Spectrum-NET is a cloud-native, horizontally scalable solution based on a Kubernetes-orchestrated microservices architecture. With the option for AWS hosting, Spectrum-NET automatically ingests and analyzes RAN data from NR and LTE RAN equipment. Spectrum-NET integrates with other tools (e.g., collaboration systems, network topology databases, trouble ticket systems, SON tools) to maximize automation. Spectrum-NET supports SMO/non-RT RIC for greenfield and brownfield implementations.
Spectrum-NET’s AI models deliver accurate and actionable results yielding meaningful performance and economic gains.
Spectrum-NET
Product Overview
State-of-the-Art Automation
ML-Driven Analysis
Spectrum-NET incorporates Deep Convolutional Neural Network models that are trained offline with labeled measurement samples (interference signatures from known interference cases) from mobile operator networks across the globe. Labeled data is added to the ML training data set and used for future classifications.
The trained CNN models classify interference types with measured accuracy of greater than 95%.
Immediate Results & Quantifiable Financial Benefits
Spectrum-NET enables operators to rapidly resolve harmful RF interference, improve network KPIs, increase network capacity, and strategically deploy scarce field resources. Operators across the globe deploy Spectrum-NET to drive RF interference mitigation processes and effectively address RF network interference. Spectrum-NET provides a valuable new perspective on RF interference by enhancing visibility into spectral efficiency, quantifying performance by RF band, and providing key insights for making sound spectrum investments and strategies.
Learn more about the most significant RF interference challenges and the benefits of Spectrum-NET.
SpectraLocate
External RF Interference Module
External RF Interference Module
- Identifies cells with external interference with high accuracy using ML-trained CNNs.
- Determines damaging external interference source types in consumer, industrial and government networks.
- Characterizes external RF interference by time, frequency and intensity.
- Aggregates all cells impacted by the common interference signature.
- Locates the interference source by analyzing interference magnitudes at each cell and applying weighted Bayesian inference. Applies horizontal and vertical antenna radiation pattern and Topography data.
- Measures the KPI impact (e.g., session drop rate, access failure rate, handover failure rate, downlink/uplink throughput and latency) from the external interference on each of the affected cells for prioritization.
External RF Interference Module
SpectraRAN
RAN Generated Interference (RGI) Module
Improve Spectral Efficiency
- Identifies cells experiencing uplink RGI with high accuracy using ML-trained CNNs.
- Characterizes RGI interference by time, frequency and intensity.
- Aggregates cells affected by the same source of the uplink RGI into clusters.
- Performs cell utilization analysis on RGI clusters.
- Determines the root cause of the RGI such as overshooting cells, insufficient separation between antennas, imbalanced traffic load among layers, or misconfigured parameters.
- Measures the KPI impact (e.g., session drop rate, access failure rate, handover failure rate, downlink/uplink throughput, latency and QoS.) from the RGI on each of the cells in the affected clusters for prioritization.
RAN Generated Interference Module
SpectraPIM
PIM Interference Module
- Identifies cells with PIM interference with high accuracy using ML-trained CNNs.
- Determines whether the PIM source is internal (in cabling or antenna) or external (beyond antenna).
- Analyzes site frequencies to determine contributors to PIM interference (IM2-IM5).
- Identifies cells with faulty hardware issues such as persistent high noise rise, defective Rx branches and crossed antenna feeders.
- Measures the KPI impact (e.g., session drop rate, access failure rate, handover failure rate, downlink/uplink throughput, latency and QoS.) from the PIM interference on each of the affected cells for prioritization.
PIM Interference Module
SpectraDucting
Ducting Module
- Identifies cells experiencing TDD self-interference for LTE and NR during periods of tropospheric ducting (victim cells).
- Aggregates cells affected by the same sources of the ducting interference.
- Identifies the dominant aggressor cells causing the majority of TDD Self-Interference.
- Reconfigures parameters on dominant aggressor cells to mitigate the impact of ducting interference while optimally preserving capacity.
- Detects the victim cells from cross-border interference and identifies the locations of cross-border interference sources.
- Measures the KPI impact (e.g., session drop rate, access failure rate, handover failure rate, downlink/uplink throughput, latency and QoS.) from ducting interference on each of the victim cells.
Ducting Module
O-RAN Apps
O-RAN RIC rApp Module
Spectrum-NET rApp for the RIC
Spectrum-NET offers flexible deployment options, including deployment on mobile operator private or public cloud, mobile operator dedicated data center server, or on Spectrum Effect-hosted AWS. The Apps are available cloud-native or in rApp form for the RIC. Spectrum-NET readily integrates with existing mobile operator tools (e.g., automation framework, collaboration system, topology database, trouble ticket system, and SON) to maximize automation and effectively bridges the gap in greenfield/brownfield environments.