Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.23682

Packet Loss Analysis Under Doppler Effects and Reinforcement-Learning-Based Resource Allocation for Direct-to-Satellite LoRaWAN

Alireza Maleki·Ebrahim Bedeer·Robert Barton
View original source ↗Read source document ↗Export metadata ↓

Abstract

Long range wide area network (LoRaWAN) has a long communication range and low power consumption, making it a potential technology for direct-to-satellite (DtS) Internet of Things (IoT) connectivity. However, packet collisions and Doppler impairments brought on by satellite motion can greatly increase the packet loss ratio (PLR) in large-scale DtS-IoT networks. In this paper, we study resource allocation in DtSIoT LoRaWAN by first developing an analytical PLR model that simultaneously takes Doppler impairments and packet collisions in the temporal and frequency domains into account. In order to maximize resource allocation, the generated PLR is then used as the reward function for two reinforcement learning (RL) schemes: deep Q-network (DQN) and proximal policy optimization (PPO). Simulation results validate the analytical PLR model against Monte Carlo simulations and demonstrate significant improvements over random resource allocation. At a target PLR of 3*10^-2, while random allocation supports only 100 end devices (EDs), PPO and DQN support approximately 650 and 690 EDs, respectively, representing nearly a seven-fold increase. Furthermore, for 800 EDs, PPO and DQN increase the total number of transmitted packets by 106.7% and 88.0%, respectively, compared with random allocation.

Explore related subjects

eess.SP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alireza Maleki, Ebrahim Bedeer, Robert Barton. 2026-10-16. Packet Loss Analysis Under Doppler Effects and Reinforcement-Learning-Based Resource Allocation for Direct-to-Satellite LoRaWAN. https://arxiv.org/abs/2610.23682

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Integrated sensing and communications in the 3GPP New Radio: sensing limits

Integrated Sensing and Communications (ISAC) is regarded as a key element of the beyond-fifth-generation (5G) and sixth-generation (6G) systems, raising the question of whether current 5G New Radio (NR) signal structures can meet the sensing accuracy requirements specified by the Third Generation Partnership Project (3GPP). This paper addresses this issue by analyzing the fundamental limits of range and velocity estimation through the Cramér-Rao lower bound (CRLB) for a monostatic unmanned aerial vehicle (UAV) sensing use case currently under consideration in the 3GPP standardization process. The study focuses on standardized signals and also evaluates the potential performance gains achievable with reference signals specifically designed for sensing purposes. The compact CRLB expressions derived in this work highlight the fundamental trade-offs between estimation accuracy and system parameters. The results further indicate that information from multiple slots must be exploited in the estimation process to attain the performance targets defined by the 3GPP. As a result, the 5G NR positioning reference signal (PRS), whose patterns may be suboptimal for velocity estimation when using single-slot resources, becomes suitable when multislot estimation is employed. Finally, we propose a two-step iterative range and radial-velocity estimator that attains the CRLB over a significantly wider range of distances than conventional maximum-likelihood (ML) estimators, for which the well-known threshold effect severely limits the distance range over which the accuracy requirements imposed by the 3GPP are satisfied.

eess.SP↗
arXiv2026

Full-Domain Coupler: A Wireless Native Neural Backbone for Channel Representation and Deduction

Data representation is a fundamental issue in deep learning. However, as wireless data scales and deeply couples across many physical domains such as time, space, and frequency, existing wireless artificial intelligence (AI) technologies lack dedicated representation solutions. Instead, they mainly rely on stitching general-purpose networks, a tool-driven paradigm that inevitably results in structural redundancy and bottlenecks in information flow. To fill this gap, this paper proposes Coupler, a wireless native-AI neural backbone designed for representation learning of channel state information (CSI)--the pivotal data in wireless systems. Leveraging the revealed physical insights of channel tensors, Coupler decomposes representation learning into individual domains on a layer-by-layer basis, and then couples the learned domain-specific features through a dimension-staggered cascade. This full-domain interleaved learning architecture enables superior parameter efficiency and fine-grained multi-domain feature fusion. Based on this backbone, we use the complex-domain multilayer perceptrons (CMLPs) as spatial and frequency domain learners, while employing three optional mechanisms--convolution, attention, or gating--to capture temporal dependencies. This results in a series of efficient channel learning schemes with diverse functionalities and extreme lightweights, showcasing the compactness, versatility and flexibility of Coupler. We evaluate these schemes on channel deduction, a general representation task encompassing channel estimation, interpolation, prediction, and feedback. Extensive experimental evaluations validate their significant performance gains and robust applicability even for real-world measured data, demonstrating the potential of Coupler as a promising basic architecture in the design of wireless foundation models.

eess.SP↗
arXiv2026

Advances in Wavelet Denoising for Communication Signals: From Parameter Selection Toward Data-Driven Optimization

Wavelet denoising suppresses nonstationary, impulsive, and interference-like disturbances in communication signals, but efficacy depends on coupled choices of transform, mother wavelet, decomposition level, thresholding rule, and shrinkage function. Pairing communication experiments with a PRISMA-aligned synthesis of 2020-2025 studies from ten sources, coded by parameter focus and domain (biomedical, industrial, geophysical, communication), this review traces a shift from defaults to similarity-, sparsity-, entropy-, energy-, sub-band-SNR-, and task-loss-driven selection, but finds communication evidence scarce. We benchmark DWT, SWT, and WPT on an impulsive-noise OFDM channel under SNR gain, MSE, BER, EVM, and real-time feasibility, with Friedman and Wilcoxon tests, efficiency-index ranking, and assessment of embedded DSP/FPGA constraints. As waveform-fidelity gains need not improve hard-decision performance, the Unified Decision Framework prioritises receiver-level validation over fidelity surrogates, validated on synthetic pilot-aided OFDM and measured IEEE 802.11n channels. On the synthetic link, the configuration attains the lowest channel-estimation NMSE, significantly outperforms a fixed-parameter wavelet $(p < 10^{-11})$, and is statistically indistinguishable from the strongest classical baseline in BER; on measured channels it attains the lowest BER and EVM. The supported claim is the lowest estimation error at statistically equivalent bit-error performance, and it is conditional on the disturbance being structured or impulsive, since under AWGN alone wavelet thresholding yields no systematic BER improvement. Within these limits the framework generalises to held-out data and adapts to the observed channel, recasting wavelet denoising into a reproducible, communication-aware design methodology extensible to deep-unfolding architectures.

eess.SP↗