English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fog Radio Access Networks (F-RAN) are gaining worldwide interests forenabling mobile edge computing for Beyond 5G. However, to realize the futurereal-time and delay-sensitive applications, F-RAN tailored radio resourceallocation and interference management become necessary. This work investigatesuser association and beamforming issues for providing energy efficient F-RANs.We formulate the energy efficiency maximization problem, where the F-RANspecific constraint to guarantee local edge processing is explicitlyconsidered. To solve this intricate problem, we design an algorithm based onthe Augmented Lagrangian (AL) method. Then, to alleviate the computationalcomplexity, a heuristic low-complexity strategy is developed, where the tasksare 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 butoutdated user Channel State Information (CSI) to account for fronthaul delays,and the second solving for beamforming in a distributed manner at each activeF-AP based on perfect but local CSIs. Simulation results show that the proposedheuristic method achieves an appreciable performance level as compared to theAL-based method, while largely outperforming the energy efficiency of thebaseline F-RAN scheme and limiting the sum-rate degradation compared to theoptimized sum-rate maximization algorithm.

Energy Efficient Resource Allocation Optimization in Fog Radio Access Networks with Outdated Channel Knowledge