2017 IEEE International Symposium on Information Theory (ISIT) 2017
DOI: 10.1109/isit.2017.8006944
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FPLinQ: A cooperative spectrum sharing strategy for device-to-device communications

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Cited by 69 publications
(107 citation statements)
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“…4. Outputs from the convolutional stage are then input into a DNN that learns from the scheduling results of the state-of-the-art FPLinQ algorithm [59] as in [56] or in an unsupervised manner as in [57] to be discussed later. The simulation results suggest that DNNs can effectively learn the network interference topology and perform scheduling to near optimum without the help of accurate CSI.…”
Section: A Supervised Learning Approach For Optimizationmentioning
confidence: 99%
“…4. Outputs from the convolutional stage are then input into a DNN that learns from the scheduling results of the state-of-the-art FPLinQ algorithm [59] as in [56] or in an unsupervised manner as in [57] to be discussed later. The simulation results suggest that DNNs can effectively learn the network interference topology and perform scheduling to near optimum without the help of accurate CSI.…”
Section: A Supervised Learning Approach For Optimizationmentioning
confidence: 99%
“…α, β, and γ are the Lagrangian multipliers for the constraints (P1c), (P4b) and (P1e), respectively. In (30), the constraints (P1c), (P4b) and (P1e) are written in vector form as y − F z = 0, v − G y = 0, and H y − h = 0, respectively. The positive parameter ρ controls the weight on the quadratic penalty terms, which also corresponds to the step size of the dual descent update in the ADMM based solution to be introduced.…”
Section: A Distributedmentioning
confidence: 99%
“…The positive parameter ρ controls the weight on the quadratic penalty terms, which also corresponds to the step size of the dual descent update in the ADMM based solution to be introduced. We only consider the dual variables of the equality constraints (P1c), (P4b), and (P1e) in (30). The rest of the constraints are omitted here for simplicity, which will be considered when solving each subproblem.…”
Section: A Distributedmentioning
confidence: 99%
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