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.

Description
Features
Data Description
Analysis and Results
Modeling
Conclusion
Contributing

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.

Hashezm/WSPR-Signal-Propagation-Analysis