MIMO Active Set Scheduling for Interference and Capacity Balance
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Solution Overview
Problem
Existing MIMO networks face challenges in efficiently forming active sets that avoid interference and capacity issues while maintaining reasonable network complexity, as iterating through all possible combinations of TRP and UE groupings is impractical.
Innovation Solution
A scheduler-based active set management scheme that determines metrics for potential combinations of active sets, combining those with the highest capacity and link quality until all sets meet threshold values, thereby optimizing MIMO network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all possible combinations of TRP and UE groupings are iterated through to form optimal active sets, then network performance (throughput, reliability) is improved, but network complexity and processing time increase significantly
Solution Approach 1:
The patent segments the exhaustive combination iteration process into two distinct phases: a first scheduling process that selects user equipment for service by a coordinating group of cells, and a second scheduling process that determines which groups of cells form the coordinating groups. This segmentation allows each process to operate independently with reduced complexity while achieving near-optimal active set formation.
Solution Approach 2:
The first scheduling process performs preliminary selection of user equipment and coordinating cell groups before the second scheduling process optimizes the active set configurations. By establishing initial groupings in advance, the system reduces the search space for the second process, avoiding exhaustive iteration while maintaining performance.
2Productivity
If larger active sets are formed to increase MIMO dimensions and capacity, then network throughput is improved, but interference between UEs increases
Solution Approach 1:
The patent dynamically adjusts active set configurations by changing key parameters: combining multiple cell groups into coordinating groups to increase MIMO dimensions and capacity, while simultaneously optimizing the composition of these groups to minimize interference. The scheduler evaluates different parameter combinations (cell groupings, active set sizes) to find the optimal balance between throughput and interference.
Data Source
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AI summary
Aspects of the disclosure relate to an active set management scheme implemented by a scheduler in a multiple-input multiple-output (MIMO) network that minimizes capacity and interference issues. For example, the scheduler can initially group each base station into a separate active set. The scheduler can then analyze each active set to determine whether the active set is a good or bad based on the level of interference in and the number of MIMO dimensions available in the respective active set. If the scheduler determines that an active set is a bad, the scheduler can determine a set of metrics that each represent a capacity and link quality that would result if the bad active set is combined with another active set. Based on the set of metrics, the scheduler can combine the bad active set with another active set, and repeat this process until no bad active sets remain.