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| license: mit |
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|
| ### Dataset Summary |
| This dataset is a real-world dataset collected using the indoor over-the-air lab at the University of Utah’s |
| POWDER testbed. The data is collected in three rounds with the corresponding |
| protocol assignment for each radio shown in the table below. For each round four sets of data are collected at different gains, |
| where a single set in the round consist of captured signals for each permutation of transmitter |
| on/off states.In total the dataset consist of 768 signals where each round consist of 256 signals and each |
| set of experiments in the round captures 64 signals. |
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|
|
| ### Expierimental Setup |
| The experimental setup consist of seven software defined radios(SDR) with one being |
| used as the receiver and six being used as transmitters. The receiving radio as well as two transmitting radios are NI/Ettus |
| X310 USRP’s and the remaining four are NI/Ettus B210 USRP’s. For the X310 the exact gains are 28.4, 25.2, 22.1, 18.9 |
| and for the B210 they are 80.8, 71.8, 62.9, 53.9. Transmitted waveforms are generated by the MATLAB wireless waveform |
| generator and consist of 5G NR, 4G LTE, and 802.11a(Wi-Fi),all with a bandwidth of 20MHz.The carrier frequency used is 2.425 GHz, the sampling |
| rate is 33.33 MS/s, and 20 million samples are collected |
| for every signal. |
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|
| ### Loading Dataset Files |
| ```python |
| |
| def load_signal(file_path = "POWDER_Cochannel_Protocol/Round1_Gain90.out/otax3102node_tx011010_id26.bin") |
| """ |
| - file_path: path the signal file |
| """ |
| samples = np.fromfile(filepath, dtype = np.complex64) |
| return samples |
| |
| def sample_signal(file_path = "POWDER_Cochannel_Protocol/Round1_Gain90.out/otax3102node_tx011010_id26.bin", sample_len = 1024, sample_offset = 10000): |
| """ |
| - file_path: path the signal file |
| - sample_len: number of timesteps to sample |
| - sample_offset: timestep to start sampling from |
| """ |
| |
| samples = np.memmap(file_path, dtype = np.complex64, mode = "r", offset=samples_offset*np.dtype(np.complex64).itemsize, shape = (sample_len,)) |
| samples = np.array(samples) |
| return IQ_samples |
| |
| ``` |
