diff --git a/applications/disturb2/designs/disturb2_unb2b_station/disturb2_unb2b_station.fpga.yaml b/applications/disturb2/designs/disturb2_unb2b_station/disturb2_unb2b_station.fpga.yaml
index 5cecb8d2e6c998d6f2f1f29cda282bcf1dc83551..4c3d100037965c843061c117c765da482d11f2f6 100644
--- a/applications/disturb2/designs/disturb2_unb2b_station/disturb2_unb2b_station.fpga.yaml
+++ b/applications/disturb2/designs/disturb2_unb2b_station/disturb2_unb2b_station.fpga.yaml
@@ -14,6 +14,7 @@ parameters:
- { name: c_N_pn_lb, value: 16 }
- { name: c_S_pn, value: 12 }
- { name: c_Q_fft, value: 2 }
+ - { name: c_P_sum, value: 2 }
- { name: c_P_sq, value: 1 + c_N_pn_lb // 2 } # = 1 + 16 // 2 = 9, on revision xsub_one only first X_sq cell is used
- { name: c_X_sq, value: c_S_pn * c_S_pn } # = 144
- { name: c_N_crosslets, value: 7 }
@@ -375,7 +376,7 @@ peripherals:
number_of_peripherals: c_N_beamsets
peripheral_span: ceil_pow2(c_P_sum) * 2 * MM_BUS_SIZE # number_of_ports = c_P_sum, mm_port_span = 2 words
parameter_overrides:
- - { name: g_nof_streams, value: 2 }
+ - { name: g_nof_streams, value: c_P_sum }
mm_port_names:
- REG_BSN_ALIGN_V2_BF
@@ -384,7 +385,7 @@ peripherals:
number_of_peripherals: c_N_beamsets
peripheral_span: ceil_pow2(c_P_sum) * 8 * MM_BUS_SIZE # number_of_ports = c_P_sum, mm_port_span = 8 words
parameter_overrides:
- - { name: g_nof_streams, value: 2 }
+ - { name: g_nof_streams, value: c_P_sum }
mm_port_names:
- REG_BSN_MONITOR_V2_RX_ALIGN_BF
@@ -400,7 +401,7 @@ peripherals:
- peripheral_name: ring/ring_lane_info
peripheral_group: bf
number_of_peripherals: c_N_beamsets
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_RING_LANE_INFO_BF
diff --git a/applications/lofar2/designs/lofar2_unb2b_ring/lofar2_unb2b_ring.fpga.yaml b/applications/lofar2/designs/lofar2_unb2b_ring/lofar2_unb2b_ring.fpga.yaml
index 3a814a4657d166ff85e6dd39b13566fe0e225b82..62cfbcc4f6713e272b3da6b49981eb8551ef34a2 100644
--- a/applications/lofar2/designs/lofar2_unb2b_ring/lofar2_unb2b_ring.fpga.yaml
+++ b/applications/lofar2/designs/lofar2_unb2b_ring/lofar2_unb2b_ring.fpga.yaml
@@ -74,7 +74,7 @@ peripherals:
- peripheral_name: dp/dp_bsn_monitor_v2
peripheral_group: ring_rx
number_of_peripherals: c_nof_lanes
- peripheral_span: ceil_pow2(c_nof_rx_monitors) * 8 * MM_BUS_SIZE
+ peripheral_span: ceil_pow2(c_nof_rx_monitors) * 8 * MM_BUS_SIZE # number_of_ports = ceil_pow2(c_nof_rx_monitors), mm_port_span = 8 words
parameter_overrides:
- { name: g_nof_streams, value: c_nof_rx_monitors }
mm_port_names:
@@ -83,7 +83,7 @@ peripherals:
- peripheral_name: dp/dp_bsn_monitor_v2
peripheral_group: ring_tx
number_of_peripherals: c_nof_lanes
- peripheral_span: ceil_pow2(c_nof_tx_monitors) * 8 * MM_BUS_SIZE
+ peripheral_span: ceil_pow2(c_nof_tx_monitors) * 8 * MM_BUS_SIZE # number_of_ports = ceil_pow2(c_nof_tx_monitors), mm_port_span = 8 words
parameter_overrides:
- { name: g_nof_streams, value: c_nof_tx_monitors }
mm_port_names:
@@ -91,27 +91,27 @@ peripherals:
- peripheral_name: ring/ring_lane_info
number_of_peripherals: c_nof_lanes
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_RING_LANE_INFO
- peripheral_name: dp/dp_xonoff
peripheral_group: lane
number_of_peripherals: c_nof_lanes
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_DP_XONOFF_LANE
- peripheral_name: dp/dp_xonoff
peripheral_group: local
number_of_peripherals: c_nof_lanes
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_DP_XONOFF_LOCAL
- peripheral_name: dp/dp_block_validate_err
number_of_peripherals: c_nof_lanes
- peripheral_span: ceil_pow2(c_nof_err_counts + 3) * MM_BUS_SIZE
+ peripheral_span: ceil_pow2(c_nof_err_counts + 3) * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = ceil_pow2(c_nof_err_counts + 3) words
parameter_overrides:
- { name: g_nof_err_counts, value: c_nof_err_counts }
mm_port_names:
@@ -119,7 +119,7 @@ peripherals:
- peripheral_name: dp/dp_block_validate_bsn_at_sync
number_of_peripherals: c_nof_lanes
- peripheral_span: 4 * MM_BUS_SIZE
+ peripheral_span: 4 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 4 words
mm_port_names:
- REG_DP_BLOCK_VALIDATE_BSN_AT_SYNC
diff --git a/applications/lofar2/designs/lofar2_unb2b_sdp_station/lofar2_unb2b_sdp_station.fpga.yaml b/applications/lofar2/designs/lofar2_unb2b_sdp_station/lofar2_unb2b_sdp_station.fpga.yaml
index 0e7f0176633908ccea087378043598f493509d3f..26b558380e9ab0e708215833ce0cd40961af3a32 100644
--- a/applications/lofar2/designs/lofar2_unb2b_sdp_station/lofar2_unb2b_sdp_station.fpga.yaml
+++ b/applications/lofar2/designs/lofar2_unb2b_sdp_station/lofar2_unb2b_sdp_station.fpga.yaml
@@ -394,7 +394,7 @@ peripherals:
- peripheral_name: ring/ring_lane_info
peripheral_group: bf
number_of_peripherals: c_N_beamsets
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_RING_LANE_INFO_BF
diff --git a/applications/lofar2/designs/lofar2_unb2c_ring/lofar2_unb2c_ring.fpga.yaml b/applications/lofar2/designs/lofar2_unb2c_ring/lofar2_unb2c_ring.fpga.yaml
index 37037abf09666e4d44b6e970a5857db05cfdf92a..c67e3352679de04db26afd02dbdbb05c966e85f9 100644
--- a/applications/lofar2/designs/lofar2_unb2c_ring/lofar2_unb2c_ring.fpga.yaml
+++ b/applications/lofar2/designs/lofar2_unb2c_ring/lofar2_unb2c_ring.fpga.yaml
@@ -74,7 +74,7 @@ peripherals:
- peripheral_name: dp/dp_bsn_monitor_v2
peripheral_group: ring_rx
number_of_peripherals: c_nof_lanes
- peripheral_span: ceil_pow2(c_nof_rx_monitors) * 8 * MM_BUS_SIZE
+ peripheral_span: ceil_pow2(c_nof_rx_monitors) * 8 * MM_BUS_SIZE # number_of_ports = ceil_pow2(c_nof_rx_monitors), mm_port_span = 8 words
parameter_overrides:
- { name: g_nof_streams, value: c_nof_rx_monitors }
mm_port_names:
@@ -83,7 +83,7 @@ peripherals:
- peripheral_name: dp/dp_bsn_monitor_v2
peripheral_group: ring_tx
number_of_peripherals: c_nof_lanes
- peripheral_span: ceil_pow2(c_nof_tx_monitors) * 8 * MM_BUS_SIZE
+ peripheral_span: ceil_pow2(c_nof_tx_monitors) * 8 * MM_BUS_SIZE # number_of_ports = ceil_pow2(c_nof_tx_monitors), mm_port_span = 8 words
parameter_overrides:
- { name: g_nof_streams, value: c_nof_tx_monitors }
mm_port_names:
@@ -91,27 +91,27 @@ peripherals:
- peripheral_name: ring/ring_lane_info
number_of_peripherals: c_nof_lanes
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_RING_LANE_INFO
- peripheral_name: dp/dp_xonoff
peripheral_group: lane
number_of_peripherals: c_nof_lanes
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_DP_XONOFF_LANE
- peripheral_name: dp/dp_xonoff
peripheral_group: local
number_of_peripherals: c_nof_lanes
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_DP_XONOFF_LOCAL
- peripheral_name: dp/dp_block_validate_err
number_of_peripherals: c_nof_lanes
- peripheral_span: ceil_pow2(c_nof_err_counts + 3) * MM_BUS_SIZE
+ peripheral_span: ceil_pow2(c_nof_err_counts + 3) * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = ceil_pow2(c_nof_err_counts + 3) words
parameter_overrides:
- { name: g_nof_err_counts, value: c_nof_err_counts }
mm_port_names:
@@ -119,7 +119,7 @@ peripherals:
- peripheral_name: dp/dp_block_validate_bsn_at_sync
number_of_peripherals: c_nof_lanes
- peripheral_span: 4 * MM_BUS_SIZE
+ peripheral_span: 4 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 4 words
mm_port_names:
- REG_DP_BLOCK_VALIDATE_BSN_AT_SYNC
diff --git a/applications/lofar2/designs/lofar2_unb2c_sdp_station/lofar2_unb2c_sdp_station.fpga.yaml b/applications/lofar2/designs/lofar2_unb2c_sdp_station/lofar2_unb2c_sdp_station.fpga.yaml
index 1b3fae928ad905928679a151c3174343e6be7922..3155c2a507d4e80854ca7c45be46bb6215f91fe9 100644
--- a/applications/lofar2/designs/lofar2_unb2c_sdp_station/lofar2_unb2c_sdp_station.fpga.yaml
+++ b/applications/lofar2/designs/lofar2_unb2c_sdp_station/lofar2_unb2c_sdp_station.fpga.yaml
@@ -394,7 +394,7 @@ peripherals:
- peripheral_name: ring/ring_lane_info
peripheral_group: bf
number_of_peripherals: c_N_beamsets
- peripheral_span: 2 * MM_BUS_SIZE
+ peripheral_span: 2 * MM_BUS_SIZE # number_of_ports = 1, mm_port_span = 2 words
mm_port_names:
- REG_RING_LANE_INFO_BF
diff --git a/applications/lofar2/images/images.txt b/applications/lofar2/images/images.txt
index d2f076bcc1a9defbd99df056e7026bbee5e8494f..a609898c8a784e705795f9441965a687b2a47de1 100644
--- a/applications/lofar2/images/images.txt
+++ b/applications/lofar2/images/images.txt
@@ -11,7 +11,7 @@ lofar2_unb2b_sdp_station_xsub_one-r087d98be6 | 2021-06-14 | R vd Walle
unb2b_minimal-rce6b96eed | 2021-08-26 | P. Donker | unb2b_minimal with new mmap, rbf maid with option --unb2_factory
lofar2_unb2b_sdp_station_full-r9ff51058a | 2022-01-12 | R vd Walle | Old Lofar2 SDP station full design for UniBoard2b without ring.
lofar2_unb2b_sdp_station_full-r2c3958e1f | 2022-04-29 | R vd Walle | Lofar2 SDP station full design for UniBoard2b.
