大规模MIMO大间距非均匀天线阵列设计

中文摘要 AI 辅助

在大规模多输入多输出（MIMO）系统中，均匀阵列通常配置为阵元间距不超过半波长，以避免栅瓣和空间混叠。然而，许多新兴的第五代和第六代（5G/6G）应用依赖于阵元间距远大于半波长的分布式阵列。本文针对大规模MIMO系统，提出了一种基于电磁互信息理论（EMIT）引导的大间距非均匀阵列（NUA）设计，以解决栅瓣和空间混叠问题，同时降低硬件成本和能耗。我们首先为非均匀平面阵列建立多径信道模型，并针对所提出的典型NUA模式，从用户间干扰、孔径效率、有利传播和信道容量等方面分析由此产生的信道特性。该模型进一步扩展到宽带场景，其中NUA由于其更紧凑的阵元分布，表现出对波束斜视更强的鲁棒性。此外，我们引入了一种用于NUA设计的EMIT方法，该方法将天线阵列的空间采样模式与所得MIMO信道的容量联系起来。由此产生两种互补的整形策略：幅度锥削和几何整形，以及它们的联合优化。数值结果表明，所提出的NUA在孔径效率、信道正交性、波束斜视缓解、容量和误码率性能方面显著优于传统均匀阵列。

英文摘要

In massive multiple-input multiple-output (MIMO) systems, uniform arrays are typically configured with inter-element spacing no greater than half a wavelength to avoid grating lobes and spatial aliasing. However, many emerging fifth- and sixth-generation (5G/6G) applications rely on distributed arrays whose inter-element spacing far exceeds half a wavelength. In this paper, we propose an electromagnetic mutual-information-theoretic (EMIT)-guided non-uniform array (NUA) design with large inter-element spacing for massive MIMO systems to address the grating lobes and spatial aliasing artifacts, and in the meantime, to reduce the hardware cost and energy consumption. We start by developing a multipath channel model for non-uniform planar arrays, and analyze the resulting channel characteristics in terms of inter-user interference, aperture efficiency, favorable propagation and channel capacity for the proposed typical NUA patterns. The model is further extended to wideband scenarios, where NUAs demonstrate improved robustness against beam squint due to their more compact element distribution. In addition, we introduce an EMIT approach to NUA design, which links the spatial sampling pattern of an antenna array to the capacity of the resulting MIMO channel. This gives rise to two complementary shaping strategies, amplitude tapering and geometric shaping, and their joint optimization. Numerical results demonstrate that the proposed NUAs significantly outperform conventional uniform arrays in aperture efficiency, channel orthogonality, beam squint mitigation, capacity, and error rate performance.

发表机构

• Stevens Institute of Technology（史蒂文斯理工学院）
• University of Surrey（萨里大学）
• Queen Mary University of London（伦敦大学玛丽女王学院）

机构由 AI 辅助整理，请以论文原文为准。