TY - GEN
T1 - ADSA
T2 - 2022 IEEE International Conference on Communications, ICC 2022
AU - Yin, Chenyang
AU - Dong, Ping
AU - Du, Xiaojiang
AU - Zhang, Yuyang
AU - Zhang, Hongke
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Cognitive Radio (CR) enabled vehicles in Vehicle Networks can use multiple Radio Access Networks (RANs) for data transmission. The simultaneous use of multiple RANs for transmission requires the design of a specific multi-path transmission protocol. Many scholars have studied the scheduling algorithm to improve the quality of multi-path transmission. However, most of the existing scheduling algorithms are difficult to deal with the challenges brought by the diversity and heterogeneity of the vehicle network. To deal with these challenges, this paper proposes an IP layer Deep Reinforcement Learning (DRL) multi-path transmission scheduling algorithm named Adaptive Dynamic Scheduling Algorithm (ADSA), which can dynamically generate the optimal scheduling policy through the interaction between agent and network environment. This paper first models the data packet scheduling strategy of multi-path transmission into an optimization problem of multi-path transmission efficiency. Then this paper transforms the optimization problem into a DRL problem and finds the optimal scheduling strategy through DRL model training. This paper evaluates the network performance of ADSA in different network scenarios compared with traditional scheduling algorithms. Simulation results show that ADSA increases the throughput by 8.9 Mbps compared with the three traditional scheduling algorithms and reduces the transmission delay by 4.3 ms.
AB - Cognitive Radio (CR) enabled vehicles in Vehicle Networks can use multiple Radio Access Networks (RANs) for data transmission. The simultaneous use of multiple RANs for transmission requires the design of a specific multi-path transmission protocol. Many scholars have studied the scheduling algorithm to improve the quality of multi-path transmission. However, most of the existing scheduling algorithms are difficult to deal with the challenges brought by the diversity and heterogeneity of the vehicle network. To deal with these challenges, this paper proposes an IP layer Deep Reinforcement Learning (DRL) multi-path transmission scheduling algorithm named Adaptive Dynamic Scheduling Algorithm (ADSA), which can dynamically generate the optimal scheduling policy through the interaction between agent and network environment. This paper first models the data packet scheduling strategy of multi-path transmission into an optimization problem of multi-path transmission efficiency. Then this paper transforms the optimization problem into a DRL problem and finds the optimal scheduling strategy through DRL model training. This paper evaluates the network performance of ADSA in different network scenarios compared with traditional scheduling algorithms. Simulation results show that ADSA increases the throughput by 8.9 Mbps compared with the three traditional scheduling algorithms and reduces the transmission delay by 4.3 ms.
KW - Cognitive radio
KW - Deep reinforcement learning
KW - Multi-path transmission
KW - Vehicle networks
UR - http://www.scopus.com/inward/record.url?scp=85137270572&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137270572&partnerID=8YFLogxK
U2 - 10.1109/ICC45855.2022.9839283
DO - 10.1109/ICC45855.2022.9839283
M3 - Conference contribution
AN - SCOPUS:85137270572
T3 - IEEE International Conference on Communications
SP - 5058
EP - 5063
BT - ICC 2022 - IEEE International Conference on Communications
Y2 - 16 May 2022 through 20 May 2022
ER -