Refiner GAN Algorithmically Enabled Deep-RL for Guaranteed Traffic Packets in Real-Time URLLC B5G Communication Systems

Abstract

Ultra-reliable and Low-latency Communications (URLLC) is expected to be one of the most critical characteristics in Beyond fifth-Generation (B5G) cellular networks with stringent low latency and high-reliability requirements. The Deep Reinforcement Learning (deep-RL) framework has been applied to predict the optimization of a Resource Block (RB) and minimize Power Allocation (PA) to guarantee a high End-to-End (E2E) reliability and low E2E latency under rate constraints. This paper proposes a novel Policy Gradient-based Actor-Critic Learning (PGACL) algorithm to optimize the policy gradient for optimal rate allocation to solve the RB, minimize power, and guarantee a solution for URLLC scheduling. The purpose of a PGACL algorithm is to provide a good policy with a closer convergence rate and a low computational cost depending on the reduced action space for every user. URLLC systems need to operate in highly reliable systems and account for extreme network conditions. Therefore, we proposed the refiner Generative Adversarial Networks (GANs) that apply enough extreme events for the deep-RL agent to generate synthetic data with high reliability similar to real data based on the regulated number of extreme events in the dataset. This refiner GAN method enables a deep-RL approach to generate large amounts of data practically used in real-time operation. Simulation results showed that the proposed deep-RL for refiner-GAN can omit the transient training time and develop deep-learning based on a controlled set of unlabeled real traffic at a relatively short time. Furthermore, the refiner GAN demonstrated 99.9999% reliability and E2E latency of less than 1.4ms.INDEX TERMS URLLC, beyond fifth-generation, end-to-end, generative adversarial networks.

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

“…Research topic Research analysis/findings Deep Reinforcement Learning [153] Resource allocation Low job completion time by utilizing less computational resources [168] Network slicing operation Resource allocation with low computational burden to achieve high long-term throughput [169] Resource and power allocation optimization Low computation cost for the optimization technique with reliable and fast packet transmission [170] Joint optimization of computation offloading and resource allocation policies Low computational overhead analysis [171] Joint optimization of beamforming matrix for base station and RIS Convergence analysis of the proposed algorithm along with network throughput [30] Handover mechanism Convergence analysis of the proposed algorithm along with downlink data rate policy learning. Network slicing aims at assigning physical resource blocks in such a manner that QoS requirements of advanced network services such as eMBB, URLLC and mMTC are fulfilled.…”

“…This mechanism can also be useful for 6G communication systems, according to the claim made by the authors. In [169], a DRL framework is presented, where a novel algorithm is proposed for optimizing resource block allocation and minimizing power allocation for URLLC service. Also, a refiner Generative Adversarial Network (GAN) method is proposed in the paper for the high reliable generation of data to help the resource and power allocation.…”

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

“…Industry 5.0 Industry 5.0 is a manufacturing paradigm change that prioritizes human-machine interaction. It has emerged due to advances in AI, distributed computing, and B5G connectivity [189,190], and it is likely to accelerate even further with the inclusion of supporting technologies such as FL and industrial edge computing [191]. In addition, industry 5.0 will minimize latency, boost overall data security and privacy, increase efficiency, and support transactions hindered by limited connection thanks to the development of UAV computing [192].…”

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence

“…However, due to the incomplete observation of the network dynamics and randomness, named network uncertainty, the MEC-aided optimization problem solution may be far from the optimal solution and further degrades the QoS or quality of experience (QoE) performance for delay-sensitive and computation-intensive applications. For example, in the ultra-reliable and lowlatency communications (URLLC) scenario, the round trip delay between a sender and a receiver can reach 1 ms [19], [36]. However, random traffic loads from a large number of devices may cause serious network congestion and severe packet loss, and the stringent QoS requirements of delay and reliability will not be satisfied [19], [20], [36].…”

“…For example, in the ultra-reliable and lowlatency communications (URLLC) scenario, the round trip delay between a sender and a receiver can reach 1 ms [19], [36]. However, random traffic loads from a large number of devices may cause serious network congestion and severe packet loss, and the stringent QoS requirements of delay and reliability will not be satisfied [19], [20], [36]. In this regard, we elaborate on network uncertainty, followed by effective RL-based strategies.…”