论文标题
5G地面 - 卫星网络中的多连接性：5G allstar解决方案
Multi-Connectivity in 5G terrestrial-Satellite Networks: the 5G-ALLSTAR Solution
论文作者
论文摘要
5G Allstar项目旨在集成地面和卫星网络，以满足5G用例的高度挑战和苛刻的要求。两个网络的集成是通过避免使用服务中断来确保挑战性沟通情况（例如紧急情况，海上，铁路等）的服务连续性的关键功能。 5G Allstar项目建议开发多连通性（MC）解决方案，以确保网络可靠性并改善用户设备（UE）和网络之间每种连接的吞吐量和延迟。在5G-Allstar视觉中，我们将GNB分为两个实体：1）GNB-CU（集中式单元）和2）GNB-DU（分布式单元）GNB-CU集成了一种创新的交通流量控制算法，能够通过协调控制的GNB DUS资源来优化网络资源，同时实施了该网络，同时实施了该网络。 MC允许同时连接每个UE（甚至不同的无线接入技术）。该解决方案导致具有包含RLC，MAC和PHY层的独立GNB-DU/S。 5G Allstar MC算法为RRC层（在GNB-CU中）提供了高级功能，该功能又能够在GNB-DU中设置SDAP，PDCP和下层。在这方面，通过在UE周围环境中的网络性能以及UE QoS的要求，将在GNB-CU中实现的基于AI的MC算法以及UE QoS的要求，将动态选择最有希望的访问点，以确保满足要求还可以实现最佳流量，以应对连接可靠性。在本文中，我们还提出了一个基于创新的基于AI的框架，其中包含在交通流控制中，能够通过实施强化学习算法来解决网络控制问题，从而解决了MC目标。
The 5G-ALLSTAR project is aimed at integrating Terrestrial and Satellite Networks for satisfying the highly challenging and demanding requirements of the 5G use cases. The integration of the two networks is a key feature to assure the service continuity in challenging communication situations (e.g., emergency cases, marine, railway, etc.) by avoiding service interruptions. The 5G-ALLSTAR project proposes to develop Multi-Connectivity (MC) solutions in order to guarantee network reliability and improve the throughput and latency for each connection between User Equipment (UE) and network. In the 5G-ALLSTAR vision, we divide the gNB in two entities: 1) gNB-CU (Centralized Unit) and 2) gNB-DU (Distributed Unit) The gNB-CU integrates an innovative Traffic Flow Control algorithm able to optimize the network resources by coordinating the controlled gNB-DUs resources, while implementing MC solutions. The MC permits to connect each UE with simultaneous multiple access points (even different radio access technologies). This solution leads to have independent gNB-DU/s that contain the RLC, MAC and PHY layers. The 5G-ALLSTAR MC algorithms offer advanced functionalities to RRC layer (in the gNB-CU) that is, in turn, able to set up the SDAP, the PDCP and the lower layers in gNB-DU. In this regard, the AI-based MC algorithms, implemented in gNB-CU, by considering the network performances in the UE surrounding environment as well as the UE QoS requirements, will dynamically select the most promising access points able to guarantee the fulfilment of the requirements also enabling the optimal traffic splitting to cope with the connection reliability. In this paper, we present also an innovative AI-based framework, included within the Traffic Flow Control, able to address the MC objectives, by implementing a Reinforcement Learning algorithm in charge of solving the network control problem.
