New version 0.0.2

Improved the SpectrumSharingDApp by exposing the noise floor value instead of the ota flag and a classifier that will label the values on the Dashboard

Renamed DemoGui to Dashboard

VERSION

@@ -1 +1 @@
-0.0.1
+0.0.2

docs/gui_uml.txt

@@ -34,7 +34,7 @@ class IQPlotter {
     + process_iq_data(iq_data)
 }
 
-class DemoGui {
+class Dashboard {
     -- Attributes --
     - BUFFER_SIZE : int
     - iq_size : int
@@ -49,6 +49,4 @@ class DemoGui {
     + stop()
 }
 
-DemoGui --> IQPlotter : "contains"
-DemoGui --> EnergyPlotter : "contains"
 @enduml

examples/spectrum_dapp.py

@@ -23,10 +23,18 @@ def stop_program(time_to_wait, dapp: SpectrumSharingDApp):
 def main(args, time_to_wait: float = 60.0):
     # with open(f"{LOG_DIR}/busy.txt", "w") as f:
     #     f.close()
-
-    dapp = SpectrumSharingDApp(ota=args.ota, save_iqs=args.save_iqs, control=args.control, link=args.link, transport=args.transport,
-                energyGui=args.energy_gui, iqPlotterGui=args.iq_plotter_gui, demoGui=args.demo_gui)
 
+    if args.ota:
+        print(f'Using OTA configuration')
+        noise_floor_threshold = 20 # this really depends on the RF conditions and should be carefully calibrated
+    else: # Colosseum
+        print(f'Using Colosseum configuration')
+        noise_floor_threshold = 53
+
+    print(f'Threshold {noise_floor_threshold}')
+
+    dapp = SpectrumSharingDApp(noise_floor_threshold=noise_floor_threshold, save_iqs=args.save_iqs, control=args.control, link=args.link, transport=args.transport,
+                energyGui=args.energy_gui, iqPlotterGui=args.iq_plotter_gui, dashboard=args.demo_gui)
     dapp.setup_connection()
 
     if args.timed:

src/spectrum/spectrum_dapp.py

@@ -25,7 +25,7 @@ class SpectrumSharingDApp(DApp):
     # Noise floor threshold needs to be calibrated
     # We receive the symbols and average them over some frames, and do thresholding.
 
-    def __init__(self, id: int = 1, ota: bool = False, save_iqs: bool = False, control: bool = False, link: str = 'posix', transport:str = 'uds', **kwargs):
+    def __init__(self, id: int = 1, noise_floor_threshold: int = 53, save_iqs: bool = False, control: bool = False, link: str = 'posix', transport:str = 'uds', **kwargs):
         super().__init__(link=link, transport=transport, **kwargs) 
 
         self.bw = 40.08e6  # Bandwidth in Hz
@@ -35,14 +35,7 @@ def __init__(self, id: int = 1, ota: bool = False, save_iqs: bool = False, contr
         self.prb_thrs = 75 # This avoids blacklisting PRBs where the BWP is scheduled (it’s a workaround bc the UE and gNB would not be able to communicate anymore, a cleaner fix is to move the BWP if needed or things like that)
         self.FFT_SIZE = 1536  
         self.Average_over_frames = 63
-
-        if ota:
-            dapp_logger.info(f'Using OTA configuration')
-            self.Noise_floor_threshold = 20 # this really depends on the RF conditions and should be carefully calibrated
-        else: # Colosseum
-            dapp_logger.info(f'Using Colosseum configuration')
-            self.Noise_floor_threshold = 53
-
+        self.noise_floor_threshold = noise_floor_threshold
         self.save_iqs = save_iqs
         self.e3_interface.add_callback(self.get_iqs_from_ran)
         if self.save_iqs:
@@ -54,7 +47,7 @@ def __init__(self, id: int = 1, ota: bool = False, save_iqs: bool = False, contr
 
         self.energyGui = kwargs.get('energyGui', False)
         self.iqPlotterGui = kwargs.get('iqPlotterGui', False)
-        self.demoGui = kwargs.get('demoGui', False)
+        self.dashboard = kwargs.get('dashboard', False)
 
         self.control_count = 1
         self.abs_iq_av = np.zeros(self.FFT_SIZE)
@@ -70,11 +63,12 @@ def __init__(self, id: int = 1, ota: bool = False, save_iqs: bool = False, contr
             iq_size = self.FFT_SIZE * 2 # double the size of ofdm_symbol_size since real and imaginary parts are interleaved
             self.iqPlotter = IQPlotter(buffer_size=500, iq_size=iq_size, bw=self.bw, center_freq=self.center_freq)    
 
