论文标题
大型智能表面收发器辅助通信系统的参数通道模型估计
Parametric Channel Model Estimation for Large Intelligent Surface-Based Transceiver-assisted Communication System
论文作者
论文摘要
连接的移动设备的数量和通过这些设备的数据流量的数量预计将在未来的通信网络中增长很多。为了支持此庞大的数据流量的规模,需要将越来越多的基站和无线终端部署在现有网络中。然而,实际上,部署大量具有大量天线阵列的基站将大大增加网络的硬件成本和功耗。提高无线通信系统覆盖范围和速率的有前途的方法是基于智能表面的收发器(LISBT),它使用空间连续的表面进行信号传输和接收。一个典型的LI由一个平面阵列组成,具有大量反射的超材料元件(例如,低成本印刷偶极子),每个偶极都可以充当相移。它也被认为是一种具有成本效益和节能的解决方案。 LISBT辅助无线通信系统中的准确渠道状态信息(CSI)对于实现这些目标至关重要。在本文中,我们根据系统的物理参数提出了一种通道估计方案。假设接收器没有噪声,则只需要五个试点信号即可完美估计通道参数。在存在噪声的情况下,我们提出了一种迭代估计算法,该算法会减少由于噪声而引起的通道估计误差。与先前在庞大的多输入多输出(MIMO)方面的工作不同,拟议方案的训练开销和计算成本不会随天线的数量增长。基于大型智能表面收发器(LISBT)辅助无线通信系统的物理特性的渠道估计方案是我们未来研究的主题。
The number of connected mobile devices and the amount of data traffic through these devices are expected to grow many-fold in future communication networks. To support the scale of this huge data traffic, more and more base stations and wireless terminals are required to be deployed in existing networks. Nevertheless, practically deploying a large number of base stations having massive antenna arrays will substantially increase the hardware cost and power consumption of the network. A promising approach for enhancing the coverage and rate of wireless communication systems is the large intelligent surface-based transceiver (LISBT), which uses a spatially continuous surface for signal transmission and receiving. A typical LIS consists of a planar array having a large number of reflecting metamaterial elements (e.g., low-cost printed dipoles), each of which could act as a phase shift. It is also considered to be a cost effective and energy efficient solution. Accurate channel state information (CSI) in LISBT-assisted wireless communication systems is critical for achieving these goals. In this paper, we propose a channel estimation scheme based on the physical parameters of the system. that requires only five pilot signals to perfectly estimate the channel parameters assuming there is no noise at the receiver. In the presence of noise, we propose an iterative estimation algorithm that decreases the channel estimation error due to noise. The proposed scheme's training overhead and computational cost do not grow with the number of antennas, unlike previous work on enormous multiple-input multiple-output (MIMO). The channel estimate scheme based on the physical properties of the Large intelligent surface-based transceiver (LISBT)-assisted wireless communication systems is the subject of our future study.