Unsupervised learning for parametric optimization in wireless networks

• Autores: Rasoul Nikbakht Silab
• Directores de la Tesis: Ángel Lozano Solsona (dir. tes.)
• Lectura: En la Universitat Pompeu Fabra ( España ) en 2021
• Idioma: español
• Tribunal Calificador de la Tesis: Luca Sanguinetti (presid.), Lorenza Giupponi (secret.), Francisco Monteiro (voc.)
• Programa de doctorado: Programa de Doctorado en Tecnologías de la Información y las Comunicaciones por la Universidad Pompeu Fabra
• Materias:
  • Matemáticas
    • Análisis y análisis funcional
      • Espacios de Hilbert
  • Ciencias tecnológicas
    • Tecnología de las telecomunicaciones
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • This thesis studies parametric optimization in cellular and cell-free networks, exploring data-based and expert-based paradigms. Power allocation and power control, which adjust the transmit power to meet different fairness criteria such as max-min or max-product, are crucial tasks in wireless communications that fall into the parametric optimization category. The state-of-the-art approaches for power control and power allocation often demand huge computational costs and are not suitable for real-time applications. To address this issue, we develop a general-purpose unsupervised-learning approach for solving parametric optimizations; and extend the well-known fractional power control algorithm.

    In the data-based paradigm, we create an unsupervised learning framework that defines a custom neural network (NN), incorporating expert knowledge to the NN loss function to solve the power control and power allocation problems. In this approach, a feedforward NN is trained by repeatedly sampling the parameter space, but, rather than solving the associated optimization problem completely, a single step is taken along the gradient of the objective function. The resulting method is applicable for both convex and non-convex optimization problems. It offers two-to-three orders of magnitude speedup in the power control and power allocation problems compared to a convex solver---whenever appliable.

    In the expert-driven paradigm, we investigate the extension of fractional power control to cell-free networks. The resulting closed-form solution can be evaluated for uplink and downlink effortlessly and reaches an (almost) optimum solution in the uplink case.

    In both paradigms, we place a particular focus on large scale gains---the amount of attenuation experienced by the local-average received power. The slow-varying nature of the large-scale gains relaxes the need for a frequent update of the solutions in both the data-driven and expert-driven paradigms, enabling real-time application for both methods.