Zeng Sheng, Ji Yuhang, Chen Weiwei, Yan Liping, Zhao Xiang
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Sensors (Basel). 2024 Aug 10;24(16):5178. doi: 10.3390/s24165178.
A coverage prediction model helps network operators find coverage gaps, plan base station locations, evaluate quality of service, and build radio maps for spectrum sharing, interference management, localization, etc. Existing coverage prediction models rely on the height and transmission power of the base station, or the assistance of a path loss model. All of these increase the complexity of large-scale coverage predictions. In this paper, we propose a multi-modal model, DNN-SS, which combines a DNN (deep neural network) and SS (semantic segmentation) to perform coverage prediction for mobile networks. Firstly, DNN-SS filters the samples with a geospatial-temporal moving average filter algorithm, and then uses a DNN to extract numerical features. Secondly, a pre-trained model is used to perform semantic segmentation of satellite images of the measurement area. Thirdly, a DNN is used to extract features from the results after semantic segmentation to form environmental features. Finally, the prediction model is trained on the dataset consisting of numerical features and environmental features. The experimental results on campus show that for random location prediction, the model achieves a RMSE (Root Mean Square Error) of 1.97 dB and a MAE (Mean Absolute Error) of 1.41 dB, which is an improvement of 10.86% and 10.2%, respectively, compared with existing models. For the prediction of a test area, the RMSE and MAE of the model are 4.32 dB and 3.45 dB, respectively, and the RMSE is only 0.22 dB lower than that of existing models. However, the DNN-SS model does not need the height, transmission power, and antenna gain of the base station, or a path loss model, which makes it more suitable for large-scale coverage prediction.
覆盖预测模型可帮助网络运营商发现覆盖漏洞、规划基站位置、评估服务质量以及构建用于频谱共享、干扰管理、定位等的无线电地图。现有的覆盖预测模型依赖于基站的高度和发射功率，或者路径损耗模型的辅助。所有这些都增加了大规模覆盖预测的复杂性。在本文中，我们提出了一种多模态模型DNN-SS，它结合了深度神经网络（DNN）和语义分割（SS）来对移动网络进行覆盖预测。首先，DNN-SS使用地理时空移动平均滤波算法对样本进行滤波，然后使用DNN提取数值特征。其次，使用预训练模型对测量区域的卫星图像进行语义分割。第三，使用DNN从语义分割后的结果中提取特征以形成环境特征。最后，在由数值特征和环境特征组成的数据集上训练预测模型。校园实验结果表明，对于随机位置预测，该模型的均方根误差（RMSE）为1.97 dB，平均绝对误差（MAE）为1.41 dB，与现有模型相比分别提高了10.86%和10.2%。对于测试区域的预测，该模型的RMSE和MAE分别为4.32 dB和3.45 dB，RMSE仅比现有模型低0.22 dB。然而，DNN-SS模型不需要基站的高度、发射功率和天线增益，也不需要路径损耗模型，这使其更适合大规模覆盖预测。
