Cannot understand the meaning of the amplitude.

Thanks for your work, I am using your library to read CSI packets generated from Nexmon CSI, but I have some questions about the data.

1. The channel statement information in 802.11 is calculated by H = Y/X, where H is the CSI. The value of this should have ranged from 0 to 1, 0 means the signal faded significantly and 1 means the signal has no fading after the channel. Due to the multipath channel, the value could be a little bit larger than 1. But the original data obtained by CSIKit are very big, the image below shows the CSI amplitude data in a linear format.
2. The data looks reasonable after setting the scaled to True, csi_data = my_reader.read_file("DATASET-pi-2\Empty-21.pcap", scaled=True). Could you explain how is the scale works?

Thanks in advance!!

[Gi-z]

Following the above thread I am trying to visualize the CSI (esp32 data) as you have shown in the visualizations. Could you share the code so I could complete the visualization?

Hi, yes, I’ll provide both code and the data used for this example. Data: https://drive.google.com/file/d/1MUuTdWIGMB03Vv1avKK6PBdga0bKS4A2/view?usp=sharing

from CSIKit.reader import CSVBeamformReader
from CSIKit.util.matlab import db, dbinv

import matplotlib.pyplot as plt
import numpy as np

def scale_csi_frame(csi: np.array, rssi: int) -> np.array:
    # This is not a true SNR ratio as is the case for the Intel scaling.
    # We do not have agc or noise values so it's just about establishing a scale against RSSI.

    # TODO: Find out whether it's RSS or RSSI being supplied with most CSI Frames.
    # I thought RSSI wasn't negative?

    subcarrier_count = csi.shape[0]

    rssi_pwr = dbinv(int(rssi))

    csi_sq = np.multiply(csi, np.conj(csi))
    csi_pwr = np.sum(csi_sq)
    csi_pwr = np.real(csi_pwr)

    # This implementation is based on the equation shown in https://doi.org/10.1109/JIOT.2020.3022573.
    # scaling_coefficient = sqrt(10^(RSS/10) / sum(CSIi^2))

    scale = rssi_pwr / (csi_pwr / subcarrier_count)
    return csi * np.sqrt(scale)

reader = CSVBeamformReader()
csi_data = reader.read_file("rss_test.csv")

rssis = [x.rssi for x in csi_data.frames]
sub1_data_unscaled = [db(abs(x.csi_matrix[-1])) for x in csi_data.frames]
sub1_data_scaled = [db(abs(scale_csi_frame(x.csi_matrix, x.rssi)[-1])) for x in csi_data.frames]

plt.title("Sub1 Unscaled")
plt.plot(sub1_data_unscaled)
plt.show()

plt.title("RSS")
plt.plot(rssis)
plt.show()

plt.title("Sub1 Scaled")
plt.plot(sub1_data_scaled)
plt.show()

[Gi-z]

There is good reason to believe that AGC process is being reversed with the CRISLoc method, however without a measure of CSI before AGC has been applied there isn’t definitive proof. I’ve got a couple of experiments planned to test this out, however it’ll be a little while before I can get the proper equipment. Hoping to have something on this soon. I’ll implement CRISLoc scaling as an optional feature in CSIKit shortly.

Going to close this issue now, but feel free to re-open and/or let me know if there’s anything else I can clear up.