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WSPR-Signal-Propagation-Analysis

A comprehensive analysis of weak signal propagation within the WSPR network, focusing on the impact of distance, frequency, and power on signal-to-noise ratios. The project includes data cleaning, statistical analysis, and linear regression modeling to predict signal reception quality and understand the factors influencing signal propagation.

WSPR Signal Propagation Analysis

Description

A comprehensive analysis of weak signal propagation within the WSPR network, focusing on the impact of distance, frequency, and power on signal-to-noise ratios. The project includes data cleaning, statistical analysis, and machine learning modeling to predict signal reception quality and understand the factors influencing signal propagation.

Table of Contents

Features

  • Data Cleaning: Handling missing values and imputing numerical data.
  • Statistical Summary: Summary statistics and outlier detection.
  • Unique Values Analysis: Counting unique values in specific columns.
  • Distance Analysis: Calculating average distance for signals with specified power.
  • Frequency Analysis: Identifying the receiving station with the most transmissions in a specified band.
  • Data Partitioning: Splitting the data into training and testing sets.
  • Linear Regression Modeling: Predicting signal-to-noise ratio based on distance, frequency, and power.
  • Model Evaluation: Assessing model accuracy and making predictions.

Data Description

The data set consists of the following columns:

  • id: Unique identifier for each reception report
  • time: Date and time of signal received (YYYY-MM-DD HH:MM:SS format)
  • band: Fixed designator for frequency band (values: -1, 0, 1, 3, …, 1296)
  • rx_sign: Call sign of station receiving signal
  • rx_lat: Latitude of receiving station
  • rx_lon: Longitude of receiving station
  • rx_loc: Grid square of receiving station
  • tx_sign: Call sign of transmitting station
  • tx_lat: Latitude of transmitting station
  • tx_lon: Longitude of transmitting station
  • tx_loc: Grid square of transmitting station
  • distance: Distance between receiving and transmitting stations (km)
  • azimuth: Compass direction of signal received from transmitting station
  • rx_azimuth: Compass direction of signal transmitted to receiving station
  • frequency: Receive frequency (Hz)
  • power: Transmission power (dBm)
  • snr: Signal-to-noise ratio of received signal (dB) in 2.5 kHz bandwidth
  • drift: Reported frequency drift
  • version: Receiver software version
  • code: Encoding type of signal

Analysis and Results

Data Cleaning

Performed data imputation on numerical columns to handle missing values.

Summary Statistics

Generated summary statistics for all numerical columns to understand data distribution and detect outliers.

Unique Values Analysis

Counted unique values in band, rx_sign, and tx_sign columns.

Distance Analysis

Calculated average distance between transmitting and receiving stations for signals with power less than 30 dBm.

Frequency Analysis

Identified the receiving station with the most transmissions on the 14 MHz band.

Modeling

Data Partitioning

Randomly partitioned the data into training (80%) and testing (20%) sets using the createDataPartition() function from the caret package.

Linear Regression Model

Generated a linear regression model to predict the signal-to-noise ratio based on distance, frequency, and power.

Model Evaluation

Evaluated the accuracy of the model using the testing data set and appropriate accuracy metrics. Discussed the performance and potential improvements.

Conclusion

Summarized the findings from the analysis and modeling. Provided insights into the factors affecting WSPR signal propagation and the effectiveness of the predictive model.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss your ideas.