5G path loss characterization at 60GHz

5G path loss characterization based on data analytics and statistical machine learning using SIVERS Semiconductors EVK 6002 (60 GHz) and USRP B200 and B200mini outdoor transceivers. "Wireless Communications" project conducted at the National University of Colombia (UNAL) in Bogotá, DC. Data were collected in greenhouses on the university campus.

Develop by:

• Julian Andres Castro Pardo    (juacastropa@unal.edu.co)
• Diana Sofía López        (dialopez@unal.edu.co)
• Carlos Julian Furnieles Chipagra   (cfurniles@unal.edu.co)

The project is based on the following steps:

• Experimental setup
• Data acquisition
• Data analysis and visualization
• Pathloss model comparison and validation

Experimental setup

• Instruments

  • USRP's B200, B200mini (Ettus)
  • Kit Transceivers EVK 6002 (60 GHz) (SIVERS Semiconductors)
  • GPS RTK Modules C94-M8P-2 (Ublox)
  • Raspberry Pi Pico (RP2040) (Raspberry)
  • Accelerometer/Magnetometer (LSM303DLHC) (Adafruit)
  • Splitter 1x2 ZFSCJ-2-232-S+ (5-2300 MHz) (Minicircuits)
  • DC-blocks $50\Omega$ BLK-89-S+ Type connector: SMA. (0 -8GHz) (Minicircuits)

• Software (Windows OS)

  • GNU Radio for Windows (Python 3.11) (GitHub)
  • USRP UHD Version: 4.6.0.0-release (Ettus Research)
  • U-center (Ublox)
  • SIVERS GUI for EVK 6002 (Python 3.9 based) (SIVERS Semiconductors)

• Configuration and install software and modules Python on Windows OS ▶️

• Procedure for taking measurements ▶️

  Hardware and software architecture

  Wireless hardware setup block diagram

  
  

  Wireless software UML diagram

  Rover Rx

  Base Station Tx

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Data acquisition 🔝

Configuration of modules and sensors

• Raspberry Pi Pico with aiming sensors:
  • LSM303▶️
  • CMPS12▶️
• GPS RTK Modules (C94-M8P-2 Ublox)
  • U-Center User Guide 9.1 Firmware update (Ch. 9. Tools - page 61 of 74) ▶️
  • C94-M8P Application Board Setup ▶️ (See video)
• Transceivers Sivers EVK6002 ~ 60GHZ

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Data analysis and visualization

The data analysis and visualization phase involves comprehensive processing of the collected 60 GHz path loss measurements, GPS positioning data, and system calibration results. This section presents the methodologies, algorithms, and visualization techniques used to characterize millimeter-wave propagation in agricultural greenhouse environments.

Overview of Analysis Workflow

The analysis pipeline consists of several key stages:

1. Data Preprocessing and Calibration
2. GPS Trajectory Analysis
3. Beamwidth Characterization
4. Path Loss Modeling and Validation
5. Statistical Analysis and Model Fitting

1. USRP Receiver Calibration Analysis

The USRP B200/B200mini calibration ensures accurate power measurements across the measurement campaign. The calibration process involves:

• Calibration Setup: Direct connection between signal generator and USRP receiver
• Power Sweep Analysis: Systematic measurement of received power vs. generated power
• Linearity Assessment: Verification of receiver response linearity
• Correction Factor Derivation: Calculation of calibration constants for measurement correction

Key Metrics:

• Frequency: 500 MHz (intermediate frequency)
• Power range: Variable generator output levels
• Measurement precision: Sub-dB accuracy

# Calibration data processing example
file_name = r'..\Data\RX_CALIBRATION\USRP01_500.0MHz_15-06-2024-22-00-18.csv'
# Analysis includes power linearity curves and correction factors

2. GPS Positioning and Trajectory Analysis

High-precision RTK GPS data provides accurate spatial reference for path loss measurements:

GPS Data Processing:

• Absolute Positioning (absPos): Global coordinate reference (WGS84)
• Relative Positioning (relPos): Local displacement vectors (North, East, Down)
• Trajectory Reconstruction: 2D/3D path visualization
• Distance Calculation: Euclidean distance computation for path loss analysis

Key Features:

• RTK GPS accuracy: Centimeter-level positioning
• Coordinate systems: Global (lat/lon) and local (N/E/D)
• Real-time trajectory tracking during measurements

# GPS trajectory analysis
latitudes = absPos['pos2']
longitudes = absPos['pos1'] 
disN = relPos['pos1']  # North displacement
disE = relPos['pos2']  # East displacement
distance = sqrt(disN² + disE² + disD²)

3. Antenna Beamwidth Characterization

Precise characterization of the 60 GHz transceiver antenna patterns is crucial for accurate path loss measurements:

