Joint Vehicular and Static Users Multiplexing Transmission With Hierarchical Modulation for Throughput Maximization in Vehicular Networks

2020 ◽  
Vol 21 (9) ◽  
pp. 3835-3847 ◽  
Author(s):  
Qian Gao ◽  
Siyu Lin ◽  
Gang Zhu
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Huang ◽  
Xinqiang Ma ◽  
Youyuan Liu ◽  
Zhigang Yang

How to improve delay-sensitive traffic throughput is an open issue in vehicular communication networks, where a great number of vehicle to infrastructure (V2I) and vehicle to vehicle (V2V) links coexist. To address this issue, this paper proposes to employ a hybrid deep transfer learning scheme to allocate radio resources. Specifically, the traffic throughput maximization problem is first formulated by considering interchannel interference and statistical delay guarantee. The effective capacity theory is then applied to develop a power allocation scheme on each channel reused by a V2I and a V2V link. Thereafter, a deep transfer learning scheme is proposed to obtain the optimal channel assignment for each V2I and V2V link. Simulation results validate that the proposed scheme provides a close performance guarantee compared to a globally optimal scheme. Besides, the proposed scheme can guarantee lower delay violation probability than the schemes aiming to maximize the channel capacity.


2010 ◽  
Vol E93-B (6) ◽  
pp. 1645-1648 ◽  
Author(s):  
Jeong-Chul SHIN ◽  
Jin-Hyuk SONG ◽  
Jee-Hoon KIM ◽  
Hyoung-Kyu SONG

2013 ◽  
Vol 32 (4) ◽  
pp. 900-904 ◽  
Author(s):  
Xiao-yang LIU ◽  
Min-you WU
Keyword(s):  

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