-lofar2_unb2b_sdp_station_full_wg-r70b28ffc3 | 2022-06-15 | R vd Walle | Do not use, has beamlet/subband weight bug, delete when rd3d2b75e1 is OK.
lofar2_unb2b_sdp_station_full_wg-rd3d2b75e1 | 2022-08-16 | R vd Walle | Lofar2 SDP station design without ADC inputs, only WG. Uses dp_clk + dp_pps instead of rx_clk + rx_sysref.
-lofar2_unb2c_sdp_station_full-r70484fd08 | 2022-04-29 | R vd Walle | Do not use, has beamlet/subband weight bug, delete when rc2b0cb728 is OK.
+lofar2_unb2b_sdp_station_full_wg-r4591ed7a1 | 2022-08-23 | R vd Walle | Lofar2 SDP station design without ADC inputs, only WG. Uses dp_clk + dp_pps instead of rx_clk + rx_sysref.
lofar2_unb2c_sdp_station_full-rc2b0cb728 | 2022-08-16 | R vd Walle | Lofar2 SDP station full design for UniBoard2c.
+lofar2_unb2c_sdp_station_full-r10b443515 | 2022-08-25 | R vd Walle | Lofar2 SDP station full design for UniBoard2c. Includes pause frame handling.
diff --git a/applications/lofar2/images/lofar2_unb2b_sdp_station_full_wg-r70b28ffc3.tar.gz b/applications/lofar2/images/lofar2_unb2b_sdp_station_full_wg-r4591ed7a1.tar.gz
similarity index 65%
rename from applications/lofar2/images/lofar2_unb2b_sdp_station_full_wg-r70b28ffc3.tar.gz
rename to applications/lofar2/images/lofar2_unb2b_sdp_station_full_wg-r4591ed7a1.tar.gz
index 3ff9e5eb8f7267e683133f0d70425b36398bada8..949589b19e2d945bcd5f997528c1e68a5b53d32e 100644
Binary files a/applications/lofar2/images/lofar2_unb2b_sdp_station_full_wg-r70b28ffc3.tar.gz and b/applications/lofar2/images/lofar2_unb2b_sdp_station_full_wg-r4591ed7a1.tar.gz differ
diff --git a/applications/lofar2/images/lofar2_unb2c_sdp_station_full-r70484fd08.tar.gz b/applications/lofar2/images/lofar2_unb2c_sdp_station_full-r10b443515.tar.gz
similarity index 65%
rename from applications/lofar2/images/lofar2_unb2c_sdp_station_full-r70484fd08.tar.gz
rename to applications/lofar2/images/lofar2_unb2c_sdp_station_full-r10b443515.tar.gz
index a0df0b6919cc24ef771d38867f4db681d4114241..22e5980a877d9447c4389d4d0946ec3c85b1898a 100644
Binary files a/applications/lofar2/images/lofar2_unb2c_sdp_station_full-r70484fd08.tar.gz and b/applications/lofar2/images/lofar2_unb2c_sdp_station_full-r10b443515.tar.gz differ
diff --git a/applications/lofar2/libraries/sdp/src/vhdl/sdp_station.vhd b/applications/lofar2/libraries/sdp/src/vhdl/sdp_station.vhd
index 30926a34d18088538bf898dd3cdca7eb570475b6..c33e0a5e37e01a4e1d0ef02d084b79b119df2e47 100644
--- a/applications/lofar2/libraries/sdp/src/vhdl/sdp_station.vhd
+++ b/applications/lofar2/libraries/sdp/src/vhdl/sdp_station.vhd
@@ -1112,13 +1112,14 @@ BEGIN
---------------
u_nw_10GbE_beamlet_output: ENTITY nw_10GbE_lib.nw_10GbE
GENERIC MAP (
- g_sim => g_sim,
- g_sim_level => 1,
- g_nof_macs => c_nof_10GbE_beamlet_output,
- g_direction => "TX_RX",
- g_tx_fifo_fill => c_fifo_tx_fill_beamlet_output,
- g_tx_fifo_size => c_fifo_tx_size_beamlet_output,
- g_ip_hdr_field_arr => c_sdp_cep_hdr_field_arr
+ g_sim => g_sim,
+ g_sim_level => 1,
+ g_nof_macs => c_nof_10GbE_beamlet_output,
+ g_direction => "TX_RX",
+ g_tx_fifo_fill => c_fifo_tx_fill_beamlet_output,
+ g_tx_fifo_size => c_fifo_tx_size_beamlet_output,
+ g_ip_hdr_field_arr => c_sdp_cep_hdr_field_arr,
+ g_xon_backpressure => TRUE
)
PORT MAP (
diff --git a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
index bc89897d52492e1ac09a26718b35e1d832bcf88d..d2a083f35100dcc9cd4e425931285a0f4ea2d87f 100644
--- a/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
+++ b/applications/lofar2/model/lofar2_station_sdp_firmware_model.ipynb
@@ -19,7 +19,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 82,
"id": "2b477516",
"metadata": {},
"outputs": [],
@@ -33,12 +33,12 @@
"id": "c2cc6c7a",
"metadata": {},
"source": [
- "## 1 SDP Parameters"
+ "# 1 SDP Parameters"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 83,
"id": "e1b6fa12",
"metadata": {},
"outputs": [
@@ -52,8 +52,10 @@
}
],
"source": [
- "# SDP\n",
+ "# General\n",
"N_complex = 2\n",
+ "\n",
+ "# SDP\n",
"N_fft = 1024\n",
"N_sub = N_fft / N_complex\n",
"f_adc = 200e6 # Hz\n",
@@ -68,7 +70,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 84,
"id": "eb325c9c",
"metadata": {},
"outputs": [
@@ -98,7 +100,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 85,
"id": "3e71626f",
"metadata": {},
"outputs": [
@@ -135,79 +137,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "def6eba7",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Conclusion: G_fft_real_input_sine = 0.5\n"
- ]
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x288 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# DFT of real sine input --> show that:\n",
- "G_fft_real_input_sine = 0.5\n",
- "G_fft_real_input_dc = 1.0\n",
- "\n",
- "# . DFT size\n",
- "N_points = 1024\n",
- "N_bins = N_points // 2 + 1 # positive frequency bins including DC and F_s/2\n",
- "\n",
- "# . select a bin\n",
- "i_bin = 200 # bin index in range(N_points // 2 )\n",
- "\n",
- "# . time and frequency axis\n",
- "f_s = f_adc # sample frequency\n",
- "f_s = 1 # normalized sample frequency\n",
- "T_s = 1 / f_s # sample period\n",
- "T_fft = N_points * T_s # DFT period\n",
- "t_axis = np.linspace(0, T_fft, N_points, endpoint=False)\n",
- "f_axis = np.linspace(0, f_s, N_points, endpoint=False)\n",
- "f_axis_fft = f_axis - f_s/2 # fftshift axis\n",
- "f_axis_rfft = f_axis[0:N_bins] # positive frequency bins\n",
- "\n",
- "f_bin = i_bin / N_points * f_s # bin frequency\n",
- "\n",
- "# . create sine at bin, use cos to see DC at i_bin = 0 \n",
- "x = np.cos(2 * np.pi * f_bin * t_axis)\n",
- "\n",
- "# . DFT using complex input fft()\n",
- "X_fft = np.fft.fftshift(np.fft.fft(x) / N_points)\n",
- "\n",
- "# . DFT using real input rfft()\n",
- "X_rfft = np.fft.rfft(x) / N_points\n",
- "\n",
- "plt.figure(figsize=(10, 4))\n",
- "plt.subplot(1, 2, 1)\n",
- "plt.title('DFT of real sine using fft')\n",
- "plt.plot(f_axis_fft, abs(X_fft))\n",
- "plt.grid()\n",
- "plt.subplot(1, 2, 2)\n",
- "plt.title('DFT of real sine using rfft')\n",
- "plt.plot(f_axis_rfft, abs(X_rfft))\n",
- "plt.grid()\n",
- "\n",
- "print(\"Conclusion: G_fft_real_input_sine =\", G_fft_real_input_sine)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
+ "execution_count": 86,
"id": "0ec00484",
"metadata": {},
"outputs": [
@@ -220,24 +150,26 @@
"subband_weight_re = 8192\n",
"subband_weight_im = 0\n",
"\n",
- "G_subband = 0.994817 * 0.5 * 2**4 * 1.0 = 7.958536\n",
- " . G_fir_dc = 0.994817\n",
- " . G_fft_real_input_sine = 0.5\n",
- " . W_sub_gain = 4\n",
- " . subband_weight_gain = 1.0\n"
+ "G_subband = 1 * 0.5 * 2**4 * 1.0 = 8.0 = 3.00 bits\n",
+ " . G_fir_dc = 1\n",
+ " . G_fft_real_input_sine = 0.5\n",
+ " . W_sub_gain = 4\n",
+ " . subband_weight_gain = 1.0\n"
]
}
],
"source": [
- "# Subband filterbank (F_sub)\n",
+ "# Gain factor G_subband between subband and signal input in the subband filterbank (F_sub)\n",
+ "\n",
"# . FIR filter DC gain\n",
- "G_fir_dc = 0.994817\n",
+ "G_fir_dc = 0.994817 # actual gain of FIR filter in LOFAR\n",
+ "G_fir_dc = 1\n",
"\n",
"# . Signal level bit growth to accomodate processing gain of FFT\n",
"W_sub_proc = np.log2(np.sqrt(N_sub))\n",
- "W_sub_gain = 4\n",
+ "W_sub_gain = 4 # use W_sub_gain instead of W_sub_proc\n",
"\n",
- "# Subband equalizer (E_sub)\n",
+ "# . Subband equalizer (E_sub)\n",
"subband_weight_gain = 1.0\n",
"subband_weight_phase = 0\n",
"subband_weight_re = int(subband_weight_gain * Unit_sub_weight * np.cos(subband_weight_phase))\n",
@@ -249,10 +181,11 @@
"print(f\"subband_weight_im = {subband_weight_im:d}\")\n",
"print()\n",
"\n",
- "# . Expected factor between subband amplitude and real signal input amplitude\n",
+ "# Expected factor from real signal input amplitude to subband amplitude\n",
