Backscatter communication networks have attracted much attention due to their small size and low power waste, but their spectrum resources are very limited and are often affected by link bursts. Channel prediction is a method to effectively utilize the spectrum resources and improve communication quality. Most channel prediction methods have failed to consider both spatial and frequency diversity. Meanwhile, there are still deficiencies in the existing channel detection methods in terms of overhead and hardware dependency. For the above reasons, we design a sequence-to-sequence channel prediction scheme. Our scheme is designed with three modules. The channel prediction module uses an encoder-decoder based deep learning model (EDChannel) to predict the sequence of channel indicator measurements. The channel detection module decides whether to perform a channel detection by a trigger that reflects the prediction effect. The channel selection module performs channel selection based on the channel coefficients of the prediction results. We use a commercial reader to collect data in a real environment, and build an EDChannel model based on the deep learning module of Tensorflow and Keras. As a result, we have implemented the channel prediction module and completed the overall channel selection process. The experimental results show that the EDChannel algorithm has higher prediction accuracy than the previous state-of-the-art methods. The overall throughput of our scheme is improved by approximately 2.9% and 14.1% over Zhao’s scheme in both stable and unstable environments.

反向散射通信网络因其体积小、功耗低而备受关注，但其频谱资源非常有限，且经常受到链路突发的影响。信道预测是一种有效利用频谱资源，提高通信质量的方法。大多数信道预测方法未能同时考虑空间和频率分集。同时，现有的信道检测方法在开销和硬件依赖方面还存在不足。由于上述原因，我们设计了一个序列到序列的信道预测方案。我们的方案设计了三个模块。通道预测模块使用基于编码器-解码器的深度学习模型 (EDChannel) 来预测通道指标测量的序列。通道检测模块通过反映预测效果的触发器来决定是否进行通道检测。通道选择模块根据预测结果的通道系数进行通道选择。我们使用商业阅读器在真实环境中采集数据，并基于 Tensorflow 和 Keras 的深度学习模块构建 EDChannel 模型。至此，我们实现了频道预测模块，完成了整个频道选择过程。实验结果表明，EDChannel算法比之前的state-of-the-art方法具有更高的预测精度。在稳定和不稳定的环境中，我们方案的整体吞吐量比赵的方案分别提高了大约 2.9% 和 14.1%。通道选择模块根据预测结果的通道系数进行通道选择。我们使用商业阅读器在真实环境中采集数据，并基于 Tensorflow 和 Keras 的深度学习模块构建 EDChannel 模型。至此，我们实现了频道预测模块，完成了整个频道选择过程。实验结果表明，EDChannel算法比之前的state-of-the-art方法具有更高的预测精度。在稳定和不稳定的环境中，我们方案的整体吞吐量比赵的方案分别提高了大约 2.9% 和 14.1%。通道选择模块根据预测结果的通道系数进行通道选择。我们使用商业阅读器在真实环境中采集数据，并基于 Tensorflow 和 Keras 的深度学习模块构建 EDChannel 模型。至此，我们实现了频道预测模块，完成了整个频道选择过程。实验结果表明，EDChannel算法比之前的state-of-the-art方法具有更高的预测精度。在稳定和不稳定的环境中，我们方案的整体吞吐量比赵的方案分别提高了大约 2.9% 和 14.1%。并基于Tensorflow和Keras的深度学习模块构建EDChannel模型。至此，我们实现了频道预测模块，完成了整个频道选择过程。实验结果表明，EDChannel算法比之前的state-of-the-art方法具有更高的预测精度。在稳定和不稳定的环境中，我们方案的整体吞吐量比赵的方案分别提高了大约 2.9% 和 14.1%。并基于Tensorflow和Keras的深度学习模块构建EDChannel模型。至此，我们实现了频道预测模块，完成了整个频道选择过程。实验结果表明，EDChannel算法比之前的state-of-the-art方法具有更高的预测精度。在稳定和不稳定的环境中，我们方案的整体吞吐量比赵的方案分别提高了大约 2.9% 和 14.1%。