Joint routing and scheduling for transmission service in software-defined full-duplex wireless networks

Li, Zhuo; Chen, Xin; Li, Lulu; Wang, Xiangkun

doi:10.1007/s12083-017-0624-y

Cited by 1 publication

(1 citation statement)

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“…Li et al [23] considered the issue of joint routing and link scheduling in software-defined full-duplex wireless networks, where an exclusive software-defined networking (SDN) controller node was involved. They formulated the optimization problem and presented a minimum-cost routing algorithm to solve it.…”

Section: B Joint Routing and Tdma Link Schedulingmentioning

confidence: 99%

Deep Learning for MANET Routing

Danilchenko,

Azoulay,

Reches

et al. 2023

Trans. Mach. Learn. Comm. Netw.

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Power control and scheduling are among the most well-known resource allocation challenges in wireless networks, and are often solved as optimization problems with constraints. However, solving these optimization challenges by using optimal algorithms often incurs a significant time complexity, which creates considerable discrepancies between the theoretical results and real-time processing required. In this study, we propose a novel machine learning-based perspective to address this issue. We propose a scheduling and power control deep neural network SPCDNet method and its modification SPCDNet R . SPCDNet solves the scheduling problem for point-to-point transmission requests while SPCDNet R solves the more complex problem, where the input transmission list is composed of ordered routes which should be satisfied. Both SPCDNet and SPCDNet R are trained in a supervised manner and show near-optimal performance on the test set. Our results demonstrate that SPCDNet and SPCDNet R can serve as a computationally inexpensive solution (regarding time complexity), compared with state-of-the-art schemes, while showing to be near-optimal approximation solutions to the time scheduling and power control challenges. Moreover, we found that both SPCDNet and SPCDNet R reach efficient solutions for large problem instances, even though they were trained on small problems.

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Section: B Joint Routing and Tdma Link Schedulingmentioning

confidence: 99%