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Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems
IEEE Transactions on Communications ( IF 8.3 ) Pub Date : 2020-11-01 , DOI: 10.1109/tcomm.2020.3017757
Walid R. Ghanem , Vahid Jamali , Yan Sun , Robert Schober

This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.

中文翻译:

多用户下行链路 MISO OFDMA-URLLC 系统的资源分配

本文考虑了下行链路多输入单输出 (MISO) 正交频分多址 (OFDMA) 超可靠低延迟通信 (URLLC) 系统的资源分配算法设计。为满足 URLLC 严格的延迟要求,采用短包传输,并在资源分配算法设计中加以考虑。资源分配被优化以最大化加权系统总和吞吐量,该总吞吐量受关于 URLLC 用户传输比特数、数据包错误概率和延迟的服务质量 (QoS) 约束。尽管由此产生的优化问题是非凸的,但通过单调优化找到了最优解。相应的最优资源分配策略可以作为次优低复杂度解决方案的性能上限。我们开发了一种基于逐次凸逼近和凸规划差分的低复杂度次优资源分配算法。我们的仿真结果揭示了使用多个天线来减少延迟和提高 URLLC 系统可靠性的重要性。此外,所提出的次优算法被证明与所提出的最优算法的性能非常接近,并且明显优于两个基线方案,尤其是当用户具有异构延迟要求时。最后,
更新日期:2020-11-01
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