The Team
Three high school students who built a physics-grounded framework for predictive wireless reliability — from Maxwell's equations to a hardware-validated switching controller.
Neha Abin
Physics derivation, instability model, and formal analysis. Developed the coherence-cliff framework from Maxwell's equations through Jake's model to the CCI risk metric and switching rule.
Sahil Shah
Controller implementation, Wi-Fi testbed construction, and experimental data collection. Built the closed-loop measurement pipeline and ran 100 validation trials across varying signal conditions.
Yajat Parmar
Interactive simulations, website, and data visualization. Implemented the Python simulation framework, Jupyter notebooks, and the web-based interactive models demonstrating the CCI framework.
Why we built this
As wireless systems push to higher frequencies — Wi-Fi 7 at 6 GHz, 5G mmWave at 28 GHz, and eventually 6G above 100 GHz — the coherence cliff problem becomes more severe. At higher carrier frequencies, even slow user movement produces large Doppler shifts, making coherence times very short.
We believe wireless reliability should be designed in from physics, not patched after failures are observed. WaveLynk provides a framework for doing exactly that.
Roadmap
- Phase 1 ✓ — Theoretical framework, CCI derivation, proxy Wi-Fi experiment
- Phase 2 — mmWave testbed validation (28 GHz SDR platform)
- Phase 3 — Multi-user MIMO extension (> 4 simultaneous users)
- Phase 4 — Real-time embedded controller for commercial access points
Where this matters
Any system where wireless link collapse has real consequences.
Telemedicine
Reliable links for remote surgery and diagnostics, where a single latency spike can be life-critical.
Autonomous Vehicles
V2X communication that predicts link failures before safety hazards arise in real-time driving.
Industrial IoT
Factory mmWave links needing sub-millisecond reliability for robotic control loops.
AR / VR
High-throughput, low-latency links where frame drops cause motion sickness and break immersion.
Rural Connectivity
Point-to-point wireless backhaul in underserved areas where infrastructure reliability is essential.
6G Infrastructure
Foundation for networks above 100 GHz where coherence cliffs are severe and unavoidable.
Common questions
What exactly is the "Coherence Cliff"?
A nonlinear instability transition in ZF beamforming. When CSI age — amplified by channel conditioning κ — causes the inversion-based beamforming weights to become destructive, the result is a sudden jump in interference, latency, and packet loss. It's a phase transition, not a gradual decline.
How is this different from standard rate adaptation?
Standard rate adaptation (e.g., Wi-Fi's MCS selection) reacts to observed degradation — low SNR, high packet loss. WaveLynk uses a physics-based predictive metric (CCI) that detects when the system is approaching the instability boundary before failure occurs. It acts on a mathematical forecast, not a symptom.
Why is this a Wi-Fi 6/7 proxy experiment rather than true mmWave?
mmWave/THz hardware (28 GHz+ SDR platforms) was not available to us. The underlying physics — Doppler shift, coherence time, channel matrix conditioning — behaves identically and scales predictably with carrier frequency. Our results demonstrate the mechanism at 6 GHz; a direct mmWave validation would further strengthen the case.
What are the limitations of this work?
The proxy experiment nature is the primary limitation. We also use 2 clients to approximate MU-MIMO; a real system might have 8–16 simultaneous users. The switching threshold γ = 0.6 is derived analytically, but empirical tuning across a wider range of channel conditions would improve robustness. Finally, we cannot measure the true channel matrix H in commercial hardware — we approximate κ(H) from indirect observations.
What comes next?
Phase 2 involves validating at 28 GHz with a software-defined radio (SDR) platform, enabling true channel matrix estimation. After that, extending to >4 simultaneous users, running >1000 trials per condition, and integrating the controller into commercial AP firmware are the priorities.