AoI-Driven Statistical Delay and Error-Rate Bounded QoS Provisioning for mURLLC Over UAV-Multimedia 6G Mobile Networks Using FBC
AoI-Driven Statistical Delay and Error-Rate Bounded QoS Provisioning for mURLLC Over UAV-Multimedia 6G Mobile Networks Using FBC
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DOI：
10.1109/jsac.2021.3088625
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发表时间：
2021-07
影响因子：
16.4
通讯作者：
Xi Zhang;Jingqing Wang;H. Poor
中科院分区：
文献类型：
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作者：
Xi Zhang;Jingqing Wang;H. Poor
大规模超可靠低延迟通信（mURLLC）已成为新的主导性6G标准服务，用于为对延迟敏感的数据传输提供统计服务质量（QoS）保障。为了衡量更新信息的时效性，信息年龄（AoI）最近已成为QoS度量的新维度。由于状态更新通常由少量信息比特组成，但需要超低延迟，因此将AoI与有限块长编码（FBC）相结合为mURLLC创造了一种有前景的替代解决方案。另一方面，为了解决mURLLC带来的大规模连接问题，无人驾驶飞行器（UAV）已被开发出来，以显著增强视距（LOS）覆盖范围，同时保证各种QoS要求。然而，如何在无人机系统中有效地整合上述新技术以实现有统计延迟和误码率限制的QoS保障，既未被很好地理解，也未得到深入研究。为了克服这些挑战，我们提出了基于FBC的有统计延迟和误码率限制的QoS保障方案，该方案利用AoI作为无人机移动网络上mURLLC的关键QoS保障技术。首先，我们开发了基于FBC的无人机系统模型。其次，我们建立了基于AoI度量的建模框架，以使用FBC对峰值AoI违反概率进行上界估计。第三，我们制定并解决了基于FBC的峰值AoI违反概率最小化问题。第四，我们联合优化峰值AoI违反概率和$\epsilon$ -有效容量，并描述它们之间的权衡。最后，我们的模拟验证并评估了我们所开发的方案。
Massive ultra-reliable and low latency communications (mURLLC) has emerged as new and dominating 6G-standard services to support statistical quality-of-services (QoS) provisioning for delay-sensitive data transmissions. To measure the freshness of updated information, age of information (AoI) has recently formed as the new dimension of QoS metric. Since status updates usually consist of a small number of information bits but warrant ultra-low latency, integrating AoI with finite blocklength coding (FBC) creates an alternative promising solution for mURLLC. On the other hand, to solve the massive connectivity issues imposed by mURLLC, unmanned aerial vehicle (UAV) has been developed to significantly enhance the line-of-sight (LOS) coverage while guaranteeing various QoS requirements. However, how to efficiently integrate the above new techniques for statistical delay and error-rate bounded QoS provisioning in UAV systems has been neither well understood nor thoroughly studied. To overcome these challenges, we propose FBC based statistical delay and error-rate bounded QoS provisioning schemes which leverage AoI as a key QoS provisioning technique for mURLLC over UAV mobile networks. First, we develop FBC based UAV system models. Second, we build up AoI-metric based modeling frameworks to upper-bound peak AoI violation probability using FBC. Third, we formulate and solve FBC based peak AoI violation probability minimization problem. Forth, we jointly optimize peak AoI violation probability and $\epsilon $ -effective capacity and characterize their tradeoffs. Finally, our simulations validate and evaluate our developed schemes.