Forced Termination Probability

The statistical arrival rate and channel holding time of licensed users directly dictate the collision hazard. A higher Markov Modulated Poisson Process (MMPP) arrival rate or longer ON periods reduce the idle window available to secondary users, sharply increasing the probability that an ongoing transmission will be truncated by a returning incumbent. The traffic load (Erlangs) is the fundamental exogenous driver of forced termination.

Imperfect sensing creates a direct vulnerability window. A missed detection event—where the sensing algorithm fails to identify an active primary user—leads to a collision and immediate forced termination. Similarly, high sensing latency delays the initiation of a handoff, causing a collision during the detection interval. The probability of forced termination is proportional to the probability of missed detection (P_md) integrated over the sensing period.

The time required to execute a spectrum handoff—including target channel selection, link re-establishment, and MAC-layer reconfiguration—is a critical interval of vulnerability. During this link maintenance gap, the secondary user is effectively deaf and cannot respond to a primary user's sudden arrival. A longer handoff delay, often caused by suboptimal Deep Q-Network policy execution or extensive channel scanning, directly increases the forced termination probability.

A spectrum handoff can only succeed if an idle backup channel exists. In dense spectrum environments with high overall occupancy, the spectrum availability window on alternative channels may be zero or too short to accommodate the remaining transmission. The probability of forced termination spikes when the number of active secondary users approaches the number of available idle channels, creating a target channel scarcity condition that forces a link drop.

Proactive handoff strategies rely on a spectrum mobility prediction engine to forecast future channel states. If the required prediction horizon—the lookahead time needed to reserve a channel and execute a seamless handoff—exceeds the model's accurate forecasting range, the prediction becomes unreliable. A mismatch between the required horizon and the model's effective range, often seen with LSTM Spectrum Predictors under concept drift, leads to incorrect proactive decisions and forced terminations.

The statistical distribution of channel holding time is rarely memoryless. A heavy-tailed distribution, such as a Phase-Type Distribution or one modeled by Extreme Value Theory (EVT), implies that while most idle periods are short, there is a non-negligible probability of an extremely long busy period. If a secondary user selects a channel during a brief idle window that is followed by a heavy-tail busy period, the forced termination probability is dominated by these rare, prolonged occupancy events.

The process by which a secondary user (SU) vacates a frequency channel upon detecting a returning primary user (PU) and transitions to a new idle channel. Forced termination occurs when this process fails or cannot be completed before a collision. Handoff strategies are categorized as:

• Proactive: The SU switches channels before the PU arrives, minimizing disruption.
• Reactive: The SU switches only after detecting the PU, resulting in higher latency and a greater risk of forced termination.

The statistical duration a secondary user can occupy a specific frequency channel before a primary user's return forces a spectrum handoff. This metric directly shapes forced termination probability: shorter holding times imply more frequent handoffs and a higher cumulative risk of failure. Channel holding time is typically modeled using phase-type distributions or empirical measurements of PU inter-arrival patterns.

A stochastic framework used to mathematically represent the temporal behavior of licensed spectrum users. Accurate modeling is critical for predicting forced termination risk. Common models include:

• ON/OFF traffic models with exponential or hyper-exponential distributions.
• Markovian arrival processes (e.g., Markov Modulated Poisson Process) to capture bursty traffic.
• Hidden Markov Models (HMMs) for inferring unobservable channel states from noisy measurements.

A handoff strategy where a secondary user predicts future channel occupancy and switches channels before a primary user arrives. This approach minimizes service disruption and directly reduces forced termination probability by avoiding collisions. Implementation relies on:

• LSTM Spectrum Predictors for multi-step channel state forecasting.
• Deep Q-Networks to learn optimal target channel selection policies.
• Maintaining a ranked list of backup channels based on predicted idle durations.

A decision-theoretic framework for spectrum mobility where the true channel state is hidden from the secondary user. The cognitive radio maintains a belief state—a probability distribution over possible channel occupancies—updated via noisy sensor observations. The POMDP policy balances:

• Exploration: Sensing channels to reduce uncertainty.
• Exploitation: Transmitting on channels with high belief of vacancy. Optimal POMDP policies minimize forced termination probability over a defined horizon.

A predicted temporal interval during which a specific frequency channel is forecasted to remain idle. This window enables a secondary user to schedule a transmission burst with a quantifiable risk of forced termination. Key characteristics:

• Prediction horizon: The lookahead window for the forecast.
• Confidence interval: Quantified via Gaussian Process Regression or Bayesian methods.
• Reservation duration: Must be shorter than the predicted idle window to maintain a safety margin.