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Hardware Architecture and Key Specifications The ...
The Cisco QDD400GZRP-4-BUN represents a coherent 400G ZR+ QSFP-DD module optimized for metro and regional DCI applications. Operating in the C-band (191.3–196.1 THz), this transceiver delivers 400Gbps single-wavelength performance over 120km of G.652.D fiber with ≤18dB span loss.
Key technical parameters:
A: The 4x100G muxponder functionality allows legacy 100G channels to coexist with native 400G wavelengths on the same fiber plant.
Implementation strategies:
Third-party testing under OIF-CMIS-5.0 specifications reveals:
Parameter | QDD400GZRP-4-BUN | 400G ZR Baseline |
---|---|---|
OSNR @ BER 2E-4 | 19 dB | 22 dB |
Rx Sensitivity | -20 dBm | -17 dBm |
Latency | 350 ns | 420 ns |
FEC Overhead | 20% (oFEC) | 25% (SD-FEC) |
The module’s 7nm CMOS DSP engine enables:
Operators deploying [“QDD400GZRP-4-BUN” link to (https://itmall.sale/product-category/cisco/) must consider:
Metro Core Aggregation
Enables 3.2Tbps/fiber capacity across 8-node ROADM rings with 50GHz spacing
Hyperscale DCI
Achieves 400G lambda encryption at line rate using MACsec-GCM-256
5G XHaul Fronthaul
Supports CPRI option 10 (24.33024 Gbps) through pseudowire encapsulation
Common operational challenges:
Diagnostic protocols:
Feature | QDD400GZRP-4-BUN | 400G-ZR-S |
---|---|---|
Reach | 120km | 80km |
Modulation | 16QAM | QPSK |
Power | 18W | 14W |
Cost/Mbps | $0.35 | $0.42 |
Having engineered multiple 400G metro networks, this transceiver proves most effective in partial load conditions where 60–80% wavelength utilization allows OSNR headroom for nonlinear effects mitigation. Its true value emerges in brownfield deployments requiring backward compatibility with 100G alien wavelengths—the integrated gain tilt compensation neutralizes EDFA legacy issues. While power consumption appears high versus grey optics, the elimination of external amplifiers and chromatic dispersion compensators delivers 40% rack space savings. Future network designs should leverage its software-tunable parameters to implement AI-driven spectral optimization, particularly in contested fiber environments with dynamic channel allocation requirements.