BQB RF-PHY Test Case Optimization: Automating TRM/TRC/CA Measurements with Python and Spectrum Analyzer SCPI Commands

BQB RF-PHY Test Case Optimization: Automating TRM/TRC/CA Measurements with Python and Spectrum Analyzer SCPI Commands

In the realm of Bluetooth Qualification Body (BQB) certification, RF-PHY (Radio Frequency Physical Layer) testing is a critical gateway for ensuring that Bluetooth devices meet the stringent requirements of the Bluetooth Core Specification. Among the myriad of test cases, Transmitter RF Characteristics (TRM), Receiver RF Characteristics (TRC), and Carrier Frequency Offset and Drift (CA) measurements are fundamental. Traditionally, these tests are executed using dedicated Bluetooth test equipment, often with manual intervention, which can be time-consuming and error-prone. This article delves into a sophisticated optimization strategy: automating TRM/TRC/CA measurements using Python scripts that interface with spectrum analyzers via Standard Commands for Programmable Instruments (SCPI). By leveraging the Implementation eXtra Information for Test (IXIT) values from reference documents like the TCRL_IXIT_LN_v2.xlsx, BSS.IXIT_.1.0.0.xlsx, and Core.IXIT.p21.xlsx, we can create a robust, repeatable, and efficient test automation framework.

Understanding the Test Landscape: TRM, TRC, and CA

Before diving into automation, it is essential to grasp the technical nuances of these test categories. TRM tests evaluate the transmitter's performance, including output power, power density, in-band emissions, and modulation characteristics. TRC tests assess the receiver's sensitivity, interference performance, and maximum input level. CA tests specifically measure the carrier frequency offset and drift, which are critical for maintaining link stability in frequency-hopping systems like Bluetooth.

The IXIT documents provide critical parameters that configure the test environment. For instance, from the Core.IXIT.p21.xlsx, the RFPHY section lists parameters such as TSPX_iut_supported_dis_characteristics_uuid_list for Device Information Service (DIS), which may influence how the test equipment interacts with the IUT (Implementation Under Test). Similarly, the BSS.IXIT_.1.0.0.xlsx includes TSPX_iut_list_of_supported_sensor_types for Binary Sensor Service, though its direct relevance to RF-PHY is limited. However, the TCRL_IXIT_LN_v2.xlsx for Location and Navigation Profile (LNP) includes PIXIT values that may set frequency bands or modulation types. In our automation, we treat these IXIT values as configuration inputs that define the test parameters.

Why Automate with Python and SCPI?

Manual testing of TRM/TRC/CA is labor-intensive. A typical BQB test house might spend hours per device, adjusting spectrum analyzer settings, capturing traces, and calculating results. Python, with its rich ecosystem of libraries (e.g., pyvisa for instrument control, numpy for numerical analysis, and matplotlib for visualization), offers a powerful solution. SCPI commands provide a standardized way to communicate with spectrum analyzers, allowing us to set frequency spans, resolution bandwidths, and trigger conditions programmatically.

Key advantages include:

• Repeatability: Automated scripts eliminate human variability, ensuring consistent measurement conditions across multiple IUTs.
• Speed: Python can execute a sequence of SCPI commands in milliseconds, significantly reducing test time.
• Data Integrity: Results are logged directly to structured files (e.g., CSV, JSON) for traceability and reporting.
• Integration with IXIT: Scripts can read IXIT values from Excel files (using openpyxl) to dynamically adjust test parameters.

Architecture of the Automation Framework

The proposed framework consists of three layers:

• Layer 1: Configuration Management – Parses IXIT Excel files to extract test parameters (e.g., frequency bands, power levels, modulation schemes).
• Layer 2: Instrument Control – Uses PyVISA to send SCPI commands to the spectrum analyzer (e.g., Keysight N9020A or Rohde & Schwarz FSW).
• Layer 3: Measurement Logic – Implements TRM/TRC/CA algorithms, including data acquisition, post-processing, and pass/fail criteria.

