Fast and Efficient Cross Band Channel Prediction Using Machine Learning
Fast and Efficient Cross Band Channel Prediction Using Machine Learning
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DOI：
10.1145/3300061.3345438
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发表时间：
2019-08
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影响因子：
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通讯作者：
Arjun Bakshi;Yifan Mao;K. Srinivasan;S. Parthasarathy
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作者：
Arjun Bakshi;Yifan Mao;K. Srinivasan;S. Parthasarathy
渠道信息在现代无线通信系统中起着重要作用。使用不同频段进行上行链路和下行链路通信的系统通常需要设备之间的反馈来交换特定的频道信息。当前的最新方法提出了一种通过识别上行链路通道的基础变量来根据观察到的上行链路的下行链路中预测通道的方法。在本文中，我们提出了一个解决方案，该解决方案大大降低了此任务的复杂性，甚至适用于单个天线设备。我们的方法使用在标准通道模型上训练的神经网络来生成通道基础变量的粗略估计。然后，我们使用简单有效的单个天线优化框架来获得更准确的变量估计，可用于下行链路通道预测。我们在软件定义的无线电上实施方法，并通过实验和仿真将其与最先进的方法进行比较。结果表明，我们的方法将时间复杂性降低至少一个数量级（10倍），同时保持相似的预测质量。
Channel information plays an important role in modern wireless communication systems. Systems that use different frequency bands for uplink and downlink communication often need feedback between devices to exchange band specific channel information. The current state-of-the-art approach proposes a way to predict the channel in the downlink based on that of the observed uplink by identifying variables underlying the uplink channel. In this paper we present a solution that greatly reduces the complexity of this task, and is even applicable for single antenna devices. Our approach uses a neural network trained on a standard channel model to generate coarse estimates for the variables underlying the channel. We then use a simple and efficient single antenna optimization framework to get more accurate variable estimates, which can be used for downlink channel prediction. We implement our approach on software defined radios and compare it to the state-of-the-art through experiments and simulations. Results show that our approach reduces the time complexity by at least an order of magnitude (10x), while maintaining similar prediction quality.