"G_subband = G_fir_dc * G_fft_real_input_sine * 2**W_sub_gain * subband_weight_gain\n",
"\n",
- "print(f\"G_subband = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_sub_gain} * {subband_weight_gain} = {G_subband}\")\n",
+ "print(f\"G_subband = {G_fir_dc} * {G_fft_real_input_sine} * 2**{W_sub_gain} * {subband_weight_gain} \\\n",
+ "= {G_subband} = {np.log2(G_subband):.2f} bits\")\n",
"print(\" . G_fir_dc =\", G_fir_dc)\n",
"print(\" . G_fft_real_input_sine =\", G_fft_real_input_sine)\n",
"print(\" . W_sub_gain =\", W_sub_gain)\n",
@@ -261,7 +194,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 101,
"id": "4d197368",
"metadata": {},
"outputs": [
@@ -269,54 +202,99 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "beamlet_weight_gain = 1.0\n",
- "beamlet_weight_phase = 0\n",
- "beamlet_weight_re = 16384\n",
- "beamlet_weight_im = 0\n",
+ "Same BF weight for all inputs:\n",
+ ". beamlet_weight_gain = 1.0\n",
+ ". beamlet_weight_phase = 0\n",
+ ". beamlet_weight_re = 16384\n",
+ ". beamlet_weight_im = 0\n",
"\n",
- "BF for coherent input:\n",
- " . W_bf_proc = 10.00 for N_ant = 10\n",
+ "N_ant_arr = [ 1 12 24 28 96]\n",
"\n",
- "BF for incoherent input:\n",
- " . W_bf_proc = 3.16 for N_ant = 10\n",
+ "N_ant = 1 : bf_proc_coh = 1.00 = 0.0 bits\n",
+ "N_ant = 12 : bf_proc_coh = 12.00 = 3.6 bits\n",
+ "N_ant = 24 : bf_proc_coh = 24.00 = 4.6 bits\n",
+ "N_ant = 28 : bf_proc_coh = 28.00 = 4.8 bits\n",
+ "N_ant = 96 : bf_proc_coh = 96.00 = 6.6 bits\n",
+ "\n",
+ "N_ant = 1 : bf_proc_incoh = 1.00 = 0.0 bits\n",
+ "N_ant = 12 : bf_proc_incoh = 3.46 = 1.8 bits\n",
+ "N_ant = 24 : bf_proc_incoh = 4.90 = 2.3 bits\n",
+ "N_ant = 28 : bf_proc_incoh = 5.29 = 2.4 bits\n",
+ "N_ant = 96 : bf_proc_incoh = 9.80 = 3.3 bits\n",
+ "\n",
+ "N_ant = 1 : G_beamlet_sum_coh = 8.00 * 1.00 * 1.0 = 8.00 = 3.0 bits\n",
+ "N_ant = 12 : G_beamlet_sum_coh = 8.00 * 12.00 * 1.0 = 96.00 = 6.6 bits\n",
+ "N_ant = 24 : G_beamlet_sum_coh = 8.00 * 24.00 * 1.0 = 192.00 = 7.6 bits\n",
+ "N_ant = 28 : G_beamlet_sum_coh = 8.00 * 28.00 * 1.0 = 224.00 = 7.8 bits\n",
+ "N_ant = 96 : G_beamlet_sum_coh = 8.00 * 96.00 * 1.0 = 768.00 = 9.6 bits\n",
+ "\n",
+ "N_ant = 1 : G_beamlet_sum_incoh = 8.00 * 1.00 * 1.0 = 8.00 = 3.0 bits\n",
+ "N_ant = 12 : G_beamlet_sum_incoh = 8.00 * 3.46 * 1.0 = 27.71 = 4.8 bits\n",
+ "N_ant = 24 : G_beamlet_sum_incoh = 8.00 * 4.90 * 1.0 = 39.19 = 5.3 bits\n",
+ "N_ant = 28 : G_beamlet_sum_incoh = 8.00 * 5.29 * 1.0 = 42.33 = 5.4 bits\n",
+ "N_ant = 96 : G_beamlet_sum_incoh = 8.00 * 9.80 * 1.0 = 78.38 = 6.3 bits\n",
+ "\n",
+ "N_ant = 1 : si_ampl_max = 2.000000 = 16384 = 14.0 bits\n",
+ "N_ant = 12 : si_ampl_max = 0.166667 = 1365 = 10.4 bits\n",
+ "N_ant = 24 : si_ampl_max = 0.083333 = 683 = 9.4 bits\n",
+ "N_ant = 28 : si_ampl_max = 0.071429 = 585 = 9.2 bits\n",
+ "N_ant = 96 : si_ampl_max = 0.020833 = 171 = 7.4 bits\n",
"\n"
]
}
],
"source": [
- "# Digital beamformer (BF)\n",
- "N_ant = 10\n",
+ "# Gain factor G_beamlet_sum between beamlet and signal input in the digital beamformer (BF)\n",
+ "# . coherent input is same signal (e.g. sine or noise) on all inputs\n",
+ "# . incoherent input is different signal (noise) on all inputs\n",
"\n",
- "# Assume all N_ant use same BF weight\n",
+ "# . Assume all N_ant use same BF weight\n",
"beamlet_weight_gain = 1.0\n",
"beamlet_weight_phase = 0\n",
"beamlet_weight_re = int(beamlet_weight_gain * Unit_bf_weight * np.cos(beamlet_weight_phase))\n",
"beamlet_weight_im = int(beamlet_weight_gain * Unit_bf_weight * np.sin(beamlet_weight_phase))\n",
"\n",
- "print(\"beamlet_weight_gain =\", beamlet_weight_gain)\n",
- "print(\"beamlet_weight_phase =\", beamlet_weight_phase)\n",
- "print(f\"beamlet_weight_re = {beamlet_weight_re:d}\")\n",
- "print(f\"beamlet_weight_im = {beamlet_weight_im:d}\")\n",
+ "print(\"Same BF weight for all inputs:\")\n",
+ "print(f\". beamlet_weight_gain = {beamlet_weight_gain}\")\n",
+ "print(f\". beamlet_weight_phase = {beamlet_weight_phase}\")\n",
+ "print(f\". beamlet_weight_re = {beamlet_weight_re:d}\")\n",
+ "print(f\". beamlet_weight_im = {beamlet_weight_im:d}\")\n",
"print()\n",
"\n",
- "si_types = [\"coherent\", \"incoherent\"]\n",
- "for si_type in si_types:\n",
- "\n",
- " # . BF processing gain\n",
- " if si_type == \"coherent\":\n",
- " bf_proc = N_ant\n",
- " else:\n",
- " bf_proc = np.sqrt(N_ant)\n",
- " \n",
- " # . Normalize BF weights to get BF DC gain is 1.0\n",
- " beamlet_weight_gain = 1 / bf_proc\n",
+ "N_ant_arr = np.array([1, 12, 24, 28, 96])\n",
+ "print(f\"N_ant_arr = {N_ant_arr}\")\n",
+ "print() \n",
+ "\n",
+ "# . BF processing gain for N_ant coherent inputs and for N_ant incoherent inputs\n",
+ "bf_proc_coh = N_ant_arr\n",
+ "bf_proc_coh_bits = np.log2(bf_proc_coh)\n",
+ "bf_proc_incoh = np.sqrt(N_ant_arr)\n",
+ "bf_proc_incoh_bits = np.log2(bf_proc_incoh)\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"N_ant = {na:2d} : bf_proc_coh = {bf_proc_coh[ni]:5.2f} = {np.log2(bf_proc_coh[ni]):.1f} bits\")\n",
+ "print() \n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"N_ant = {na:2d} : bf_proc_incoh = {bf_proc_incoh[ni]:5.2f} = {np.log2(bf_proc_incoh[ni]):.1f} bits\")\n",
+ "print()\n",
"\n",
- " # . Expected factor between beamlet amplitude and real signal input amplitude\n",
- " G_beamlet_sum = N_ant * beamlet_weight_gain * G_subband\n",
+ "# Expected factor from real signal input amplitude to beamlet amplitude\n",
+ "G_beamlet_sum_coh = G_subband * bf_proc_coh * beamlet_weight_gain\n",
+ "G_beamlet_sum_incoh = G_subband * bf_proc_incoh * beamlet_weight_gain\n",
+ "\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"N_ant = {na:2d} : G_beamlet_sum_coh = {G_subband:.2f} * {bf_proc_coh[ni]:5.2f} * {beamlet_weight_gain} \\\n",
+ "= {G_beamlet_sum_coh[ni]:7.2f} = {np.log2(G_beamlet_sum_coh[ni]):.1f} bits\")\n",
+ "print() \n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"N_ant = {na:2d} : G_beamlet_sum_incoh = {G_subband:.2f} * {bf_proc_incoh[ni]:5.2f} * {beamlet_weight_gain} \\\n",
+ "= {G_beamlet_sum_incoh[ni]:6.2f} = {np.log2(G_beamlet_sum_incoh[ni]):.1f} bits\")\n",
+ "print()\n",
"\n",
- " print(f\"BF for {si_type} input:\")\n",
- " print(f\" . bf_proc = {bf_proc:.2f} for N_ant = {N_ant}\")\n",
- " print()\n"
+ "# Maximum signal input amplitude\n",
+ "si_ampl_max = 2**(W_beamlet_sum - 1) / G_beamlet_sum_coh\n",
+ "for ni, na in enumerate(N_ant_arr):\n",
+ " print(f\"N_ant = {na:2d} : si_ampl_max = {si_ampl_max[ni] / FS:f} = {si_ampl_max[ni]:6.0f} = {np.log2(si_ampl_max[ni]):5.1f} bits\")\n",
+ "print()"
]
},
{
@@ -324,17 +302,26 @@
"id": "d942fcc6",
"metadata": {},
"source": [
- "## 2 Quantization model"
+ "# 2 Quantization model"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 88,
"id": "f66c5028",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "P_bit_dB = 6.02 dB\n"
+ ]
+ }
+ ],
"source": [
"# Bit\n",
+ "# . Each bit yields a factor 2 in voltage, so a factor 2**2 = 4 in power \n",
"P_bit = 2**2\n",
"P_bit_dB = 10 * np.log10(P_bit)\n",
"print(f\"P_bit_dB = {P_bit_dB:.2f} dB\")"
@@ -342,12 +329,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 89,
"id": "a9fca052",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "P_quant = 0.083333\n",
+ "P_quant_dB = -10.79 dB = -1.8 bit\n",
+ "sigma_quant = 0.29 q\n"
+ ]
+ }
+ ],
"source": [
"# Quantization noise\n",
+ "# . The quantization noise power is q**2 * 1 / 12, so the standard deviation\n",
+ "# of the quantization noise is q * sqrt(1 / 12) < q = one LSbit\n",
+ "# . The quantization noise power is at a level of -10.79 dB or -1.8 bit.\n",
+ "# . The 0 dB power level or 0 bit level corresponds to the power of one LSbit, so q**2 \n",
"P_quant = 1 / 12 # for W >> 1 [2]\n",
"P_quant_dB = 10 * np.log10(P_quant)\n",
"sigma_quant = np.sqrt(P_quant)\n",
