AI 中文总结
针对异构6G RAN的资源分配挑战，提出DG-RA框架，通过多层面量化弹性，在保持相当吞吐量的同时，能快速恢复故障后的高吞吐量，将退化作为弹性储备。
AI 中文摘要
异构6G无线接入网（RAN）需在干扰、延迟限制、不完美信道状态信息（CSI）和架构多样性下可靠分配资源。我们提出一种感知退化资源分配（DG-RA）框架，将多架构编排建模为概率博弈，与单解优化不同，该框架刻意倾向于可由众多结构不同但性能等价策略轮廓实现的分配方案。通过退化加权路径鲁棒性（DWPR）、功能替代评分（FSS）和算法弹性商（ARQ）在三个层面量化弹性。在集中式（C-RAN）、开放式（O-RAN）、虚拟化（V-RAN）及混合RAN架构上，以分数规划优化器为基准，DG-RA在静态工作点达到与现有技术相当的吞吐量和中断率，随后利用其等价集在单个资源单元故障时，通过一次切换恢复约99%的吞吐量，而单解优化器需数十次迭代才能重新收敛。该结果将退化重新定义为吞吐量提升因素之外，用于抗干扰6G编排的预计算弹性储备。
英文摘要
Heterogeneous 6G radio access networks (RANs) must allocate resources reliably under interference, latency limits, imperfect channel state information (CSI), and architectural diversity. We propose a degeneracy-aware resource allocation (DG-RA) framework that casts multi-architecture orchestration as a probabilistic game and, unlike single-solution optimization, deliberately favors allocations realizable by many structurally distinct yet performance-equivalent strategy profiles. Resilience is quantified across three layers through Degeneracy-Weighted Path Robustness (DWPR), Functional Substitution Score (FSS), and an Algorithmic Resilience Quotient (ARQ). Across centralized (C-RAN), open (O-RAN), virtualized (V-RAN), and hybrid RAN architectures, and benchmarked against a fractional-programming optimizer, DG-RA matches the state-of-the-art throughput and outage at the static operating point, then exploits its equivalence set to recover $\sim$$99\%$ of throughput from a resource-unit failure with a single switch, where a single-solution optimizer needs tens of iterations to re-converge. The results recast degeneracy not as a rate booster but as a precomputed resilience reserve for disruption-tolerant 6G orchestration.
Comments5 pages, 5 figures, submitted to IEEE Wireless Communications Letters