-        if self.demoGui:
-            from visualization.dashboard import DemoGui
+        if self.dashboard:
+            from visualization.dashboard import Dashboard
             self.demo_queue = multiprocessing.Queue()
             iq_size = self.FFT_SIZE * 2 # double the size of ofdm_symbol_size since real and imaginary parts are interleaved
-            self.demo = DemoGui(buffer_size=100, iq_size=iq_size) 
+            classifier = kwargs.get('classifier', None)
+            self.demo = Dashboard(buffer_size=100, iq_size=iq_size, classifier=classifier) 
 
     def get_iqs_from_ran(self, data):
         dapp_logger.debug(f'Triggered callback')
@@ -93,7 +87,7 @@ def get_iqs_from_ran(self, data):
         if self.iqPlotterGui:
             self.iq_queue.put(iq_arr)
 
-        if self.demoGui:
+        if self.dashboard:
             self.demo_queue.put(("iq_data", iq_arr))
 
         if self.control:
@@ -106,9 +100,6 @@ def get_iqs_from_ran(self, data):
             self.control_count += 1
             dapp_logger.debug(f"Control count is: {self.control_count}")
 
-            if self.iqPlotterGui:
-                self.iq_queue.put(iq_arr)
-
             if self.control_count == self.Average_over_frames:
                 abs_iq_av_db =  20 * np.log10(1 + (self.abs_iq_av/(self.Average_over_frames)))
                 abs_iq_av_db_offset_correct = np.append(abs_iq_av_db[self.First_carrier_offset:self.FFT_SIZE],abs_iq_av_db[0:self.First_carrier_offset])
@@ -119,10 +110,9 @@ def get_iqs_from_ran(self, data):
                 # last_5_max_abs_iq_av_db_offset_correct = np.sort(abs_iq_av_db_offset_correct)[-20:]
                 # dapp_logger.info(f'Last 20 max values (abs_iq_av_db_offset_correct): {last_5_max_abs_iq_av_db_offset_correct}')
 
-
                 # PRB blocking based on the noise floor threshold
                 f_ind = np.arange(self.FFT_SIZE)
-                blklist_sub_carrier = f_ind[abs_iq_av_db_offset_correct > self.Noise_floor_threshold]
+                blklist_sub_carrier = f_ind[abs_iq_av_db_offset_correct > self.noise_floor_threshold]
                 np.sort(blklist_sub_carrier)
                 dapp_logger.info(f'blklist_sub_carrier: {blklist_sub_carrier}')
                 prb_blk_list = np.unique((np.floor(blklist_sub_carrier/self.Num_car_prb))).astype(np.uint16)
@@ -143,7 +133,7 @@ def get_iqs_from_ran(self, data):
                 if self.energyGui:
                     self.sig_queue.put(abs_iq_av_db)
 
-                if self.demoGui:
+                if self.dashboard:
                     self.demo_queue.put(("prb_list", prb_blk_list))
 
                 # Reset the variables
@@ -154,16 +144,18 @@ def _control_loop(self):
         if self.energyGui:
             abs_iq_av_db = self.sig_queue.get()
             self.energyPlotter.process_iq_data(abs_iq_av_db)
+
         if self.iqPlotterGui:
             iq_data = self.iq_queue.get()
             self.iqPlotter.process_iq_data(iq_data)
-        if self.demoGui:
+
+        if self.dashboard:
             message = self.demo_queue.get()
             self.demo.process_iq_data(message)
 
     def _stop(self):        
         if self.save_iqs:
             self.iq_save_file.close()
 