@@ -359,21 +361,37 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 90,
"id": "d9972b6b",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "# System noise\n",
+ "# Impact of quantization on the system noise\n",
+ "# The quantization noise has sigma_quant = 0.29 q, this increases the system noise.\n",
+ "# The system noise has sigma_sys = n * q. For n = 2 the quantization increases the\n",
+ "# total power by 2% (so sigma_sys increase by sqrt(2 %) is about 1 %).\n",
"n = np.arange(1,9)\n",
- "sigma_sys = n\n",
+ "sigma_sys = n # = n * q, so sigma of n LSbits\n",
"P_sys = sigma_sys**2\n",
"P_tot = P_sys + P_quant\n",
"sigma_tot = np.sqrt(P_tot)\n",
"\n",
"plt.figure()\n",
"plt.plot(n, (P_tot / P_sys - 1) * 100)\n",
- "plt.title(\"Increase in total noise power due to sampling\")\n",
+ "plt.title(\"Increase in total noise power due to quantization\")\n",
"plt.xlabel(\"sigma_sys [q]\")\n",
"plt.ylabel(\"[%]\")\n",
"plt.grid()"
@@ -381,12 +399,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 91,
"id": "be2d952f",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "W_adc = {W_adc} bits\n",
+ "FS = 8192\n",
+ "sigma_fs_sine = 5792.6 q\n",
+ "P_fs_sine_dB = 75.26 dB = 12.5 bit\n"
+ ]
+ }
+ ],
"source": [
- "# FS sine\n",
+ "# Full scale (FS) sine\n",
"P_fs_sine = FS**2 / 2\n",
"P_fs_sine_dB = 10 * np.log10(P_fs_sine)\n",
"print(\"W_adc = {W_adc} bits\")\n",
@@ -397,12 +426,21 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 92,
"id": "a9e7fabc",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "SNR_dB = P_fs_sine_dB - P_quant_dB = 75.26 - -10.79 = 86.05 dB\n"
+ ]
+ }
+ ],
"source": [
- "# SNR\n",
+ "# Signal to noise ratio (SNR)\n",
"SNR = P_fs_sine / P_quant\n",
"SNR_dB = 10 * np.log10(SNR)\n",
"\n",
@@ -412,27 +450,46 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 93,
"id": "92852a53",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Power at -50dBFS = 25.26 dB corresponds to:\n",
+ " . sigma = 18.3 q\n",
+ " . Noise range 3 sigma = +-55 q\n",
+ " . Sine with amplitude A = = sigma * sqrt(2) = 25.9 q\n",
+ "\n",
+ "sigma = 16 q corresponds to:\n",
+ " . Power = 24.08 dB, so at -51.2 dBFS\n",
+ " . Noise range 3 sigma = +-48 q\n",
+ " . Sine with amplitude A = sigma * sqrt(2) = 22.6 q\n"
+ ]
+ }
+ ],
"source": [
- "# -50 dbFS level\n",
+ "# Signal level relative to FS sine\n",
"Power_50dBFS = P_fs_sine_dB - 50 \n",
"sigma_50dBFS = 10**(Power_50dBFS / 20)\n",
"ampl_50dBFS = sigma_50dBFS * np.sqrt(2)\n",
"\n",
- "print(f\"Power at -50dBFS = {Power_50dBFS:.2f} dB, so sigma = {sigma_50dBFS:.1f} q, ampl = {ampl_50dBFS:.1f} q\")\n",
+ "print(f\"Power at -50dBFS = {Power_50dBFS:.2f} dB corresponds to:\")\n",
+ "print(f\" . sigma = {sigma_50dBFS:.1f} q\")\n",
+ "print(f\" . Noise range 3 sigma = +-{3 * sigma_50dBFS:.0f} q\")\n",
+ "print(f\" . Sine with amplitude A = = sigma * sqrt(2) = {ampl_50dBFS:.1f} q\")\n",
"\n",
- "# Assume sigma = 16 q\n",
- "sigma = 16\n",
- "Power = sigma**2\n",
- "Power_dB = 10 * np.log10(Power)\n",
+ "# Assume signal with sigma = 16 q\n",
+ "sigma_16q = 16\n",
+ "Power_16q = sigma_16q**2\n",
+ "Power_16q_dB = 10 * np.log10(Power_16q)\n",
"print()\n",
- "print(f\"sigma = {sigma:.0f} q corresponds to:\")\n",
- "print(f\" . Power = {Power_dB:.2f} dB, so at {Power_dB - P_fs_sine_dB:.1f} dBFS\")\n",
- "print(f\" . Range 3 sigma = +-{3 * sigma:.0f} q\")\n",
- "print(f\" . Sine with amplitude A = sigma * sqrt(2) = {np.sqrt(2) * sigma:.1f} q\")\n"
+ "print(f\"sigma = {sigma_16q:.0f} q corresponds to:\")\n",
+ "print(f\" . Power = {Power_16q_dB:.2f} dB, so at {Power_16q_dB - P_fs_sine_dB:.1f} dBFS\")\n",
+ "print(f\" . Noise range 3 sigma = +-{3 * sigma_16q:.0f} q\")\n",
+ "print(f\" . Sine with amplitude A = sigma * sqrt(2) = {np.sqrt(2) * sigma_16q:.1f} q\")\n"
]
},
{
@@ -440,69 +497,132 @@
"id": "77bb14cc",
"metadata": {},
"source": [
- "## 3 Expected signal levels in the SDP FW"
+ "# 3 Expected signal levels in the SDP FW"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f7fff7a0",
+ "metadata": {},
+ "source": [
+ "## 3.1 Signal input power and DC level"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 94,
"id": "a04af043",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ADC sigma = 5792.6 q = 12.5 bits: P_ast = 6.710886e+15, uses 52.6 bits, is 0 dBFS = FS sine\n",
+ "ADC sigma = 18.3 q = 4.2 bits: P_ast = 6.710886e+10, uses 36.0 bits, is -50dBFS\n",
+ "ADC sigma = 16.0 q = 4.0 bits: P_ast = 5.120000e+10, uses 35.6 bits\n"
+ ]
+ }
+ ],
"source": [
- "# ADC power statistic (AST)\n",
- "sigma = sigma_fs_sine\n",
- "sigma_bits = np.log2(sigma)\n",
- "P_ast = (sigma)**2 * N_int\n",
- "print(f\"ADC sigma = {sigma:6.1f} q = {sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is 0 dBFS = FS sine\")\n",
- "\n",
- "sigma = sigma_50dBFS\n",
- "sigma_bits = np.log2(sigma)\n",
+ "# Signal input power statistic for ADC / WG (AST)\n",
+ "si_sigma = sigma_fs_sine\n",
+ "si_sigma_bits = np.log2(si_sigma)\n",
+ "P_ast = (si_sigma)**2 * N_int\n",
+ "print(f\"ADC sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is 0 dBFS = FS sine\")\n",
+ "\n",
+ "si_sigma = sigma_50dBFS\n",
+ "si_sigma_bits = np.log2(si_sigma)\n",
+ "P_ast = (si_sigma)**2 * N_int\n",
+ "print(f\"ADC sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is -50dBFS\")\n",
+ "\n",
+ "si_sigma = sigma_16q\n",
+ "si_sigma_bits = np.log2(si_sigma)\n",
"P_ast = (sigma)**2 * N_int\n",
- "print(f\"ADC sigma = {sigma:6.1f} q = {sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits, is -50dBFS\")\n",
+ "print(f\"ADC sigma = {si_sigma:6.1f} q = {si_sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ce94d23",
+ "metadata": {},
+ "source": [
+ "From measured P_ast and DC_ast to signal input sigma:\n",
"\n",
- "sigma = 16\n",
- "sigma_bits = np.log2(sigma)\n",
- "P_ast = (sigma)**2 * N_int\n",
- "print(f\"ADC sigma = {sigma:6.1f} q = {sigma_bits:4.1f} bits: P_ast = {P_ast:e}, uses {np.log2(P_ast):.1f} bits\")"
+ "* si_rms = sqrt(P_ast / N_int)\n",
+ "* si_mean = DC_ast / N_int\n",
+ "* si_sigma = sqrt(si_rms^2 - si_mean^2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5cb555b6",
+ "metadata": {},
+ "source": [
+ "## 3.2 Subband statistics (SST)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 95,
"id": "0b2ac36c",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Signal input level --> Expected subband level and SST level:\n",
+ "\n",
+ " si_ampl sub_ampl #bits SST #bits\n",
+ " 8192.0 65536.0 16.0000 8.388608e+14 49.6\n",
+ " 25.9 207.2 7.6952 8.388608e+09 33.0\n",
+ " 22.6 181.0 7.5000 6.400000e+09 32.6\n"
+ ]
+ }
+ ],
"source": [
- "# Subband filterbank (F_sub)\n",
- "ampl_sub_fs = FS * G_subband # subband amplitude for FS signal input sine\n",
- "SST_fs = ampl_sub_fs**2 * N_int_sub\n",
+ "# SST for Subband filterbank (F_sub)\n",
+ "sub_ampl_fs = FS * G_subband # subband amplitude for FS signal input sine\n",
+ "SST_fs = sub_ampl_fs**2 * N_int_sub\n",
"\n",
- "ampl_sub_50dBFS = ampl_50dBFS * G_subband # subband amplitude -50dBFS signal input sine\n",
- "SST_50dBFS = ampl_sub_50dBFS**2 * N_int_sub\n",
+ "sub_ampl_50dBFS = ampl_50dBFS * G_subband # subband amplitude -50dBFS signal input sine\n",
+ "SST_50dBFS = sub_ampl_50dBFS**2 * N_int_sub\n",
"\n",
- "ampl_si_16q = 16 # [q], so 16 q is 4 bits amplitude\n",
- "ampl_sub_16q = ampl_si_16q * G_subband # subband amplitude for signal input sine with A = 16 q\n",
- "SST_ampl_16q = ampl_sub_16q**2 * N_int_sub\n",
- "\n",
- "sigma_si_16q = 16 * np.sqrt(2) # [q], so A = 16 * sqrt(2) q for sigma = 16 q\n",
- "sigma_sub_16q = sigma_si_16q * G_subband # subband sigma for arbitrary signal input with sigma = 16 q\n",
- "SST_sigma_16q = sigma_sub_16q**2 * N_int_sub\n",
+ "si_ampl_s16q = sigma_16q * np.sqrt(2)\n",
