Closed-loop rate adaptation and error-control depends on the availability of feedback, which is necessary to maintain efficient and reliable wireless links. In the 6G era, many Internet of Things (IoT) devices may not be able to support feedback transmissions due to stringent energy constraints. This calls for new transmission techniques and design paradigms to maintain reliability in feedback-free IoT networks. In this context, this paper proposes a novel open-loop rate adaptation (OLRA) scheme for reliable feedback-free IoT networks. In particular, large packets are fragmented to operate at a reliable transmission rate. Furthermore, transmission of each fragment is repeated several times to improve the probability of successful delivery. Using tools from stochastic geometry and queueing theory, we develop a novel spatiotemporal framework to determine the number of fragments and repetitions needed to optimize the network performance in terms of transmission reliability and latency. To this end, the proposed OLRA is bench-marked against conventional closed-loop rate adaptation (CLRA) to highlight the impact of feedback in large-scale IoT networks. The obtained results concretely quantify the energy saving of the proposed feedback-free OLRA scheme at the cost of transmission reliability reduction and latency increment.

翻译：闭环速率自适应与差错控制依赖于反馈的可用性，这对于维持高效可靠的无线链路至关重要。在6G时代，许多物联网设备因严格的能量约束可能无法支持反馈传输。这要求采用新的传输技术与设计范式，以在无反馈的物联网网络中维持可靠性。在此背景下，本文提出了一种用于可靠无反馈物联网网络的新型开环速率自适应方案。具体而言，将大数据包分片处理以在可靠传输速率下工作，并通过多次重复传输每个分片来提高成功交付概率。利用随机几何与排队论工具，我们构建了一个新颖的时空框架，用于确定优化网络传输可靠性与延迟性能所需的分片数量与重复次数。为此，将所提出的OLRA与传统闭环速率自适应方案进行基准对比，以揭示反馈在大规模物联网网络中的影响。获得的结果定量量化了所提出的无反馈OLRA方案在牺牲传输可靠性及增加延迟代价下的能量节省效果。