KRSE1: Spectrum Monitoring

This page is still under development and the experiment described on this page is not yet generally available.

1) Experiment Overview

Experiment Definition: The user logs remotely to open N6820ES Signal Surveyor 4D software and its extensions. The software is integrated with N6841A Sensor Management Tool.

Experiment Scenarios Applicable: S2, S3, S4, S5, and S6. These scenarios correspond to various different user experimentation scenarios in AERPAW.

Experiment Goals: The main goal of this experiment is to develop a basic understanding of the energy detection process, signal external parametric data extraction, and saving process to a permanent SQL database. We will consider monitoring the Wi-Fi 2.4 GHz and 700 MHz-800 MHz bands. Please note that the AERPAW Surveyor 4D setup can monitor the Frequency range from 20 MHz to 6 GHz simultaneously.

Experiment Requirements: It is assumed that the users are familiar with the Surveyor 4D interface. For a brief introduction, users are recommended to check this page.

Setting up the Wi-Fi Spectrum Monitoring: First, please check whether the 'Search On/Off' button is green. If it is red, then click on it to start the sweeping process.

Then click the 'Search type' icon and select the ‘Directed Search’ as the search type and click ok. Note that the ‘Directed search’ can be used to monitor multiple-segmented spectrum bands simultaneously. The software will take some time to restart the frequency sweeping process.

Then click the ‘Search Setup’ Icon to monitor the 700 MHz-800 MHz band. Select Band # 1 and click on the ‘Modify Single Band’ option below.

A Direct Search Band Setup window will appear. Click on the ‘Start Freq’ and ‘Stop Freq’ options successively to set the values 700 MHz and 800 MHz, respectively. There are some other important parameters on the right side of this window, such as the ‘Status’ (should be set to ‘Active’), ‘Antenna’ (should be ‘Antenna 1’), ‘Average Type’ (can be ‘Peak’), ‘Averages’ (can be 16). Overall, the Direct Search Band Setup window should be as depicted below. Note that for Peak averaging, the number of averages specifies how many samples to take while keeping track of the largest signal values. Increasing the number of averages increases the probability of intercepting a transient signal.

Now repeat the procedure starting by clicking the ‘Search Setup’ Icon and selecting Band # 2 for Wi-Fi spectrum monitoring. Set the values of ‘Start Freq’ and ‘Stop Freq’ to be 2.4 GHz and 2.45 GHz, respectively.

Next, select the Detection Setup dialog box is used to set parameters that detect a signal of interest in the measured frequency span. Please note that it is critically important to check the ‘Enable Energy Detection’ option. Detection entries are made in the Energy History display only if this option is checked. Users can choose any Threshold Modes such as ‘Level’, ‘Auto’, ‘Environment’, ‘File’, ‘Segment Average’, and ‘Point Average’. Here, we consider the option ‘Level’ with an approximate value of -90 dBm.

If the bandwidth (BW) of the typical signals of interest is known such as 20 MHz BW for Wi-Fi 2.4 GHz signals, click the ‘Hints ..’ button as marked in red color in the figure above and select the Wi-Fi band. This dialog box is used to define parts of the spectrum where signals have known center frequencies and bandwidths. It is possible to specify the bandwidth and, optionally, frequency values that are recorded in the New Energy Log and the Energy History for energy detected in these bands. Click ‘Modify Single Hint’’ and change the ‘Status’ to ‘Active’. Provide the Start and Stop Frequency as 2.40 GHz and 2.50 GHz, respectively, and set the Bandwidth and Channelize options to 20 MHz and ‘Yes’, respectively as shown below. Click ‘OK’ to continue.

Then select the center tab, titled ‘Energy’, and click ‘Modify Energy History Setup’. Notice the “Active to Past Energy Transition” setup. This facilitates an orderly transition of data to a “Past Energy History” database and ultimately to the SQL database. To see this in action, click the button reading 'After [5 Seconds]' and change it to 'After [1 second]'. Now check the box “Use Past Energy”. Change the Past Energy Removal setting to 2 minutes and click “Apply”.

Then click the ‘View’ menu from the Menu bar at the top and select the ‘Energy History Pane’ which displays as many as eight of the energy features that are tracked for each detected energy. The Status bar below the pane displays the statistics on how much data is stored in the Energy History Database.

Next, click (highlight) the entry in the Energy History Database and select View > Entry (in the Energy History pane). This shows all the signal parametric data Surveyor 4D is collecting and summarizing – as seen below

Post filtering Energy History Data:

The post-filter provides another way to minimize the number of entries in the energy history. Click on the ‘Energy History Filter’ on the tool bar. Check the ‘Enabled’ option for the Energy History Post-Filter Setup as highlighted in the below figure. Select Filter #1 and Click on the ‘Modify Post-Filter’ option. Choose ‘Age Filter’ as the Post-Filter type and consider ‘Removal After [5.00] Seconds of Inactivity’ as shown, and finally, click ‘Apply’. Select Filter #2 and modify it to a Duration filter with the Removal option when the duration exceeds 300 seconds.

From Surveyor 4D Energy History Database to the SQL Database:

On the Toolbar, click the “Exporting” Icon as shown below.

This dialogue box defines the SQL transition from Surveyor 4D. Change the Initial Update to ‘1 seconds’ and the Update Interval settings to ‘1 minute’. Also, change the Database duration to ‘1 day’ and then check the two boxes to ‘Enable Export’ as shown below.

Surveyor 4 provides a built-in SQL Visualizer which can be started after clicking the ‘Visualization Tool…’ option as shown above. A new window will appear where set the appropriate ‘Start’ and ‘Stop’ dates by double-clicking on them as shown in the figure below. Then click the ‘Update Sample >’ button which will create a .bat file required for visualization.

Finally, click ‘Run Batch File’. Several Matlab plots such as amplitude and bandwidth plots, waterfall plots, etc., will appear. These plots will be saved at the location ‘C:\E3238s\SqlVisualizer\my_energies\System Ref [Lat, Long]’, where Lat and Long will be the latitude and longitude of the RF sensor N6841A. The detected energies will be saved in CSV format with the provided start and stop dates at the location ‘C:\E3238s\SqlVisualizer’.

Now, start the PostGreSQL pgAdmin 4 application (START menu >PostgreSQL 10(x86)> pgAdmin 4 ) and connect to the database by right-clicking the entry under Servers, selecting Connect and enter password: !E3238s!

In the dashboard, you can observe the data collection activity. Navigate to the E3238S Tables menu and look for “energies” and the day, and hour of the collection as shown below. Right-click the “energies” table and select “View Data” and “View all rows” to see a table populated with the energy history database with a new entry every second.

This experiment illustrates the capability of Surveyor 4D to detect spectral energy, extract signal parametric data and make database entries based on the results. Further, the data can be exported to a searchable, sortable relational database where additional scripting can be developed to produce customized reports, charts, and graphs. This is of high value to frequency managers, regulators, and any organization attempting to enforce a wireless or spectrum policy.

Below is an example output of the SQL Visualizer Tool provided with Surveyor 4D. It is a 2-hour spectrogram of maximum amplitude detections in our bands of interest.

Users can use MATLAB to visualize the extracted .CSV files from the SQL. We have shown below the max amplitude variations of the bands with respect to time. The sample code is provided in this link .