GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Enabling Communication Technologies for Automated Unmanned Vehicles in Industry 4.0

Within the context of Industry 4.0, mobile robot systems such as automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) are one of the major areas challenging current communication and localization technologies. Due to stringent requirements on latency and reliability, several of the existing solutions are not capable of meeting the performance required by industrial automation applications. Additionally, the disparity in types and applications of unmanned vehicle (UV) calls for more flexible communication technologies in order to address their specific requirements. In this paper, we propose several use cases for UVs within the context of Industry 4.0 and consider their respective requirements. We also identify wireless technologies that support the deployment of UVs as envisioned in Industry 4.0 scenarios.Comment: 7 pages, 1 figure, 1 tabl

Big Data Network Optimization for Mobile Cellular Networks in 5G

5G ensures the provision of intelligent network and application services by means of connectivity to remote sensors, massive amounts of Internet of Things data, and fast data transmissions. Through the utilization of distributed compute architectures and by supporting massive connectivity across diverse devices like sensors, gateways, and controllers, 5G brings about a transformative revolution in the conversion of both big data at rest and data in motion into real-time intelligence. Big Data Analytics play an important role in the evolution of 5G standards, enabling intelligence across networks, applications, and businesses. Administrators of mobile organizations have access to a plethora of opportunities to enhance service quality through big data. Network optimization serves as a crucial method to achieve this task, with network prediction forming the foundation for such optimization. Ensuring network stability and security is essential for 5G mobile communication, considering its significance as an important tool in national life. Therefore, this work focuses on presenting big data network optimization for mobile cellular networks within the context of 5G. In order to improve the Quality of Experience (QoE) for users, this work explores various methods for integrating network optimization and Big Data analytics. The performance of the presented model is evaluated in terms of QoE, Throughput, handover rate, mobility, reliability, and network slicing