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| author | andreas128 <Andreas> | 2017-08-29 10:54:19 +0200 |
|---|---|---|
| committer | andreas128 <Andreas> | 2017-08-29 10:54:19 +0200 |
| commit | 11d79073f3301d85e6c4314fcca60db30afc0138 (patch) | |
| tree | 0e6faa7723fc3692c0e3c3cd323397b33226e131 /dpd | |
| parent | e6ffca1849a56508245d726185344122bf4f873c (diff) | |
| download | dabmod-11d79073f3301d85e6c4314fcca60db30afc0138.tar.gz dabmod-11d79073f3301d85e6c4314fcca60db30afc0138.tar.bz2 dabmod-11d79073f3301d85e6c4314fcca60db30afc0138.zip | |
Fix uniform sampler
Diffstat (limited to 'dpd')
| -rw-r--r-- | dpd/src/Model.py | 49 |
1 files changed, 31 insertions, 18 deletions
diff --git a/dpd/src/Model.py b/dpd/src/Model.py index e0f9c62..ae9f7b3 100644 --- a/dpd/src/Model.py +++ b/dpd/src/Model.py @@ -36,12 +36,13 @@ class Model: tx_hist, ccdf_edges = np.histogram(txframe_aligned_abs, bins=n_bins, range=(ccdf_min, ccdf_max)) - tx_choice = np.zeros(tx_hist[-1] * n_bins, dtype=np.complex64) - rx_choice = np.zeros(tx_hist[-1] * n_bins, dtype=np.complex64) - n_choise = tx_hist[-1] - for idx, bin in enumerate(tx_hist[:-1]): - indices = np.where((txframe_aligned >= ccdf_edges[idx]) & - (txframe_aligned <= ccdf_edges[idx+1]))[0] + n_choise = np.min(tx_hist) + tx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) + rx_choice = np.zeros(n_choise * n_bins, dtype=np.complex64) + + for idx, bin in enumerate(tx_hist): + indices = np.where((txframe_aligned_abs >= ccdf_edges[idx]) & + (txframe_aligned_abs <= ccdf_edges[idx+1]))[0] indices_choise = np.random.choice(indices, n_choise, replace=False) rx_choice[idx*n_choise:(idx+1)*n_choise] = rxframe_aligned[indices_choise] tx_choice[idx*n_choise:(idx+1)*n_choise] = txframe_aligned[indices_choise] @@ -109,18 +110,6 @@ class Model: tx_range = np.linspace(0, 2) phase_range_dpd = dpd_phase(tx_range) - tx_abs = np.abs(rx_choice) - tx_A = np.vstack([tx_abs, - tx_abs ** 3, - tx_abs ** 5, - tx_abs ** 7, - tx_abs ** 9, - ]).T - tx_dpd = np.sum(tx_A * new_coefs, axis=1) - - tx_dpd_norm = tx_dpd * ( - np.median(np.abs(tx_choice)) / np.median(np.abs(tx_dpd))) - rx_A_complex = np.vstack([rx_choice, rx_choice * rx_abs ** 2, rx_choice * rx_abs ** 4, @@ -207,10 +196,34 @@ class Model: label="RX Frame", linestyle="--", linewidth=0.5) + + rx_abs = np.abs(rxframe_aligned) + rx_A = np.vstack([rx_abs, + rx_abs ** 3, + rx_abs ** 5, + rx_abs ** 7, + rx_abs ** 9, + ]).T + rx_dpd = np.sum(rx_A * self.coefs_am, axis=1) + rx_dpd = rx_dpd * ( + np.median(np.abs(tx_choice)) / np.median(np.abs(rx_dpd))) + ax.plot(np.abs(rx_dpd[:128]), label="RX DPD Frame", linestyle="-.", linewidth=0.5) + + tx_abs = np.abs(np.abs(txframe_aligned[:128])) + tx_A = np.vstack([tx_abs, + tx_abs ** 3, + tx_abs ** 5, + tx_abs ** 7, + tx_abs ** 9, + ]).T + tx_dpd = np.sum(tx_A * new_coefs, axis=1) + tx_dpd_norm = tx_dpd * ( + np.median(np.abs(tx_choice)) / np.median(np.abs(tx_dpd))) + ax.plot(np.abs(tx_dpd_norm[:128]), label="TX DPD Frame Norm", linestyle="-.", |