Rate Adaptation in Delay-Sensitive and Energy-Constrained Large-Scale IoT Networks
Mostafa Emara, Nour Kouzayha, Hesham ElSawy, Tareq Y. Al-Naffouri
TL;DR
The paper tackles rate adaptation for delay-sensitive, energy-constrained IoT in large-scale networks by explicitly modeling imperfect feedback. It develops a coupled spatiotemporal framework that combines stochastic geometry with absorbing Markov chains to analyze CLRA and OLRA schemes, using a beta-approximated meta-distribution for forward fragment delivery and a closed-form expression for feedback success. The key contributions include a detailed CLRA/OLRA analysis under feedback impairments, a discretized FSD-class approach for tractable evaluation, and quantitative comparisons of PSD, latency, and energy, validated by Monte Carlo simulations. The results show fragmentation improves reliability but increases latency, while OLRA can substantially reduce receiver energy consumption, albeit at some loss in reliability depending on feedback quality. Overall, the work provides design insights for rate adaptation strategies in IoT networks balancing reliability, delay, and energy under realistic feedback conditions.
Abstract
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 An idealistic feedback assumption is no longer valid for large-scale Internet of Things (IoT), which has energy-constrained devices, susceptible to interference, and serves delay-sensitive applications.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.