+ "sub_ampl_s16q = si_ampl_s16q * G_subband # subband amplitude for signal input sine with sigma = 16 q\n",
+ "SST_ampl_s16q = sub_ampl_s16q**2 * N_int_sub\n",
"\n",
"print(\"Signal input level --> Expected subband level and SST level:\")\n",
"print()\n",
- "print(\" ampl_si ampl_sub #bits SST #bits\")\n",
- "print(f\"{FS:8.1f} {ampl_sub_fs:10.1f} {np.log2(ampl_sub_fs):8.4f} {SST_fs:e} {np.log2(SST_fs):.1f}\")\n",
- "print(f\"{ampl_50dBFS:8.1f} {ampl_sub_50dBFS:10.1f} {np.log2(ampl_sub_50dBFS):8.4f} {SST_50dBFS:e} {np.log2(SST_50dBFS):.1f}\")\n",
- "print(f\"{ampl_si_16q:8.1f} {ampl_sub_16q:10.1f} {np.log2(ampl_sub_16q):8.4f} {SST_ampl_16q:e} {np.log2(SST_ampl_16q):.1f}\")\n",
- "print()\n",
- "print(\"sigma_si sigma_sub #bits SST #bits\")\n",
- "print(f\"{sigma_si_16q:8.1f} {sigma_sub_16q:10.1f} {np.log2(sigma_sub_16q):8.4f} {SST_sigma_16q:e} {np.log2(SST_sigma_16q):.1f}\")"
+ "print(\" si_ampl sub_ampl #bits SST #bits\")\n",
+ "print(f\"{FS:8.1f} {sub_ampl_fs:10.1f} {np.log2(sub_ampl_fs):8.4f} {SST_fs:e} {np.log2(SST_fs):.1f}\")\n",
+ "print(f\"{ampl_50dBFS:8.1f} {sub_ampl_50dBFS:10.1f} {np.log2(sub_ampl_50dBFS):8.4f} {SST_50dBFS:e} {np.log2(SST_50dBFS):.1f}\")\n",
+ "print(f\"{si_ampl_s16q:8.1f} {sub_ampl_s16q:10.1f} {np.log2(sub_ampl_s16q):8.4f} {SST_ampl_s16q:e} {np.log2(SST_ampl_s16q):.1f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2c02740",
+ "metadata": {},
+ "source": [
+ "From measured SST to SSTq in q^2 units and subband amplitude in q units\n",
+ "\n",
+ "* SSTq = SST / N_int_sub / G_subband^2\n",
+ "* sub_ampl = sqrt(SSTq) # ampl real = ampl imag = std complex = sqrt(power complex)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66d49365",
+ "metadata": {},
+ "source": [
+ "## 3.3 Beamlet statistics (BST)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 96,
"id": "f0b09a83",
"metadata": {},
"outputs": [],
@@ -512,10 +632,87 @@
"# . uses BF weights to form beamlets from sum of weighted subbands\n"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "d2086ec5",
+ "metadata": {},
+ "source": [
+ "# Appendix 1: DFT of real input sine + DC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 97,
+ "id": "def6eba7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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yzpQ0bJM0U9cAJyU5MckhwIuArcvG/BmjrTqSHM1ot/mNs4spSeuyAPUrns0nDdyqzVRVPQC8Gng/8AngoqraluSNSU5rhr0f+EKS64HLgZ+tqi+0FVqSJmH9kjQPE82ZqqpLgUuXPfa6sdsF/HTzI0kLo+v6Nbpop7umpCHzCuiSJElTsJmSpBYF90tJQ2czJUkt8rv5pOGzmZKktrlrSho0mylJapHfzScNn82UJEnSFGymJKlFiUf5pKGzmZKkFjn/XBo+mylJkqQp2ExJUosSv5tPGjqbKUmSpCnYTElSy9wxJQ2bzZQktcgroEvDt6HrAFq/K299gA2fuoNnnHR011EG7Yrtd3D7V+7l0V0HkbRPl//j7dyz60F+4ImP7TqKDkDumeqxt113Hy95x9Vdxxi8F7/9av7bhR/vOoZ6Kl4cYS5edv41vOo9H+06hg5QNlOS1DLP5pOGzWZKklrknClp+GymJEmSpmAzJUktCl4aQRo6mylJapGH+aThs5mSpJa5Z0oaNpspSWpRErspaeBspiRJkqZgMyVJLXICujR8NlOS1CYnoEuDZzMlSS1zz5Q0bDZTktSi4AR0aehspiRJkqZgMyVJLfLKCNLw2UxJkiRNwWZKklrkyXzS8E3UTCU5NckNSbYnOWc/456fpJJsnl1ESVq/ruuX380nDd+qzVSSg4FzgWcDJwNnJDl5hXGHAz8JXD3rkJK0HotSv5wzJQ3bJHumTgG2V9WNVbULuAA4fYVxvwS8Cbh3hvkkaRqd168Qym5KGrQNE4w5Brh57P4O4CnjA5I8GTiuqi5J8rP7WlCSs4GzATZt2sTS0tKaA7dh586dC5NlPfqYvY+Z+/o+6WvuGem8ft100y6gevk76ON7Z2lpqZe5oZ/rG/qbG2aXfZJmar+SHAS8BThztbFVdR5wHsDmzZtry5Yt0778TCwtLbEoWdbksksA+pW9x5k3btzYr9yN3r6/52Ae9esju26AG7f38nfQq/fOWG3pVe4x5p6/WWWf5DDfLcBxY/ePbR7b43DgCcBSks8A3wVsdRK6pAXQef1y/rk0fJM0U9cAJyU5MckhwIuArXuerKq7quroqjqhqk4ArgJOq6prW0ksSZNbiPrllClp2FZtpqrqAeDVwPuBTwAXVdW2JG9MclrbASVpvRaifnltBGnwJpozVVWXApcue+x1+xi7ZfpYkjQb1i9JbfMK6JLUoj37pcrrI0iDZTMlSZI0BZspSWrRnilT7piShstmSpJaFC+OIA2ezZQkzYE7pqThspmSpBY9fJjPdkoaKpspSZKkKdhMSVKLHro0QqcpJLXJZkqSWuQF0KXhs5mSpDlwypQ0XDZTktSiNLumygN90mDZTEmSJE3BZkqS5sDDfNJw2UxJkiRNwWZKklrk2XzS8NlMSVKL9nw3n4f5pOGymZIkSZqCzZQkteih7+bz0gjSYNlMSZIkTcFmSpJa9NB387ljShosmylJapFn80nDZzMlSXPgjqn5KHcBqgM2U5LUoocvjeAf+XlwNasLNlOSpMGwl1IXbKYkqUUPXxpB87DbXVPqgM2UJGkw7KXUBZspSZoD/8jPhxdHVRdspiSpRfE431zZtKoLNlOSJElTsJmSpBY9dAV0d03NhXum1IWJmqkkpya5Icn2JOes8PxPJ7k+yXVJ/irJ8bOPKklrZ/06sNi0qgurNlNJDgbOBZ4NnAyckeTkZcP+HthcVU8ELgbePOugkrRWi1C/Hpoy5d/4udjtelYHJtkzdQqwvapurKpdwAXA6eMDquryqrqnuXsVcOxsY0rSunRevx4+zKd58Erz6sKGCcYcA9w8dn8H8JT9jD8L+POVnkhyNnA2wKZNm1haWposZct27ty5MFnWo4/Z+5i5r++Tvuaekc7r16duuh+AK664gsMP6de3HvfxvfM3f/O37L7v7t7lhn6ub+hvbphd9kmaqYkleQmwGXjmSs9X1XnAeQCbN2+uLVu2zPLl121paYlFybIml10C0K/sPc68cePGfuVu9Pb9PWdt1a+b/u4z8IltPO1pT+OojYfOJuyc9Oq903xOn/6MZ/D3V1/Rn9xjerW+x/Q1N8wu+yTN1C3AcWP3j20e20uSZwGvBZ5ZVfdNnUySpmf9OtB4lE8dmGTO1DXASUlOTHII8CJg6/iAJE8C3gacVlW3zz6mJK1L5/XLa3bOl2fzqQurNlNV9QDwauD9wCeAi6pqW5I3JjmtGfYbwEbgj5N8LMnWfSxOkuZmEepXv2ZJ9dP4pHPP5lMXJpozVVWXApcue+x1Y7efNeNckjQTi1K/PMmsPePr1rP51AWvgC5JbWqO83n4qT27xxoo17K6YDMlSeq18QbKHVPqgs2UJLXooTlT/pFvzV6H+VzR6oDNlCSp1/ZqoOyl1AGbKUlqkZdGaN/4ninP5lMXbKYkqUVpDvQ5l2c+PMynLthMSZJ6ba+z+eyl1AGbKUlq0cOH+fwr35ZyypQ6ZjMlSeq1vS+NYDul+bOZkiZkkdZ67Lk0gm+f9pSH+dQxmylpQtZorUf8cr7WedFOdc1mSpLmwL/x7andY7dd0+qAzVRPechJ6oeHL43gZ7Yt4w2Uq1ldsJmSJPWaZ/OpazZTPeXW1/y5zrUuey6N4PunNZ7Np67ZTPWU5WL+XOfSYtrrbL4Oc+jAZTMlSS3yZL72uWdKXbOZ6ikLhtQPid/N1za/TkZds5mSJPWbE9DVMZupnrJgzJ/rXOvx0BXQfQe1xot2qms2U5KkXtv70gh2U5o/m6mecutr/lznWo94aYTWedFOdc1mqqfc+pL64aFmqtsYgzbeQO22m1IHbKYkSb3m2Xzqms1UT1kw5s9VrvXwu/na56pV12ymJEmDYWOlLthMSZOySGsdnDPVPs/mU9dspiRJvebZfOqazVRPWTDmz1WuafiZbc/ee6ak+bOZ6il3Zc+fa1zrsee7+XwHtWf8bD4vjaAu2ExJknrNr5NR1yZqppKcmuSGJNuTnLPC84cmubB5/uokJ8w8qfZiwZAm03X9emi/lJ/Z1uy9bl3Rmr9Vm6kkBwPnAs8GTgbOSHLysmFnAV+sqscDvwW8adZBJWmtrF8HCiegq1sbJhhzCrC9qm4ESHIBcDpw/diY04HXN7cvBt6aJDWDq9Tt+OI9vOwPrpl2Mft19z338MiP/nWrrzFrD46t2u97S7+yQz8zv+HKr/KW6/qXu83398bDNvCnr3x6K8uekU7r1+g1R/89653XcuiGfs2s6Ett3PXg7odu/9SFH2P3/ff2IvdyfVnfy/U1N8B/PP5BtsxgOZM0U8cAN4/d3wE8ZV9jquqBJHcBRwF3jA9KcjZwNsCmTZtYWlpa9cW/dO9ujsiuCWKu38ZDd3Nwvtrqa8xc4M5Dio2HHMQRPcq+6WvCg7t39yrzcYcfxFd2FY89rIfvE9p9fx/6QCb6HHeo0/oF8MC9uznlMcXug+5bT/5O9aY2boBHP+ZgHig47OBdPHhQT3Iv05v1vUxfcwPU/Q/MpIZN0kzNTFWdB5wHsHnz5tqyZctE/9/zTm0xFLC0tMSkWRaJuefL3Ae29dYvgCMP6+fvoK/vHXPPV19zw+yyT7LP+RbguLH7xzaPrTgmyQbgCOALU6eTpOlYvyS1bpJm6hrgpCQnJjkEeBGwddmYrcBLm9svAD44q/kGkjQF65ek1q16mK+ZQ/Bq4P3AwcDvV9W2JG8Erq2qrcA7gD9Ksh24k1HBkqROWb8kzcNEc6aq6lLg0mWPvW7s9r3AC2cbTZKmZ/2S1LZ+nacrSZK0YGymJEmSpmAzJUmSNAWbKUmSpCmkqzOAk3weuKmTF/+XjmbZ1Y57wtzzZe7pHV9Vj+k6xLTWUb8W6XewFuaeL3PP31qy77N+ddZMLZIk11bV5q5zrJW558vcWq++/g7MPV/mnr9ZZfcwnyRJ0hRspiRJkqZgMzVyXtcB1snc82VurVdffwfmni9zz99MsjtnSpIkaQrumZIkSZqCzZQkSdIUDshmKsnXJvnLJJ9q/vvo/Yx9VJIdSd46z4z7yLJq7iTfkeTKJNuSXJfkh7vI2mQ5NckNSbYnOWeF5w9NcmHz/NVJTugg5r8wQe6fTnJ9s37/KsnxXeRcbrXcY+Oen6SS9PJU5kU24Pf8v03y0SQPJHlBFxlXMtTPapJXJPmHJB9L8rdJTu4i53J9rTETrO8zk3y+Wd8fS/Kja36RqjrgfoA3A+c0t88B3rSfsf8LeA/w1j7kBr4ZOKm5/TjgNuDIDrIeDHwa+EbgEODjwMnLxrwS+L3m9ouACxdgHU+S+3uAr2lu/3hfcjfjDgc+BFwFbO4695B+Bv6ePwF4IvCHwAu6zryG3L38rAKPGrt9GnBZH3I34xaqxky4vs+c9m/8AblnCjgdeGdz+53A81YalOTfAJuAv5hPrFWtmruqPllVn2pu3wrcDnRxxelTgO1VdWNV7QIuYJR/3Pi/52Lge5NkjhlXsmruqrq8qu5p7l4FHDvnjCuZZH0D/BLwJuDeeYY7QAz5Pf+ZqroO2N1FwH0Y7Ge1qr48dveRwCKcKdbXGjNp7qkcqM3Upqq6rbn9WUYN016SHAT8JvCaeQZbxaq5xyU5hVEn/um2g63gGODmsfs7msdWHFNVDwB3AUfNJd2+TZJ73FnAn7eaaDKr5k7yZOC4qrpknsEOIAfKe35RDPazCpDkVUk+zeiIxH+dU7b96WuNmfR98vzmcPDFSY5b64tsWG+6RZfkA8DXr/DUa8fvVFUlWanrfyVwaVXtmOeG4wxy71nOY4E/Al5aVYu0NTkYSV4CbAae2XWW1TQbB29htDtbOqD06bO6R1WdC5yb5D8BvwC8tONI+9XzGvM+4L1VdV+SH2O09/jfrWUBg22mqupZ+3ouyeeSPLaqbmuajttXGPZU4LuTvBLYCBySZGdV7XPS3SzMIDdJHgVcAry2qq5qKepqbgHGu/tjm8dWGrMjyQbgCOAL84m3T5PkJsmzGDW4z6yq++aUbX9Wy3048ARgqdk4+Hpga5LTqurauaUctkG/5xfQUD+ry10A/G6riSbT1xqz6vquqvHP4NsZ7Q1cm64nh3XxA/wGe0/kfvMq489kMSagr5qb0WG9vwJ+quOsG4AbgRN5eNLfty0b8yr2nox70QKs40lyP4nRodOTus67ltzLxi+xAJNDh/Qz5Pf82NjzWZwJ6IP9rI7nBZ4LXNuH3MvGL0SNmXB9P3bs9n8Arlrz63T9D+1o5R7VNByfAj4AfG3z+Gbg7SuMX5RmatXcwEuA+4GPjf18R0d5nwN8silmr20eeyNwWnP7MOCPge3Ah4Fv7HodT5j7A8Dnxtbv1q4zT5J72diFKHRD+xnwe/47Gc01uZvRnrRtXWeeMHcvP6uMziLf1mS+nP00LYuUe9nYhakxE6zvX2vW98eb9f2v1/oafp2MJEnSFA7Us/kkSZJmwmZKkiRpCjZTkiRJU7CZkiRJmoLNlCRJ0hRspiRJkqZgMyVJkjSF/w8KlcSyweKhhAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 720x288 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# DFT of real sine input\n",
