2017 IEEE 6th Global Conference on Consumer Electronics (GCCE) 2017
DOI: 10.1109/gcce.2017.8229456
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Validation of theoretical evaluation on user cooperative mobility for QoS improvement in ad-hoc networks

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Cited by 2 publications
(3 citation statements)
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“…Ryo Hamamoto et al [10] introduced an AP selection method based on movable APs and users. In addition, references [7], [8] and Tianran Luo et al evaluated the system throughput characteristics of a single movable user and considered the capture effect or dynamic back-off time. Nonetheless, the scheme of multiple user mobility by the cooperative method has not been explored due to sophisticated transmission strategies and optimization criteria.…”
Section: B User Cooperative Mobility Approachmentioning
confidence: 99%
“…Ryo Hamamoto et al [10] introduced an AP selection method based on movable APs and users. In addition, references [7], [8] and Tianran Luo et al evaluated the system throughput characteristics of a single movable user and considered the capture effect or dynamic back-off time. Nonetheless, the scheme of multiple user mobility by the cooperative method has not been explored due to sophisticated transmission strategies and optimization criteria.…”
Section: B User Cooperative Mobility Approachmentioning
confidence: 99%
“…Ryo Hamamoto et al [23] introduced an AP selection method based on collaboration among APs and user's mobility under the single user and the limited distances. In addition, Tianran Luo et al [24], [25] evaluated system throughput features of the single movable user and validated the best location under capture effect or dynamic back-off time.…”
Section: B User Cooperative Mobility Approachmentioning
confidence: 99%
“…Finally, in phase 3 (line [25][26][27][28][29][30][31][32][33][34][35][36][37][38], with the search area slowly expanding, it compares old and new throughput values that are newly generated from optimal locations. If the new throughput is larger than the old value, it updates the optimal throughput and continues to expand the search range G, where we define ∆G as a per expansion area.…”
Section: A Maximum Throughput Algorithm For Optimal Positionmentioning
confidence: 99%