Merge pull request #1 from ctarver/conjugate_branch

Conjugate branch

ILA_DPD.m

@@ -36,6 +36,8 @@
         nIterations   % Number of iterations used in the ILA learning
         block_size    % Block size used for each iteration in the learning
         coeffs        % DPD coefficients
+        use_conj      % Use a conjugate branch as well
+        use_dc_term   % use a dc term
     end
 
     methods
@@ -47,6 +49,8 @@
                 params.memory_depth = 3;
                 params.nIterations = 3;
                 params.block_size = 50000;
+                params.use_conj = 0;
+                params.use_dc_term = 0;
             end
 
             if mod(params.order, 2) == 0
@@ -58,6 +62,9 @@
             obj.nIterations = params.nIterations;
             obj.block_size = params.block_size;
 
+            obj.use_conj = params.use_conj;
+            obj.use_dc_term = params.use_dc_term;
+
             % Start DPD coeffs being completely linear (no effect)
             obj.coeffs = zeros(obj.convert_order_to_number_of_coeffs, obj.memory_depth);
             obj.coeffs(1) = 1;
@@ -137,6 +144,7 @@ function perform_learning(obj, x, pa)
             number_of_basis_vectors = obj.memory_depth * obj.convert_order_to_number_of_coeffs;
             X = zeros(length(x), number_of_basis_vectors);
 
+            % Main branch
             count = 1;
             for i = 1:2:obj.order
                 branch = x .* abs(x).^(i-1);
@@ -147,6 +155,24 @@ function perform_learning(obj, x, pa)
                     count = count + 1;
                 end
             end
+
+            if obj.use_conj
+                % Conjugate branch
+                for i = 1:2:obj.order
+                    branch = conj(x) .* abs(x).^(i-1);
+                    for j = 1:obj.memory_depth
+                        delayed_version = zeros(size(branch));
+                        delayed_version(j:end) = branch(1:end - j + 1);
+                        X(:, count) = delayed_version;
+                        count = count + 1;
+                    end
+                end
+            end
+
+            % DC
+            if obj.use_dc_term
+                X(:, count) = 1;
+            end
         end
 
 
@@ -158,7 +184,16 @@ function perform_learning(obj, x, pa)
             if nargin == 1
                 order = obj.order;
             end
+
             number_of_coeffs = (order + 1) / 2;
+
+            if obj.use_conj
+                number_of_coeffs = 2 * number_of_coeffs;
+            end
+
+            if obj.use_dc_term
+                number_of_coeffs = number_of_coeffs + 1;
+            end
         end
 
 

README.md

@@ -44,3 +44,12 @@ The DPD is a nonlinear function that approximates an inverse of the PA's nonline
 One strong advantage of this model is that the FIR filter and hence any coefficients of the model are after their corresponding nonlinearities. This means the output of the model can be expressed as a linear system, *y = X b*. Here, X is a matrix where each column is a different nonlinear branch of the form *x|x|^{i-1}* where *i* is the order which can only be odd, and *x* is the input signal. If we have input/output samples, this can be a linear least-squares regression problem since the model coefficients are linear with regards to the input. Here we want to *minimize_b || y - X b ||* for some experimental *x* and *y.* This minimizes the sum of squared residuals and gives us the best fit. 
 
 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 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
+```

example.m

@@ -6,10 +6,10 @@
 addpath(genpath('WARPLab-Matlab-Wrapper'))
 addpath(genpath('Power-Amplifier-Model'))
 
-rms_input = 0.20;
+rms_input = 0.50;
 
 % Setup the PA simulator or TX board
-PA_board = 'WARP'; % either 'WARP', 'webRF', or 'none'
+PA_board = 'webRF'; % either 'WARP', 'webRF', or 'none'
 switch PA_board
     case 'WARP'
         warp_params.nBoards = 1;         % Number of boards
@@ -20,12 +20,13 @@
         board = PowerAmplifier(7, 4);
         Fs = 40e6;    % WARP board sampling rate.
     case 'webRF'
-        board = webRF();
+        dbm_power = -26;
+        board = webRF(dbm_power);
         Fs = 200e6;   % webRF sampling rate.
 end
 
 % Setup OFDM
-ofdm_params.nSubcarriers = 300;
+ofdm_params.nSubcarriers = 600;
 ofdm_params.subcarrier_spacing = 15e3; % 15kHz subcarrier spacing
 ofdm_params.constellation = 'QPSK';
 ofdm_params.cp_length = 144; % Number of samples in cyclic prefix.
@@ -38,21 +39,32 @@
 tx_data = normalize_for_pa(upsampled_tx_data, rms_input);
 
 % Setup DPD
-dpd_params.order = 7;
-dpd_params.memory_depth = 3;
-dpd_params.nIterations = 3;
+dpd_params.order = 11;
+dpd_params.memory_depth = 4;
+dpd_params.nIterations = 2;
 dpd_params.block_size = 50000;
+
+dpd_params.use_conj = 1;
+dpd_params.use_dc_term = 1;
+conj_dpd = ILA_DPD(dpd_params);
+
+dpd_params.use_conj = 0;
+dpd_params.use_dc_term = 0;
 dpd = ILA_DPD(dpd_params);
 
 %% Run Expierement
 w_out_dpd = board.transmit(tx_data);
+conj_dpd.perform_learning(tx_data, board);
+w_conj_dpd = board.transmit(conj_dpd.predistort(tx_data));
+
 dpd.perform_learning(tx_data, board);
 w_dpd = board.transmit(dpd.predistort(tx_data));
 
 %% Plot
 plot_results('psd', 'Original TX signal', tx_data, 40e6)
 plot_results('psd', 'No DPD', w_out_dpd, 40e6)
-plot_results('psd', 'With DPD', w_dpd, 40e6)
+plot_results('psd', 'With Normal DPD', w_dpd, 40e6)
+plot_results('psd', 'With Conjug DPD', w_conj_dpd, 40e6)
 
 %% Some helper functions
 function out = up_sample(in, Fs, sampling_rate)

webRF.m

@@ -11,9 +11,12 @@
     end
 
     methods
-        function obj = webRF()
+        function obj = webRF(dbm_power)
             %webRF Construct an instance of this class
-            obj.RMSin = -21;
+            if nargin == 0
+               dbm_power = -24; 
+            end
+            obj.RMSin = dbm_power;
             obj.synchronization.sub_sample = 1;
         end