Amplifiers for Cable Broadband
Quantum Bandwidth™ HFC amplifiers deliver unmatched RF performance, low-power consumption, and seamless DOCSIS 4.0® readiness. Engineered with advanced GaN technology, these amplifiers optimize high-split spectrum allocations while maintaining legacy compatibility with GainMaker® and Motorola housings. Features include remote diagnostics via QuantumLink™, field-upgradeable diplex filters, and precision RF tuning for optimal network efficiency.
Quantum12 | 1.2GHz
Quantum18 | 1.8GHz
1.8GHz Node
Quantum18™ GSN 1.8GHz Node delivers scalable, high-output RF performance for DOCSIS 3.1 and 4.0 networks. Built for seamless integration with GS7000 housings, this node features advanced thermal management, flexible spectrum allocations, and remote Bluetooth configuration. With multi-split support, AI-powered diagnostics, and robust signal amplification, Quantum18™ ensures future-ready network reliability with minimal field intervention.
QuantumLink™: System Amplifier Remote Management
QuantumLink™ transforms HFC amplifier and node management with AI-driven remote diagnostics, automated ingress monitoring, and firmware updates. Built on LoRaWAN architecture, QuantumLink™ reduces OPEX, truck rolls, and manual RF balancing while ensuring network stability with real-time telemetry and predictive maintenance alerts. Seamlessly configure amplifiers, monitor RF levels, and optimize performance—all from a centralized interface.
QuantumWave™: Revolutionizing Fiber Optics with High-Speed Transceivers
QuantumWave™ brings high-speed transceiver solutions to meet the growing demand for fast, reliable data transmission in today's interconnected world. QuantumWave™ transceivers, available in 10Gb and 25Gb, utilize cutting-edge fiber optic technology to facilitate swift and secure data transfer across extended distances. Suitable for an array of applications, these transceivers underscore our commitment to pushing the boundaries of fiber optic technology and empowering your network to handle even the most demanding workloads.
