Deep Unsupervised Learning for Network Resource Allocation Problems with Convex and Non-Convex Constraints

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dc.contributor.advisorTabassum, Hina
dc.contributor.authorAlizadeh, Mehrazin
dc.date.accessioned2023-03-28T21:16:03Z
dc.date.available2023-03-28T21:16:03Z
dc.date.copyright2022-10-19
dc.date.issued2023-03-28
dc.date.updated2023-03-28T21:16:02Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractDeep neural networks (DNNs) are currently emerging as a potential solution to solve NP-hard wireless resource allocation problems. However, in the presence of intricate constraints, e.g., users' quality-of-service (QoS) constraints or base station quota, guaranteeing constraint satisfaction becomes a fundamental challenge. In this thesis, I propose a novel unsupervised learning framework to solve the classical power control and user assignment problem in a multi-user interference channel, where the objective is to maximize the network sum-rate with QoS, power budget, and base station quota constraints. The proposed method utilizes a differentiable projection function, defined both implicitly and explicitly, to project the output of the DNN to the feasible set of the problem. Extensive simulations depict that the proposed DNN solutions not only improve the achievable data rate, but also achieve zero constraint violation probability, compared to the existing DNNs, and also outperform the optimization-based benchmarks in computation time.
dc.identifier.urihttp://hdl.handle.net/10315/40982
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectElectrical engineering
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subject.keywordsPower control
dc.subject.keywordsLearning to optimize (L2O)
dc.subject.keywordsDeep learning (DL)
dc.subject.keywordsUnsupervised learning
dc.subject.keywordsDifferentiable projection
dc.subject.keywordsMulti-user
dc.subject.keywordsInterference
dc.subject.keywordsResource allocation
dc.subject.keywordsUser assignment
dc.subject.keywordsMinimum rate constraint
dc.subject.keywordsMachine learning
dc.subject.keywordsConstraint optimization
dc.subject.keywordsWireless communication
dc.titleDeep Unsupervised Learning for Network Resource Allocation Problems with Convex and Non-Convex Constraints
dc.typeElectronic Thesis or Dissertation

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