Machine Learning Assisted Capacity Optimization for B5G/6G Integrated Access and Backhaul Networks
Machine Learning Assisted Capacity Optimization for B5G/6G Integrated Access and Backhaul Networks
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DOI：
10.1109/infocomwkshps57453.2023.10225946
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
O. Ajayi;Shuai Zhang;Yu-long Cheng
中科院分区：
文献类型：
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作者：
O. Ajayi;Shuai Zhang;Yu-long Cheng
由于对超可靠低延迟通信（URLLC）的严格要求，在超5G（B5 G）/6 G集成接入和回程（IAB）网络中对业务的路由和无线回程链路的调度的跨层设计已经持续引起关注。分割总带宽或将静态分区分配给一些回程链路的努力没有提高总速率，并且在共享频谱中使用近似算法进行资源调度已经造成了显著的计算开销。在本文中，我们提出了一个两阶段的机器学习（ML）框架的容量优化，在模拟IAB多跳网络的调度结构，过去的优化实例进行了探索和利用，以加速解决一个新的优化实例与线性规划。我们评估了ML方法在不同的多商品流（MCF）部署在上行链路和下行链路，并实现了高达94%的平均吞吐量相比，延迟列生成（DCG）基准算法。此外，我们的ML方法将计算时间显著减少了至少95%。
The cross-layer design on the routing of traffic and scheduling of wireless backhaul links in the beyond 5G (B5G)/6G integrated access and backhaul (IAB) networks has continued to draw attention, owing to the stringent requirement of ultra-reliable low latency communication (URLLC). Efforts to split the total bandwidth or allocate a static partition to some backhaul links have not improved the sum rate, and the use of approximation algorithms for resource scheduling in the shared spectrum have posed significant computation overhead. In this paper, we propose a two-stage machine learning (ML) framework for capacity optimization, where the scheduling structure of past optimization instances in a simulated IAB multi-hop network are explored and exploited to accelerate the solution of a new optimization instance with linear programming. We evaluate the ML method on different multi-commodity flow (MCF) deployments in uplink and downlink, and achieved up to 94% average throughput in comparison with the delayed column generation (DCG) benchmark algorithm. Further to that, our ML method significantly reduces the computation time by at least 95 %.