论文标题
使用统计无线电图的超可靠通信的预测率选择
Predictive Rate Selection for Ultra-Reliable Communication using Statistical Radio Maps
论文作者
论文摘要
本文提出，通过构造统计无线电图来预测任何相关的通道统计信息以帮助通信，从而利用了无线通道统计数据的空间相关性。具体而言，从网络中以前用户获取的存储的通道样本中，我们使用高斯进程（GPS）使用非参数模型估算新位置处的通道分布的分位数。然后使用此先前信息来选择某些目标级别的可靠性级别的传输速率。该方法通过合成数据测试，从城市微型细胞环境中进行模拟，强调了建议的解决方案如何有助于减少训练估计阶段，这对于超级可靠的低延迟（URLLC）部署固有的紧密延迟约束特别有吸引力。
This paper proposes exploiting the spatial correlation of wireless channel statistics beyond the conventional received signal strength maps by constructing statistical radio maps to predict any relevant channel statistics to assist communications. Specifically, from stored channel samples acquired by previous users in the network, we use Gaussian processes (GPs) to estimate quantiles of the channel distribution at a new position using a non-parametric model. This prior information is then used to select the transmission rate for some target level of reliability. The approach is tested with synthetic data, simulated from urban micro-cell environments, highlighting how the proposed solution helps to reduce the training estimation phase, which is especially attractive for the tight latency constraints inherent to ultra-reliable low-latency (URLLC) deployments.
