OFDM Signal Intelligence (SIGINT)

Extract the fundamental physical-layer parameters of an unknown OFDM signal without prior knowledge or demodulation. This capability is foundational for non-cooperative signal analysis.

• FFT Size Detection: Identifies the number of subcarriers by analyzing cyclostationary signatures or the autocorrelation lag profile.
• Cyclic Prefix Length: Determines the guard interval duration by detecting correlation peaks at specific lags, enabling classification between normal and extended CP modes.
• Subcarrier Spacing: Estimates the frequency separation between subcarriers using spectral correlation density analysis or spectrogram ridge detection.
• Occupied Bandwidth: Measures the active transmission bandwidth by detecting the edges of the resource block allocation.

Decode the broadcast and synchronization channels to identify the specific wireless standard, cell identity, and network configuration of an intercepted OFDM transmission.

• PSS/SSS Decoding: Detects Zadoff-Chu and m-sequences to recover the Physical Cell Identity (PCI) and achieve slot and frame synchronization in LTE and 5G NR signals.
• MIB Extraction: Decodes the Master Information Block from the PBCH to reveal the downlink bandwidth, system frame number, and CORESET configuration.
• SSB Beam Indexing: Identifies the beam index from 5G NR Synchronization Signal Blocks to determine the spatial direction of the transmission.
• PRACH Format Classification: Detects and classifies the random access preamble format to infer uplink timing and cell radius.

Distinguish between OFDM variants and other modulation families using statistical signatures and deep learning classifiers operating on raw IQ samples.

• CP-OFDM vs. DFT-s-OFDM: Separates multi-carrier downlink waveforms from single-carrier uplink waveforms by analyzing PAPR statistical distributions and spectral correlation patterns.
• Numerology Classification: Identifies the 5G NR numerology (μ = 0 to 4) by estimating subcarrier spacing and symbol duration from cyclostationary features.
• OFDM vs. Single-Carrier: Discriminates OFDM from non-OFDM waveforms using the unique spectral correlation signature generated by the cyclic prefix.
• Standard Fingerprinting: Identifies the specific wireless protocol (LTE, 5G NR, WiFi, DVB-T) by matching pilot patterns, preamble structures, and frame timing.

Exploit advanced signal processing and machine learning techniques to extract intelligence from weak or overlapping OFDM signals in dense electromagnetic environments.

• Cyclostationary Feature Exploitation: Leverages the unique spectral correlation density of OFDM signals for robust detection and classification at low signal-to-noise ratios where energy detection fails.
• Blind Source Separation: Applies independent component analysis to separate co-channel OFDM signals without knowledge of the mixing matrix or antenna array geometry.
• Deep Learning Classifiers: Deploys convolutional neural networks trained on spectrograms or raw IQ samples to automatically identify OFDM variants under challenging channel impairments.
• Channel Impairment Compensation: Preprocesses signals to mitigate the effects of multipath fading, frequency offset, and phase noise before parameter extraction.

Reconstruct the two-dimensional time-frequency resource grid to visualize and analyze the allocation of control and data channels within an intercepted OFDM frame.

• CORESET Detection: Identifies the time-frequency regions configured for downlink control channel (PDCCH) transmission in 5G NR.
• DMRS Extraction: Locates and decodes Demodulation Reference Signals to map active resource block allocations and user-specific transmissions.
• PTRS Tracking: Detects Phase Tracking Reference Signals in millimeter-wave 5G NR signals to analyze phase noise compensation strategies.
• Bandwidth Part Identification: Maps the configured Bandwidth Parts (BWPs) to understand the bandwidth adaptation and power-saving strategies of the target user equipment.

Identify specific hardware implementations and vendor configurations by analyzing subtle, unintentional features embedded in the transmitted OFDM waveform.

• OFDM Protocol Fingerprinting: Identifies vendor-specific pilot patterns, preamble structures, and frame timing deviations that distinguish one equipment manufacturer from another.
• RF Fingerprinting: Detects microscopic hardware imperfections in the transmitted waveform, such as I/Q imbalance, oscillator phase noise, and power amplifier non-linearity, for physical-layer device authentication.
• Zadoff-Chu Root Sequence Analysis: Estimates the root sequence index of CAZAC sequences used in synchronization and random access to infer cell planning and vendor configuration.
• PAPR Signature Profiling: Analyzes the statistical distribution of the peak-to-average power ratio to identify specific power amplifier models and digital pre-distortion implementations.