Key Technologies Of Proactive Mobile Network For Ultra-Low Latency Communication

Given the escalating need for low-latency performance in mobile communication networks due to various advanced applications and the rapid advancement of artificial intelligence technology,a new network architecture,referred to as the proactive mobile network,has been developed.The primary objective of this architectural framework is to facilitate the provision of mobile communication services with very low latency.Additionally,it aims to drive the development of network intelligence by adopting a user-centric strategy to effectively manage,coordinate,and schedule various generic service resources.The architectural design of this system incorporates several functionalities in sensing,communication,and computing,facilitating the network’s potential to demonstrate self-organization and flexibility.The system integrates emerging technologies,including open-loop transmission,virtual cells,and predictive mobility management,to provide a range of useful features.In recent years,this design has garnered significant interest and study from both business and academia,leading to its constant enrichment and improvement.Nevertheless,the implementation of current real-time signaling-based control systems has become more difficult due to disruptive designs in the proactive mobile network.The emergence of these developments has presented novel technological obstacles and concerns,namely pertaining to the assurance of dependable data transmission and the overall administration and coordination of services.This study undertakes a comprehensive examination of the fundamental technological components of the proactive mobile network.(1)Wireless Resource Management for Upstream Open-Loop Communication:Addressing the critical challenge of ensuring data transmission reliability in the open-loop transmission mode.This paper introduces a bilateral approach for managing wireless resources,wherein the processes of transmission decision and resource management are separated into recommended resource allocation and intelligent resource utilization.These two decision processes are implicitly interconnected through the utilization of shared external environmental information.The research also examines the process of choosing appropriate reinforcement learning algorithms tailored to different network environments encountered in real-world scenarios.The simulation findings demonstrate that the suggested methodology achieves a performance level that is within a 2.9%margin of the theoretical lower limit in testing circumstances.Furthermore,when integrated with suitable redundancy or coding techniques,it has the capability to fulfill the Ultra-Reliable Low Latency Communication(URLLC)requirements.(2)Network Transmission Control for Downstream Open-Loop Communication:This chapter focuses on enhancing the reliability of transmission decisions,with the network-side anchor node being responsible for downstream transmission control.The anchor node exhibits a wide range of sensing capabilities and has a significant capacity for data processing,which allows for intelligent decision-making in transmission.Nevertheless,it is important to take into account other facets of system performance,such as energy efficiency.This study presents an enhanced version of the proactive network’s downstream transmission management structure,specifically designed to cater to the demands of several objectives.The proposed approach involves the development of iterative algorithms that use Tchebyshev and deep reinforcement learning techniques to leverage both immediate and long-term network states.The findings from the simulation indicate a notable enhancement of 40%in terms of dependability and an 11%increase in efficiency when compared to previous studies.(3)Interference Mitigation with Multi-RIS Assistance in Open-Loop Communication:The focus of this study is to address the issue of inter-user interference that arises from the use of virtual cell technology in proactive networks.This study presents a proposal for the incorporation of reconfigurable intelligent surface(RIS)technology as a means to enhance downstream transmission.In order to address the issue of managing RIS in a proactive network,which lacks direct access to real-time accurate channel state information,this study proposes the use of an asynchronous advantage actor-critic method.This algorithm is employed to provide a centralized management scheme for multiple RIS.The aforementioned methodology employs historical data and prediction skills as a means to address the absence of real-time information.The simulation results provide empirical evidence supporting the substantial benefits of the suggested strategy when compared to scenarios where RIS assistance is absent.Specifically,the findings provide an estimated 173%enhancement in connection capacity.(4)Data-Driven Management of Generalized Service Resource Mobility:Efficient mobility management of resources is essential in the proactive network to provide the provision of low-latency and efficient generalized noncommunication services to users.This entails the management of spatial and temporal disparities in the availability and demand of resources over a broad geographical region in order to optimize the use of scarce resources for the prompt and efficient provision of high-quality services.This study presents a comprehensive examination of modeling and analytic techniques pertaining to resource mobility restrictions and scheduling procedures,including both macro and micro viewpoints.In addition,it formulates a methodology for managing scheduling.The present methodology involves the development of a model for analyzing the capacity of area services.This model aims to align regional service resources with corresponding demands,hence mitigating supply-demand disparities.To do this,a bipartite graph algorithm is used.In the context of local single-resource scheduling,the proposed technique takes into account the selection of movement paths within the confines of external environmental limitations.This is achieved by integrating route planning with resource preallocation.The simulation findings indicate a substantial decrease of around 76%in the overall user service time and a potential energy conservation of over 70%.