Reactive Spectrum Handoff

The handoff process is initiated exclusively by a sensing event—the detection of a primary user's signal on the current operating channel.

• Mechanism: Energy detection, matched filtering, or cyclostationary feature detection triggers the handoff.
• No Prediction: Unlike proactive handoff, there is no forecasting of future channel states.
• Consequence: The secondary user must immediately cease transmission to avoid harmful interference, per regulatory requirements.

After vacating the current channel, the secondary user must perform real-time wideband sensing to find a new idle frequency.

• Sensing Overhead: This introduces a significant link maintenance delay as the radio scans candidate channels sequentially.
• Selection Criteria: Channels are typically selected based on the first available idle frequency, signal-to-noise ratio, or a pre-defined backup channel list.
• Trade-off: The lack of a pre-computed target channel list increases the total service disruption time compared to proactive methods.

The defining performance penalty of reactive handoff is the extended handoff latency.

• Components of Delay: Total latency = time to detect PU + time to vacate channel + time to sense target channels + time to re-establish the link.
• Forced Termination Risk: If no idle channel is found within a critical timeout window, the ongoing secondary transmission is forcibly dropped.
• QoS Impact: This strategy is unsuitable for delay-intolerant applications like real-time video or voice over cognitive radio.

The primary advantage of reactive handoff is its architectural simplicity.

• No Model Training: Eliminates the computational cost of training and updating LSTM, HMM, or Deep Q-Network predictors.
• No Concept Drift: Immune to prediction model degradation caused by changing primary user traffic patterns.
• Deterministic Response: The system reacts to a verified signal detection, avoiding false-positive handoffs triggered by prediction errors.

Reactive handoff inherently carries a non-zero collision probability with the returning primary user.

• Detection Latency: A collision occurs during the interval between the primary user's actual arrival and the secondary user's sensing-based detection.
• Missed Detection: If the sensing mechanism fails due to shadow fading or the hidden node problem, a prolonged collision results.
• Regulatory Compliance: Strict detection thresholds (e.g., IEEE 802.22 requires -116 dBm sensitivity) are mandated to minimize this window.

Reactive handoff represents one end of the spectrum mobility strategy continuum.

• Proactive Handoff: Uses predictive models (e.g., Hidden Markov Models, LSTM Spectrum Predictors) to switch channels before the primary user arrives, minimizing latency.
• Reactive Handoff: Switches only upon detection, trading higher latency for zero prediction complexity.
• Hybrid Approaches: Some architectures use a reactive fallback when the proactive prediction confidence interval is too wide.

A handoff strategy where the secondary user predicts future channel occupancy and switches channels before a primary user arrives. This contrasts directly with reactive handoff by minimizing service disruption time and packet loss. Proactive approaches rely on predictive models like Hidden Markov Models or LSTM Spectrum Predictors to forecast Spectrum Availability Windows.

The likelihood that an ongoing secondary user transmission is prematurely dropped due to a collision with a returning primary user. This is the primary performance penalty of reactive handoff. A high forced termination probability indicates that the Channel Holding Time was overestimated or the sensing mechanism failed to detect the primary user with sufficient speed.

A stochastic framework used to mathematically represent the temporal behavior of licensed spectrum users. Common models include:

• ON/OFF traffic models with exponential or Phase-Type Distributions
• Markov Modulated Poisson Processes (MMPP) for bursty traffic
• Markovian arrival processes for complex correlation structures Reactive handoff does not require this model, but its performance is entirely governed by the underlying PU activity pattern.

The neural network architectures and signal processing techniques responsible for detecting primary users and identifying spectrum occupancy holes. Reactive handoff is critically dependent on the latency and accuracy of these sensing mechanisms. Techniques include matched filter detection, cyclostationary feature detection, and deep learning-based Automatic Modulation Classification to distinguish PU signals from noise and interference.

A decision-theoretic framework for spectrum mobility where the true channel state is hidden. The cognitive radio must maintain a belief state updated via noisy sensor observations. While often used for optimal proactive handoff policy derivation, a POMDP also formally models the reactive case: the agent acts only when the belief state of 'PU present' crosses a confidence threshold, incurring the cost of sensing delay.

The statistical duration a secondary user can occupy a specific frequency channel before a primary user's return forces a spectrum handoff. In reactive handoff, the actual holding time is unknown a priori. The secondary user transmits until a collision or sensing trigger occurs, making the effective throughput highly sensitive to the Hurst Exponent and tail behavior of the PU's idle period distribution.