Reinforcement Learning-Based Resource Management Model for Fog Radio Access Network Architectures in 5G

Abstract

The need to cope with the continuously growing number of connected users and the increased demand for mobile broadband services in the Internet of Things has led to the notion of introducing the fog computing paradigm in fifth generation (5G) mobile networks in the form of fog radio access network (F-RAN). The F-RAN approach emphasises bringing the computation capability to the edge of the network so as to reduce network bottlenecks and improve latency. However, despite the potential, the management of computational resources remains a challenge in F-RAN architectures. Thus, this paper aims to overcome the shortcomings of conventional approaches to computational resource allocation in F-RANs. Reinforcement learning (RL) is presented as a method for dynamic and autonomous resource allocation, and an algorithm is proposed based on Q-learning. RL has several benefits in resource allocation problems and simulations carried out show that it outperforms reactive methods. Furthermore, the results show that the proposed algorithm improves latency and thus has the potential to have a major impact in 5G applications, particularly the Internet of Things.

Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues

“…Some IoT applications are delay-sensitive and cannot tolerate such delays. To handle this problem, F-RAN is a critical solution for the Fifth-generation (5G) communication systems to support the URLLC requirement for IoT devices and applications [97]. The fog nodes are capable of performing signal processing, computation, and RF functionalities.…”

Distributed reinforcement learning for dynamic spectrum allocation in cognitive radio‐based internet of things

“…In the last few years, RL and DRL techniques have increasingly attracted research community interest, due to their robustness and dynamicity. They have demonstrated them superlatively in the context of dynamic channel allocation, and many papers [47][48][49][50][51][52][53][54][55] have mainly based on the Q-learning, SARSA, expected SARSA, Monte Carlo, and Actor-Critic (A2C), to allocate radio or edge/fog resources to network slices, in order to maximise the operator revenue, QoS satisfaction, and resource utilisation. To deal with scalability issues faced by RL-based approaches, the approaches in Ref.…”

SIMAD: Secure Intelligent Method for IoT-Fog Environments Attacks Detection

“…The Internet of Things (IoT) offers tremendous occasions to the engineering and industrial activities [5,17]. Undoubtedly, this innovation is expected to be far more dynamic with the up and coming Fifth-Generation (5G) mobile network [18]. Nevertheless, the huge IoT utilization in basic areas, leads to the production of plentiful delicate and real time information.…”