Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

Abstract

The heterogeneous novelty applications present in the 5th generation (5G) era, including machine-type communication (mMTC), enhanced mobile broadband (eMBB) communication, and ultra-reliable low latency communication (URLLC), which required mobile edge computing (MEC) for local computation and services. The next-generation radio networking (NGRN) will rely on new radio (NR) with the millimeter-wavelength (mmWave) technologies that enable ultra-dense connectivities of the deployed heterogeneous mobile terminal gateways (MTG). However, the mission-critical mMTC applications will suffer from inadequate radio resource management and orchestration (MANO), which will diminish end-to-end (E2E) communication reliability in edge areas. This paper proposed optimal MTG selections and resource allocation (RA) based on the complementary between MTG loading prediction based on recurrent neural network-based long short-term memory (RNN-LSTM) and MTG loading adjustment based on the applied deep reinforcement learning (DRL) approaches, respectively. Furthermore, the RNN-LSTM enhances offloading and handover decisions with discrete-time predictions, while the DRL plays an essential role in adjusting the determined MTG during congestion situations. The proposed method contributed to remarkable outcomes in crucial performance metrics over reference approaches regarding computation and communication quality of service (QoS).

Priority-Aware Resource Management for Adaptive Service Function Chaining in Real-Time Intelligent IoT Services

“…A variety of congestion intervals leads DQN-RM to consist of deficient action selection, particularly when parallel service requests occur. By solely tackling the resource predetermination f r s j(i) , the efficiency of resources is high; however, it remains an optimizable mechanism for real-time intelligent IoT services [33][34][35]. This reference scheme observes the same states as the proposed priority-aware scheme.…”

An intelligent resource allocation strategy for machine type communication environment

“…Sa Math et al 23 have developed the recurrent‐based long short term memory framework to enhance the resource management performance and reduce the communication delay between the end to end servers. It is the heterogeneous network to perform the MTC with 5 the generation application.…”

“…The attack interruption can collapse the communication process and affects the QoS Math et al 23 Recurrent-based long short term memory framework…”

Retracted: Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”