Robust inference-aware AirMoE aggregation under wireless impairments

Establish robust inference-aware AirMoE aggregation methods that remain effective under imperfect channel-state information, synchronization errors, interference, and device mobility.

Background

The AirMoE framework relies on accurate channel knowledge, synchronized simultaneous transmissions, and a relatively controlled uplink environment to aggregate gating-weighted expert outputs over the air. The paper identifies robustness under realistic wireless impairments—including imperfect CSI, synchronization errors, interference, and device mobility—as an unresolved research direction, with the goal of preserving reliable MoE inference when these assumptions fail.

References

From a broader perspective, AirMoE highlights several open problems in wireless MoE serving systems. Robust AirMoE aggregation can be revisited from an inference-aware perspective under imperfect CSI, synchronization errors, interference, and device mobility.

— AirMoE: Realizing Over-the-Air Distributed Mixture-of-Experts Inference at the Wireless Edge  (2608.22932 - Yang et al., 24 Aug 2026) in Concluding Remarks, Section VI

From a broader perspective, AirMoE highlights several open problems in wireless MoE serving systems. Robust AirMoE aggregation can be revisited from an inference-aware perspective under imperfect CSI, synchronization errors, interference, and device mobility. Beyond empirical layer-sensitivity calibration, analytical models are needed to characterize how over-the-air aggregation distortion propagates across MoE layers and affects E2E inference performance.

— AirMoE: Realizing Over-the-Air Distributed Mixture-of-Experts Inference at the Wireless Edge  (2608.22932 - Yang et al., 24 Aug 2026) in Concluding Remarks, Section VI