Antenna Array Interference Sensing for Autonomous Spectrum Sharing
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Solution Overview
Problem
The need for a system that dynamically manages spectrum allocation in near real-time to address rapidly-changing scenarios, particularly in the mid-band shared by DoD and commercial 5G systems, and to prioritize DoD users over commercial users, while managing interference from adjacent channel interference.
Innovation Solution
A method using mMIMO antenna arrays and RICs for dynamic co-channel interference sensing and autonomous spectrum management, employing DCSS algorithms and AI/ML to detect and mitigate interference, enabling rapid spectrum sharing and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional spectrum management systems are used, then system complexity is reduced, but detection latency increases and near-real-time interference mitigation cannot be achieved
Solution Approach 1:
The system segments the cell coverage area into multiple three-dimensional segments using antenna arrays, allowing parallel interference detection in each segment. This segmentation enables simultaneous processing of multiple spatial regions, reducing overall detection latency while distributing system complexity across multiple processing units.
Solution Approach 2:
The patent introduces three-dimensional spatial segmentation as an additional dimension for interference detection, moving beyond traditional two-dimensional or single-point monitoring. By creating 3D segments and placing detection points throughout the volume, the system achieves comprehensive coverage and faster detection without proportionally increasing overall system complexity.
2Measurement precision
If antenna arrays with three-dimensional segmentation are deployed, then interference detection precision is improved, but device complexity increases
Solution Approach 1:
The antenna array system divides the coverage area into multiple 3D segments with detection points in each segment. This segmentation allows precise localization of interferers by identifying which specific segment contains the anomaly, improving detection precision while managing complexity through modular segment-based processing.
Solution Approach 2:
The system uses anomaly signatures as intermediaries between the raw radio measurements and the final interference detection decision. By comparing measurements against pre-established anomaly signatures, the system achieves high detection precision without requiring overly complex real-time analysis algorithms.
3Productivity
If dynamic spectrum management is implemented, then spectrum utilization efficiency is improved, but control system complexity increases
Solution Approach 1:
The system implements dynamic spectrum management by continuously monitoring radio measurements across 3D segments and adapting resource allocation in real-time. The controller dynamically adjusts spectrum assignment based on detected interferers and anomaly signatures, improving utilization efficiency while managing complexity through event-driven updates rather than continuous reconfiguration.
Solution Approach 2:
The system employs feedback mechanisms where radio measurements are continuously compared against anomaly signatures, and detection results feed back into spectrum allocation decisions. This closed-loop feedback enables efficient spectrum utilization through automated responses to interference conditions without requiring overly complex manual control systems.
4Reliability
If co-channel interference detection is performed in real-time, then federal user prioritization is achieved, but processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing anomaly signatures for federal user patterns before actual interference events occur. These pre-characterized signatures enable rapid matching and identification of federal users during real-time operation, ensuring reliable prioritization without requiring complex real-time analysis of every signal characteristic.
Data Source
AI summary
A controller of a RAN detects an interferer is present in a region covered by antenna array(s) that provide a 3D view of segments of a cell, using at least a mapping from the segments to corresponding anomaly signatures, and using radio measurements taken in the segments, to determine segment(s) affected by the interferer. The controller performs mitigation of interference in the segment(s). A base station controlling the antenna array(s) determines that an event has been detected because radio measurement(s) of the segments of the cell meet an event detection threshold, and performs and sends to the controller multiple symbol-level radio measurements. The controller also causes channel sensing and radio measurements to be performed by the base station for steady state and states of interest in the cell. The controller correlates these radio measurements to map from segments to anomaly signatures and uses the mapping to perform interference mitigation.