PROJECT TITLE :

Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks

ABSTRACT:

Taking full advantage of both heterogeneous networks and cloud access radio access networks, heterogeneous cloud radio access networks (H-CRANs) are presented to reinforce each spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high knowledge rates for users with top quality of service (QoS) needs, whereas the high-power node (HPN) is deployed to ensure seamless coverage and serve users with low-QoS requirements. To mitigate the intertier interference and improve energy efficiency (EE) performances in H-CRANs, characterizing user association with RRH/HPN is taken into account in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based mostly on the RRH/HPN association constraint and the improved S-FFR, an energy-efficient optimization drawback with the resource assignment and power allocation for the orthogonal-frequency-division-multiple-access-based mostly H-CRANs is formulated as a nonconvex objective function. To handle the nonconvexity, the same convex feasibility drawback is reformulated, and closed-kind expressions for the energy-economical resource allocation answer to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition methodology. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation answer will enhance the EE significantly.