Efficient DRL-Based Selection Strategy in Hybrid Vehicular Networks

Yacheur, Badreddine Yacine; Ahmed, Toufik; Mosbah, Mohamed

doi:10.1109/tnsm.2023.3300653

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…Reliability gains are seen with the constructive combination of ITS-G5 and C-V2X PC5. Two more recent studies in [6,7] have considered Deep Reinforcement Learning (DRL)-based selection strategy between DSRC and C-V2X PC5 mode 3. Specifically, the selection is based on the channel load, SNIR and latency.…”

Section: Related Workmentioning

confidence: 99%

“…Among the available technologies, the focus is on minimizing the load on stochastic queues and maximizing the throughput using the LSTM prediction technique. The implementation of mode 3 in [6][7][8] necessitates additional infrastructure and control signals, with mandatory base station coverage. An evolutionary game-based technology selection approach is considered in [9] for selection between DSRC and C-V2X mode 4 with a goal to maximize the transmission throughput.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the broadcasting of both payoff and population profile to the V2X node incurs an extra communication link. Studies in [5][6][7][8][9] exclusively focus on short-range technologies, potentially resulting in coverage limitations. Furthermore, these studies lack consideration for various C-ITS messages and the adaptation of message rates.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Hybrid V2X Communication: An Intelligent Technology Selection Algorithm Using 5G, C-V2X PC5 and DSRC

Khalid,

Maglogiannis,

Naudts

et al. 2024

Future Internet

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Cooperative communications advancements in Vehicular-to-Everything (V2X) are bolstering the autonomous driving paradigm. V2X nodes are connected through communication technology, such as a short-range communication mode (Dedicated Short Range Communication (DSRC) and Cellular-V2X) or a long-range communication mode (Uu). Conventional vehicular networks employ static wireless vehicular communication technology without considering the traffic load on any individual V2X communication technology and the traffic dynamics in the vicinity of the V2X node, and are hence inefficient. In this study, we investigate hybrid V2X communication and propose an autonomous and intelligent technology selection algorithm using a decision tree. The algorithm uses the information from the received Cooperative Intelligent Transport Systems (C-ITS) Cooperative Awareness Messages (CAMs) to collect statistics such as inter vehicular distance, one-way end-to-end latency and CAM density. These statistics are then used as input for the decision tree for selecting the appropriate technology (DSRC, C-V2X PC5 or 5G) for the subsequent scheduled C-ITS message transmission. The assessment of the intelligent hybrid V2X algorithm’s performance in our V2X test setup demonstrates enhancements in one-way end-to-end latency, reliability, and packet delivery rate when contrasted with the conventional utilization of static technology.

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Section: Related Workmentioning

confidence: 99%