Memory-Based Predictive Resource Allocation for NR-V2X Traffic

New Radio Vehicle-to-Everything (NR-V2X) safety scheduling is difficult because burst episodes increase urgent arrivals, lower transmission success, and create retransmissions that compete for future slots. A scheduler that waits for the visible queue can react late, whereas always reserving extra safety physical resource blocks (PRBs) consumes the best-effort (BE) capacity after the stress has passed. This study proposes Memory-Based Predictive Allocation (MPA), a finite-action PRB allocation rule for safety and BE coexistence. MPA combines the deadline queue and retry state with a decayed transient-deficit memory, online success calibration, and a recoverability-aware BE cost guard. At each slot, it tests feasible safety PRB increments and chooses the action that first limits urgent safety loss, then reduces next-slot carryover, and finally avoids unnecessary PRB use. The model uses an NR-V2X resource pool interpretation and a calibrated signal-to-interference-plus-noise-ratio (SINR)-to-success mapping with hybrid automatic repeat request (HARQ)-like combining. Monte Carlo results show that MPA lowers safety misses relative to queue-reactive scheduling while preserving more BE throughput than a maximum safety reservation. In dense non-line-of-sight (NLOS) stress, MPA keeps the 95th-percentile (p95) delivered packet delay within the three-millisecond budget and preserves 0.892 normalized BE throughput, versus 0.534 under fixed maximum reservation.