+ "# . Show that DFT of real sine with A = 1 yields bin phasor with amplitude 0.5. For DC at bin 0\n",
+ "# the real input gain is 1.0, because DC has only one phasor component of frequency 0.\n",
+ "G_fft_real_input_sine = 0.5\n",
+ "G_fft_real_input_dc = 1.0\n",
+ "\n",
+ "# . DFT size\n",
+ "N_points = 1024\n",
+ "N_bins = N_points // 2 + 1 # positive frequency bins including DC and f_s/2\n",
+ "\n",
+ "# . select a bin\n",
+ "i_bin = 200 # bin index in range(N_bins)\n",
+ "dc_bin = 0 # DC\n",
+ "\n",
+ "# . time and frequency axis\n",
+ "f_s = f_adc # sample frequency\n",
+ "f_s = 1 # normalized sample frequency\n",
+ "T_s = 1 / f_s # sample period\n",
+ "T_fft = N_points * T_s # DFT period\n",
+ "t_axis = np.linspace(0, T_fft, N_points, endpoint=False)\n",
+ "f_axis = np.linspace(0, f_s, N_points, endpoint=False)\n",
+ "f_axis_fft = f_axis - f_s/2 # fftshift axis\n",
+ "f_axis_rfft = f_axis[0:N_bins] # positive frequency bins\n",
+ "\n",
+ "f_bin = i_bin / N_points * f_s # bin frequency\n",
+ "\n",
+ "# . create sine at bin + DC, use cos to see DC at i_bin = 0 \n",
+ "s = np.cos(2 * np.pi * f_bin * t_axis)\n",
+ "dc = np.cos(2 * np.pi * dc_bin * t_axis) # equivalent to dc = 1\n",
+ "\n",
+ "x = s + dc\n",
+ "\n",
+ "# . DFT using complex input fft()\n",
+ "X_fft = np.fft.fftshift(np.fft.fft(x) / N_points)\n",
+ "\n",
+ "# . DFT using real input rfft()\n",
+ "X_rfft = np.fft.rfft(x) / N_points\n",
+ "\n",
+ "plt.figure(figsize=(10, 4))\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.title('DFT of real sine using fft')\n",
+ "plt.plot(f_axis_fft, abs(X_fft))\n",
+ "plt.grid()\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.title('DFT of real sine using rfft')\n",
+ "plt.plot(f_axis_rfft, abs(X_rfft))\n",
+ "plt.grid()"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "8713e865",
+ "id": "2e386180",
"metadata": {},
"outputs": [],
"source": []
diff --git a/applications/lofar2/model/signal_statistics.ipynb b/applications/lofar2/model/signal_statistics.ipynb
index 4a1f8fbc19bb9b89aad3bd2aadc7b49fab78a42f..fbd1d2772cd42bf0394cf85bcbffb3203318b1b3 100644
--- a/applications/lofar2/model/signal_statistics.ipynb
+++ b/applications/lofar2/model/signal_statistics.ipynb
@@ -12,9 +12,9 @@
"Purpose: Model the SNR of a beamformer and a correlator\n",
"\n",
"Status:\n",
- "* coherent summator (= voltage beamformer): SNR of coherent input improves by the number of inputs N\n",
- "* incoherent summator (= auto correlation, power beamformer): SNR does not improve, but accuracy of power mean measurement (its variance) does improves by factor N. Summing powers from N inputs or summing N powers from one input is equivalent.\n",
- "* correlator: SNR of coherent input improves by sqrt(N) for integration over N cross powers in time. Hence if the input SNR of the input signal is -20 dB (i.e. sigma_coh / sigma_sys = 0.1) then it takes N = 10000 to improve the SNR by a factor 100 = +20 dB to 0 dB.\n",
+ "* coherent summator (sums voltages, e.g. voltage beamformer): SNR of coherent input improves by the number of inputs N\n",
+ "* incoherent summator (sums powers, e.g. auto power statistics, power beamformer): SNR does not improve, but accuracy of mean power measurement does improve by factor N, so the std of the mean power measurement reduces by N. Summing powers from N inputs (like in an incoherent beamformer) or summing N powers in time from 1 input (like in subband auto power statistics) is equivalent.\n",
+ "* correlator: SNR of coherent input improves by sqrt(N) for integration over N cross powers in time. Hence if the input SNR of the input signal is -20 dB (i.e. sigma_coh / sigma_sys = 0.1) then it takes integration over N = 10000 cross powers in time to improve the SNR of the correlator output by a factor 100 = +20 dB to 0 dB.\n",
"\n",
"References:\n",
"\n",
@@ -23,7 +23,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 17,
"id": "2b477516",
"metadata": {},
"outputs": [],
@@ -39,25 +39,37 @@
"source": [
"## 1 Statistics basics:\n",
"\n",
- "* dc = mean # direct current\n",
- "* sigma = std # standard deviation\n",
- "* var = std**2 # variance\n",
- "* mean power = var + mean**2\n",
- "* rms = sqrt(mean power) = sqrt(var + mean**2)\n",
+ "Signal statistics\n",
+ "\n",
+ "* dc = mean # direct current, average value of a signal\n",
+ "* sigma = std = sqrt(var) # standard deviation, measure for fluctuating portion of a signal\n",
+ "* var = std**2 # variance, power of the fluctuating portion of a signal\n",
+ "* power = var + mean**2\n",
+ "* rms = sqrt(power) = sqrt(var + mean**2)\n",
+ "\n",
+ "If mean = 0 then var = power and std = rms.\n",
+ "\n",
+ "For a complex signal (like subbands and beamlets):\n",
+ "\n",
+ "* power complex = power real + power imag = (std real)^2 + (std imag)^2\n",
+ "* power real = power imag = power complex / 2\n",
+ "* std real = std imag = std complex / sqrt(2)\n",
+ "* std complex = sqrt(power complex)\n",
+ "* ampl real = ampl imag = std complex = std real * sqrt(2) = std imag * sqrt(2)\n",
+ "\n",
+ "Coherent and incoherent signals. With S signals, the std of their sum signal:\n",
"\n",
- "Coherent and incoherent signals. With S signals, the std of their sum:\n",
- " \n",
"* increases by S for coherent signals\n",
"* increases by sqrt(S) for incoherent signals\n",
"\n",
- "Coherent averaging by summing the signal voltages improves the SNR of a signal by a factor N^2 / N = N, because the signal power increases by a factor N^2, while the incoherent noise adds as powers, so the noise power increases by a factor N.\n",
+ "Coherent averaging by summing voltage signals improves the SNR of a signal by a factor N^2 / N = N, because the coherent signal power increases by a factor N^2, while the incoherent noise adds as powers, so the noise power increases by a factor N.\n",
"\n",
- "Incoherent averaging by summing the signal powers does not improve the SNR, because the phase information of the signal is lost in the powers. Incoherent averaging does reduce the variance of the signal power estimate by a factor N, so it mkes the measurement more accurate."
+ "Incoherent averaging by summing power signals does not improve the SNR, because the phase information of the signal is lost in the powers. Incoherent averaging does reduce the std of the signal power estimate by a factor N, so incoherent averaging makes the signal power measurement more accurate."
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 18,
"id": "9c55fb7b",
"metadata": {},
"outputs": [],
@@ -74,7 +86,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 19,
"id": "74edfe32",
"metadata": {},
"outputs": [
@@ -82,9 +94,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "mean(si) = -0.207540, expected -0.2\n",
+ "mean(si) = -0.205130, expected -0.2\n",
"std(si) = 0.500000, expected 0.5\n",
- "rms(si) = 0.541362, expected 0.538516\n"
+ "rms(si) = 0.540443, expected 0.538516\n"
]
}
],
@@ -114,7 +126,7 @@
"Two types:\n",
"\n",
"1. Coherent summation in voltage beamformer (e.g. digital BF in LOFAR2 Station, TAB in ARTS)\n",
- "2. Incoherent summation in power beamformer (e.g. IAB in ARTS)"
+ "2. Incoherent summation in power statistics (e.g. SST, BST), power beamformer (e.g. IAB in ARTS)"
]
},
{
@@ -130,18 +142,18 @@
"2. Incoherent signal, add up as power\n",
"\n",
"In the voltage beamformer the sky signal in the beamlet direction adds coherently and the sky\n",
- "signals from other directions and from the receivers noise add incoherently. Hence the SNR of the beamlet signal improves by factor S/sqrt(S) = sqrt(S)."
