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Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems ?

机译:网络物理系统中有效的5G无线下行链路调度和资源分配?

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In emerging Cyber-Physical Systems (CPS), the demand for higher communication performance and enhanced wireless connectivity is increasing fast. To address the issue, in our recent work, we proposed a dynamic programming algorithm with polynomial time complexity for effective cross-layer downlink Scheduling and Resource Allocation (SRA) considering the channel and queue state, while supporting fairness. In this paper, we extend the SRA algorithm to consider 5G use-cases, namely enhanced Machine Type Communication (eMTC), Ultra-Reliable Low Latency Communication (URLLC) and enhanced Mobile BroadBand (eMBB). In a simulation study, we evaluate the performance of our SRA algorithm in comparison to an advanced greedy cross-layer algorithm for eMTC, URLLC and LTE (long-term evolution). For eMTC and URLLC, our SRA method outperforms the greedy approach by up to 17.24%, 18.1%, 2.5% and 1.5% in terms of average goodput, correlation impact, goodput fairness and delay fairness, respectively. In the case of LTE, our approach outperforms the greedy method by 60%, 2.6% and 1.6% in terms of goodput, goodput fairness and delay fairness compared with tested baseline.
机译:在新兴的网络物理系统(CPS)中,对更高的通信性能和增强的无线连接的需求正在快速增长。为了解决该问题,在我们最近的工作中,我们提出了一种具有多项式时间复杂度的动态规划算法,用于在考虑公平性的同时考虑信道和队列状态的有效跨层下行链路调度和资源分配(SRA)。在本文中,我们将SRA算法扩展为考虑5G用例,即增强型机器类型通信(eMTC),超可靠低延迟通信(URLLC)和增强型移动宽带(eMBB)。在仿真研究中,我们与eMTC,URLLC和LTE(长期演进)的高级贪婪跨层算法相比,评估了SRA算法的性能。对于eMTC和URLLC,我们的SRA方法在平均吞吐量,相关影响,吞吐量公平性和延迟公平性方面分别比贪婪方法高出17.24%,18.1%,2.5%和1.5%。就LTE而言,与经过测试的基准相比,我们的方法在吞吐量,吞吐量公平性和延迟公平性方面的性能优于贪婪方法的60%,2.6%和1.6%。

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