This paper investigates the joint resource block group (RBG) scheduling and beamforming optimization problem for weighted sum-rate (WSR) maximization in multi-cell multiple-input multiple-output (MIMO) downlink networks. While the Fast Fractional Programming (FastFP) framework provides a reliable model-driven solution, it suffers from conservative continuous beamforming updates and prohibitive computational overhead during the discrete RBG matching phase. To address these bottlenecks, we propose a joint deep unfolding framework comprising two core modules: P-Net and K-Net. For continuous beamforming, P-Net learns an adaptive relaxation factor along the analytical FastFP update direction. By strictly constraining this factor within an ascent-preserving interval, P-Net accelerates the optimization trajectory while rigorously retaining monotonic improvement and stationary-point convergence guarantees. For discrete RBG scheduling, K-Net learns a long-horizon priority policy that guides a low-complexity greedy assignment, effectively preserving the assignment quality while bypassing the high complexity of Hungarian matching. Both networks leverage analytical algorithmic priors and utilize recurrent parameter sharing, enabling flexible inference beyond the training horizon. Extensive simulations demonstrate that the proposed joint framework achieves higher WSR and faster execution times than conventional model-driven baselines, while generalizing robustly across unseen network scales, antenna configurations, and channel conditions without retraining.

翻译：本文研究多小区多输入多输出（MIMO）下行链路中加权和速率（WSR）最大化的联合资源块组（RBG）调度与波束赋形优化问题。快速分式规划（FastFP）框架虽提供了可靠的模型驱动解决方案，但其连续波束赋形更新过于保守，且在离散RBG匹配阶段面临高昂的计算开销。为突破这些瓶颈，我们提出一种联合深度展开框架，包含P-Net和K-Net两大核心模块。针对连续波束赋形，P-Net沿解析FastFP更新方向学习自适应松弛因子。通过将该因子严格约束于保持上升的区间内，P-Net加速优化轨迹，同时严格保留单调改进与驻点收敛保证。针对离散RBG调度，K-Net学习长视优先级策略，引导低复杂度贪婪分配，在绕过匈牙利匹配高计算复杂度的同时有效保障分配质量。两网络均利用解析算法先验知识并采用循环参数共享机制，实现超越训练时域长度的灵活推理。大量仿真表明，与传统模型驱动基线相比，所提联合框架在实现更高WSR与更快执行时间的同时，能鲁棒泛化至未见过的网络规模、天线配置及信道条件，且无需重新训练。