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

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 frequency division multiple access (OFDMA) based H-CRANs is formulated as a non-convex objective function. To deal with the non-convexity, an equivalent convex feasibility problem is reformulated, and closedform expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the energy efficiency significantly.