To improve the efficiency of scarce radio-frequency (RF) resources in next-generation wireless systems, an intelligent transceiver architecture based on stacked intelligent metasurfaces (SIM) has recently emerged, where multiple programmable metasurface layers are cascaded and each layer comprises passive meta-atoms that perform beamforming directly in the wave domain. In parallel, inter-band carrier aggregation enables multi-band transmission with high spectral efficiency. Their integration in multi-band multiuser downlink transmission is challenging because a single SIM phase configuration must remain effective across all subcarriers, while user scheduling and power allocation vary across scheduling intervals. To address these challenges, we propose an alternating-optimization framework that decomposes the joint design into a power-constrained precoder update and a SIM phase update. For the SIM phase subproblem, we develop a physically consistent multi-band deep-unfolding network (MBDU-Net) that unrolls projected-gradient phase updates into a compact trainable architecture. Each stage computes an analytic gradient from the cascaded SIM channel model and learns lightweight parameters, including per-stage step sizes and band-aware scaling, enabling fast convergence. Numerical results for multi-band multiuser downlink scenarios demonstrate reliable convergence and consistent sum-rate gains on unseen channel realizations.

翻译：为提高下一代无线系统中稀缺射频资源的利用效率，一种基于堆叠智能超表面的智能收发器架构应运而生。该架构通过级联多个可编程超表面层实现，每层包含可在波域直接执行波束成形的无源超原子。与此同时，频段间载波聚合技术实现了高频谱效率的多频段传输。将二者集成于多频段多用户下行链路传输面临挑战：单次SIM相位配置需在所有子载波上保持有效，而用户调度与功率分配随调度间隔变化。为解决这些问题，我们提出一种交替优化框架，将联合设计分解为功率约束预编码器更新与SIM相位更新两个子问题。针对SIM相位子问题，我们开发了一种物理一致的多频段深度展开网络，将投影梯度相位更新展开为紧凑的可训练架构。每一级从级联SIM信道模型计算解析梯度，并学习轻量级参数（包括每级步长和频段感知缩放因子），从而实现快速收敛。多频段多用户下行链路场景的数值仿真表明，该方法在未见信道实现上具有可靠的收敛性和稳定的和速率增益。