论文标题
用于上行无链路细胞的杂交波束形成和自适应RF链激活大量的MIMO系统
Hybrid Beamforming and Adaptive RF Chain Activation for Uplink Cell-Free Millimeter-Wave Massive MIMO Systems
论文作者
论文摘要
在这项工作中，我们研究了用于上行无链路单元格（CF）毫米波（MMWAVE）的混合模拟数字波束成形（HBF）体系结构大量多输入多输入（MIMO）系统。 {我们首先提出了两个HBF方案，即分散的HBF（D-HBF）和半偏心的HBF（SC-HBF）。在前者中，基于本地通道状态信息（CSI），在每个AP上独立生成数字和模拟波束形式。相比之下，在后者中，仅在AP处局部获得数字波束形式，而模拟波束形成矩阵是基于从所有APS接收的全局CSI在中央处理单元（CPU）生成的。 We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement.} Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters基于CSI的APS处（ADC）和相变（PSS）。为了激活RF链，提出了低复杂性算法，可以在总可实现的速率上有边际损失，可以显着提高能效（EE）。
In this work, we investigate hybrid analog-digital beamforming (HBF) architectures for uplink cell-free (CF) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. {We first propose two HBF schemes, namely, decentralized HBF (D-HBF) and semi-centralized HBF (SC-HBF). In the former, both the digital and analog beamformers are generated independently at each AP based on the local channel state information (CSI). In contrast, in the latter, only the digital beamformer is obtained locally at the AP, whereas the analog beamforming matrix is generated at the central processing unit (CPU) based on the global CSI received from all APs. We show that the analog beamformers generated in these two HBF schemes provide approximately the same achievable rates despite the lower complexity of D-HBF and its lack of CSI requirement.} Furthermore, to reduce the power consumption, we propose a novel adaptive radio frequency (RF) chain-activation (ARFA) scheme, which dynamically activates/deactivates RF chains and their connected analog-to-digital converters (ADCs) and phase shifters (PSs) at the APs based on the CSI. For the activation of RF chains, low-complexity algorithms are proposed, which can achieve significant improvement in energy efficiency (EE) with only a marginal loss in the total achievable rate.
