Computational Experiments and Comparative Analysis of Signal Detection Algorithms in Vehicular Ad Hoc Networks

In the era of rapid development of vehicular ad hoc networks (VANETs), ensuring the reliability and security of vehicle-to-vehicle communication has become a top priority. This paper comprehensively analyzes the performance of various signal detection algorithms in different scenarios. To intelligen...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of radio frequency identification (Online) Vol. 8; pp. 402 - 411
Main Authors: Li, Yi, Hao, Conghui, Xie, Yupei, Han, Shuangshuang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Automobile industry
Bit error rate
Intelligent transportation systems
Interference cancellation
Mobile ad hoc networks
Mountainous areas
Nakagami distribution
Network reliability
parallel intelligence
Parallel processing
Performance measurement
Rician channels
Rural environments
Scenario generation
scenario-based channels
Signal detection
signal detection algorithms
Transportation
Transportation networks
Urban environments
Vehicular ad hoc networks
ISSN:2469-7281, 2469-729X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the era of rapid development of vehicular ad hoc networks (VANETs), ensuring the reliability and security of vehicle-to-vehicle communication has become a top priority. This paper comprehensively analyzes the performance of various signal detection algorithms in different scenarios. To intelligently choose different signal detection algorithms in the context of VANETs, the study covers diverse scenarios such as urban environments, rural areas, highways, parking lots, and mountainous regions, aiming to capture subtle variations in the performance of different signal detection algorithms across these scenarios. The paper employs strict performance metrics, such as bit error rate and algorithmic complexity, to quantify and compare the performance of different signal detection algorithms. The focus is on the role of signal detection algorithms in achieving parallel intelligence in VANETs, including the simultaneous processing of signals from multiple vehicles to enhance overall network efficiency and reliability. This research holds significance by providing insights into the strengths and limitations of signal detection algorithms in VANETs, guiding their development for efficient and accurate performance, thereby contributing to academic understanding and informing decision-making in the automotive industry and intelligent transportation systems.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2469-7281
2469-729X
DOI:10.1109/JRFID.2024.3355298