+ "signals from other directions and the signals from the receivers noise add incoherently. Hence the SNR of the beamlet signal improves by factor S/sqrt(S) = sqrt(S)."
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 20,
"id": "89845ec3",
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -153,7 +165,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -246,7 +258,7 @@
"id": "fd6ffb94",
"metadata": {},
"source": [
- "Incoherent summation of powers from S inputs is equivalent to incoherent summation of S powers in time from a single input. Therefore the incoherent summation does not improve the SNR of the signal, but it does improve the accuracy of the power measurement by a factor S. Hence instead of measuring with one dish for S intervals it is equivalent to sum the powers of S dishes for 1 interval. Hence the field of view of the summed power beam is the same as the field of view of one dish."
+ "Incoherent summation of powers from S inputs is equivalent to incoherent summation of S powers in time from a single input. Incoherent summation does not improve the SNR of the signal, but it does improve the accuracy of the power measurement by a factor S. Hence instead of measuring with one dish for S intervals it is equivalent to sum the powers of S dishes for 1 interval. Hence the field of view of the summed array power beam is the same as the field of view of one dish."
]
},
{
@@ -267,13 +279,13 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 21,
"id": "8713e865",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -285,7 +297,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -297,7 +309,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -324,31 +336,50 @@
"\n",
"# Input signal\n",
"sigma = 1.0\n",
- "pow_mean = sigma**2\n",
+ "expected_pow_mean = sigma**2\n",
"\n",
"# Auto correlator mean(A * A)\n",
"auto_mean_arr = []\n",
- "auto_std_arr = []\n",
- "measure_SNR_arr = []\n",
- "measure_SNR_dB_arr = []\n",
+ "auto_mean_SNR_arr = []\n",
+ "auto_mean_SNR_dB_arr = []\n",
"for N_samples in N_samples_arr:\n",
" # Signal input A\n",
" sA = np.random.randn(N_samples)\n",
" sA *= sigma / np.std(sA)\n",
"\n",
" # Auto correlate A\n",
+ " # . the auto_mean is the mean power\n",
" auto_mean = np.mean(sA * sA)\n",
" auto_mean_arr.append(auto_mean)\n",
- " auto_std = np.std(sA * sA)\n",
- " auto_std_arr.append(auto_std)\n",
+ " # . the np.std(sA * sA) is not useful, because for powers all info is already in the auto_mean\n",
"\n",
" # Accuracy of the power measurement\n",
- " measure_SNR = auto_mean / np.abs(auto_mean - pow_mean)\n",
- " measure_SNR_dB = 10 * np.log10(measure_SNR)\n",
- " measure_SNR_arr.append(measure_SNR)\n",
- " measure_SNR_dB_arr.append(measure_SNR_dB)\n",
+ " auto_mean_SNR = auto_mean / np.abs(auto_mean - expected_pow_mean)\n",
+ " auto_mean_SNR_dB = 10 * np.log10(auto_mean_SNR)\n",
+ " auto_mean_SNR_arr.append(auto_mean_SNR)\n",
+ " auto_mean_SNR_dB_arr.append(auto_mean_SNR_dB)\n",
+ " \n",
+ " #print(f\"{N_samples}, {auto_mean:9.6f}, {auto_mean_SNR_dB:.0f}\")\n",
" \n",
- " #print(f\"{N_samples}, {auto_mean:9.6f}, {auto_std:9.6f}, {measure_SNR_dB:.0f}\")\n",
+ "# Determine accuracy of the auto_mean by using N_measure to measure auto_mean_std using std()\n",
+ "# instead of using auto_mean_SNR = auto_mean - expected_pow_mean\n",
+ "N_measure = 10\n",
+ "\n",
+ "auto_mean_std_log10_arr = []\n",
+ "for N_samples in N_samples_arr:\n",
+ " am_arr = []\n",
+ " for R in range(N_measure):\n",
+ " # Signal input A\n",
+ " sA = np.random.randn(N_samples)\n",
+ " sA *= sigma / np.std(sA)\n",
+ "\n",
+ " # Auto correlate A\n",
+ " am = np.mean(sA * sA)\n",
+ " am_arr.append(am)\n",
+ " auto_mean_std = np.std(np.array(am_arr))\n",
+ " auto_mean_std_log10 = 10 * np.log10(auto_mean_std)\n",
+ " auto_mean_std_log10_arr.append(auto_mean_std_log10)\n",
+ "\n",
"\n",
"plt.figure(1)\n",
"plt.plot(N_samples_arr, auto_mean_arr, 'g')\n",
@@ -358,18 +389,18 @@
"plt.grid()\n",
"\n",
"plt.figure(2)\n",
- "plt.plot(N_samples_arr, auto_std_arr, 'g')\n",
- "plt.title(\"Auto correlator std\")\n",
- "plt.xlabel(\"Number of samples\")\n",
- "plt.ylabel(\"Auto power std\")\n",
- "plt.grid()\n",
- "\n",
- "plt.figure(3)\n",
- "plt.plot(N_samples_arr_log, measure_SNR_dB_arr, 'r')\n",
+ "plt.plot(N_samples_arr_log, auto_mean_SNR_dB_arr, 'r')\n",
"plt.title(\"Auto correlator\")\n",
"plt.xlabel(\"Number of samples (log10)\")\n",
"plt.ylabel(\"SNR of power measurement [dB]\")\n",
- "plt.grid()"
+ "plt.grid()\n",
+ " \n",
+ "plt.figure(3)\n",
+ "plt.plot(N_samples_arr_log, auto_mean_std_log10_arr, 'g')\n",
+ "plt.title(\"Auto correlator mean power std\")\n",
+ "plt.xlabel(\"Number of samples (log10)\")\n",
+ "plt.ylabel(\"Auto mean power std (log10)\")\n",
+ "plt.grid()\n"
]
},
{
@@ -389,9 +420,18 @@
"### 3.2 Cross powers"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "4fc1cbf5",
+ "metadata": {},
+ "source": [
+ "**Conclusion:**\n",
+ "The expected coherent cross power is pow_coh and the measurement of cross_coh_mean = pow_coh becomes more accurate when N_samples increases. The incoherent cross power is cross_incoh_mean and goes to zero. The SNR of the coherent correlator is proportional to 1 / cross_incoh_mean. Dividing by almost zero causes the SNR to fluctuate, but in general the SNR of the coherent signal improves by sqrt(N_samples)."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 22,
"id": "470fd269",
"metadata": {},
"outputs": [
@@ -404,7 +444,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -416,19 +456,7 @@
},
{
"data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -443,7 +471,7 @@
"# Number of samples N_samples in time for input A and input B, try N_samples in N_samples_arr\n",
"N_steps = 100\n",
"\n",
- "N_min = 10\n",
+ "N_min = 100\n",
"N_max = 10000000\n",
"n_incr = (N_max / N_min)**(1 / N_steps)\n",
"N_samples_arr = []\n",
@@ -463,11 +491,8 @@
"\n",
"# Correlator mean(A * B)\n",
"cross_coh_mean_arr = []\n",
- "cross_coh_std_arr = []\n",
"cross_incoh_mean_arr = []\n",
- "cross_incoh_std_arr = []\n",
"cross_sys_mean_arr = []\n",
- "cross_sys_std_arr = []\n",
"cross_SNR_arr = []\n",
"cross_SNR_dB_arr = []\n",
"for N_samples in N_samples_arr:\n",
@@ -487,18 +512,11 @@
" # Correlate A and B\n",
" cross_coh_mean = np.mean(si_coh * si_coh)\n",
" cross_coh_mean_arr.append(cross_coh_mean)\n",
- " cross_coh_std = np.std(si_coh * si_coh)\n",
- " cross_coh_std_arr.append(cross_coh_std)\n",
" cross_incoh_mean = np.mean(sA_incoh * sB_incoh)\n",
" cross_incoh_mean_arr.append(cross_incoh_mean)\n",
- " cross_incoh_std = np.std(sA_incoh * sB_incoh)\n",
- " cross_incoh_std_arr.append(cross_incoh_std)\n",
" cross_sys_mean = np.mean(sA_sys * sB_sys)\n",
" cross_sys_mean_arr.append(cross_sys_mean)\n",
- " cross_sys_std = np.std(sA_sys * sB_sys)\n",
- " cross_sys_std_arr.append(cross_sys_std)\n",
" #print(f\"{N_samples}, {cross_coh_mean:9.6f}, {cross_incoh_mean:9.6f}, {cross_sys_mean:9.6f}\")\n",
- " #print(f\"{N_samples}, {cross_coh_std:9.6f}, {cross_incoh_std:9.6f}, {cross_sys_std:9.6f}\")\n",
"\n",
" # SNR definitions of the coherent correlator\n",
" # . using cross_coh_mean shows the cross_SNR imrpovement for all N_max\n",
@@ -511,8 +529,9 @@
" cross_SNR = np.abs(cross_coh_mean / cross_incoh_mean)\n",
" #cross_SNR = np.abs(1 / (cross_coh_mean - pow_coh))\n",
" #cross_SNR = np.abs(1 / cross_incoh_mean)\n",
- " cross_SNR = np.abs(cross_sys_mean / cross_incoh_mean)\n",
+ " #cross_SNR = np.abs(cross_sys_mean / cross_incoh_mean)\n",
" #cross_SNR = np.abs(cross_sys_mean / (cross_sys_mean - cross_coh_mean))\n",
+ " \n",
" cross_SNR_dB = 10 * np.log10(cross_SNR)\n",
" cross_SNR_arr.append(cross_SNR)\n",
" cross_SNR_dB_arr.append(cross_SNR_dB)\n",
@@ -527,14 +546,6 @@
"plt.grid()\n",
"\n",
"plt.figure(2)\n",
- "plt.plot(N_samples_arr, cross_coh_std_arr, 'g', N_samples_arr, cross_incoh_std_arr, 'b', N_samples_arr, cross_sys_std_arr, 'r')\n",
- "plt.title(\"Correlator std\")\n",
- "plt.xlabel(\"Number of samples\")\n",
- "plt.ylabel(\"Cross power std\")\n",
- "plt.legend(['cross_coh', 'cross_incoh', 'cross_sys'])\n",
- "plt.grid()\n",
- "\n",
- "plt.figure(3)\n",
"plt.plot(N_samples_arr_log, cross_SNR_dB_arr, 'r')\n",
"plt.title(\"Correlator\")\n",
"plt.xlabel(\"Number of samples (log10)\")\n",
@@ -542,15 +553,6 @@
"plt.grid()"
]
},
- {
- "cell_type": "markdown",
- "id": "4fc1cbf5",
- "metadata": {},
- "source": [
- "**Conclusion:**\n",
- "The expected coherent cross power is pow_coh and the measurement of cross_coh_mean = pow_coh becomes more accurate when N_samples increases. The incoherent cross power is cross_incoh_mean and goes to zero. The SNR of the coherent correlator is proportional to 1 / cross_incoh_mean. Dividing by almost zero causes the SNR to fluctuate, but in general the SNR of the coherent signal improves by sqrt(N_samples)."
