Routing and Resource Allocation for IAB Multi-Hop Network in 5G Advanced
Routing and Resource Allocation for IAB Multi-Hop Network in 5G Advanced
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DOI：
10.1109/tcomm.2022.3200673
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发表时间：
2022-10
影响因子：
8.3
通讯作者：
Hao Yin;S. Roy;Liu Cao
中科院分区：
文献类型：
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作者：
Hao Yin;S. Roy;Liu Cao
集成接入和回程（IAB）是用于基于共享/有效分配传统上保留用于接入的所有者频谱来扩展5G蜂窝网络中的网络覆盖的新颖特征。然而，由于超可靠性和低延迟（URLLC）要求是5G高级服务的关键组成部分，因此提供此类服务对IAB多跳网络设计提出了严峻的挑战。为了满足IAB网络中的URLLC要求，我们提出了当前第三代合作伙伴计划（3GPP）5G标准下的路由和资源分配的跨层设计。首先，我们制定了IAB多跳网络，最大限度地减少延迟，同时满足可靠性要求的路由问题。随后，我们提出了一个强化学习（RL）框架来解决资源分配和路由问题的基础上的每个代理（IAB节点）在环境中的本地信息。之后，我们提出了一种新的基于熵的强化学习算法与联邦学习（FL）机制，以提高整体性能，以及加快收敛速度。通过仿真，该算法优于基线算法的延迟和可靠性的角度来看，分别。同时，通过使用FL，该算法的收敛速度也得到了提高。
Integrated access and backhaul (IAB) is a novel feature for extending the network coverage in 5G cellular networks, based on sharing/efficient allocation of owner’s spectrum traditionally reserved for access. However, since ultra-reliability and low latency (URLLC) requirements are a key component of 5G advanced services, provisioning such services present stringent challenges for IAB multi-hop network design. To fulfill the URLLC requirements in the IAB network, we propose a cross-layer design on routing and resource allocation under the current 3rd Generation Partnership Project (3GPP) 5G standards. We first formulate a routing problem for the IAB multi-hop network, which minimizes the latency while satisfying the reliability requirement. Subsequently, we present a reinforcement learning (RL) framework to solve the resource allocation and routing problem based on the local information of each agent (IAB node) in the environment. Afterward, we propose a novel entropy-based RL algorithm with federated learning (FL) mechanism to improve the overall performance as well as accelerate the convergence speed. Via the simulation, the proposed algorithm outperforms baseline algorithms from the latency and reliability perspective, respectively. Meanwhile, the convergence speed with the proposed algorithm also improves by using FL.