-        if self.demoGui:
+        if self.dashboard:
             self.demo.stop()

src/visualization/dashboard.py

@@ -13,8 +13,8 @@
                     exit(-1)
 import numpy as np
 
-class DemoGui:
-    def __init__(self, buffer_size=100, iq_size=1536, bw=46.08e6, center_freq=3.6192e9):
+class Dashboard:
+    def __init__(self, buffer_size=100, iq_size=1536, bw=46.08e6, center_freq=3.6192e9, classifier=None):
         # TODO double check killing conditions
         # Flask app setup
         self.app = Flask(__name__)
@@ -30,6 +30,8 @@ def __init__(self, buffer_size=100, iq_size=1536, bw=46.08e6, center_freq=3.6192
         # Route setup
         self.app.add_url_rule("/", view_func=self.index)
 
+        self.classifier = classifier if classifier is not None else None
+
         # SocketIO event handlers
         self.socketio.on_event("connect", self.handle_initial_connection)
         self._initialize_plot()
@@ -63,6 +65,7 @@ def handle_initial_connection(self):
         data_buffer = {
             "magnitude": np.zeros((self.BUFFER_SIZE, iqs_to_show)).tolist(),
             "num_prbs": num_prbs,
+            "predicted_label": self.classifier is not None
         }
         self.socketio.emit("initialize_plot", data_buffer)
 
@@ -88,6 +91,11 @@ def process_iq_data(self, message):
             if self.sampling_counter >= self.sampling_threshold:
                 magnitude_dB = self._process_iq_data(iq_data)[::-1].tolist() # visualization processing is delegated to client
                 self.socketio.emit("update_plot", {"magnitude": magnitude_dB})
+
+                if self.classifier:
+                    label = self.classifier.predict(iq_data)
+                    self.socketio.emit("update_plot", {"predicted_label": label})
+
                 self.sampling_counter = 0
 
         elif plot == "prb_list":
@@ -101,4 +109,4 @@ def stop(self):
             self.run_thread.join()
 
 if __name__ == "__main__":
-    server_app = DemoGui()
+    server_app = Dashboard()

src/visualization/templates/index.html

@@ -11,6 +11,7 @@
 <body>
     <div id="prbs"></div>
     <div id="magnitude"></div>
+    <div id="predicted_label"></div>
 
     <script>
         var waterfall = [{
@@ -22,7 +23,7 @@
         var waterfallLayout = {
             title: "Magnitude of the Sensed Spectrum",
             xaxis: {
-                title: "OFDM Symbols"
+                title: "Subcarriers of the OFDM Symbol"
             },
             yaxis: {
                 title: "Time samples",
@@ -152,8 +153,18 @@
             }
         };
 
+        var classifierLayout = {
+            title: {
+                text: "Predicted Label: N/A",
+                font: { size: 24 }
+            },
+            xaxis: { visible: false },
+            yaxis: { visible: false }
+        };      
+
         var waterfall_buffer;
         var prbs_buffer;
+        var predictedLabel;
 
         var socket = io.connect('http://' + document.domain + ':' + location.port);
         socket.on('initialize_plot', function (data) {
@@ -163,6 +174,9 @@
             prbs[0].z = prbs_buffer;
             Plotly.newPlot('magnitude', waterfall, waterfallLayout);
             Plotly.newPlot('prbs', prbs, prbsLayout);
+            if(data.predicted_label){
+                Plotly.newPlot("predicted_label", [], classifierLayout);
+            }
         });
 
         socket.on('update_plot', function (data) {
@@ -184,6 +198,11 @@
                 prbs_buffer[0].fill(0);
                 data.prb_list.forEach(index => prbs_buffer[0][index] = 1);
                 Plotly.restyle('prbs', { z: [prbs_buffer] });
+            } else if ('predicted_label' in data) {
+                console.log("Predicted label is: ", data.predicted_label);
+                Plotly.relayout('predicted_label', {
+                    'title.text': `Predicted Label: ${data.predicted_label}`
+                });
             } else {
                 console.error('Error unknown values in data ', data);
             }