In this paper, we investigate cell-free massive MIMO (CF-mMIMO) systems in which access points (APs) are equipped with fluid antennas (FAs) and develop a comprehensive framework for channel estimation, antenna port selection, and uplink spectral efficiency (SE) optimization. We propose a generalized LMMSE-based uplink channel estimation scheme that dynamically activates FA ports during pilot transmission, efficiently exploiting antenna reconfigurability under practical training constraints. Building on this, we design a distributed port selection strategy that minimizes per-AP channel estimation error by exploiting spatial correlation among FA ports. We systematically analyze the impact of antenna geometry and spatial correlation using the Jakes' channel model for different AP array configurations, including uniform linear and planar arrays. We then derive SINR expressions for centralized and distributed uplink processing and obtain a closed-form uplink SE expression for centralized maximum-ratio combining using the use-and-then-forget bound. Finally, we propose an alternating-optimization framework to select FA port configurations that maximize the uplink sum SE. Numerical results show that the proposed FA-aware channel estimation and port optimization strategies greatly reduce channel estimation error and significantly improve sum-SE over fixed-antenna and non-optimized FA baselines, confirming FAs as a key enabler for scalable, adaptive CF-mMIMO networks.
翻译:本文研究了接入点配备流体天线的无蜂窝大规模MIMO系统,并建立了一个涵盖信道估计、天线端口选择与上行链路频谱效率优化的完整框架。我们提出了一种基于广义LMMSE的上行信道估计方案,该方案在导频传输期间动态激活流体天线端口,在实际训练约束下高效利用天线的可重构特性。在此基础上,我们设计了一种分布式端口选择策略,通过利用流体天线端口间的空间相关性来最小化每个接入点的信道估计误差。我们采用Jakes信道模型,针对包括均匀线性阵列与平面阵列在内的不同接入点阵列配置,系统分析了天线几何结构与空间相关性的影响。随后,我们推导了集中式与分布式上行链路处理下的信干噪比表达式,并利用“使用后遗忘”界得到了集中式最大比合并下的闭合形式上行业谱效率表达式。最后,我们提出一种交替优化框架来选择能最大化上行总频谱效率的流体天线端口配置。数值结果表明,所提出的流体天线感知信道估计与端口优化策略能大幅降低信道估计误差,并在总频谱效率上显著优于固定天线及未经优化的流体天线基线方案,从而证实流体天线是实现可扩展、自适应无蜂窝大规模MIMO网络的关键使能技术。