Dynamic MIMO channel modeling and parameter calculating method for internet of vehicles

Abstract

The invention discloses a dynamic MIMO channel modeling and parameter calculating method for the internet of vehicles, the invention provides a V2V dynamic MIMO channel modeling method, and various factors influencing propagation of radio waves, such as the movement of the transceiving vehicle, the movement of the scatterers, the movement speed, the track changes are comprehensively considered; the V2V dynamic MIMO channel modeling method and the parameter calculation method provided support time-varying channel model parameters, are suitable for any three-dimensional antenna array and the propagation scene of any vehicle moving track, and the continuity of the output channel fading phase can be ensured.

Description

Technical field: [0001] The invention relates to modeling and simulation of a mobile communication channel of the Internet of Vehicles, belonging to the field of wireless communication, in particular to a dynamic MIMO channel modeling method and a channel parameter calculation method considering random changes in vehicle speeds under complex traffic conditions. Background technique: [0002] With the rapid increase of road traffic density, road safety and traffic flow management issues need to be solved urgently. Vehicle-to-vehicle (V2V) communication system can effectively improve the safety and convenience of road traffic, and has become one of the key transmission technologies concerned by the fifth generation of mobile communication. Multiple Input Multiple Output (MIMO) technology can realize high-speed data transmission without increasing bandwidth and transmission power, and significantly improve the capacity and reliability of communication systems. Therefore, const...

AI Technical Summary

Problems solved by technology

At present, most V2V channel models assume that the mobile terminal moves in a specific direction at a constant speed. However, in the actual traffic environment, due to the influence of surrounding vehicles and facilities and traffic lights, the vehicle will experience acceleration or deceleration during the movement process. , and on turns and uneven roads, the vehicle will change its direction of motion

Changes in the magnitude and direction of motion speed will have an important impact on channel characteristics, but there are few V2V channel models that consider vehicle speed changes

On the other hand, most of the current non-stationary channel modeling methods do not take into account the continuity between the channel states of different scene segments, resulting in the output Doppler frequency and phase not matching the actual

Images