时延敏感与能量受限的大规模IoT网络中的速率自适应
Rate Adaptation in Delay-Sensitive and Energy-Constrained Large-Scale IoT Networks
AI总结：
本文研究能量受限和时延敏感的大规模IoT网络中反馈对速率自适应的影响，提出基于随机几何与排队论的时空框架，优化闭环与开环方案的数据分片及重复策略，并量化反馈对可靠性、延迟和能耗的作用。
摘要：
反馈传输用于确认正确接收的数据包、触发错误数据包的重传，并自适应调整传输参数（如速率和功率）。尽管反馈在建立可靠通信链路中起着至关重要的作用，但大多数文献通过假设依赖完美且瞬时反馈的genie-aided系统而忽略了其影响。这种理想化的反馈假设对于大规模物联网已不再适用，因为IoT设备能量受限、易受干扰，且服务于时延敏感的应用。此外，对于具有严格能量限制的IoT接收器，必须采用无反馈操作。在此背景下，本文明确考虑了能量受限和时延敏感的大规模IoT网络中反馈的影响。我们考虑了一个采用闭环和开环速率自适应方案的时隙系统，其中数据包被分片，以在满足数据包交付截止期限的可靠传输速率下运行。在闭环方案中，每个分片的交付通过易错反馈信道进行确认。开环方案没有反馈机制，因此采用预定的分片重复策略来提高传输可靠性。利用随机几何和排队论工具，我们开发了一个新颖的时空框架，以优化两种方案的分片数量以及开环方案的重复次数。为此，我们量化了反馈在传输可靠性、延迟和能耗方面对网络性能的影响。
英文摘要：
Feedback transmissions are used to acknowledge correct packet reception, trigger erroneous packet re-transmissions, and adapt transmission parameters (e.g., rate and power). Despite the paramount role of feedback in establishing reliable communication links, the majority of the literature overlooks its impact by assuming genie-aided systems relying on flawless and instantaneous feedback. An idealistic feedback assumption is no longer valid for large-scale Internet of Things (IoT), which has energy-constrained devices, susceptible to interference, and serves delay-sensitive applications. Furthermore, feedback-free operation is necessitated for IoT receivers with stringent energy constraints. In this context, this paper explicitly accounts for the impact of feedback in energy-constrained and delay-sensitive large-scale IoT networks. We consider a time-slotted system with closed-loop and open-loop rate adaptation schemes, where packets are fragmented to operate at a reliable transmission rate satisfying packet delivery deadlines. In the closed-loop scheme, the delivery of each fragment is acknowledged through an error-prone feedback channel. The open-loop scheme has no feedback mechanism, and hence, a predetermined fragment repetition strategy is employed to improve transmission reliability. Using tools from stochastic geometry and queueing theory, we develop a novel spatiotemporal framework to optimize the number of fragments for both schemes and repetitions for the open-loop scheme. To this end, we quantify the impact of feedback on the network performance in terms of transmission reliability, latency, and energy consumption.
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