Wireless Channel Prediction in Different Locations Using Transfer Learning
Wireless Channel Prediction in Different Locations Using Transfer Learning
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DOI：
10.1145/3565287.3617634
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Jieying Chen;Abdalaziz Sawwan;Shuhui Yang;Jie Wu
中科院分区：
文献类型：
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作者：
Jieying Chen;Abdalaziz Sawwan;Shuhui Yang;Jie Wu
迁移学习是指将特定领域的知识迁移到相关领域。在源学习器和目标学习器具有相似分布和参数的情况下，迁移学习可以降低学习和目标学习器的构建成本，提高目标学习器的性能。在无线自组织网络中，用户根据服务位置连接到网络，并且具有不同服务质量（QoS）水平的各种网络通道可用。无线信道代表特定的无线电频率范围。当用户从一个位置移动到另一位置时，移动应用程序可以切换频道以获得良好的服务质量。本文根据用户的位置预测无线信道。由于基于位置的信道预测在一个城市是可行的，因此一个城市的信道预测知识可以转移到另一个城市。因此，迁移学习在此类应用中是适用且有效的。该论文使用两个城市的无线地图数据集来预测网络通道，并使用迁移学习基于另一个城市的模型来预测一个城市的网络通道。在训练过程中使用不同初始学习率以及不同源域和目标域数据比例的实验表明，迁移学习对于不同城市之间的网络预测是可行的。
Transfer learning refers to transferring the knowledge of a specific domain to a related domain. In cases where the source and the target learner have similar distribution and parameters, transfer learning can reduce the cost of learning and the construction of the target learner and improve the performance of the target learner. In wireless ad-hoc networks, the users connect to networks based on the service location, and various network channels with different levels of quality-of-service (QoS) are available. The wireless channels represent specific ranges of radio frequencies. When the users move from one location to another, the mobile application may switch channels for good quality of service. This paper predicts the wireless channel based on the user's location. Since channel prediction based on location is feasible in one city, the knowledge of channel prediction in one city can be transferred to another city. Thus, transfer learning is applicable and effective in such applications. The paper uses two cities' wireless mapping datasets to predict network channels and uses transfer learning to predict one city's network channels based on the other city's model. Experiments using different initial learning rates during training and different source and target domain data ratios show that transfer learning is feasible for network prediction among different cities.