首页|Findings from Technical University Braunschweig (TU Braunschweig) Broaden Unders tanding of Machine Learning (Approaching Globally Optimal Energy Efficiency In I nterference Networks Via Machine Learning)
Findings from Technical University Braunschweig (TU Braunschweig) Broaden Unders tanding of Machine Learning (Approaching Globally Optimal Energy Efficiency In I nterference Networks Via Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting originating in Brauns chweig, Germany, by NewsRx journalists, research stated, "This work presents a m achine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find." Financial support for this research came from Federal Ministry of Education & Research (BMBF). The news reporters obtained a quote from the research from Technical University Braunschweig (TU Braunschweig), "In the literature, either simple but suboptimal approaches or optimal methods with high complexity are proposed. In contrast, w e propose an unsupervised machine learning framework to approach the global opti mum. While the neural network (NN) training takes moderate time, application wit h the trained model requires very low computational complexity. In particular, w e introduce a novel objective function based on stochastic actions to solve the non-convex optimization problem. Besides, we design a dedicated NN architecture SINRnet for the power allocation problems in the interference channel that is pe rmutation-equivariant. We encode our domain knowledge into the NN design and she d light into the black box of machine learning. Training and testing results sho w that the proposed method without supervision and with reasonable computational effort achieves an EE close to the global optimum found by the branch-and-bound algorithm and outperform the successive convex approximation (SCA) algorithm." According to the news reporters, the research concluded: "Hence, the proposed ap proach balances between computational complexity and performance."
BraunschweigGermanyEuropeCyborgsEmerging TechnologiesMachine LearningTechnical University Braunschweig (TU B raunschweig)
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