Occupied Bandwidth (OBW) is the calculated frequency span containing 99% of a signal's total mean power, with 0.5% of the power remaining above and below the band. Unlike the necessary bandwidth defined by modulation rate, OBW accounts for real-world transmitter imperfections, including spectral regrowth caused by power amplifier nonlinearity, and is a fundamental metric for quantifying spectral efficiency and regulatory compliance.
Glossary
Occupied Bandwidth (OBW)

What is Occupied Bandwidth (OBW)?
Occupied Bandwidth is the frequency range containing a specified percentage of the total integrated power of a modulated signal, typically 99%, defining the spectral footprint of a transmission.
OBW is measured using the integrated power method, where total power across a wide frequency sweep is computed and the lower and upper frequency boundaries are iteratively determined until the 99% power threshold is met. It is analyzed alongside Adjacent Channel Leakage Ratio (ACLR) and spectral mask compliance to validate that digital predistortion (DPD) systems are effectively containing emissions within licensed allocations.
Key Characteristics of Occupied Bandwidth
Occupied Bandwidth (OBW) is the frequency range containing 99% of a signal's total integrated power. It is a critical regulatory metric that, alongside ACLR, defines a transmitter's spectral efficiency and compliance.
The 99% Power Containment Rule
OBW is defined by the ITU-R SM.328 standard as the bandwidth containing 99% of the mean transmitted power. The remaining 0.5% of power is distributed equally (0.25%) in the upper and lower out-of-band regions.
- Measurement Method: Integrate total power across the spectrum, then sweep inward from the band edges until 0.5% of total power remains outside each boundary.
- Contrast with 3 dB Bandwidth: Unlike the simple half-power bandwidth, OBW accounts for spectral regrowth and modulation side-lobes, making it a true measure of real-world spectral occupancy.
- Regulatory Relevance: Spectrum regulators use OBW to define emission masks and license boundaries, ensuring adjacent channel licensees are protected from interference.
OBW vs. Necessary Bandwidth
While often used interchangeably, Necessary Bandwidth is a theoretical calculation based on modulation type and data rate, whereas Occupied Bandwidth is an empirical measurement of a specific transmitter's actual spectral spread.
- Necessary Bandwidth Formula: For a given modulation scheme (e.g., QPSK), symbol rate, and filtering, the necessary bandwidth is calculated mathematically.
- OBW Variability: OBW is always wider than necessary bandwidth due to non-ideal filter roll-off, IQ modulator impairments, and power amplifier nonlinearity.
- Spectral Regrowth Impact: A nonlinear PA causes spectral regrowth that directly expands the measured OBW, potentially pushing a compliant design out of its licensed channel.
Measurement Integration Techniques
Accurate OBW measurement requires precise power integration, typically performed by a spectrum analyzer or vector signal analyzer in zero-span or swept-frequency mode.
- RMS Detection: Modern analyzers use root-mean-square (RMS) detection to accurately sum the power of noise-like modulated signals, avoiding the errors of peak detection.
- Integration Bandwidth: The analyzer must sweep a span significantly wider than the expected OBW to capture all spectral regrowth components before applying the 99% power threshold.
- Trace Averaging: Multiple sweeps are averaged to reduce measurement variance, but excessive averaging can mask intermittent transient spectral spreading caused by thermal memory effects.
Relationship with Crest Factor Reduction
Crest Factor Reduction (CFR) directly influences OBW by limiting signal peaks before the power amplifier, preventing the amplifier from being driven into deep compression.
- Hard Clipping: Abruptly truncates peaks, causing severe spectral regrowth that expands OBW dramatically.
- Peak Windowing: Applies a smooth window to peaks, containing spectral regrowth within a tighter bandwidth and minimizing OBW expansion.
- Trade-off: Aggressive CFR reduces OBW expansion but introduces in-band Error Vector Magnitude (EVM) degradation. The optimal CFR algorithm balances OBW containment against modulation fidelity.
OBW in 5G NR and Wideband Systems
5G New Radio (NR) signals with 100 MHz component carriers and high-order MIMO place extreme demands on OBW characterization.
- Channel Bandwidth vs. OBW: A 100 MHz NR channel has a specified transmission bandwidth configuration, but the actual OBW must remain within the spectral mask limits to avoid adjacent channel interference.
- OBW for Multi-Cluster Signals: Intra-band carrier aggregation creates multi-cluster signals where OBW is defined across the entire aggregated bandwidth, including the gap between clusters.
- mmWave Challenges: At millimeter-wave frequencies, phase noise and PA nonlinearity combine to expand OBW, requiring integrated DPD and CFR solutions to maintain spectral containment.
OBW as a Diagnostic for PA Health
Monitoring OBW drift over time provides a non-invasive diagnostic of power amplifier degradation and antenna VSWR faults.
- Thermal Runaway Detection: A gradual OBW expansion during continuous transmission indicates rising junction temperatures and worsening PA linearity.
- Load Mismatch: A sudden OBW increase can signal an antenna impedance mismatch, causing the PA to operate outside its designed load-pull contour.
- Preventive Maintenance: Integrating OBW trending into base station self-organizing network (SON) algorithms enables proactive PA replacement before catastrophic failure and regulatory violation.
Frequently Asked Questions
Essential questions and answers about Occupied Bandwidth (OBW) measurement, its relationship to spectral regrowth, and its role in transmitter compliance testing.
Occupied Bandwidth (OBW) is the frequency range containing a specified percentage—typically 99%—of the total integrated power of a modulated signal. It is measured by integrating the power spectral density (PSD) across the entire frequency span, calculating the total power, then identifying the lower and upper frequency boundaries where 0.5% of the total power lies outside each edge. Modern spectrum analyzers and vector signal analyzers automate this process using the % power method, sweeping from the channel center outward until the integrated power reaches the specified threshold. The OBW measurement is distinct from the channel bandwidth allocation; a signal with 10 MHz channel spacing may exhibit an OBW of 9.8 MHz depending on pulse shaping and nonlinear distortion. Regulatory bodies like the FCC and ITU-R mandate OBW measurements to verify that transmitters do not exceed their authorized spectrum allocation and to characterize spectral containment before assessing Adjacent Channel Leakage Ratio (ACLR).
OBW vs. Other Bandwidth Metrics
Comparison of Occupied Bandwidth with related spectral containment and emission metrics used in regulatory compliance and transmitter characterization.
| Feature | Occupied Bandwidth (OBW) | Adjacent Channel Leakage Ratio (ACLR) | Spectral Mask |
|---|---|---|---|
Primary Purpose | Quantifies spectral containment of modulated signal power | Measures power leakage into adjacent channels | Defines regulatory emission limits vs. frequency offset |
Measurement Basis | Integrated power percentage (typically 99%) | Ratio of in-channel to adjacent channel power | Absolute power spectral density envelope |
Regulatory Compliance Metric | |||
Typical Units | Hz or MHz (frequency range) | dBc (relative to carrier) | dBm/Hz or dBm per measurement bandwidth |
Directly Measures Spectral Regrowth | |||
Sensitive to Nonlinear Distortion | |||
Used with Modulated Signals Only | |||
Defined by Standards Body | ITU-R SM.328, FCC 2.202 | 3GPP TS 36.104, 38.104 | FCC Part 24/27, ETSI EN 301 502 |
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Related Terms
Key metrics and concepts that define how effectively a transmitter confines its power within the assigned channel, directly impacting regulatory compliance and system performance.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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