Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks
Mugen Peng, Kecheng Zhang, Jiamo Jiang, Jiaheng Wang, and Wenbo Wang

TL;DR
This paper proposes an energy-efficient resource assignment and power allocation scheme for H-CRANs, combining user association, enhanced S-FFR, and convex optimization to significantly improve energy efficiency in heterogeneous cloud radio access networks.
Contribution
It introduces a novel joint resource and power allocation method for H-CRANs using convex reformulation and Lagrange dual decomposition, enhancing energy efficiency.
Findings
Significant energy efficiency improvements demonstrated through simulations.
Effective joint resource and power allocation strategy developed.
Enhanced user association and interference mitigation contribute to performance gains.
Abstract
Taking full advantages of both heterogeneous networks (HetNets) and cloud access radio access networks (CRANs), heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both the spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, while the high power node (HPN) is deployed to guarantee the seamless coverage and serve users with low QoS requirements. To mitigate the inter-tier interference and improve EE performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal…
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