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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Catalysis)
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  2. ; ; ; ; ; ( , 2018 IEEE International Conference on Communications (ICC))
    There is an increase in usage of smaller cells or femtocells to improve performance and coverage of next-generation heterogeneous wireless networks (HetNets). However, the interference caused by femtocells to neighboring cells is a limiting performance factor in dense HetNets. This interference is being managed via distributed resource allocation methods. However, as the density of the network increases so does the complexity of such resource allocation methods. Yet, unplanned deployment of femtocells requires an adaptable and self-organizing algorithm to make HetNets viable. As such, we propose to use a machine learning approach based on Q-learning to solve the resource allocation problemmore »in such complex networks. By defining each base station as an agent, a cellular network is modeled as a multi-agent network. Subsequently, cooperative Q-learning can be applied as an efficient approach to manage the resources of a multi-agent network. Furthermore, the proposed approach considers the quality of service (QoS) for each user and fairness in the network. In comparison with prior work, the proposed approach can bring more than a four-fold increase in the number of supported femtocells while using cooperative Q-learning to reduce resource allocation overhead.« less
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  3. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available March 1, 2023
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available January 1, 2023
  5. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of High Energy Physics)
    A bstract A search is presented for new particles produced at the LHC in proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb − 1 , collected in 2017–2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with anmore »earlier search based on a data sample of 36 fb − 1 , collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.« less
    Free, publicly-accessible full text available November 1, 2022
  6. ; ; ; ; ; ; ; ; ; ; et al ( , Physics Letters B)
    Free, publicly-accessible full text available September 1, 2022
  7. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available September 1, 2022
  8. ; ; ; ; ; ; ; ; ; ; et al ( , Physics Letters B)
    Free, publicly-accessible full text available August 1, 2022
  9. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
    Free, publicly-accessible full text available August 1, 2022
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Instrumentation)
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