2021
DOI: 10.3991/ijim.v15i21.22475
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Vehicular Networks Performance Evaluation Based on Downlink Scheduling Algorithms for High-Speed Long Term Evolution – Vehicle

Abstract: <p class="Abstract"><span lang="EN-US">Moving is the key to modern life. Most things are in moving such as vehicles and user mobiles, so the need for high-speed wireless networks to serve the high demand of the wireless application becomes essential for any wireless network design. The use of web browsing, online gaming, and on-time data exchange like video calls as an example means that users need a high data rate and fewer error communication links. To satisfy this, increasing the bandwidth avail… Show more

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Cited by 10 publications
(5 citation statements)
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“…For example, in [30] a radio resource allocation using a genetic algorithm (GA) in heterogeneous networks is proposed to optimize user association and resource block allocation and realize multi-QoS objectives. Some resource allocation solutions are especially designed for vehicular scenarios, for example [32], where interference level of neighboring vehicles is counted in the scheduling function.…”
Section: Related Workmentioning
confidence: 99%
“…For example, in [30] a radio resource allocation using a genetic algorithm (GA) in heterogeneous networks is proposed to optimize user association and resource block allocation and realize multi-QoS objectives. Some resource allocation solutions are especially designed for vehicular scenarios, for example [32], where interference level of neighboring vehicles is counted in the scheduling function.…”
Section: Related Workmentioning
confidence: 99%
“…The network coverage area is 2000 * 2000 meters. For peer-to-peer data, transmission vehicles create constant bit rate (CBR) traffic as [26][27][28]. The simulation parameters are shown in Table 1.…”
Section: Gateway Selectionmentioning
confidence: 99%
“…Ref. [37] developed an enhanced best-CQI scheduling technique to increase the network's throughput. To test the proposed algorithm's throughput performance, they compared it to three user scheduling algorithms.…”
Section: Related Workmentioning
confidence: 99%