摘要
无线传播模型由于其对无线电波路径损耗的精准预测及对通信速率与覆盖范围等指标的估算起重要支撑作用,被广泛应用于民用和军用的通信系统设计。近年来,随着人工智能技术的发展,无线传播模型的发展方向也由传统的经验模型向基于数据驱动的智能无线传播模型发展,该类方法可有效地扩展无线传播模型的适用范围并减小预测误差。然而,由于在不同环境下智能无线传播模型的适用特征可能并不相同,如何针对不同场景最优地为智能无线传播模型设计以及选择输入特征是一个重要的研究问题。立足以上需求,提出了一种自适应智能无线传播模型。首先,该模型借鉴经验模型在不同场景下对频率、距离等特征的不同处理方式,对现有的输入特征集合进行了扩充;然后,基于在建模区域采集的训练数据,该模型利用模拟退火算法来自适应地针对当前建模区域为智能无线传播模型选择最优的输入特征子集,从而避免受到无关特征的影响;最后,基于优化过程所搜索到的最优输入特征子集,该模型利用采集到的全部数据对智能无线传播模型进行重新训练,并将该智能无线传播模型进行部署,以预测该区域的路径损耗。仿真结果表明,在复合地形下的LTE网络数据以及其他典型数据集下,与传统的经验模型以及现有的智能无线传播模型相比,所提模型对各种传播场景均具有适用性,且进一步减小了路径损耗的预测误差。
The wireless propagation model,which can accurately predict the path loss of radio waves,plays an important role in the estimation of communication rate,coverage and interference.It plays a fundamental role in the design of communication systems in civil and military fields.With the advance in artificial intelligence,there appears a significant trend to develop intelligent wireless propagation model that replaces the empirical formula with machine learning algorithms to fit the path loss.The intelligent wireless propagation model effectively extends the applicability of the propagation model and reduces the error in predicting path loss.However,because the optimal input features set of the intelligent wireless propagation model may be different in diffe-rent propagation environments,it is important to optimally design and select the input features for different scenarios.Therefore,this paper proposes a self-adaptive intelligent wireless propagation model(SAIWP).Firstly,inspired by the processing methods of empirical model for features in different scenarios,the SAIWP model extends the input features set of the intelligent wireless propagation model.And then,the SAIWP model uses the simulated annealing algorithm to self-adaptively select the optimal input feature subset to reduce the error in the prediction of path loss.Finally,the SAIWP model exploits the optimal input feature subset in the optimization process and all data set to train the intelligent wireless propagation model.Simulation results show that,in the LTE networks and the smart campus,compared with traditional empirical models and intelligent wireless propagation models,the SAIWP model predict accurately in various terrains and distances,and effectively reduces the error in the prediction of path loss.
作者
高士顺
赵海涛
张晓瀛
魏急波
GAO Shi-shun;ZHAO Hai-tao;ZHANG Xiao-ying;WEI Ji-bo(College of Electronic Science,National University of Defense Technology,Changsha 410073,China)
出处
《计算机科学》
CSCD
北大核心
2021年第7期324-332,共9页
Computer Science
基金
国家自然科学基金重点项目(61931020)。
关键词
深度学习
无线传播模型
模拟退火算法
经验模型
Deep learning
Wireless propagation model
Simulated annealing algorithm
Empirical model
