Periodic Reporting for period 1 - mmChannel (millimeter Wave Channel Modelling Considering Environmental Factors)
Période du rapport: 2020-09-01 au 2022-08-31
In order to meet the requirement of high-volume data throughput for future wireless telecommunication networks, new radio frequency spectrums for communication networks have to be exploited. The millimetre wave (mmWave) frequency band from 30 GHz to 300 GHz, which offers a wide bandwidth up to 4 GHz, is a promising candidate for 5G networks. Different from the radio frequency below 6 GHz, the mmWave has a higher path loss, verified by measurements on 28 GHz, 38 GHz, 60 GHz and 73 GHz recently. Furthermore, these measurements showed that the mmWave channel path loss is subject to various environmental factors.
The future wireless networks aim to provide ubiquitous wireless connections with high data throughput, especially in network application environments with high mobility and high heterogeneity. To guarantee the high quality-of-service of the networks, the mmWave wireless networks deployment needs to consider environmental impacts on the mmWave communication channels. Furthermore, with advanced channel models considering the characteristics of radio wave propagations, the network design and optimisation engineering industry benefit from highly efficient and accurate deployment of future wireless networks.
However, despite many recent research efforts to measure and to characterize the mmWave channel, a channel model developed for future wireless networks planning is still challenging. In order to develop such a channel model for future wireless network design applications, the following challenges need to be tackled. First, a parameterized channel model needs to be developed. Second, channel measurements need to be carried out to extract parameters for the developed channel model. Last, channel models need to be applied to evaluate and optimise the performance of the 5G networks in real-world environments. In this project, we developed advanced channel propagation models by addressing the above challenges specifically in the 5G and future wireless networks.
The overall technical objectives of this project are development and improvement of channel models for mmWave network design applications. These objectives are implemented in three stages in the project. In the first stage, new parameterised mmWave channel models with environmental factors are developed. In the second stage, channel data are acquired and used in training the model parameters. In the third stage, these newly developed models with new parameter values are applied to real-world wireless network application and demonstrated their effectiveness as network design tools. The achievements of these objectives offer a new set of tools for planning and optimising future wireless networks and yield new insights into future network performance.
The conclusions of this action include the following. In the first step, we developed parameterised channel models focusing on the mmWave frequency characteristics and environmental factors. In the second stage, we developed statistical method to train the modelling parameters using channel measurement data. In the third stage, the parameters and the models are demonstrated to be accurate and efficient tools in planning and optimisation of future wireless networks. The models and techniques developed in this action will benefit the research and development of future wireless networks in the long term.
The future wireless networks aim to provide ubiquitous wireless connections with high data throughput, especially in network application environments with high mobility and high heterogeneity. To guarantee the high quality-of-service of the networks, the mmWave wireless networks deployment needs to consider environmental impacts on the mmWave communication channels. Furthermore, with advanced channel models considering the characteristics of radio wave propagations, the network design and optimisation engineering industry benefit from highly efficient and accurate deployment of future wireless networks.
However, despite many recent research efforts to measure and to characterize the mmWave channel, a channel model developed for future wireless networks planning is still challenging. In order to develop such a channel model for future wireless network design applications, the following challenges need to be tackled. First, a parameterized channel model needs to be developed. Second, channel measurements need to be carried out to extract parameters for the developed channel model. Last, channel models need to be applied to evaluate and optimise the performance of the 5G networks in real-world environments. In this project, we developed advanced channel propagation models by addressing the above challenges specifically in the 5G and future wireless networks.
The overall technical objectives of this project are development and improvement of channel models for mmWave network design applications. These objectives are implemented in three stages in the project. In the first stage, new parameterised mmWave channel models with environmental factors are developed. In the second stage, channel data are acquired and used in training the model parameters. In the third stage, these newly developed models with new parameter values are applied to real-world wireless network application and demonstrated their effectiveness as network design tools. The achievements of these objectives offer a new set of tools for planning and optimising future wireless networks and yield new insights into future network performance.
The conclusions of this action include the following. In the first step, we developed parameterised channel models focusing on the mmWave frequency characteristics and environmental factors. In the second stage, we developed statistical method to train the modelling parameters using channel measurement data. In the third stage, the parameters and the models are demonstrated to be accurate and efficient tools in planning and optimisation of future wireless networks. The models and techniques developed in this action will benefit the research and development of future wireless networks in the long term.
