Dig Deeper.

Think Smarter.

Survive the Chaos.

The Reznov-Jackal™ — a GPS-free, emission-free solution to autonomously protect logistical groups from RF-based threats.

Decision Drivers for Choosing a MIMO System

Decision Driver Why It's Important How It Affects MIMO Choice
1. Application Requirements Different use cases need different speeds, reliability, and latency. High-throughput apps (like 5G, VR streaming) need higher-order MIMO (8×8 or more). Simple IoT devices may only need basic MIMO (2×2).
2. Channel Conditions (Environment) Real-world factors like reflections, obstacles, and user movement matter. Rich multipath environments benefit from more antennas. Line-of-sight (LoS) systems (e.g., backhaul) might benefit more from beamforming rather than spatial multiplexing.
3. Device Size and Complexity Physical and cost constraints limit the number of antennas. Smartphones can't fit 8×8 arrays easily; base stations can. High MIMO orders mean more RF chains, increasing complexity and heat.
4. Power Consumption Each antenna and RF chain consumes power. Battery-powered devices (e.g., smartphones) might limit MIMO to save energy, opting for 2×2 or 4×4.
5. Cost (CapEx and OpEx) More antennas = more hardware cost and maintenance. Budget constraints might restrict base station MIMO layers even if theoretically beneficial.
6. Spectrum Band Different frequency bands have different behaviors. Higher frequencies (mmWave 5G) easily support large MIMO arrays (64×64) because antenna elements are physically smaller.
7. Backhaul and Network Support The rest of the network must handle increased throughput. Upgrading radio access must be matched by backhaul (fiber, microwave) upgrades.
8. Standard and Regulatory Requirements Must comply with regional wireless standards. Some regions cap MIMO layer usage for certain services (e.g., Wi-Fi in 2.4 GHz bands).
9. Mobility and Channel Coherence Time High mobility shortens coherence time, making MIMO harder. For high-speed trains, simpler MIMO setups with fast tracking are better than complex massive MIMO.
10. Latency and Reliability Goals Some applications require ultra-low latency and ultra-high reliability (URLLC in 5G). Massive MIMO with smart scheduling and coding might be necessary to meet stringent demands.

Decision Drivers of Multi-Input Mutli-Output (MIMO) Systems

Mine, Harvest, and Isolate with Confidence

Whether you're just starting or looking to master advanced maneuvers, EiganUSA is your trusted resource for all things FPV drones.

We specialize in beginner-friendly tutorials, build guides, gear reviews, simulator walkthroughs, and safety tips — all designed to help you operate smarter.

Schema for WDARMS-drone

Excavate Beneath the Battlefield: Using Autonomous Tunneling Robots to Evade Enemy Drones with WDARMS — the WDARMS Reznov-Jackal™

Engineered for swarm communication and EW operations, the WDARMS system boasts a robust chassis, a modular payload bay, and a high-bandwidth radio suite. These features make it suitable for subterranean adaptation. The robot can burrow through soil and debris by integrating a front-mounted auger or rotary cutter. An onboard conveyor moves excavated material while rear-deployed foam sprayers or shoring rods reinforce the tunnel. The compact design allows it to navigate tight urban subsurfaces—ideal for collapsed buildings, bunkers, or enemy tunnel networks; this system rides on reinforced treads with a low-profile chassis, designed for maneuverability in tight, irregular subterranean environments. Unlike wheeled robots, this configuration provides superior traction through rubble, narrow pipes, and collapsed infrastructure, making it perfect for disaster zones, tunnel systems , and bunkers; the visible dual-fan thermal management system and enclosed circuitry indicate MIL-SPEC-grade environmental hardening. This means the robot can operate continuously in dusty, humid, and high-temperature underground environments without thermal failure—a critical asset for long-duration extraction tasks — soft robotic actuators, grippers, and tunnel support structures can be printed on-site and installed quickly. This enables mission-specific customization: e.g., printing a soft-extraction arm for a narrow debris channel or modular soft shoring braces.

What is the Objective of WDARMS?

To deploy this countermeasure system on active logistics platforms and unmanned systems, providing silent, autonomous threat neutralization in RF-contested zones. Our goal is full mission integrated, platform alignment, and acquisition support.

What Makes the WDARMS System Different?

It does not jam. It does not emit. It does not need comms or GPS. Instead, the WDARMS system uses environmental airflow to arm, and enemy signals to target. It is completely passive unit it activates — enabling unmatched stealth and survivability.

Where is the WDARMS System Applicable?

Unmanned systems (UAS / UUV), perimeter defense, shipboard ISR denial, and expeditionary launch platforms. It is modular, small form-factor, and adaptable to multiple deployment scenarios.

What is the Current State of Robustness for the WDARMS System?

EiganUSA™ has validated the functional elements: wind calibration, RF-target discrimination, lithiunm ignition, and projectile containment. EiganUSA™ is now preparing for system-level integration and operational field evaluation.

How Scalable is the WDARMS Solution?

The WDARMS system used 3D-printed, tensor-encoded structures that scale by geometry — not complexity. The digital driver files are adaptable for various platform sizes, and can be field-fabricated if needed using containerized additive systems.

Meet the President of EiganUSA

Alexander Paul Eul - President of EiganUSA at a drone technology event
Alexander Paul Eul — Innovator, Engineer, and Patent Holder

Alexander Paul Eul is the President of EiganUSA and a driving force in bridging the worlds of biomedical innovation, drone technology, and AI-based additive manufacturing

He is the named inventor of U.S. Patent No. 12105501, a system for generating three-dimensional models using advanced tensor encoding and volumetric data

In addition to hardware innovation, Alexander is the creator of the open-source Node.js tool mariinsky — a powerful library that allows drone pilots

Alexander’s career also includes serving in the U.S. Navy's Naval Sea Systems Command (NAVSEA)

At present, he is also the Director of Monetization at Pluripotent Analytics, a company focused on scalable innovation

Credentials & Expertise

  • 📜 Patent Holder:
    U.S. Patent No. 12105501
  • 🖥️ Software Developer:
    Author of mariinsky
  • 🎯 Certifications:
    CRISPR/Cas9 Additive Manufacturing Deep Neural Networks
  • 🗣️ Languages:
    English (Native) Hebrew (Professional)
  • 🎓 Education:
    California State University (Mathematics)
    UCSD Extended Studies (Infrared Systems)
    CSU Northridge – CSUN (Computer Engineering)
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