I generated a human body model through "Blender". Now I hope to simulate the reflection path of radio signals on the human body through sionna, but I can't achieve it. May I ask what the reason is? Here is my code and computational results in the image # %% import numpy as np import drjit as dr import mitsuba as mi import matplotlib as mpl # import tensorflow as tf from sionna.rt import LambertianPattern, DirectivePattern, BackscatteringPattern,\ load_scene, Camera, Transmitter, Receiver, PlanarArray,\ PathSolver, RadioMapSolver, cpx_abs, cpx_convert no_preview = True # Toggle to False to use the preview widget from sionna.rt import load_scene, PlanarArray, Transmitter, Receiver, Camera, watt_to_dbm,\ PathSolver, RadioMapSolver, subcarrier_frequencies import matplotlib.pyplot as plt # %% [markdown] # 导入 # %% scene = load_scene("/data2/yty/Blender Scene/Human_test/Human_test.xml") # %% if not no_preview: scene.preview(); # %% # Only availabe if a preview is open if not no_preview: scene.render(camera="preview", num_samples=512); if not no_preview: scene.render_to_file(camera="preview", filename="scene.png", resolution=[650,500]); # %% # Create new camera with different configuration my_cam = Camera(position=[-100,150,250], look_at=[0,0,0]) # Render scene with new camera* scene.render(camera=my_cam, resolution=[650, 500], num_samples=512); # Increase num_samples to increase image quality # %% scene.objects # floor = scene.get("floor") # print("Position (x,y,z) [m]: ", floor.position) # print("Orientation (alpha, beta, gamma) [rad]: ", floor.orientation) # print("Scaling: ", floor.scaling) # %% # test_scene——tx:[15,90,5];rx:[20,20,20] # Human/skeleton——tx:[20,20,5];rx:[20,-20,5] # Configure antenna array for all transmitters scene.tx_array = PlanarArray(num_rows=1, num_cols=1, vertical_spacing=0.5, horizontal_spacing=0.5, pattern="tr38901", polarization="V") # Configure antenna array for all receivers scene.rx_array = PlanarArray(num_rows=1, num_cols=1, vertical_spacing=0.5, horizontal_spacing=0.5, pattern="dipole", polarization="cross") # Create transmitter tx = Transmitter(name="tx", position=[20,20,5], display_radius=2) # Add transmitter instance to scene scene.add(tx) # Create a receiver rx = Receiver(name="rx", position=[20,-20,5], display_radius=2) # Add receiver instance to scene scene.add(rx) tx.look_at(rx) # Transmitter points towards receiver # Compute radio map rm_solver = RadioMapSolver() rm = rm_solver(scene, max_depth=5, # Maximum number of ray scene interactions samples_per_tx=10**7 , # If you increase: less noise, but more memory required cell_size=(5, 5), # Resolution of the radio map center=[0, 0, 0], # Center of the radio map size=[400, 400], # Total size of the radio map orientation=[0, 0, 0]) # Orientation of the radio map, e.g., could be also vertical # %% # Visualize path gain rm.show(metric="path_gain"); # Visualize received signal strength (RSS) rm.show(metric="rss"); # Visulaize SINR rm.show(metric="sinr"); # %% tx.power_dbm = 24 rm = rm_solver(scene, max_depth=5, samples_per_tx=10**7, cell_size=(5, 5), center=[0, 0, 0], size=[400, 400], orientation=[0, 0, 0]) rm.show(metric="rss"); # %% print(f"Bandwidth: ", scene.bandwidth.numpy(), "[Hz]") print(f"Temperature: ", scene.temperature.numpy(), "[K]") print(f"Thermal noise power: ", watt_to_dbm(scene.thermal_noise_power).numpy(), "[dBm]") # %% # Instantiate a path solver # The same path solver can be used with multiple scenes p_solver = PathSolver() # Compute propagation paths paths = p_solver(scene=scene, max_depth=5, los=True, specular_reflection=True, diffuse_reflection=False, refraction=False, synthetic_array=False, seed=41) # %% # if no_preview: # scene.render(camera=my_cam, paths=paths, clip_at=20); # else: # scene.preview(paths=paths, clip_at=20); scene.preview(paths=paths, clip_at=20); # %% a, tau = paths.cir(normalize_delays=True, out_type="numpy") # Shape: [num_rx, num_rx_ant, num_tx, num_tx_ant, num_paths, num_time_steps] print("Shape of a: ", a.shape) # Shape: [num_rx, num_rx_ant, num_tx, num_tx_ant, num_paths] print("Shape of tau: ", tau.shape) # %% t = tau[0,0,0,0,:]/1e-9 # Scale to ns a_abs = np.abs(a)[0,0,0,0,:,0] a_max = np.max(a_abs) # And plot the CIR plt.figure() plt.title("Channel impulse response") plt.stem(t, a_abs) plt.xlabel(r"$\tau$ [ns]") plt.ylabel(r"$|a|$"); # %% # OFDM system parameters num_subcarriers = 1024 subcarrier_spacing=30e3 # Compute frequencies of subcarriers relative to the carrier frequency frequencies = subcarrier_frequencies(num_subcarriers, subcarrier_spacing) # Compute channel frequency response h_freq = paths.cfr(frequencies=frequencies, normalize=True, # Normalize energy normalize_delays=True, out_type="numpy") # Shape: [num_rx, num_rx_ant, num_tx, num_tx_ant, num_time_steps, num_subcarriers] print("Shape of h_freq: ", h_freq.shape) # Plot absolute value plt.figure() plt.plot(np.abs(h_freq)[0,0,0,0,0,:]); plt.xlabel("Subcarrier index"); plt.ylabel(r"|$h_\text{freq}$|"); plt.title("Channel frequency response"); # %% taps = paths.taps(bandwidth=100e6, # Bandwidth to which the channel is low-pass filtered l_min=-6, # Smallest time lag l_max=100, # Largest time lag sampling_frequency=None, # Sampling at Nyquist rate, i.e., 1/bandwidth normalize=True, # Normalize energy normalize_delays=True, out_type="numpy") print("Shape of taps: ", taps.shape) plt.figure() plt.stem(np.arange(-6, 101), np.abs(taps)[0,0,0,0,0]); plt.xlabel(r"Tap index $\ell$"); plt.ylabel(r"|$h[\ell]|$"); plt.title("Discrete channel taps"); # %% scene.get("tx").velocity = [10, 0, 0] # Recompute propagation paths paths_mob = p_solver(scene=scene, max_depth=5, los=True, specular_reflection=True, diffuse_reflection=False, refraction=True, synthetic_array=True, seed=41) # Compute CIR with time-evolution num_time_steps=100 sampling_frequency = 1e4 a_mob, _ = paths_mob.cir(sampling_frequency=sampling_frequency, num_time_steps=num_time_steps, out_type="numpy") # Inspect time-evolution of a single path coefficient plt.figure() plt.plot(np.arange(num_time_steps)/sampling_frequency*1000, a_mob[0,0,0,0,0].real); plt.xlabel("Time [ms]"); plt.ylabel(r"$\Re\{a_0(t) \}$"); plt.title("Time-evolution of a path coefficient"); # %% rm_solver = RadioMapSolver() rm = rm_solver(scene=scene, max_depth=5, cell_size=[1,1], samples_per_tx=10**6) if no_preview: scene.render(camera=my_cam, radio_map=rm); else: scene.preview(radio_map=rm); # scene.preview(radio_map=rm); 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