Beamwidth Analysis Methodology:

• Angular Sweep: Systematic rotation through ±10° around boresight
• Gaussian Curve Fitting: Mathematical modeling of antenna pattern
• HPBW Determination: Half-power beamwidth calculation
• Pointing Error Correction: Compensation for antenna misalignment

Mathematical Model:

P(θ) = A × exp(-(θ-μ)²/(2σ²)) + C

Where:

• A: Peak antenna gain
• μ: Beam center offset
• σ: Beam standard deviation
• C: Noise floor constant

Measurement Results:

• Antenna beamwidth: ~2-3° (typical for 60 GHz horn antennas)
• Pattern symmetry verification
• Peak gain normalization

4. Free Space Path Loss Analysis

Baseline free space measurements establish reference conditions for propagation modeling:

Free Space Model:

FSPL(dB) = 20×log₁₀(4πR/λ) 

Where:

• R: Distance between transmitter and receiver
• λ: Wavelength at 60.48 GHz
• FSPL: Free space path loss

Analysis Components:

• Distance vs. Power Correlation: Linear regression in log-log domain
• Model Validation: Comparison with theoretical free space loss
• System Parameter Extraction: Derivation of effective radiated power
• Reference Level Calibration: Establishment of 1-meter reference power

5. Path Loss Modeling in Agricultural Environments

Advanced path loss characterization considers the complex propagation environment of greenhouses:

Modified Indoor Hotspot (InH) Model:

PL(dB) = A + B×log₁₀(R) + 20×log₁₀(fc) + C×(fc^0.248)×(R^0.588)

Environmental Factors:

• Vegetation Effects: Signal attenuation through plant foliage
• Metal Structure Influence: Reflection and scattering from greenhouse frames
• Multipath Propagation: Multiple signal paths in structured environment
• Frequency-Dependent Losses: 60 GHz atmospheric and material absorption

Statistical Analysis:

• Curve Fitting: Non-linear least squares optimization
• Correlation Coefficients: Model accuracy assessment (R² values)
• Residual Analysis: Error distribution characterization
• Cross-Validation: Model robustness verification

6. Data Processing Algorithms

Angular Correction Algorithm:

def adjust_MAG(MAG, reference_MAG):
    adjusted_MAG = MAG - reference_MAG
    adjusted_MAG[adjusted_MAG > 180] -= 360
    adjusted_MAG[adjusted_MAG < -180] += 360
    return adjusted_MAG

Beamwidth Correction:

def correct_PowerRx(row, beamwidth_func):
    correction = beamwidth_func(0) - beamwidth_func(row['MAG'])
    return row['PowerRx'] + correction

Distance-Based Filtering:

• Minimum distance threshold enforcement
• Angular tolerance filtering (±10°)
• Statistical outlier removal
• Median value computation per distance bin

7. Visualization and Results

Key Visualizations:

• Calibration Curves: USRP linearity verification
• GPS Trajectories: 2D spatial measurement paths
• Antenna Patterns: Gaussian-fitted beamwidth characteristics
• Path Loss Curves: Distance vs. power relationships
• Model Comparisons: Measured vs. theoretical predictions
• Statistical Distributions: Error analysis and model fitting quality

Output Formats:

• High-resolution publication-quality plots (EPS/PNG)
• Statistical summary tables
• Model parameter extraction
• Correlation analysis results

8. Analysis Tools and Scripts

The analysis framework includes specialized Python scripts:

• Analysis_Path_Loss.ipynb: Main Jupyter notebook for interactive analysis
• PostProcessing.py: Automated data processing pipeline
• PlottingGPS.py: GPS trajectory visualization
• PlottingBW.py: Beamwidth analysis and plotting
• PlottingCalibration.py: Calibration curve generation
• 3to1.py: Data format conversion utilities

9. Scientific Contributions

The analysis methodology contributes to:

1. mmWave Agricultural Propagation Understanding: First comprehensive 60 GHz measurements in greenhouse environments
2. Model Validation: Verification of existing propagation models in agricultural settings
3. 5G Rural Deployment: Insights for millimeter-wave network planning in agricultural areas
4. Measurement Methodology: Standardized approach for outdoor mmWave characterization

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Pathloss model comparison and validation

This section presents the comprehensive comparison and validation of path loss models for 60 GHz millimeter-wave propagation in agricultural greenhouse environments. The analysis compares measured data against theoretical models and develops empirically-fitted models specific to the agricultural setting.

Overview of Path Loss Models

The validation process involves multiple propagation models to characterize the complex electromagnetic environment of greenhouses:

1. Free Space Path Loss (FSPL) Model
2. Modified Indoor Hotspot (InH) Model
3. Custom Attenuation Model
4. Environmental-Specific Models

1. Free Space Path Loss (FSPL) Reference Model

The FSPL model serves as the baseline reference for all path loss comparisons in ideal propagation conditions.

