Massive MIMO Load Adjustment for Traffic Capacity Bottlenecks
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Solution Overview
Problem
Massive MIMO cells face limitations in beamforming and multi-user spatial division pairing capabilities, leading to a limited improvement in traffic capacity.
Innovation Solution
A load adjustment method that involves acquiring performance indicators, determining load states, and implementing strategies such as user migration between cells to optimize traffic distribution, including migrating users between neighboring cells and the massive MIMO cell based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If massive MIMO cells configure hundreds or thousands of antenna arrays to improve spectrum efficiency, then spectrum efficiency is greatly improved, but beamforming and multi-user spatial division pairing capabilities cannot be well performed, resulting in limited traffic improvement
Solution Approach 1:
The patent introduces a load adjustment mechanism as an intermediary between the massive MIMO cell and neighboring cells. This mechanism dynamically adjusts user connections by migrating users between cells based on real-time load states and performance indicators, thereby optimizing traffic distribution and improving overall system throughput without requiring additional antenna configurations
Solution Approach 2:
The patent implements dynamic load adjustment by continuously monitoring performance indicators (such as reference signal received power and signal-to-interference-plus-noise ratio) and adjusting user connections in real-time. The system dynamically migrates users between massive MIMO cells and neighboring cells based on changing load states, enabling adaptive optimization of traffic capacity
2Productivity
If load adjustment strategies are implemented to optimize traffic distribution, then traffic capacity is improved, but system complexity increases due to user migration management
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring performance indicators (reference signal received power, signal-to-interference-plus-noise ratio) and using this information to adjust load states and migrate users. The feedback loop automatically triggers load adjustment strategies when performance thresholds are met, simplifying management through automated decision-making based on real-time system state
Solution Approach 2:
The system performs self-optimization by automatically monitoring its own performance indicators and initiating load adjustment strategies without external intervention. The massive MIMO cell and neighboring cells autonomously manage user migrations based on their respective load states, reducing the need for complex centralized control and simplifying overall system management
Data Source
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AI summary
Provided are a load adjustment method, a server, and a storage medium, which belong to the communication field. The method includes steps described below. An indicator value of a first performance indicator of a massive Multiple-Input-Multiple-Output, MIMO, cell is acquired; a load state of the massive MIMO cell is determined according to the indicator value of the first performance indicator; and a load adjustment strategy matching the load state of the massive MIMO cell is determined and performed, where the load adjustment strategy includes one of: migrating a first user of a neighboring cell of the massive MIMO cell into the massive MIMO cell, migrating a second user in the massive MIMO cell into a neighboring cell, migrating a first user of a neighboring cell into the massive MIMO cell and migrating a second user in the massive MIMO cell into the neighboring cell, or maintaining the status quo.