlab-Wrapper).
 
@@ -51,8 +52,9 @@ Once the DPD is trained, you can predistort any samples with the model using the
 Check out the example.m to see more. 
 
 ## Result: 
-Running this code on the WARP board, I obtained the following power spectral density plot. Here, I am using a 5 MHz LTE like signal. With DPD, we can see less spectral regrowth around the carrier.
-![PSD](https://raw.githubusercontent.com/ctarver/ILA-DPD/master/psd_example.png?token=ACLnMTVWU6jnNqXKfcndnWRs5eeq5Ph8ks5bZG90wA%3D%3D "WARP Board PSD")
+Running this code on RFWebLab, I obtained the following power spectral density plot. Here, I am using a 20 MHz LTE like signal. With DPD, we can see less spectral regrowth around the carrier. 
+Here, ***P*** represents the nonlinearity order, ***M*** is the memory depth, and ***L*** is the lag/lead amount. 
+![PSD](https://raw.githubusercontent.com/ctarver/ILA-DPD/master/psd_example.png?token=ACLnMTVWU6jnNqXKfcndnWRs5eeq5Ph8ks5bZG90wA%3D%3D "RFWebLab PSD")
 
 ## Theory of Operation:
 The DPD is a nonlinear function that approximates an inverse of the PA's nonlinearities so that the sum output is linear. The DPD in principle can be modeled in any fashion. I have chosen to use a Parallel Hammerstein model. This consists of multiple nonlinear branches where each is followed by an FIR filter to model memory effects. The parallel Hammerstein model is widely chosen for its balance between modeling performance and complexity. 
@@ -62,10 +64,30 @@ One strong advantage of this model is that the FIR filter and hence any coeffici
 The DPD can't easily be modeled in this method directly because we don't know the desired predistorter output. We just know the desired PA output, actual PA output, and the original input signal. The indirect learning architecture allows us to circumvent this. When fully converged, the PA output should be linear, and so the input to the pre and post distorters would be equivalent, their outputs would be the same, and the error signal would be zero. When training, we use the postdistorter.  We want the output of the postdistorter to be equal to the output of the predistorter so that there is no error. We can run this to train and get some PA output signal. Then for the postdistorter, we have input sample (the PA ouput) and a desired postdistorter output (the predistorter output). We can start with some DPD coefficients (such as a pure linear DPD) then perform a LS fit to find the best coefficients to fit the postdistorter.  We copy this to the predistorter and repeat for a few iterations.
 
 ## References:
+For Generalized memory polynomials:
+```
+D. R. Morgan, Z. Ma, J. Kim, M. G. Zierdt and J. Pastalan, "A Generalized Memory Polynomial Model for Digital Predistortion of RF Power Amplifiers," in IEEE Transactions on Signal Processing, vol. 54, no. 10, pp. 3852-3860, Oct. 2006.
+doi: 10.1109/TSP.2006.879264
+```
+
 For the conjugate branch:
 ```
 L. Anttila, P. Handel and M. Valkama, "Joint Mitigation of Power Amplifier and I/Q Modulator Impairments in Broadband Direct-Conversion Transmitters," in IEEE Transactions on Microwave Theory and Techniques, vol. 58, no. 4, pp. 730-739, April 2010.
 doi: 10.1109/TMTT.2010.2041579
 keywords: {modulators;power amplifiers;radio transmitters;telecommunication channels;broadband direct-conversion transmitters;frequency-dependent power amplifier;I/Q modulator impairments;direct-conversion radio transmitters;extended parallel Hammerstein structure;parameter estimation stage;indirect learning architecture;adjacent channel power ratio;Broadband amplifiers;Power amplifiers;Radio transmitters;Digital modulation;Predistortion;Local oscillators;Wideband;Nonlinear distortion;Radiofrequency amplifiers;Frequency estimation;Digital predistortion (PD);direct-conversion radio;in-phase and quadrature (I/Q) imbalance;I/Q modulator;local oscillator (LO) leakage;mirror-frequency interference (MFI);power amplifier (PA);spectral regrowth},
 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5431085&isnumber=5446455
 ```
+
+## History
+
+### v1.1
+Sept 27th, 2019
+
+* Now works for GMP and includes a lag/lead crossterm.
+
+### v1.0
+April 2019
+
+* First official tagged release with a DOI. 
+* Works for standard memory polynomials.
+* Supports conjugate branch processing for IQ imbalance