Energy Efficient Resource Allocation Optimization in Fog Radio Access Networks with Outdated Channel Knowledge

TL;DR

This paper proposes an energy-efficient resource allocation strategy for Fog Radio Access Networks that accounts for outdated channel information, combining centralized user association with distributed beamforming to improve performance and reduce complexity.

Contribution

It introduces a novel heuristic algorithm that balances energy efficiency and computational complexity in F-RANs with outdated CSI, incorporating both centralized and distributed processing.

Findings

Heuristic method achieves near-AL performance with lower complexity.

Proposed scheme outperforms baseline F-RAN in energy efficiency.

Limited sum-rate degradation compared to sum-rate maximization.

Abstract

Fog Radio Access Networks (F-RAN) are gaining worldwide interests for enabling mobile edge computing for Beyond 5G. However, to realize the future real-time and delay-sensitive applications, F-RAN tailored radio resource allocation and interference management become necessary. This work investigates user association and beamforming issues for providing energy efficient F-RANs. We formulate the energy efficiency maximization problem, where the F-RAN specific constraint to guarantee local edge processing is explicitly considered. To solve this intricate problem, we design an algorithm based on the Augmented Lagrangian (AL) method. Then, to alleviate the computational complexity, a heuristic low-complexity strategy is developed, where the tasks are split in two parts: one solving for user association and Fog Access Points (F-AP) activation in a centralized manner at the cloud, based on global but outdated user Channel State Information (CSI) to account for fronthaul delays, and the second solving for beamforming in a distributed manner at each active F-AP based on perfect but local CSIs. Simulation results show that the proposed heuristic method achieves an appreciable performance level as compared to the AL-based method, while largely outperforming the energy efficiency of the baseline F-RAN scheme and limiting the sum-rate degradation compared to the optimized sum-rate maximization algorithm.