Exploring LTE‐V link‐level performance under V‐CPS by geometry enhanced winner II channel model

Abstract

The reliable and efficient simulation of a Long‐Term Evolution‐vehicle requires a channel model that considers practical features of vehicle ad hoc networks, such as large‐scale and small‐scale fading effects from static and mobile objects. However, existing vehicle‐to‐vehicle (V2V) channel models seldom consider vehicle ad hoc networks. In this paper, we propose a geometry‐enhanced WINNER II V2V channel model, which simulates channel responses among vehicles in arbitrary locations with detailed geographical information (eg, placement of buildings). After exploiting the outlines of vehicles and buildings, line‐of‐sight/nonline‐of‐sight links are evaluated. In addition, multiple‐interaction reflections and diffractions are calculated. Then, signal variations and multilink time delays are generated. Therefore, it reflects practical communication conditions, are more reliable than existing Long‐Term Evolution V2V channel models. The model is applied to our developed link‐level simulator, which conforms to the Third‐Generation Partnership Project Release 14, and is compared with two other common channel models. Results validate the importance of the surrounding geometry compared with models with simple large‐scale and small‐scale propagation paths.

An efficient artificial rabbits optimization‐based user association problems in ultradense network using deep learning model

“…The WINNER channel model is a popular channel that is used for many applications 26 . The data for this study are obtained using the WINNER channel model and by distributing the UE around each SBS.…”

“…The WINNER channel model is a popular channel that is used for many applications. 26 The data for this study are obtained using the WINNER channel model and by distributing the UE around each SBS. In total, 1500 samples were collected, and 80% of data samples were utilized for training, and 20% of data samples were utilized for testing purposes.…”

Grid-Based Channel Modeling Technique for Scenario-Specific Wireless Channel Emulator Based on Path Parameters Interpolation

“…R ECENT technological advancement in communication has driven many industries toward automation systems, such as cooperative intelligent transport systems [1], droneassisted vehicle communication system [2], smart factory [3], smart farm [4], and smart healthcare [5]. In these systems, many wireless communication links between sensors are constantly used.…”

“…The performance of the emulator is significantly dependent on the accuracy of the channel models applied. For example, the work in [1] showed that the block error rate curve of a vehicle-to-vehicle wireless system can vary depending on the channel model. As demonstrated in previous studies [11], [12], the statistical properties, including the K-factor, delay spread, path gain, and spatial correlation function, can exhibit significant variations when different channel models are employed in dynamic scenarios.…”

Distributed Estimation Framework for Beyond 5G Intelligent Vehicular Networks

“…The idea of average consensus, which originated from the field of automata theory and distributed computing [74], has attracted numerous interests for its applications in multiagent/multi-vehicle networks [75], which can efficiently avoid the double counting of data and can facilitate the averaging of the vehicles' initial estimates. This is achieved by sharing their information between vehicles and iteratively update their local estimates.…”