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

2026-07-27

AI Summary

This work addresses the latency bottleneck in heterogeneous multi-user systems under low-feedback scenarios—such as non-terrestrial networks and massive IoT—where conventional protocols struggle due to their reliance on frequent feedback and the presence of unknown, asymmetric channels. The authors propose ONOMA, a cross-layer transmission scheme that integrates random linear network coding with symbol-aware non-orthogonal multiple access (NOMA). By leveraging user listening and acknowledgment timing, ONOMA implicitly infers channel strength without requiring channel state information at the transmitter, enabling adaptive power allocation. Furthermore, symbol reconstruction ensures interference-free decoding for strong users, effectively decoupling user latencies. Experimental results demonstrate that ONOMA reduces completion time by up to 34% over TDMA, FDMA, multicast, inter-session coding, and classical NOMA in two-user settings, with gains reaching 50% in large-scale 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.

Problem

• low-feedback
• multi-user networks
• latency decoupling
• asymmetric channels
• completion latency

Innovation

• Overhearing-driven NOMA
• Latency Decoupling
• Low-Feedback Networks
• Random Linear Network Coding
• Cross-layer Design