Spatial Inter-Cell Interference Coordination via Neural Network Prediction
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Solution Overview
Problem
Inter-cell interference in wireless communication systems, particularly in next-generation networks with massive MIMO antennas, leads to significant signal degradation at cell edges, reducing data rates and complicating link adaptation, especially for latency-sensitive applications.
Innovation Solution
A neural network is trained to predict the impact of neighbor base station transmit beams on user equipment, allowing for coordinated interference mitigation by prohibiting interfering beams during specific resource usage, thereby reducing spatial inter-cell downlink interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If massive MIMO antennas are deployed to increase network capacity and spectral efficiency, then system productivity and data rates are improved, but inter-cell interference increases causing signal degradation at cell edges
Solution Approach 1:
The system performs preliminary actions by predicting future interference conditions using neural networks before actual interference occurs. The base station predicts spatial interference that will affect user equipment, and proactively coordinates with neighbor base stations to protect resources in advance, preventing interference degradation before it impacts data rates
Solution Approach 2:
The system implements dynamic resource protection by continuously updating interference predictions and adapting resource allocation in real-time. The neural network dynamically identifies changing interference patterns from massive MIMO antennas, and the system dynamically adjusts which time-frequency resources are protected for each user equipment based on current spatial conditions
2Reliability
If resource protection coordination is implemented between base stations to mitigate interference, then signal quality at cell edges is improved, but system complexity increases
Solution Approach 1:
The neural network acts as an intermediary that automatically processes interference prediction and generates coordination decisions. Instead of complex manual coordination between base stations, the neural network intermediary translates spatial interference patterns into automated resource protection commands, simplifying the coordination process while maintaining signal quality
Solution Approach 2:
The system replaces mechanical coordination complexity with intelligent automation. The neural network substitutes for traditional complex coordination mechanisms by using machine learning to predict interference and automatically determine resource protection strategies, reducing the burden of inter-base-station coordination
3Object-affected harmful factors
If traditional interference mitigation techniques are used, then some interference reduction is achieved, but data rate maintenance and link adaptation accuracy are insufficient for latency-sensitive applications
Solution Approach 1:
The system implements feedback by continuously monitoring actual interference conditions and using neural networks to predict future interference patterns. This feedback loop enables the system to adapt resource protection strategies in real-time, maintaining data rates by anticipating interference before it degrades signal quality and adjusting coordination accordingly
Data Source
AI summary
A method of wireless communication by a first network device includes predicting spatial inter-cell downlink interference experienced by a UE. The method also includes communicating with a second network device to reduce the spatial inter-cell downlink interference in a direction of the UE by protecting resources across selected resource sets.