Energy Efficient Resource Allocation for Control Data Separation Architecture based H-CRAN with Heterogeneous Fronthaul

TL;DR

This paper proposes an energy-efficient resource allocation method for control data separation architecture-based H-CRANs with heterogeneous fronthaul, demonstrating significant energy efficiency gains over traditional networks through convex optimization techniques.

Contribution

It introduces a modified power consumption model and an optimal resource allocation solution for CDSA-based H-CRANs with heterogeneous fronthaul, validated by system-level simulations.

Findings

Achieves up to 8% energy efficiency gain over static algorithms.

Realizes up to 16% energy efficiency improvement compared to conventional networks.

Effective resource allocation under fronthaul capacity constraints.

Abstract

Control data separation architecture (CDSA) is a more efficient architecture to overcome the overhead issue than the conventional cellular networks, especially for the huge bursty traffic like Internet of Things, and over-the-top (OTT) content service. In this paper, we study the optimization issue of network energy efficiency of the CDSA-based heterogeneous cloud radio access networks (H-CRAN) networks, which has heterogeneous fronthaul between control base station (CBS) and data base stations (DBSs). We first present a modified power consumption model for the CDSA-based H-CRAN, and then formulate the optimization problem with constraint of overall capacity of wireless fronthaul. We work out the resource assignment and power allocation by the convex relaxation approach Using fractional programming method and Lagrangian dual decomposition method, we derive the close-form optimal solution and verify it by comprehensive system-level simulation. The simulation results show that our proposed algorithm has 8% EE gain compared to the static algorithm, and the CDSA-based H-CRAN networks can achieve up to 16% EE gain compared to the conventional network even under strict fronthaul capacity limit.