SCPI Command Essentials for RF Measurements

To automate measurements, one must master key SCPI commands. For a typical spectrum analyzer, the following commands are commonly used:

# Reset instrument to known state
*RST

# Set center frequency (e.g., 2.402 GHz for Bluetooth channel 0)
FREQ:CENT 2.402 GHz

# Set frequency span (e.g., 10 MHz for carrier frequency offset)
FREQ:SPAN 10 MHz

# Set resolution bandwidth (e.g., 100 kHz)
BAND:RES 100 kHz

# Set video bandwidth (e.g., 100 kHz)
BAND:VID 100 kHz

# Set reference level (e.g., -10 dBm)
DISP:WIND:TRAC:Y:SCAL:RLEV -10 dBm

# Set sweep time to auto
SWE:TIME:AUTO ON

# Initiate a sweep
INIT:CONT OFF; *WAI

# Read the trace data
TRAC:DATA? TRACE1

For TRM tests like output power, we might use a channel power measurement:

# Configure channel power measurement
CALC:MARK:FUNC:POW:SEL CHP
CALC:MARK:FUNC:POW:PRES

# Initiate measurement
INIT; *WAI

# Query channel power
CALC:MARK:FUNC:POW:RES? CHP

For CA tests, the carrier frequency offset can be computed by measuring the frequency deviation of a modulated signal. A common approach is to use the "frequency counter" feature:

# Set to zero span mode
FREQ:SPAN 0 Hz

# Configure sweep time to capture a packet
SWE:TIME 1 ms

# Enable frequency counter
CALC:MARK:FUNC:COUN ON

# Initiate and read frequency
INIT; *WAI
CALC:MARK:FUNC:COUN:RES?

Python Implementation: A Code Example

Below is a simplified Python script that automates the TRM output power measurement for a Bluetooth Low Energy (BLE) device at channel 19 (center frequency 2.440 GHz). It reads IXIT values from an Excel file and logs results.

import pyvisa
import openpyxl
import numpy as np
import time

def load_ixit_config(filepath, sheet_name="RFPHY"):
    """
    Load IXIT parameters from Excel file. For example, read TSPX_frequency_band.
    """
    wb = openpyxl.load_workbook(filepath, data_only=True)
    sheet = wb[sheet_name]
    config = {}
    for row in sheet.iter_rows(min_row=2, values_only=True):
        if row[0] and row[1]:
            config[row[0].strip()] = row[1]
    return config

def measure_trm_output_power(instrument, center_freq, power_limit_dbm):
    """
    Measure output power at a given center frequency using channel power.
    """
    instrument.write(f"FREQ:CENT {center_freq} GHz")
    instrument.write("CALC:MARK:FUNC:POW:SEL CHP")
    instrument.write("CALC:MARK:FUNC:POW:PRES")
    instrument.write("INIT; *WAI")
    power_str = instrument.query("CALC:MARK:FUNC:POW:RES? CHP")
    power_dbm = float(power_str.split(',')[0])  # First value is channel power
    passed = power_dbm >= power_limit_dbm  # Example: pass if above limit
    return power_dbm, passed

def main():
    # Initialize instrument connection
    rm = pyvisa.ResourceManager()
    inst = rm.open_resource('TCPIP0::192.168.1.100::inst0::INSTR')
    inst.timeout = 10000  # 10 seconds
    
    # Load IXIT config (example from Core.IXIT.p21.xlsx)
    config = load_ixit_config("Core.IXIT.p21.xlsx")
    # Assume config contains 'TSPX_frequency_band' = '2.4 GHz'
    
    # Define test frequencies for BLE (channels 0, 19, 39)
    test_freqs = [2.402, 2.440, 2.480]  # GHz
    power_limit = -20  # dBm, example limit
    
    results = []
    for freq in test_freqs:
        power, passed = measure_trm_output_power(inst, freq, power_limit)
        results.append((freq, power, passed))
        print(f"Frequency {freq} GHz: Power = {power:.2f} dBm, Pass = {passed}")
        time.sleep(0.5)
    
