Bocharova et al., 2019 - Google Patents
Characterizing packet losses in vehicular networksBocharova et al., 2019
- Document ID
- 3009677281615153699
- Author
- Bocharova I
- Kudryashov B
- Rabi M
- Lyamin N
- Dankers W
- Frick E
- Vinel A
- Publication year
- Publication venue
- IEEE Transactions on Vehicular Technology
External Links
Snippet
To enable testing and performance evaluation of new connected and autonomous driving functions, it is important to characterize packet losses caused by degradation in vehicular (V2X) communication channels. In this paper we suggest an approach to constructing …
- 238000000034 method 0 abstract description 26
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Bocharova et al. | Characterizing packet losses in vehicular networks | |
Pokhrel et al. | A decentralized federated learning approach for connected autonomous vehicles | |
CN107396322B (en) | Indoor positioning method based on path matching and coding-decoding cyclic neural network | |
Ishwar et al. | On rate-constrained distributed estimation in unreliable sensor networks | |
Khayam et al. | Markov-based modeling of wireless local area networks | |
US20220383118A1 (en) | Generating variable communication channel responses using machine learning networks | |
Cheng et al. | Multi-bit & sequential decentralized detection of a noncooperative moving target through a generalized Rao test | |
Dey et al. | Remote estimation with noisy measurements subject to packet loss and quantization noise | |
López et al. | Statistical tools and methodologies for ultrareliable low-latency communication—A tutorial | |
Nguyen et al. | Applying time series analysis and neighbourhood voting in a decentralised approach for fault detection and classification in WSNs | |
CN113364543B (en) | Edge calculation model training method based on federal reinforcement learning | |
CN114531729A (en) | Positioning method, system, storage medium and device based on channel state information | |
Jia et al. | Recursive state estimation for a class of quantized coupled complex networks subject to missing measurements and amplify-and-forward relay | |
CN115600051B (en) | Orbit maneuver intelligent detection method and device based on short-arc space-based optical observation | |
Park et al. | Dropout autoencoder fingerprint augmentation for enhanced Wi-Fi FTM-RSS indoor localization | |
CN116841732A (en) | Federal learning resource optimization design method based on single-bit quantization | |
CN115086375A (en) | Method, device, system and medium for compensating motion state information delay of networked vehicle | |
CN109039531B (en) | Method for adjusting LT code coding length based on machine learning | |
Zhu et al. | Sensors scheduling for remote state estimation over an unslotted CSMA/CA channel | |
Blanc et al. | Delay independence of mutual-information rate of two symbolic sequences | |
Hu et al. | Semharq: Semantic-aware harq for multi-task semantic communications | |
Bano et al. | A federated channel modeling system using generative neural networks | |
CN116527180A (en) | SCMA method based on CWGAN-GP satellite-ground link channel modeling | |
Bocharova et al. | Modeling packet losses in communication networks | |
Kim et al. | Partial sample transmission and deep neural decoding for URLLC-based V2X systems |