4 research outputs found
Energy Efficient Resource Allocation Optimization in Fog Radio Access Networks with Outdated Channel Knowledge
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outdated user Channel State Information (CSI) to account for fronthaul delays,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