论文标题
最佳用户链接选择和关节ML检测
Study of Cloud-Aided Multi-Way Multiple-Antenna Relaying with Best-User Link Selection and Joint ML Detection
论文作者
论文摘要
在这项工作中，我们为多路多通道中继系统提供了一个云辅助上行链路框架，该框架促进了云中关节线性最大似然（ML）符号检测，并选择用户同时通过继电器互相传播。我们还针对使用基于云的缓冲区和物理层网络编码的提议的云辅助上行链路框架研究了继电器选择技术。特别是，我们根据最佳链接的选择开发了一种新颖的多路继电器选择协议，该协议被称为多路云辅助最佳用户 - 链接（MWC-test-Best-user-link）。然后，我们将最大最小距离继电器选择标准与算法合并到提出的MWC-最佳用户 - 用户 - 用户 - 用户 - 链接协议中。模拟表明，MWC-最佳用户链接在平均延迟，总和和位错误率方面优于先前的作品。
In this work, we present a cloud-aided uplink framework for multi-way multiple-antenna relay systems which facilitates joint linear Maximum Likelihood (ML) symbol detection in the cloud and where users are selected to simultaneously transmit to each other aided by relays. We also investigate relay selection techniques for the proposed cloud-aided uplink framework that uses cloud-based buffers and physical-layer network coding. In particular, we develop a novel multi-way relay selection protocol based on the selection of the best link, denoted as Multi-Way Cloud-Aided Best-User-Link (MWC-Best-User-Link). We then devise the maximum minimum distance relay selection criterion along with the algorithm that is incorporated into the proposed MWC-Best-User-Link protocol. Simulations show that MWC-Best-User-Link outperforms previous works in terms of average delay, sum-rate and bit error rate.
