As the dense deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems presents significant challenges, per-AP coverage can be expanded using large-scale antenna arrays (LAAs). However, this approach incurs high implementation costs and substantial fronthaul demands due to the need for dedicated RF chains for all antennas. To address these challenges, we propose a hybrid beamforming framework that integrates wave-domain beamforming via stacked intelligent metasurfaces (SIM) with conventional digital processing. By dynamically manipulating electromagnetic waves, SIM-equipped APs enhance beamforming gains while significantly reducing RF chain requirements. We formulate a joint optimization problem for digital and wave-domain beamforming along with fronthaul compression to maximize the weighted sum-rate for both uplink and downlink transmission under finite-capacity fronthaul constraints. Given the high dimensionality and non-convexity of the problem, we develop alternating optimization-based algorithms that iteratively optimize digital and wave-domain variables. Numerical results demonstrate that the proposed hybrid schemes outperform conventional hybrid schemes, that rely on randomly set wave-domain beamformers or restrict digital beamforming to simple power control. Moreover, the proposed scheme employing sufficiently deep SIMs achieves near fully-digital performance with fewer RF chains in the high signal-to-noise ratios regime.

翻译：随着无蜂窝大规模多输入多输出（CF-mMIMO）系统中接入点（AP）的密集部署带来显著挑战，可利用大规模天线阵列（LAA）扩展单AP覆盖范围。然而，由于所有天线均需配备专用射频链，该方法导致高昂的实现成本与巨大的前传需求。为应对这些挑战，我们提出一种混合波束赋形框架，该框架通过堆叠智能超表面（SIM）实现的波域波束赋形与常规数字处理相结合。通过动态调控电磁波，配备SIM的AP在提升波束赋形增益的同时，显著降低了射频链需求。我们构建了数字与波域波束赋形及前传压缩的联合优化问题，旨在有限容量前传约束下最大化上下行传输的加权和速率。针对该问题的高维性与非凸性，我们开发了基于交替优化的算法，以迭代方式优化数字与波域变量。数值结果表明，所提出的混合方案优于依赖随机设置波域波束赋形器或将数字波束赋形限制为简单功率控制的传统混合方案。此外，所提方案在采用足够深度的SIM时，能在高信噪比区域以更少的射频链实现接近全数字处理的性能。