A quantum fuzzy logic-enhanced evolutionary framework for energy-latency optimization in 6G Edge-IioT
Siavash Siavashian Rashidi, Ali Broumandnia, Abbas Mirzaei, Ramin Karimi · 2026
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Description
Siavash Siavashian Rashidi and colleagues built an orchestration framework in 2026 for 6G edge industrial networks balancing energy against strict latency. Dense, fast-changing edge systems make resource scheduling NP-hard, and classic methods stall or converge early. Their QFLN-AMRO design pairs a fractional-order evolutionary engine with a quantum fuzzy logic network that tunes parameters on the fly, plus a Lyapunov resilience guard.
Tested on real spatial, edge-payload, and cluster-trace data, the framework beat deep reinforcement and metaheuristic rivals on energy while holding latency thresholds, with statistical tests confirming the edge. Saturation and queue drift under extreme latency stress were curbed by the resilience mechanism. The approach suits factories where millisecond guarantees and power budgets collide.
Catalog record
| CREATOR | Siavash Siavashian Rashidi, Ali Broumandnia, Abbas Mirzaei, Ramin Karimi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 204 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |