The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if...The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.展开更多
Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research.The performance of new advanced models and technologies heavily depends on the accuracy of the wi...Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research.The performance of new advanced models and technologies heavily depends on the accuracy of the wireless CSI(Channel State Information).This study examined the randomness of the wireless channel parameters based on the characteristics of the radio propagation environment.The diversity of the statistical properties of wireless channel parameters inspired us to introduce the concept of the tomographic channel model.With this model,the static part of the CSI can be extracted from the huge amount of existing CSI data of previous measurements,which can be de ned as the wireless channel feature.In the proposed scheme for obtaining CSI with the tomographic channel model,the GMM(Gaussian Mixture Model)is applied to acquire the distribution of the wireless channel parameters,and the CNN(Convolutional Neural Network)is applied to automatically distinguish di erent wireless channels.The wireless channel feature information can be stored oine to guide the design of pilot symbols and save pilot resources.The numerical results based on actual measurements demonstrated the clear diversity of the statistical properties of wireless channel parameters and that the proposed scheme can extract the wireless channel feature automatically with fewer pilot resources.Thus,computing and storage resources can be exchanged for the nite and precious spectrum resource.展开更多
Existing mobility models have limitations in their ability to simulate the movement of Wireless Body Area Network（WBAN） since body nodes do not exactly follow either classic mobility models or human contact distribu...Existing mobility models have limitations in their ability to simulate the movement of Wireless Body Area Network（WBAN） since body nodes do not exactly follow either classic mobility models or human contact distributions. In this paper, we propose a new mobility model called Body Gauss–Markov Mobility（BGMM） model,which is oriented specially to WBAN. First, we present the random Gauss-Markov mobility model as the most suitable theoretical basis for developing our new model, as its movement pattern can reveal real human body movements. Next, we examine the transfer of human movement states and derive a simplified mathematical Human Mobility Model（HMM）. We then construct the BGMM model by combining the RGMM and HMM models. Finally,we simulate the traces of the new mobility model. We use four direct metrics in our proposed mobility model to evaluate its performance. The simulation results show that the proposed BGMM model performs with respect to the direct mobility metrics and can effectively represent a general WBAN-nodes movement pattern.展开更多
文摘The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.
基金This work is supported by the National Natural Science Foundation of China(No.61631013)National Key Basic Research Program of China(973 Program)(No.2013CB329002)+1 种基金National Major Project(No.2014ZX03003002-002)Program for New Century Excellent Talents in University(No.NCET-13-0321).
文摘Wireless channel modeling has always been one of the most fundamental highlights of the wireless communication research.The performance of new advanced models and technologies heavily depends on the accuracy of the wireless CSI(Channel State Information).This study examined the randomness of the wireless channel parameters based on the characteristics of the radio propagation environment.The diversity of the statistical properties of wireless channel parameters inspired us to introduce the concept of the tomographic channel model.With this model,the static part of the CSI can be extracted from the huge amount of existing CSI data of previous measurements,which can be de ned as the wireless channel feature.In the proposed scheme for obtaining CSI with the tomographic channel model,the GMM(Gaussian Mixture Model)is applied to acquire the distribution of the wireless channel parameters,and the CNN(Convolutional Neural Network)is applied to automatically distinguish di erent wireless channels.The wireless channel feature information can be stored oine to guide the design of pilot symbols and save pilot resources.The numerical results based on actual measurements demonstrated the clear diversity of the statistical properties of wireless channel parameters and that the proposed scheme can extract the wireless channel feature automatically with fewer pilot resources.Thus,computing and storage resources can be exchanged for the nite and precious spectrum resource.
基金supported by the National Natural Science Foundation of China(Nos.61171107 and 61271257)General program of science and technology development project of Beijing Municipal Education Commission(Nos.KM201510012005,SQKM201610012008,and SQKM201710012006)+1 种基金the research project of the China Scholarship Council(Nos.201509970037 and 201609970003)Beijing Institute of Fashion Technology College of Special Plan Young Top-notch Talent Project(No.BIFTBJ201803)
文摘Existing mobility models have limitations in their ability to simulate the movement of Wireless Body Area Network（WBAN） since body nodes do not exactly follow either classic mobility models or human contact distributions. In this paper, we propose a new mobility model called Body Gauss–Markov Mobility（BGMM） model,which is oriented specially to WBAN. First, we present the random Gauss-Markov mobility model as the most suitable theoretical basis for developing our new model, as its movement pattern can reveal real human body movements. Next, we examine the transfer of human movement states and derive a simplified mathematical Human Mobility Model（HMM）. We then construct the BGMM model by combining the RGMM and HMM models. Finally,we simulate the traces of the new mobility model. We use four direct metrics in our proposed mobility model to evaluate its performance. The simulation results show that the proposed BGMM model performs with respect to the direct mobility metrics and can effectively represent a general WBAN-nodes movement pattern.
