Near Space Network (NSN) Virtual Network Management & Orchestration Architecture

Working with NASA, we developed an architecture concept to support  the evolution of the Near Space Network.

Our team defined a scalable, cloud-first enterprise architecture for ground network management and space communications service delivery. We then evaluated available government, commercial, and hybrid capabilities to identify an optimal implementation strategy.

Working with NASA, we ensured that the LEGS antenna network met key functional and performance requirements.

Our team developed high-fidelity signal chain models for the LEGS antennas and signal processing subsystems. Using these models, we identified and mitigated component performance limitations and potential sources of interference to ensure successful mission link closure.

Working with NASA, we standardized and enhanced the mission onboarding and integration process.

Our team developed standardized document formats and processes to capture mission interface requirements and service-level agreements. Leveraging these processes, we supported the onboarding of multiple major missions, including Artemis/Orion, NISAR, PACE, and SPHEREx.

Working with NASA, we enabled automated matchmaking between mission requirements and network services.

Our team developed a data model and database architecture to manage NSN network service, commercial provider, and user mission data. Leveraging this model, we designed a real-time, on-demand mission

requirement collection and mission-to-provider matchmaking capability.

Working with NASA, we investigated anomalies in the processing of Artemis launch telemetry.

In our role as ground system subject matter experts, we conducted a high-visibility, high-priority anomaly investigation in support of the Artemis I

launch campaign. Our team identified and validated a low-risk, low-cost

strategy to meet Artemis launch requirements.

Working with a major healthcare technology provider, we developed an optimized data processing engine.

Our team implemented a high-performance data processing engine to support an aggregate input of 20+ million discrete patient and provider records per day. Our optimizations reduced total daily record ingest and processing times by more than 50%.