RF-JAM ZERO ML
Every Node Learns. The Whole Site Adapts.
RF-JAM ZERO ML turns a facility into a self-optimizing denial mesh. Distributed inhibition nodes and an IoT sensor fabric feed one predictive ML controller that coordinates power across zones in real time, re-optimizing in under 8 s and holding external spillover ≤ the regulatory limit across every site it manages.
From a Jammer to a Learning Network
A single closed loop optimizes one enclosure. RF-JAM ZERO ML optimizes an entire estate: every node reports telemetry, every perimeter sensor reports leakage, and a predictive controller anticipates load and environment changes before coverage drops — coordinating power the way a network schedules traffic.
Predictive ML Controller
A time-series model forecasts RF environment and occupancy patterns and pre-positions power set-points, cutting reaction latency to < 8 s re-convergence and < 45 s initial convergence versus reactive-only control.
IoT Sensor & Node Mesh
Up to 64 inhibition nodes and 128 perimeter sensors on one controller, each addressable over a redundant fabric with < 50 ms telemetry latency and automatic mesh self-healing on link loss.
Multi-Zone / Multi-Site Orchestration
One controller manages unlimited GeoJSON zones across multiple buildings, each with independent power profiles, schedules and spillover budgets, coordinated to avoid inter-zone interference.
Digital Twin & Analytics
A live model of the site records coverage, spillover and compliance per zone, replays incidents, and exports audit-grade reports; ML flags anomalies (coverage holes, drift, tamper) for operators.
The Result
- Re-convergence < 8 s · initial convergence < 45 s across the managed estate
- Up to 64 nodes + 128 sensors on a single predictive controller
- Per-zone spillover budgets held ≤ regulatory limit, coordinated site-wide
- Self-healing mesh with graceful degradation on node, sensor or link loss
Technical Specifications
ML Control & Optimization
| Method | Predictive time-series + constrained gradient descent |
| Constraint | Per-zone spillover ≤ regulatory limit (hard) |
| Update rate | 1–5 s (configurable) |
| Initial convergence | < 45 s |
| Re-convergence | < 8 s |
| Power resolution | Per module · 1% |
Ordering Information
Part numbers and configuration options
| Part Number | Description | Availability |
|---|---|---|
| SI-RJM-CTRL | Predictive ML controller · digital twin | Standard |
| SI-RJM-ND8 | 8-module inhibition node (160–240 W) | Standard |
| SI-RJM-ND4 | 4-module inhibition node (80–120 W) | Standard |
| SI-RJM-SN | Perimeter RF sensor · IP65 | Standard |
| SI-RJM-ANT-D | Directional panel antenna · 8–12 dBi | Standard |
| SI-RJM-SW-ML | Predictive ML + digital-twin license | Standard |
| SI-RJM-FAB-R | Redundant fabric switch kit | Optional |
| SI-RJM-UPS | Online double-conversion UPS · rack 2U | Optional |
Implementation Process
Site Survey
- Multi-building RF mapping
- Zone boundary capture
- Occupancy pattern baseline
- Regulatory limits per zone
System Design
- Node/sensor placement across sites
- Mesh fabric topology
- Per-zone spillover budgets
- Digital-twin model build
Installation
- Node mounting
- Sensor grid
- Redundant fabric cabling
- Controller commissioning
Calibration
- Per-zone ALC set-points
- Predictive model training
- Spillover verification
- Telemetry baseline
Training
- Operator training
- Administrator training
- Compliance training
- Digital-twin and reporting workflows
Go-Live
- Estate-wide activation
- Predictive optimization enabled
- Monitoring transition
- Support transition
Typical deployment timeline: 4-8 weeks depending on facility size and complexity
Frequently Asked Questions
Deploy a Denial Network That Optimizes Itself
Request a demonstration of predictive, estate-wide adaptive jamming.
RF-JAM ZERO
The single-site closed-loop flagship, upgradeable into an ML-managed mesh.