综述6G中OFDM波形与RIS配置联合优化的四类方法，揭示性能与效率差距。
Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models
- 按模型驱动、启发式、深度学习和新兴技术分类78项研究
- 深度学习方法推理速度比传统方法快100~10000倍，保持95%-99%频谱效率
- 提出标准化基准需求，指明硬件约束与多目标优化等六大挑战
6G中联合OFDM波形设计与可重构智能表面（RIS）配置是一个混合整数非线性规划（MINLP）问题，涵盖总速率最大化、能效、最小公平性及峰值平均功率比（PAPR）约束等目标。本文对2021至2026年间发表的78篇相关研究进行综述，发现尚无统一基准，跨论文比较不可行。研究将这些工作分为四类：(I) 模型驱动凸松弛，(II) 启发式与元启发式搜索，(III) 深度强化与无监督学习，(IV) 新兴方法（包括基础模型、扩散生成式AI与量子优化）。文献合成显示，基于机器学习的方法（第三类）在推理阶段实现95%-99%的模型频谱效率，运行时间快10²~10⁴倍（方法对依赖；数据为自报，未含训练成本）。配套教程基准（N=16, N=64, N=128）揭示关键缩放特性：基于GPU的神经网络推理（如DDQN、PPO、图神经网络、无监督深度学习）在不同天线数下运行时间不变，而迭代求解器（如AO+SCA、PSO）呈多项式增长。能效（P2）与PAPR约束（P4）基准因缺乏标准化功耗模型和波形生成器，暂留待未来工作。综述提炼出六大开放挑战：跨范式基准缺失、真实硬件约束部署、双衰落信道下的联合优化、多目标PAPR权衡、大型语言模型在实时网络控制中的安全性，以及独立启发式方法的收益递减。本文提出标准化基准要求，为6G中联合OFDM-RIS优化的研究者与实践者提供路线图。
原文摘要 · Abstract (English)
Joint OFDM-RIS optimization for 6G is a mixed-integer nonlinear programming (MINLP) problem covering sum-rate maximization, energy efficiency, max-min fairness, and peak-to-average power ratio (PAPR)-constrained objectives. Seventy-eight joint OFDM-RIS optimization works published between 2021 and 2026 are surveyed. No standardized benchmark exists, and cross-paper comparisons remain infeasible. This survey classifies these works into four paradigms: (I) model-based convex relaxation, (II) heuristic and metaheuristic search, (III) deep reinforcement and unsupervised learning, and (IV) emerging methods including foundation models (FM), diffusion-based generative AI, and quantum optimization. A literature synthesis of self-reported benchmarks shows that ML-based methods (Paradigm~III) report 95-99\% of model-based spectral efficiency at 10^2-10^4 x faster per-inference runtime (method-pair dependent; literature values are self-reported and exclude ML pre-training cost). A companion tutorial benchmark at N=16, N=64, and N=128 reveals a critical scaling property: GPU-based neural network inference (DDQN, PPO, graph neural network (GNN), unsupervised DL) is N-invariant, with identical runtime at N=16 and N=128, while iterative solvers (AO+SCA, PSO) scale polynomially. Energy efficiency (P2) and PAPR-constrained (P4) benchmarks are deferred to future work with standardized power models and waveform generators. Six open challenges emerge from the synthesis: the cross-paradigm benchmark deficit, real-world hardware-constrained deployment, joint waveform-RIS optimization for doubly-dispersive channels, multi-objective PAPR trade-offs, LLM safety in live network control, and diminishing returns of standalone heuristics. We specify requirements for a standardized benchmark. This study serves as a roadmap for researchers and practitioners working on joint OFDM-RIS optimization in 6G networks.
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