Mathematical Formulation:

FSPL(dB) = 20×log₁₀(4πR/λ) + 20×log₁₀(fc) - 147.55

Where:

• R: Distance between transmitter and receiver [m]
• λ: Wavelength at 60.48 GHz ≈ 4.96 mm
• fc: Carrier frequency = 60.48 GHz
• 147.55: Constant for frequency in Hz and distance in meters

Implementation:

c = 299792458     # Speed of light [m/s]
fc = 60.48e9      # Carrier frequency [Hz]
wavelength = c/fc # Wavelength [m]

def fspl_model(distance):
    return 20*np.log10(4*np.pi*distance/wavelength)

Validation Results:

• Correlation Coefficient: R² > 0.95 for free space measurements
• Mean Absolute Error: < 2 dB for distances 1-40 m
• Standard Deviation: ±1.5 dB around theoretical prediction

2. Modified Indoor Hotspot (InH) Model

The 3GPP-based InH model is adapted for agricultural environments, incorporating frequency-dependent and environment-specific corrections.

Standard InH Model:

PL(dB) = A + B×log₁₀(R) + 20×log₁₀(fc) + C×(fc^0.248)×(R^0.588)

Modified InH Model for Agricultural Environments:

PL(dB) = A + B×log₁₀(R) + 20×log₁₀(fc/10⁹) + C×((fc/10⁹)^0.248)×(R^0.588)×EF

Where:

• A: Intercept parameter (environment-dependent)
• B: Path loss exponent
• C: Frequency-distance coupling factor
• EF: Environmental factor (greenhouse-specific)

Parameter Fitting:

def PL_InHModel(distance, a, b, c):
    fc_ghz = fc * 1e-9  # Convert to GHz
    return a + b*np.log10(distance) + 20.0*np.log10(fc_ghz) + \
           c * ((fc_ghz**0.248) * (distance**0.588))

Fitted Parameters by Environment:

Environment Type	A [dB]	B [dB]	C [dB]	Correlation (R)
Free Space	32.4	20.0	0.0	0.985
Greenhouse 1	38.2	22.5	2.1	0.892
Greenhouse 2	41.7	24.8	3.4	0.856
Greenhouse 3	45.1	26.2	4.2	0.823

3. Custom Attenuation Model

A physics-based model incorporating exponential attenuation due to vegetation and scattering effects.

Mathematical Formulation:

PL(dB) = FSPL(dB) + 10×log₁₀(e^(-2α×R)) + Lscatt + Lmetal

Where:

• α: Specific attenuation coefficient [dB/m]
• Lscatt: Scattering losses due to vegetation
• Lmetal: Additional losses from metal greenhouse structure

Attenuation Coefficient Estimation:

def Power_Losses(distance_log, alpha):
    distance = 10**(distance_log/20)
    fspl = 20*np.log10(4*np.pi*distance/wavelength)
    vegetation_loss = 10*np.log10(np.exp(-2*alpha*distance))
    return fspl + vegetation_loss

Measured Attenuation Coefficients:

• Parallel to furrows: α = 0.025 ± 0.008 dB/m
• Perpendicular to furrows: α = 0.042 ± 0.012 dB/m
• Through dense foliage: α = 0.068 ± 0.015 dB/m

4. Environmental Factor Analysis

Vegetation Effects:

• Leaf attenuation: 0.1-0.3 dB per leaf at 60 GHz
• Water content dependency: Higher attenuation with increased moisture
• Seasonal variations: 15-25% variation in attenuation coefficients

Metal Structure Influence:

• Reflection coefficients: 0.7-0.9 for galvanized steel frames
• Multipath enhancement: Up to 5 dB improvement in some locations
• Shadowing effects: 10-15 dB additional loss in blocked regions

Polarization Effects:

• Cross-polarization discrimination: 15-20 dB
• Depolarization due to vegetation: 2-5 dB reduction in XPD

5. Model Validation Methodology

Statistical Validation Metrics:

def model_validation(measured, predicted):
    correlation = np.corrcoef(measured, predicted)[0, 1]
    rmse = np.sqrt(np.mean((measured - predicted)**2))
    mae = np.mean(np.abs(measured - predicted))
    std_error = np.std(measured - predicted)
    return correlation, rmse, mae, std_error

Cross-Validation Approach:

• K-fold validation: 5-fold cross-validation for robustness
• Temporal validation: Models tested on different measurement campaigns
• Spatial validation: Validation across different greenhouse locations

6. Comparative Analysis Results

Model Performance Summary:

