Latency Decoupling in Low-Feedback Multi-User Networks via Overhearing-Driven NOMA
2026-07-27 • Information Theory
Information Theory
AI summaryⓘ
The authors address the challenge of slow communication in wireless networks where feedback is limited, such as in satellite or large IoT systems. They introduce ONOMA, a new method that lets devices listen in on transmissions and use timing clues to estimate channel quality without needing constant updates. This approach helps faster devices decode messages without waiting for slower ones, reducing delays. Tests show ONOMA speeds up data completion noticeably compared to older methods.
Hybrid Automatic Repeat Request (HARQ)Non-Orthogonal Multiple Access (NOMA)Random Linear Network Coding (RLNC)Channel State Information (CSI)Feedback timingPower allocationAsymmetric channelsWireless latencyMulti-user systemsCompletion time
Authors
Mohsen Abedi, Ahmed Badawy, Amr Mohamed
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.