T-Arshad/POWDER_CoChannel_Protocol_Dataset

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.

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.

Loading Dataset Files

    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