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PRB-RUPFormer: A Recursive Unified Probabilistic Transformer for Residual PRB Forecasting

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arXiv:2605. 15363v1 Announce Type: new Abstract: Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and spectrum-aware decision making in cellular systems, where residual PRBs serve as a practical proxy for short- and medium-term spectrum availability.

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Authors: Saad Masrur, Yuxuan Jiang, Matti Hiltunen, Ajay Rajkumar, Ismail Guvenc
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because PRB-RUPFormer: A Recursive Unified Probabilistic Transformer for Residual PRB Forecasting overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Only continuous soft prefixes hit both EM axes at once on Qwen-2.5-7B-Instruct: discrete prompt searches split between the alignment objective and the distributional objective, and both discretizations of the soft prefix collapse (MODERATE confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: rect, under, eval, source, rate, control, alone, trained. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.15363

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