ECI ‘s Converged Interconnect Network Solution for Cable Operators

Streamlines operations, reduces costs and improved customer experience

As the appetite for bandwidth continues to grow, multiple system operators (MSOs) are transitioning from centralized access architectures to distributed access architectures. The transition not only requires decentralizing and virtualizing head-end functionality but also streamlining the transport and aggregation networks. This includes transitioning from many separate solutions, each for a different type of service, to multi-service platforms capable of supporting many services with different service profiles and requirements.

CIN offers greatly reduced costs, improved customer experience, and a platform for launching new, high-value services, including 5G backhaul.

ECI uses its Neptune portfolio and advanced Muse software suite to provide an agile, multiservice packet transport platform for CIN. Neptune’s unique Elastic MPLS functionality provides the ability to choose the best transport technology for each of today’s services and an easy, programmable evolution path to distributed access architecture (DAA). In addition, ECI’s solutions are deployable in the physical locations used by cable operators, from cable node/street cabinet to head-end.

ECI’s unique Elastic MPLS combines support for IP/MPLS, MPLS-TP and Ethernet on the same platform and enables the stitching between these domains, as required. Elastic MPLS also allows for smooth migration of existing services while supporting new service types in the future. The platforms come with flexible, high capacity (nx100G), long-range interfaces which permit a reduction in the number of core nodes and sites. Moreover, the Neptune form factors have been optimized for installation in street cabinets with reduced power consumption, reduced noise generation, hardened design and extended temperature range (from -25° C to +65° C). The compact design (300mm deep with all front access) and high density pluggable cards, ensure they maximize density per footprint. To learn more, visit:

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