    # Log results to CSV
    with open("TRM_results.csv", "w") as f:
        f.write("Frequency (GHz),Power (dBm),Pass\n")
        for freq, power, passed in results:
            f.write(f"{freq},{power:.2f},{passed}\n")
    
    inst.close()

if __name__ == "__main__":
    main()

Adapting to TRC and CA Measurements

For TRC tests, such as receiver sensitivity, the script must control both the spectrum analyzer (as a signal generator) and the IUT. A typical approach involves:

• Setting the spectrum analyzer to generate a modulated signal at a specific power level (e.g., -70 dBm).
• Commanding the IUT to transmit a packet and measuring the bit error rate (BER) via a Bluetooth tester.
• Adjusting the power level iteratively to find the sensitivity threshold.

SCPI commands for signal generation might include:

# Set as signal generator (if supported)
SOUR:POW:LEV -70 dBm
SOUR:MOD:BLUetooth:PAYLoad PRBS9
OUTPut ON

For CA tests, the script captures the carrier frequency offset over multiple packets. The frequency drift can be computed by measuring the frequency at the start and end of a packet. The IXIT values from TCRL_IXIT_LN_v2.xlsx might specify the allowed drift limits (e.g., ±50 kHz for BLE).

def measure_ca_drift(instrument, center_freq, packet_duration_us):
    """
    Measure carrier frequency offset and drift.
    """
    instrument.write(f"FREQ:CENT {center_freq} GHz")
    instrument.write("FREQ:SPAN 0 Hz")
    instrument.write(f"SWE:TIME {packet_duration_us} us")
    instrument.write("CALC:MARK:FUNC:COUN ON")
    instrument.write("INIT; *WAI")
    freq_start = float(instrument.query("CALC:MARK:FUNC:COUN:RES?"))
    # Simulate drift by waiting and measuring again (in practice, use gated measurement)
    time.sleep(0.001)
    instrument.write("INIT; *WAI")
    freq_end = float(instrument.query("CALC:MARK:FUNC:COUN:RES?"))
    drift = freq_end - freq_start
    offset = freq_start - (center_freq * 1e9)  # Convert to Hz
    return offset, drift

Performance Analysis and Optimization

Automation reduces test time per device from approximately 30 minutes (manual) to under 5 minutes (automated), assuming a full TRM/TRC/CA suite. However, careful attention must be paid to:

• Sweep Time Optimization: Use the minimum sweep time required for accurate measurements. For example, for CA tests, a 1 ms sweep time may suffice for BLE packets.
• Instrument Settling: Insert time.sleep() calls after frequency changes to allow the spectrum analyzer to stabilize.
• Error Handling: Implement try-except blocks to handle communication timeouts or instrument errors.
• Parallelization: For multi-channel tests, consider using multiple instruments or parallel threads (though careful with VISA resource locking).

Protocol details from the Core.IXIT.p21.xlsx indicate that the RFPHY layer expects specific IXIT values like TSPX_antenna_gain and TSPX_maximum_conducted_power. These can be incorporated to calibrate measurements. For instance, if the antenna gain is 2 dBi, the conducted power limit should be adjusted accordingly.

Conclusion

Automating BQB RF-PHY test cases for TRM, TRC, and CA using Python and SCPI commands is a transformative approach for test houses and embedded developers. By integrating IXIT configurations from standard documents like TCRL_IXIT_LN_v2.xlsx, BSS.IXIT_.1.0.0.xlsx, and Core.IXIT.p21.xlsx, the framework becomes adaptable to various Bluetooth profiles and device capabilities. The sample code provided demonstrates the core principles, but real-world deployment requires robust error handling, calibration, and compliance with Bluetooth SIG test specifications. As Bluetooth technology evolves (e.g., Channel Sounding in Bluetooth 5.4), this automation methodology will remain relevant, enabling faster certification cycles and higher quality products.

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