Skip to content
NewA calmer, faster ZeroHour

FPTR Refinement Ensures Feasible Allocations for Wireless Schedulers

Original title: FPTR: Feasibility-Preserving Transactional Refinement for Deadline-Constrained Joint Beam and Resource Allocation

arXiv cs.AI / cs.LG / cs.CLby Minghao Kong ()1 min read
AI summary · x-ai/grok-4.7Summaries by Vani AI

Feasibility-Preserving Transactional Refinement maintains validated solutions while refining beam and resource allocations before deadlines.

FPTR keeps an incumbent feasible allocation unchanged during refinement stages for joint beam and resource allocation. Candidates are built and evaluated separately, replacing only if complete, feasible, and improved. On synthetic instances, it outperforms baselines in traffic and utility, ensuring all outputs meet 100 ms deadlines.

Key facts
  • Four refinement stages for buffer-aware selection, user sharing, beam-mask, and reassignment
  • Raises traffic by 3.83% over beam-first reference
  • All 750 runs returned feasible allocations
Explain this AI news with Vani AIWhat was announced, what is new and why it matters, in a new chat

From the original article

1 min read at arxiv.org

Short-interval wireless schedulers must return a complete, feasible beam-and-resource allocation before each execution deadline, and a late or invalid output gives no utility in the current control cycle. Searching longer can increase transmitted traffic, but it may also miss the deadline or leave only an infeasible intermediate solution.

arXiv cs.AI / cs.LG / cs.CL

By Minghao Kong · arxiv.org

Read the full article at arXiv cs.AI / cs.LG / cs.CL

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.