Spectrum Resource Management and Access Mode Selection in D2D-Enabled Vehicular Networks

Author(s):  
Can Chen ◽  
Jinghang Huang ◽  
Haixia Cui
IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 17725-17735 ◽  
Author(s):  
Shi Yan ◽  
Xinran Zhang ◽  
Hongyu Xiang ◽  
Wenbin Wu

2015 ◽  
Vol 62 (12) ◽  
pp. 7938-7951 ◽  
Author(s):  
Rong Yu ◽  
Xumin Huang ◽  
Jiawen Kang ◽  
Jiefei Ding ◽  
Sabita Maharjan ◽  
...  

2020 ◽  
Vol 68 (1) ◽  
pp. 654-666
Author(s):  
Shi Yan ◽  
Lin Qi ◽  
Yangcheng Zhou ◽  
Mugen Peng ◽  
G. M. Shafiqur Rahman

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 203
Author(s):  
Nalliyanna Goundar Veerappan Kousik ◽  
Yuvaraj Natarajan ◽  
Kallam Suresh ◽  
Rizwan Patan ◽  
Amir H. Gandomi

In the past decade, low power consumption schemes have undergone degraded communication performance, where they fail to maintain the trade-off between the resource and power consumption. In this paper, management of resource and power consumption on small cell orthogonal frequency-division multiple access (OFDMA) networks is enacted using the sleep mode selection method. The sleep mode selection method uses both power and resource management, where the former is responsible for a heterogeneous network, and the latter is managed using a deactivation algorithm. Further, to improve the communication performance during sleep mode selection, a semi-Markov sleep mode selection decision-making process is developed. Spectrum reuse maximization is achieved using a small cell deactivation strategy that potentially identifies and eliminates the sleep mode cells. The performance of this hybrid technique is evaluated and compared against benchmark techniques. The results demonstrate that the proposed hybrid performance model shows effective power and resource management with reduced computational cost compared with benchmark techniques.


2019 ◽  
Vol 25 (4) ◽  
pp. 58-61
Author(s):  
Katja Gilly ◽  
Sonja Filiposka ◽  
Salvador Alcaraz Carrasco ◽  
Anastas Mishev

The Internet of Vehicles requires high bandwidth and low latency services to unleash the potential of fully connected vehicles. Thus, the offloading proposals that successfully manage massive real-time service requests from vehicle nodes are needed. In this paper, we analyse the dynamic resource management for intelligent vehicular networks based on Multi-access Edge Computing architecture services. Using a combination of CloudSim and SUMO simulators, we present a case study of infotainment services in the city centre of Alicante, in Spain, that shows a high degree of optimality both in service allocation and migration when considering dense urban environments.


2021 ◽  
Vol 28 (5) ◽  
pp. 59-65
Author(s):  
Haixia Peng ◽  
Huaqing Wu ◽  
Xuemin Sherman Shen

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