  Millimeter-wave self-backhauled small cells are a key component of
next-generation wireless networks. Their dense deployment will increase data
rates, reduce latency, and enable efficient data transport between the access
and backhaul networks, providing greater flexibility not previously possible
with optical fiber. Despite their high potential, operating dense
self-backhauled networks optimally is an open challenge, particularly for radio
resource management (RRM). This paper presents, RadiOrchestra, a holistic RRM
framework that models and optimizes beamforming, rate selection as well as user
association and admission control for self-backhauled networks. The framework
is designed to account for practical challenges such as hardware limitations of
base stations (e.g., computational capacity, discrete rates), the need for
adaptability of backhaul links, and the presence of interference. Our framework
is formulated as a nonconvex mixed-integer nonlinear program, which is
challenging to solve. To approach this problem, we propose three algorithms
that provide a trade-off between complexity and optimality. Furthermore, we
derive upper and lower bounds to characterize the performance limits of the
system. We evaluate the developed strategies in various scenarios, showing the
feasibility of deploying practical self-backhauling in future networks.
