Skip to content

Latency Decoupling in Low-Feedback Multi-User Networks via Overhearing-Driven NOMA

Jul 2026 · arXiv.org · Vol abs/2607.24609 · 0 citations · 21 references
Computer Science Mathematics

TL;DR

Analytical and simulation results show that ONOMA outperforms TDMA, multicast, FDMA, inter-session, and classical NOMA, reducing completion time by up to 34% in two-user and 50% in larger asymmetric networks.

Abstract

Conventional wireless protocols such as Hybrid Automatic Repeat Request (HARQ) rely on frequent and timely feedback, which becomes impractical in low-feedback regimes including non-terrestrial networks and massive IoT. This limitation is particularly critical in heterogeneous multi-user systems with unknown and asymmetric channels, where a single weak user can dominate the overall completion latency. We propose Overhearing-driven Non-Orthogonal Multiple Access (ONOMA), a novel cross-layer transmission scheme that minimizes latency without requiring instantaneous or statistical CSI at the transmitter. ONOMA integrates Random Linear Network Coding (RLNC) with symbol-aware NOMA and explicitly exploits overhearing and acknowledgment timing. In the first phase, users overhear RLNC transmissions, and the relative timing of acknowledgments is used to implicitly infer channel strength ordering. In the second phase, symbol reconstruction enables interference-free decoding for strong users, effectively decoupling user latencies. An adaptive power allocation policy is derived from acknowledgment timing-based channel estimates. Analytical and simulation results show that ONOMA outperforms TDMA, multicast, FDMA, inter-session, and classical NOMA, reducing completion time by up to 34% in two-user and 50% in larger asymmetric networks.

View source

Similar papers

2026

Collision-Isolated Asynchronous Access for High-Density IoT Networks

Low Power Wide Area Networks (LPWANs) such as LoRa commonly adopt unslotted ALOHA for its simplicity, but the resulting throughput ceiling (0.183 frames/frame-time) limits scalability. We propose a heterogeneous multi-channel collision resolution system that partitions $M$ channels into contention channels and dedicate...

Song Fan, Zhi Dou, Jun-Bae Seo et al. · 0 citations
Conference Sep 2026

Resolving the high-dynamics vs. low-signaling dilemma: a PPS-aided, shared-sequence ALOHA framework for LEO direct-to-satellite burst access

High-precision frequency compensation and high-throughput concurrent access constitute the core bottlenecks for burst access reliability in LEO satellite IoT. Conventional solutions, hampered by large open-loop residual errors, high closed-loop signaling overhead, and onboard multidimensional search and storage costs g...

Yi-Wei Chen, Hai-Su Zhang, Qing-Song Li et al. · 0 citations
Preprint Sep 2026

GNN-Based Polarforming for Multi-User MISO Short-Packet URLLC under Imperfect CSI

This paper investigates polarization-aware transmission for multi-user multiple-input single-output (MU-MISO) short-packet ultra-reliable low-latency communications (URLLC) under imperfect channel state information (CSI). We consider a system in which the base station (BS) and users are equipped with polarization-recon...

Zahra Mehrzad, Hamed Aghaei-Karkaj, Rahman Saadat Yeganeh et al. · 0 citations
Preprint Aug 2026

Digital Twin-Aided Prescreening for User Scheduling in MU-MIMO Downlink Systems

Digital Twin User pre-Screening (DiTUS), a digital-twin (DT)-aided framework that identifies promising users before instantaneous CSI acquisition, demonstrating that DT-based prescreening preserves substantial multiuser-diversity gains by identifying strong, spatially compatible users before acquiring effective-channel...

Namhyun Kim, Mahmoud Saad Abouamer, Jeonghun Park et al. · 0 citations
#machine learning Preprint Sep 2026

Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling

To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition over multiple-access channels (MACs) for analog model aggregation. However, coherent AirFL typically relies on stringent PHY-layer condition...

Hai-Feng Wen, Nicolò Michelusi, Osvaldo Simeone et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.