Link Blockage Modelling for Channel State Prediction in Higher Frequencies Using Deep Learning

S. K. Chari, G. Koudouridis
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Abstract

Wireless communications using higher frequencies is now possible due to the advancements in the field of high gain antennas. Using such technologies has enabled accessing wireless media within a short range supplying frequency bands with capacity worth multi-gigabits. Higher frequencies are however exposed to blockage events that can hinder the wireless system performance by reducing the throughput and losing user connectivity. The blockage effect becomes more severe with the addition of mobile blockers like vehicles. In order to understand the blockage events induced by a mobile vehicle, an efficient blockage model is required that can assist in the maintenance of the user connection. This paper proposes using a four state channel model based on the user’s signal strength for describing the occurrence of blockage events at high frequencies. Signal strength prediction and the channel state classification are then conducted and evaluated using two deep learning neural network disciplines. The high accuracy of the simulation results observed suggest the possibility and implementation of the model in real systems.
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基于深度学习的高频信道状态预测链路阻塞建模
由于高增益天线领域的进步,使用更高频率的无线通信现在是可能的。使用这些技术可以在短范围内访问无线媒体,提供容量为数千兆比特的频带。然而,较高的频率暴露于阻塞事件中,这会通过降低吞吐量和失去用户连接来阻碍无线系统的性能。随着车辆等移动拦阻物的加入,堵塞效果变得更加严重。为了理解由移动车辆引起的阻塞事件,需要一个有效的阻塞模型来帮助维护用户连接。本文提出了一种基于用户信号强度的四状态信道模型来描述高频阻塞事件的发生。然后使用两个深度学习神经网络学科进行信号强度预测和信道状态分类并进行评估。仿真结果具有较高的精度,说明该模型在实际系统中是可行的。
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