Use the imageDatastore function to load training images with the spectrogram of radar and wireless communication signals. ZipFile = fullfile(saveFolder, 'RadarCommSpectrumSensingData.zip') HelperGenerateRadarCommData(fs,wav,radartx,radarchan,commchan,rxpos_horiz_minmax,rxpos_vert_minmax,numTrainingData,trainingFolder,imageSize) TrainingFolder=fullfile(saveFolder, 'RadarCommTrainData') Because we are looking for the waveform type and the occupied bandwidth, the helper function below generates a spectrogram.ĬlassNames = To generate the training data, define the data folder and class names for noise only data, LTE data, 5G NR data, and radar data. 'ScattererSpecificationSource', 'Input port') 'ReceiveArrayMotionSource', 'Input port'. 'TransmitArrayOrientationAxes',commtxaxes. 'ReceiveArray',phased.ConformalArray( 'Element',phased.IsotropicAntennaElement). 'ScattererSpecificationSource', 'Input port') Ĭommchan=phased.ScatteringMIMOChannel( 'TransmitArray',phased.ConformalArray( 'Taper',10). 'TransmitArrayOrientationAxes',radartxaxes. Radarchan=phased.ScatteringMIMOChannel( 'TransmitArray',ant. The receivers are assumed to be randomly placed in a 2 km x 2km region and equipped with isotropic antennas. įor the wireless signals, the gain and the power of the transmitter may change from frame to frame. For more details on how to generate these types of signals, please refer to the example "Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals". Since 5G and LTE signals are present in the vicinity of airport radars, we define a set of signals for these wireless standards using 5G Toolbox and LTE Toolbox. Radartx = phased.Transmitter( 'PeakPower',power_ASR, 'Gain',gain_ASR) Wav = phased.RectangularWaveform( 'SampleRate',fs, 'PulseWidth',pulseWidth, 'PRF',prf, 'NumPulses',3) Īntele=design(reflectorParabolic( 'Exciter',horn),fc) Īnt=phased.ConformalArray( 'Element',antele) Prf=fs/ceil(fs/1050) % pulse repetition rate The radar operates at 2.8 GHz and uses a reflector antenna with a gain of 32.8 dB. Setup the SceneĬonsider an airport surveillance radar located at the scenario origin. The trained network is then used to identify the signals and the corresponding occupied bandwidth of these signals. This data is used to train and test a deep learning network. We first synthesize a data set that consists of radar and wireless communications signals. This example shows how to model a spectrum sensing system. ![]() This requires that future radar and wireless communication systems include spectrum sensing to detect occupied space to avoid conflicts. As a result, the spectrum of radar systems and wireless communication systems may overlap, which drives the need for spectrum sharing. These higher frequency bands are at ranges traditionally used by radar systems. This volume, Wireless, Networking, Radar, Sensor Array Processing, and Nonlinear Signal Processing, provides complete coverage of the foundations of signal processing related to wireless, radar, space–time coding, and mobile communications, together with associated applications to networking, storage, and communications.Due to the increasing demands for higher speeds and greater coverage, modern wireless communication systems are moving to higher frequency bands and larger signal bandwidths. Drawing on the experience of leading engineers, researchers, and scholars, the three-volume set contains 29 new chapters that address multimedia and Internet technologies, tomography, radar systems, architecture, standards, and future applications in speech, acoustics, video, radar, and telecommunications. ![]() Encompassing essential background material, technical details, standards, and software, the second edition reflects cutting-edge information on signal processing algorithms and protocols related to speech, audio, multimedia, and video processing technology associated with standards ranging from WiMax to MP3 audio, low-power/high-performance DSPs, color image processing, and chips on video. Now available in a three-volume set, this updated and expanded edition of the bestselling The Digital Signal Processing Handbook continues to provide the engineering community with authoritative coverage of the fundamental and specialized aspects of information-bearing signals in digital form.
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