Coordinated MIMO Switching Power Control for LTE Interference
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Solution Overview
Problem
Current MIMO systems in LTE networks face challenges in achieving large capacity gains and improving cell-edge user performance due to interference, particularly in coordinating power distribution across multiple-input multiple-output (MIMO) channels.
Innovation Solution
The method involves coordinating downlink power per layer at an enhanced NodeB (eNodeB) by receiving feedback from user equipment, calculating MIMO gradients, and exchanging gradient power information with neighboring eNodeBs, utilizing a network utility maximization framework to optimize power settings across the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If MIMO transmission is used to increase network capacity, then throughput is improved, but interference increases which limits capacity gains
Solution Approach 1:
The system uses CQI feedback from UEs to calculate MIMO gradients and adjust power settings. The eNB receives CQI measurements from UEs, computes gradients based on this feedback, and iteratively adjusts downlink power per layer. This closed-loop feedback mechanism enables the system to adapt to changing channel conditions and interference levels, resolving the contradiction between maintaining high throughput and managing interference.
Solution Approach 2:
The system dynamically changes power allocation parameters across different MIMO layers and time-frequency resources. By adjusting the downlink power per layer based on calculated MIMO gradients and CQI feedback, the system optimizes the balance between achieving capacity gains through MIMO transmission and controlling interference that limits overall network performance.
2Reliability
If coordinated power control is implemented across multiple eNBs, then cell-edge user performance is improved, but system complexity increases
Solution Approach 1:
The coordination problem is segmented into individual eNB decisions based on local gradient calculations. Each eNB independently computes MIMO gradients using its own CQI feedback and adjusts its power settings without requiring complex centralized coordination. This segmentation reduces system complexity while still achieving coordinated power control benefits for cell-edge users through distributed optimization.
Solution Approach 2:
Each eNB autonomously calculates its own power adjustments based on received CQI feedback and computed MIMO gradients. The system enables self-service coordination where eNBs independently make optimization decisions using local information, eliminating the need for complex inter-eNB signaling and coordination protocols while still improving cell-edge user performance through distributed power control.
3Productivity
If downlink power per layer is adjusted based on MIMO gradients, then throughput is improved, but computational requirements increase
Solution Approach 1:
The system computes MIMO gradients only for the necessary power adjustment decisions rather than performing exhaustive optimization calculations. By calculating gradients based on received CQI feedback and applying incremental power adjustments, the system achieves throughput improvement without requiring excessive computational resources for complete system optimization.
Data Source
AI summary
An eNB is configured to perform a method for coordinating downlink power per layer at an eNB in a multiple-input, multiple-output (MIMO) network. The method includes receiving feedback from at least one UE; calculating a plurality of MIMO gradients based on the received feedback; changing a power per layer according to the calculated MIMO gradients; and exchanging gradient power information between the eNB and at least one neighboring eNB.