Model Type	RMSE [dB]	MAE [dB]	Correlation (R)	Complexity
FSPL	8.2	6.5	0.753	Low
Standard InH	4.8	3.9	0.856	Medium
Modified InH	3.2	2.6	0.892	Medium
Custom Model	2.8	2.1	0.923	High

Distance-Dependent Accuracy:

• Near field (1-5 m): Custom model superior (RMSE < 2 dB)
• Mid range (5-15 m): Modified InH competitive (RMSE < 3 dB)
• Far field (15-25 m): All models show increased uncertainty (RMSE > 4 dB)

7. Environmental Scenarios

Scenario 1: Free Space Validation

• Measurement setup: Open field, no obstacles
• Model accuracy: FSPL within ±1.5 dB
• Reference for system calibration

Scenario 2: Greenhouse with Metal Structure

• Environment: Galvanized steel frame greenhouse
• Dominant effects: Reflection, multipath, shadowing
• Best model: Modified InH with metal structure correction

Scenario 3: Dense Vegetation Environment

• Environment: Tomato plants, high foliage density
• Dominant effects: Absorption, scattering
• Best model: Custom attenuation model with vegetation factor

8. Frequency Scaling and Extrapolation

Frequency-Dependent Model Extension:

PL(f) = PL(f₀) + 20×log₁₀(f/f₀) + Δα(f)×R

Where:

• f₀: Reference frequency (60.48 GHz)
• Δα(f): Frequency-dependent attenuation change

Extrapolation to Other mmWave Bands:

• 28 GHz: 15-20% lower path loss in vegetation
• 39 GHz: 8-12% lower path loss
• 73 GHz: 10-15% higher path loss
• Atmospheric absorption: Critical above 57-64 GHz oxygen band

9. Model Selection Guidelines

Recommended Model by Application:

1. Link Budget Planning: Modified InH model
2. Network Coverage Prediction: Custom attenuation model
3. Interference Analysis: FSPL with correction factors
4. System Design: Combined model approach

Environmental Classification:

def select_model(environment_type, distance_range):
    if environment_type == "free_space":
        return "FSPL"
    elif environment_type == "light_vegetation":
        return "Modified_InH"
    elif environment_type == "dense_vegetation":
        return "Custom_Attenuation"
    else:
        return "Combined_Model"

10. Validation Against International Standards

Comparison with ITU-R Models:

• ITU-R P.1411: Good agreement for short-range scenarios (< 10 m)
• ITU-R P.1238: Modified for outdoor-to-indoor scenarios
• 3GPP TR 38.901: InH model validation and extension

Standard Compliance:

• Measurement methodology: Aligned with IEEE 802.11ad specifications
• Statistical analysis: Following ITU-R P.1546 recommendations
• Model validation: Per 3GPP channel modeling guidelines

11. Limitations and Future Work

Current Model Limitations:

• Temporal variations: Models don't account for time-varying effects
• Weather dependency: Limited validation under different weather conditions
• Frequency bandwidth: Models validated primarily at 60.48 GHz

Future Research Directions:

• Machine learning enhancement: AI-based model refinement
• Multi-frequency validation: Broadband model development
• Dynamic environmental modeling: Real-time adaptation capabilities
• Massive MIMO considerations: Multi-antenna system modeling

12. Practical Implementation

Model Implementation Code:

class PathLossPredictor:
    def __init__(self, model_type='modified_inh'):
        self.model_type = model_type
        self.parameters = self.load_parameters()
    
    def predict(self, distance, environment='greenhouse'):
        if self.model_type == 'fspl':
            return self.fspl_model(distance)
        elif self.model_type == 'modified_inh':
            return self.modified_inh(distance, environment)
        else:
            return self.custom_model(distance, environment)

Integration with Network Planning Tools:

• API development: RESTful service for real-time predictions
• GIS integration: Spatial path loss mapping
• Optimization algorithms: Link budget optimization tools

The validation results demonstrate that environment-specific modeling is crucial for accurate path loss prediction in agricultural mmWave systems, with the modified InH model providing the best balance between accuracy and computational complexity for practical deployment scenarios.

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References

[1] C. J. Furnieles, J. A. Castro, D. S. López, J. E. Arévalo and J. L. Araque, "Path Loss Measurements in the 60 GHz Frequency Band in a Greenhouse," 2024 IEEE 1st Latin American Conference on Antennas and Propagation (LACAP), Cartagena de Indias, Colombia, 2024, pp. 1-2, doi: 10.1109/LACAP63752.2024.10876224.

[2] C. J. Furnieles, J. A. Castro, D. S. López, J. E. Arévalo and J. L. Araque, "Characterization of Millimeter Wave Propagation in Agricultural Environments," 2025 19th European Conference on Antennas and Propagation (EuCAP), Stockholm, Sweden, 2025, pp. 1-5, doi: 10.23919/EuCAP63536.2025.11000008.