Large intelligent surfaces (LISs) constitute a new and promising wireless communication paradigm that relies on the integration of a massive number of antenna elements over the entire surfaces of man-made structures. The LIS concept provides many advantages, such as the capability to provide reliable and space-intensive communications by effectively establishing line-of-sight (LOS) channels. In this paper, the system spectral efficiency (SSE) of an uplink LIS system is asymptotically analyzed under a practical LIS environment with a well-defined uplink frame structure. In order to verify the impact on the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically studied and a theoretical bound on its performance is derived. Given this performance bound, an optimal pilot training length for multi-LIS systems subjected to pilot contamination is characterized and, subsequently, the performance-maximizing number of devices that the LIS system must service is derived. Simulation results show that the derived analyses are in close agreement with the exact mutual information in presence of a large number of antennas, and the achievable SSE is limited by the effect of pilot contamination and intra/inter-LIS interference through the LOS path, even if the LIS is equipped with an infinite number of antennas. Additionally, the SSE obtained with the proposed pilot training length and number of scheduled devices is shown to reach the one obtained via a brute-force search for the optimal solution.
翻译:大型智能表面(LISs)构成了一个新的、有希望的无线通信模式,它依赖于将大量天线元素综合到人为结构的整个表面,LIS概念提供了许多优势,例如通过有效建立一线观察(LOS)渠道提供可靠和空间密集通信的能力。在本文中,一个上链接LIS系统的系统光谱效率(SSE)在现实的LIS环境下进行模拟分析,并有一个明确界定的上链框架结构结构。为了核实试点污染对SSE的影响,多LIS系统SE的SE正在进行无序研究,其性能有理论约束。鉴于这一作用,受试点污染的多LIS系统的最佳试点培训长度具有特征,随后,将LIS系统必须使用的性能最大化装置的数量推算出来。 模拟结果显示,衍生分析与大量天线存在的确切相互信息非常接近,而SSESSESE系统可实现的SESESSSSSSSSSSSSSSSSSSSSSSSSSSSSSSSS范围图上的最佳搜索范围,如果具有最深的试验性、通过LILLILLILLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLL的试定的试尾卡,则,其最深路段,因此显示最佳搜索范围,则限制,因此显示最佳搜索范围,其最深、最深、最深、、最深、最深、最深、、、、、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最、最、最、、最深、最深、最深、最深、最深、最深、最深、最深、、最接近的搜索、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、最深、