- ]
- },
{
"cell_type": "code",
"execution_count": null,
diff --git a/libraries/base/dp/src/vhdl/dp_bsn_source_reg_v2.vhd b/libraries/base/dp/src/vhdl/dp_bsn_source_reg_v2.vhd
index ead48b48a7d79a60e916b27ef1e746237bca17de..37e704250c6f5a32d96f54bcc80a5fa734afee49 100644
--- a/libraries/base/dp/src/vhdl/dp_bsn_source_reg_v2.vhd
+++ b/libraries/base/dp/src/vhdl/dp_bsn_source_reg_v2.vhd
@@ -167,21 +167,21 @@ BEGIN
CASE TO_UINT(sla_in.address(c_mm_reg.adr_w-1 DOWNTO 0)) IS
-- Read Block Sync
WHEN 0 =>
- sla_out.rddata(0) <= mm_on_status;
- sla_out.rddata(1) <= mm_on_pps;
+ sla_out.rddata(0) <= mm_on_status;
+ sla_out.rddata(1) <= mm_on_pps;
WHEN 1 =>
- sla_out.rddata(31 DOWNTO 0) <= mm_nof_clk_per_sync;
+ sla_out.rddata(c_word_w - 1 DOWNTO 0) <= mm_nof_clk_per_sync;
-- Read current BSN
WHEN 2 =>
- sla_out.rddata(31 DOWNTO 0) <= mm_current_bsn(31 DOWNTO 0);
- mm_current_bsn_hi <= mm_current_bsn(63 DOWNTO 32); -- first read low part and preserve high part
+ sla_out.rddata(c_word_w - 1 DOWNTO 0) <= mm_current_bsn(31 DOWNTO 0);
+ mm_current_bsn_hi <= mm_current_bsn(63 DOWNTO 32); -- first read low part and preserve high part
WHEN 3 =>
- sla_out.rddata(31 DOWNTO 0) <= mm_current_bsn_hi; -- then read preserved high part
+ sla_out.rddata(c_word_w - 1 DOWNTO 0) <= mm_current_bsn_hi; -- then read preserved high part
-- Read current bsn_time_offset
WHEN 4 =>
- sla_out.rddata <= RESIZE_UVEC(mm_bsn_time_offset, c_word_w);
+ sla_out.rddata(c_word_w - 1 DOWNTO 0) <= RESIZE_UVEC(mm_bsn_time_offset, c_word_w);
WHEN OTHERS => NULL; -- not used MM addresses
END CASE;
diff --git a/libraries/io/nw_10GbE/src/vhdl/nw_10GbE.vhd b/libraries/io/nw_10GbE/src/vhdl/nw_10GbE.vhd
index 2df5755b77df0eb5972e90bcb37344756cd6b1a9..b35b0ad82280486c67c72a9cd83aed33be87f67b 100644
--- a/libraries/io/nw_10GbE/src/vhdl/nw_10GbE.vhd
+++ b/libraries/io/nw_10GbE/src/vhdl/nw_10GbE.vhd
@@ -52,6 +52,7 @@ ENTITY nw_10GbE IS
g_tx_fifo_size : NATURAL := 256; -- 2 * 32b * 256 = 2 M9K (DP interface has 64b data, so at least 2 M9K needed)
g_rx_fifo_size : NATURAL := 256; -- 2 * 32b * 256 = 2 M9K (DP interface has 64b data, so at least 2 M9K needed)
g_word_alignment_padding : BOOLEAN := FALSE;
+ g_xon_backpressure : BOOLEAN := FALSE;
g_arp_period_s : NATURAL := 30;
g_ip_hdr_field_arr : t_common_field_arr
);
@@ -232,7 +233,8 @@ BEGIN
g_tx_fifo_fill => g_tx_fifo_fill,
g_tx_fifo_size => g_tx_fifo_size,
g_rx_fifo_size => g_rx_fifo_size,
- g_word_alignment_padding => g_word_alignment_padding
+ g_word_alignment_padding => g_word_alignment_padding,
+ g_xon_backpressure => g_xon_backpressure
)
PORT MAP (
-- Transceiver PLL reference clock
diff --git a/libraries/io/tr_10GbE/src/vhdl/tr_10GbE.vhd b/libraries/io/tr_10GbE/src/vhdl/tr_10GbE.vhd
index df979c50d6399d4f9b3b0b1ea33b536025b8cf28..280f008eb693f5c4f4962d86fae11725d9b8644b 100644
--- a/libraries/io/tr_10GbE/src/vhdl/tr_10GbE.vhd
+++ b/libraries/io/tr_10GbE/src/vhdl/tr_10GbE.vhd
@@ -45,7 +45,19 @@
--
-- to avoid that the packet transmission will get a gap that will abort it.
-- The average DP data rate depends on dp_clk and on the DP data valid.
---
+--
+-- g_xon_backpressure can be enabled to set xon = 0 when the TX fill fifo is
+-- full. This also makes use of an extra fifo of size g_tx_fifo_size to
+-- buffer the last incoming frame when xon = 0 to prevent corrupting the frame.
+--
+-- Remark
+-- . Note that the snk_out_arr().xon is used to indicate when the MAC cannot
+-- receive new frames. If xon = 0 is ignored, the TX FIFO can overflow.
+-- . xon can become low when the MAC has no link. When g_xon_backpressure
+-- = TRUE, xon = 0 can also occur when the ready of the MAC is 0 for a long
+-- time filling up the TX fifo. If this fifo is almost full xon is set to 0,
+-- the remainder of the incoming frame is captured by an extra fifo. Therefore
+-- using g_xon_backpressure = TRUE uses extra RAM.
LIBRARY IEEE, common_lib, dp_lib, diag_lib, technology_lib, tech_mac_10g_lib, tech_eth_10g_lib, tr_xaui_lib;
USE IEEE.std_logic_1164.ALL;
@@ -72,7 +84,8 @@ ENTITY tr_10GbE IS
g_tx_fifo_fill : NATURAL := 10; -- Release tx packet only when sufficiently data is available,
g_tx_fifo_size : NATURAL := 256; -- 2 * 32b * 256 = 2 M9K (DP interface has 64b data, so at least 2 M9K needed)
g_rx_fifo_size : NATURAL := 256; -- 2 * 32b * 256 = 2 M9K (DP interface has 64b data, so at least 2 M9K needed)
- g_word_alignment_padding : BOOLEAN := FALSE
+ g_word_alignment_padding : BOOLEAN := FALSE;
+ g_xon_backpressure : BOOLEAN := FALSE
);
PORT (
-- Transceiver PLL reference clock
@@ -146,11 +159,12 @@ ARCHITECTURE str OF tr_10GbE IS
SIGNAL eth_rx_clk_arr : STD_LOGIC_VECTOR(g_nof_macs-1 DOWNTO 0);
SIGNAL eth_rx_rst_arr : STD_LOGIC_VECTOR(g_nof_macs-1 DOWNTO 0);
- SIGNAL dp_fifo_dc_tx_src_out_arr : t_dp_sosi_arr(g_nof_macs-1 DOWNTO 0);
- SIGNAL dp_fifo_dc_tx_src_in_arr : t_dp_siso_arr(g_nof_macs-1 DOWNTO 0);
+ SIGNAL dp_fifo_sc_tx_src_out_arr : t_dp_sosi_arr(g_nof_macs-1 DOWNTO 0);
+ SIGNAL dp_fifo_sc_tx_src_in_arr : t_dp_siso_arr(g_nof_macs-1 DOWNTO 0);
SIGNAL dp_fifo_fill_tx_src_out_arr : t_dp_sosi_arr(g_nof_macs-1 DOWNTO 0);
SIGNAL dp_fifo_fill_tx_src_in_arr : t_dp_siso_arr(g_nof_macs-1 DOWNTO 0);
+ SIGNAL dp_fifo_fill_tx_snk_out_arr : t_dp_siso_arr(g_nof_macs-1 DOWNTO 0);
SIGNAL mac_10g_src_out_arr : t_dp_sosi_arr(g_nof_macs-1 DOWNTO 0);
SIGNAL mac_10g_src_in_arr : t_dp_siso_arr(g_nof_macs-1 DOWNTO 0);
@@ -227,7 +241,48 @@ BEGIN
eth_rx_clk_arr => eth_rx_clk_arr,
eth_rx_rst_arr => eth_rx_rst_arr
);
-
+
+ ---------------------------------------------------------------------------------------
+ -- TX FIFO for buffering last packet when xon = 0 to prevent corrupt frames.
+ ---------------------------------------------------------------------------------------
+ gen_xon_backpressure : IF g_xon_backpressure GENERATE
+ gen_dp_fifo_sc_tx : FOR i IN 0 TO g_nof_macs-1 GENERATE
+ u_dp_fifo_sc_tx : ENTITY dp_lib.dp_fifo_sc
+ GENERIC MAP (
+ g_technology => g_technology,
+ g_data_w => c_xgmii_data_w,
+ g_empty_w => c_tech_mac_10g_empty_w,
+ g_use_empty => TRUE,
+ g_fifo_size => g_tx_fifo_size
+ )
+ PORT MAP (
+ rst => dp_rst,
+ clk => dp_clk,
+
+ snk_out => snk_out_arr(i),
+ snk_in => snk_in_arr(i),
+
+ src_in => dp_fifo_sc_tx_src_in_arr(i),
+ src_out => dp_fifo_sc_tx_src_out_arr(i)
+ );
+ END GENERATE;
+
+ -- When MAC receives pause frames, it's ready signal is low for a long time
+ -- Set xon to (xon AND ready) to enable flushing frames using external dp_xonoff.
+ p_fifo_sc_tx : PROCESS(dp_fifo_fill_tx_snk_out_arr)
+ BEGIN
+ dp_fifo_sc_tx_src_in_arr <= dp_fifo_fill_tx_snk_out_arr;
+ FOR i IN 0 TO g_nof_macs-1 LOOP
+ dp_fifo_sc_tx_src_in_arr(i).xon <= dp_fifo_fill_tx_snk_out_arr(i).xon AND dp_fifo_fill_tx_snk_out_arr(i).ready;
+ END LOOP;
+ END PROCESS;
+ END GENERATE;
+
+ gen_no_xon_backpressure : IF NOT g_xon_backpressure GENERATE
+ dp_fifo_sc_tx_src_out_arr <= snk_in_arr;
+ snk_out_arr <= dp_fifo_fill_tx_snk_out_arr;
+ END GENERATE;
+
---------------------------------------------------------------------------------------
-- TX: FIFO: dp_clk -> tx_clk and with fill level/eop trigger so we can deliver packets to the MAC fast enough
---------------------------------------------------------------------------------------
@@ -248,19 +303,17 @@ BEGIN
rd_rst => eth_tx_rst_arr(i),
rd_clk => eth_tx_clk_arr(i),
- snk_out => snk_out_arr(i),
- snk_in => snk_in_arr(i),
+ snk_out => dp_fifo_fill_tx_snk_out_arr(i),
+ snk_in => dp_fifo_sc_tx_src_out_arr(i),
src_in => dp_fifo_fill_tx_src_in_arr(i),
src_out => dp_fifo_fill_tx_src_out_arr(i)
);
END GENERATE;
-
---------------------------------------------------------------------------------------
-- ETH MAC + PHY
---------------------------------------------------------------------------------------
-
u_tech_eth_10g : ENTITY tech_eth_10g_lib.tech_eth_10g
GENERIC MAP (
g_technology => g_technology,