Physical AI
AI can’t optimize what it can’t observe.
Inference distance
Effects are easy to see: latency, ECN marks. Causes are the hard part: queue growth, retry timing, aggregation depth. Closing the distance between the two is what makes control stable and learning effective.
Fi-Wi collapses it. One centralized state graph, fed by abundant radio heads and abundant inexpensive sensors placed where signals land and from where they originate, gives causal observability: the causes themselves. The heads multiply what can be learned; the sensors make it continuous.
What the scheduler actually does
The scheduler applies a pre-computed policy, refreshed at the rate the observed dynamics actually change, and the learning is physics-informed, combining Shannon capacity theory with learned corrections.
Fi-Wi provides dynamic point selection, intelligent frequency reuse and centralized MAC scheduling, on commodity Wi-Fi silicon. RF phase control, distributed MIMO and coordinated simultaneous transmission stay outside the architecture: each transmission originates from a single selected radio head.
Read the work
- Fi-Wi Forwarding Plane v2 The technical paper, and ground truth for the architecture.
- Fi-Wi in a snapshot The architecture in one diagram: concentrator, fiber plant, radio heads, sensors.