Joint Allocation on Communication and Computing Resources for Fog Radio Access Networks

Abstract

To improve the user experience, an increasing number of mobile applications offload their computing tasks to servers with powerful computing capabilities. The fog radio access network (F-RAN) incorporates the concept of "fog computing" into the access network architecture, endowing an edge network with computing, storage, communication and control functions. In this paper, we consider a multiple fog access point (F-AP) and a multiuser F-RAN, where each user generates two different tasks: communication and computation. To satisfy the diverse quality of service requirements of different users, we jointly optimize the spectrum access, computation offloading and radio resource allocation. The problem is modeled as a mixed integer nonlinear programming problem, which is difficult to solve. In view of this, we propose a genetic algorithm based on convex optimization, i.e., the genetic convex optimization algorithm (GCOA), which divides the mixed integer nonlinear programming problem into two parts, i.e., optimization and convex optimization, to solve it in polynomial time. Simulation results are provided to verify the effectiveness of the algorithm.

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

“…Via extensive numerical results, we have validated the convergence of the proposed optimization algorithms, the performance gain of C-RAN architecture as compared to D-RAN, and the impact of optimized computational resource allocation of collaborative cloud and edge computing. As future work, we mention the extension to collaborative AR [13], heterogeneous C-RAN and mobile computing integrated systems [42]- [44], the robust design with imperfect CSI [45], and the energy-efficient design [3], [4] for energy-limited mobile UEs. Also, it would be relevant to verify the effectiveness of the proposed algorithms by deriving a tight lower bound on the optimal latency values.…”

A Reinforcement Learning based Computing Offloading and Resource Allocation Scheme in F-RAN

“…, Xi'an, China. 4 Department of Communications and information systems, Xi'an University of Posts and Telecommunications, Xi'an, China.…”

“…However, due to the limited computational resource on FAPs, how and where to offload the UE's tasks while optimizing the computational resources in FAPs are hot issues in academic research [3]. Particularly, for computational tasks of UEs with different latency or energy requirements, how to strategically make an optimal offloading and computational resources allocation policy to ensure that UEs' quality of service (QoS) as well as to reduce the total cost is an essential task that many valuable works have been carried [4] - [7]. For instance, Ref.…”

A reinforcement learning-based computing offloading and resource allocation scheme in F-RAN

“…However, due to the limited computational resource on FAPs, how and where to offload the UE's tasks while optimizing the computational resource in FAPs are hot issues in academic research [3]. Particularly, for computational tasks of UEs with different latency or energy requirements, how to strategically make an optimal offloading and computational resource allocation policy to ensure that UEs' quality of service (QoS) as well as to reduce the total cost is an essential task that many valuable works have been carried [4][5][6][7]. For instance, Ref.…”

“…For instance, Ref. [4] jointly optimizes the offloading policy and radio resource allocation to satisfy the diverse QoS requirements of multi-UEs in the scenario of multiple FAPs by utilizing the genetic algorithm. With the same scenario as considered in Ref.…”