Radio propagation measurement and cluster-based analysis for millimeter-wave cellular systems in dense urban environments

2021 ◽  
Vol 22 (4) ◽  
pp. 471-487
Author(s):  
Peize Zhang ◽  
Haiming Wang ◽  
Wei Hong
Keyword(s):  
Millimeter Wave ◽  
Urban Environments ◽  
Cellular Systems ◽  
Radio Propagation ◽  
Propagation Measurement

scholarly journals Radio Propagation Measurements and Cluster-Based Analysis for 5G Millimeter-Wave Cellular Systems in Dense Urban Environments

2020 ◽  
Author(s):  
Peize Zhang ◽  
Bensheng Yang ◽  
Haiming Wang ◽  
Cheng-Xiang Wang ◽  
Xiaohu You
Keyword(s):  
Millimeter Wave ◽  
High Efficiency ◽  
Channel Modeling ◽  
Urban Environments ◽  
Cellular Systems ◽  
Radio Propagation ◽  
Delay Spread ◽  
Multiple Reflections ◽  
Cluster Number ◽  
Channel Characteristics

Empirical channel modeling is necessary for the deployment of the fifth-generation (5G) millimeter-wave (mmWave) cellular system in actual environments. In this paper, cluster-based analyses of mmWave channel characteristics in two typical dense urban environments are performed. First, radio propagation measurement campaigns are conducted at two primary 5G bands of 28 GHz and 39 GHz in a central business district and a dense residential area. The custom-designed channel sounder supports high-efficiency directional scanning sounding, which helps to collect sufficient data for statistical channel modeling. Next, using an improved autoclustering algorithm, multipath clusters and their scattering sources are identified. Mapping results show that multiple reflections from exterior walls and diffraction over building corners or rooftops enhance the coverage for non-line-of-sight (NLoS) links and the influences of these propagation mechanisms are intuitively embodied as changes in the topologies of deployment environments. Finally, an appropriate measure for cluster-level channel characteristics is provided including cluster number, Ricean K-factor, root mean squared (RMS) delay spread, RMS angular spread, and their correlations. Comparisons of these parameters across two mmWave bands are also given. The measurement and modeling results shed light on a fully understanding of mmWave channels in dense urban environments across multiple bands.

scholarly journals Radio Propagation Measurements and Cluster-Based Analysis for 5G Millimeter-Wave Cellular Systems in Dense Urban Environments

2020 ◽  
Author(s):  
Peize Zhang ◽  
Bensheng Yang ◽  
Haiming Wang ◽  
Cheng-Xiang Wang ◽  
Xiaohu You
Keyword(s):  
Millimeter Wave ◽  
High Efficiency ◽  
Channel Modeling ◽  
Urban Environments ◽  
Cellular Systems ◽  
Radio Propagation ◽  
Delay Spread ◽  
Multiple Reflections ◽  
Cluster Number ◽  
Channel Characteristics

Empirical channel modeling is necessary for the deployment of the fifth-generation (5G) millimeter-wave (mmWave) cellular system in actual environments. In this paper, cluster-based analyses of mmWave channel characteristics in two typical dense urban environments are performed. First, radio propagation measurement campaigns are conducted at two primary 5G bands of 28 GHz and 39 GHz in a central business district and a dense residential area. The custom-designed channel sounder supports high-efficiency directional scanning sounding, which helps to collect sufficient data for statistical channel modeling. Next, using an improved autoclustering algorithm, multipath clusters and their scattering sources are identified. Mapping results show that multiple reflections from exterior walls and diffraction over building corners or rooftops enhance the coverage for non-line-of-sight (NLoS) links and the influences of these propagation mechanisms are intuitively embodied as changes in the topologies of deployment environments. Finally, an appropriate measure for cluster-level channel characteristics is provided including cluster number, Ricean K-factor, root mean squared (RMS) delay spread, RMS angular spread, and their correlations. Comparisons of these parameters across two mmWave bands are also given. The measurement and modeling results shed light on a fully understanding of mmWave channels in dense urban environments across multiple bands.

Radio propagation path loss models for 5G cellular networks in the 28 GHZ and 38 GHZ millimeter-wave bands

2014 ◽  
Vol 52 (9) ◽  
pp. 78-86 ◽  
Author(s):  
Ahmed Iyanda Sulyman ◽  
Almuthanna T. Nassar ◽  
Mathew K. Samimi ◽  
George R. Maccartney ◽  
Theodore S. Rappaport ◽  
...  
Keyword(s):  
Cellular Networks ◽  
Millimeter Wave ◽  
Path Loss ◽  
Radio Propagation ◽  
Propagation Path ◽  
Loss Models

scholarly journals Directional Radio Propagation Path Loss Models for Millimeter-Wave Wireless Networks in the 28-, 60-, and 73-GHz Bands

2016 ◽  
Vol 15 (10) ◽  
pp. 6939-6947 ◽  
Author(s):  
Ahmed Iyanda Sulyman ◽  
Abdulmalik Alwarafy ◽  
George R. MacCartney ◽  
Theodore S. Rappaport ◽  
Abdulhameed Alsanie
Keyword(s):  
Wireless Networks ◽  
Millimeter Wave ◽  
Path Loss ◽  
Radio Propagation ◽  
Propagation Path ◽  
Loss Models

scholarly journals On the Realistic Radio and Network Planning of IoT Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3264 ◽  
Author(s):  
Loizos Kanaris ◽  
Charalampos Sergiou ◽  
Akis Kokkinis ◽  
Aris Pafitis ◽  
Nikos Antoniou ◽  
...  
Keyword(s):  
Large Scale ◽  
Open Systems ◽  
Urban Environments ◽  
Radio Propagation ◽  
Correct Usage ◽  
Open Systems Interconnection ◽  
Physical Information ◽  
Network Simulators ◽  
And Performance ◽  
Real Test

Planning and deploying a functional large scale Wireless Sensor Network (WSN) or a Network of Internet of Things (IoTs) is a challenging task, especially in complex urban environments. A main network design bottleneck is the existence and/or correct usage of appropriate cross layer simulators that can generate realistic results for the scenario of interest. Existing network simulators tend to overlook the complexity of the physical radio propagation layer and consequently do not realistically simulate the main radio propagation conditions that take place in urban or suburban environments, thus passing inaccurate results between Open Systems Interconnection (OSI) layers. This work demonstrates through simulations and measurements that, by correctly passing physical information to higher layers, the overall simulation process produces more accurate results at the network layer. It is demonstrated that the resulting simulation methodology can be utilized to accomplish realistic wireless planning and performance analysis of the deployed nodes, with results that are very close to those of real test-beds, or actual WSN deployments.

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