Firstly, we developed parameterised ray-based channel models for advanced networks design applications. We parameterised the path loss values of reflections and transmission in ray-based propagation models in mmWave frequency. In addition, we have integrated these parameters into the ray-based propagation simulation model for network design applications. The models developed have been used as new networks design tools for mmWave networks. The models are further integrated in the commercial wireless networks design software product Ranplan Professional.
Secondly, we acquired channel measurement data in both indoor and outdoor environments. The channel measurement data are processed and used for training the channel models developed in the first stage. Furthermore, we used these channel measurement data to train the models and to determine the model parameters using statistical methods. The parameter values trained by the data are used as standard values in the commercial wireless networks design software product Ranplan Professional.
Lastly, we applied these newly developed models with trained parameters to evaluate the performance of communication networks in real world network application scenarios. Two specific network application scenarios are simulated: channel capacity in indoor environment and network performance in tunnel environment. In both cases, the new models have been applied to evaluate the performance of wireless networks. Implemented as a channel prediction module in the commercial networks design software product Ranplan Professional, the newly developed models with parameters values demonstrated their effectiveness in planning and optimisation of future wireless networks.
The results developed in this project have been presented at the weekly meetings of Ranplan’s research and development teams.
Secondly, we acquired channel measurement data in both indoor and outdoor environments. The channel measurement data are processed and used for training the channel models developed in the first stage. Furthermore, we used these channel measurement data to train the models and to determine the model parameters using statistical methods. The parameter values trained by the data are used as standard values in the commercial wireless networks design software product Ranplan Professional.
Lastly, we applied these newly developed models with trained parameters to evaluate the performance of communication networks in real world network application scenarios. Two specific network application scenarios are simulated: channel capacity in indoor environment and network performance in tunnel environment. In both cases, the new models have been applied to evaluate the performance of wireless networks. Implemented as a channel prediction module in the commercial networks design software product Ranplan Professional, the newly developed models with parameters values demonstrated their effectiveness in planning and optimisation of future wireless networks.
The results developed in this project have been presented at the weekly meetings of Ranplan’s research and development teams.
The project developed new channel models for future wireless network planning and optimisation. The models developed specifically addressed the challenges in mmWave channel modelling and characterisation. These models have been integrated and used in the commercial network design software product Ranplan Professional, for the planning and optimisation of 5G and future wireless networks. With the deployment of the mmWave frequency, such models and tools will continue to impact the planning and optimisation of future wireless networks.
The project host’s commercial network design software product Ranplan Professional is industrial-leading and widely adopted by both major wireless network operators and major network equipment providers. With a wide industrial user base, the Ranplan Professional software has an impact on the designing and optimisation of current and future wireless networks. The models and techniques developed in this project strengthen the host's market-leading position in wireless network design software.
The project host Ranplan deployed a cloud-based radio channel models to provide online 24/7 services for both mobile networks and smart city applications. As mmWave channel is affected by environmental conditions, the project results are critical for smart city and mobile network operations. Wireless networks are key infrastructures that will enable smart building and smart city applications. Radio channel conditions decide the performance of wireless networks. Thus, the models and techniques developed in this project will benefit the performance of wireless networks in the long term.
Furthermore, the during this action the research fellow has been trained through inter-sectoral collaborations between the telecommunication industry and academia. The action also benefits the host from the knowledge transfer between the fellow and the host. The results from this project will benefit the public, businesses and organizations from better use of mmWave spectrum for high data and reliable wireless networks.
The project host’s commercial network design software product Ranplan Professional is industrial-leading and widely adopted by both major wireless network operators and major network equipment providers. With a wide industrial user base, the Ranplan Professional software has an impact on the designing and optimisation of current and future wireless networks. The models and techniques developed in this project strengthen the host's market-leading position in wireless network design software.
The project host Ranplan deployed a cloud-based radio channel models to provide online 24/7 services for both mobile networks and smart city applications. As mmWave channel is affected by environmental conditions, the project results are critical for smart city and mobile network operations. Wireless networks are key infrastructures that will enable smart building and smart city applications. Radio channel conditions decide the performance of wireless networks. Thus, the models and techniques developed in this project will benefit the performance of wireless networks in the long term.
Furthermore, the during this action the research fellow has been trained through inter-sectoral collaborations between the telecommunication industry and academia. The action also benefits the host from the knowledge transfer between the fellow and the host. The results from this project will benefit the public, businesses and organizations from better use of mmWave spectrum for high data and reliable wireless networks.