OFDM Signal Bandwidth Estimation

The most fundamental approach to bandwidth estimation involves applying a power spectral density (PSD) estimate and identifying the frequency edges where the signal power drops below a calibrated noise floor. This non-coherent method requires no prior knowledge of the signal structure but is highly sensitive to the signal-to-noise ratio (SNR) and filter roll-off. The accuracy depends on selecting an appropriate threshold, often derived using Otsu's method or a constant false alarm rate (CFAR) algorithm to separate active subcarriers from noise.

A robust technique that exploits the cyclostationary nature of OFDM signals. Unlike energy detection, SCD analysis measures the correlation between spectral components separated by specific cycle frequencies, such as the subcarrier spacing. This allows the estimator to distinguish the OFDM signal from stationary noise and interference. The alpha profile of the SCD reveals the exact spectral support of the cyclostationary features, providing a highly accurate bandwidth measurement even in negative SNR environments.

This method identifies the sharp transitions at the band edges of an OFDM spectrum by computing the first-order derivative of the PSD estimate. The locations of the maximum and minimum derivative peaks correspond to the lower and upper band edges. This technique is effective for signals with steep spectral roll-off, such as those using a high number of subcarriers. Pre-processing with a Savitzky-Golay filter can smooth the PSD estimate without distorting the edge transitions, improving detection accuracy.

This blind estimation technique leverages the autocorrelation function of the received signal. The cyclic prefix introduces correlation peaks at a lag equal to the useful symbol duration. By analyzing the correlation magnitude across different frequency sub-bands, the estimator can identify which sub-bands contain the structured OFDM signal versus empty guard bands. The transition from a high correlation zone to a noise-like zone marks the occupied bandwidth boundary, making this method resilient to frequency-selective fading.

Modern approaches use convolutional neural networks (CNNs) trained on spectrograms or raw IQ samples to directly regress the occupied bandwidth. The model learns to identify the spectral shape and edge features implicitly, without explicit thresholding. Input features often include the normalized power spectrum and its cumulative distribution function. These models can generalize across different OFDM numerologies and channel conditions, providing a single-shot estimate of both bandwidth and center frequency.

For standardized signals like LTE or 5G NR, bandwidth estimation involves decoding the Master Information Block (MIB) or analyzing the power distribution across the resource block grid. By measuring the received signal strength indicator (RSSI) per resource block, a heatmap of active allocations is generated. The total number of consecutively active resource blocks defines the channel bandwidth (e.g., 50 RBs for 10 MHz LTE). This method provides the most precise measurement but requires partial protocol decoding.

A blind parameter estimation technique that identifies the number of subcarriers in an OFDM signal. This is a prerequisite for accurate bandwidth measurement, as the occupied bandwidth equals the product of active subcarriers and subcarrier spacing.

• Analyzes cyclostationary signatures at multiples of the symbol rate
• Uses autocorrelation properties to distinguish between FFT sizes (e.g., 128, 256, 512, 1024, 2048)
• Essential for distinguishing LTE from 5G NR numerologies

A two-dimensional function measuring the correlation between spectral components at different frequencies. This reveals the cyclostationary features generated by the cyclic prefix and pilot subcarriers, which are exploited for robust parameter estimation.

• Peaks in the spectral correlation plane indicate subcarrier spacing and symbol duration
• Enables bandwidth estimation at low SNR where energy detection fails
• Discriminates OFDM from single-carrier modulations

A time-frequency analysis technique that identifies the energy ridges corresponding to individual subcarriers in an OFDM signal. By processing the spectrogram as an image, computer vision algorithms can trace subcarrier boundaries.

• Enables visual estimation of subcarrier spacing and symbol duration
• Detects active subcarrier edges for bandwidth measurement
• Effective for signals with frequency-hopping or dynamic resource allocation

A technique that estimates the cyclic prefix duration of an unknown OFDM signal by analyzing the correlation lag profile. This enables classification between normal and extended CP modes, which directly impacts the effective bandwidth calculation.

• Normal CP: ~4.7 µs (LTE), extended CP: ~16.7 µs
• Correlation plateau length reveals CP duration
• Critical for distinguishing LTE from 5G NR frame structures

A neural network model, typically a convolutional neural network (CNN), trained on IQ samples or spectrograms to automatically identify OFDM variants and their physical-layer parameters without explicit feature extraction.

• Jointly estimates bandwidth, FFT size, CP length, and modulation order
• Trained on synthetic datasets covering LTE, 5G NR, WiFi, and DVB-T waveforms
• Eliminates the need for hand-crafted cyclostationary feature detectors