arXiv cs.AI· Minghao Kong, Zirong Liu, Jianhao Wu, Muyao Chen, Jianxin Wu, Rongjie Wang·· 3 小时前AI 评分27
FPTR：面向截止期约束的波束与资源联合分配可行性保持事务式优化方法
FPTR: Feasibility-Preserving Transactional Refinement for Deadline-Constrained Joint Beam and Resource Allocation
AI 导读
论文提出 FPTR（可行性保持事务式优化）框架，始终保留一个经过验证的可行波束与资源分配方案（incumbent），每个优化阶段生成独立候选，只有完整、通过可行性验证且严格改进目标时才替换。
正文 · 原文
Abstract: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. We propose Feasibility-Preserving Transactional Refinement (FPTR), which always keeps a validated feasible allocation, the incumbent. Each refinement stage builds a separate candidate and leaves the incumbent unchanged. A candidate replaces the incumbent only if it is complete, passes the feasibility validator before its stage cutoff, and strictly improves the objective; otherwise it is discarded. Four refinement stages use this rule: buffer-aware resource selection, compatibility-group (CG) user sharing, beam-mask rebuilding from remaining demand, and joint reassignment of two resources. On 150 fixed synthetic instances, the full pipeline (Full) raises traffic by 3.83% on average over a lightweight beam-first reference and by 1.79% over its Base constructor. In a separate suite with matched inputs, Full exceeds six independent search baselines (ALNS, tabu search, GA, simulated annealing, iterated local search, and GRASP) by 6.68-8.35%, where outputs later than 100 ms receive zero utility. All 750 Full runs in the internal suite returned feasible allocations, and none exceeded 100 ms. Separating mandatory output construction from optional refinement lets spare computation time improve the allocation without endangering the incumbent. The open-source implementation supports independent reproduction.
| Comments: | 14 pages, 4 figures; includes appendix. Code: this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Performance (cs.PF) |
| Cite as: | arXiv:2611.06491 [cs.AI] |
| (or arXiv:2611.06491v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06491 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Minghao Kong [view email]
[v1]
Wed, 4 Nov 2026 13:27:57 UTC (481 KB)
来源：arXiv cs.AI · arxiv.org