Sub-band User Correlation for Massive MIMO Scheduling
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Solution Overview
Problem
Conventional Radio Access Networks (RANs) face inefficiencies in user pairing for Massive MIMO due to the use of a single wideband correlation metric, which does not accurately capture user correlation profiles across the entire bandwidth, leading to sub-optimal MU MIMO performance and increased computational complexity with a greater number of RRC connected users.
Innovation Solution
Implementing sub-band correlation computation among scheduled users to efficiently pair users across groups of Physical Resource Blocks (PRBs), allowing for flexible sub-band selection based on bandwidth and using a correlation matrix specific to each sub-band, while accommodating more RRC connected users by scheduling SRS over multiple groups and sharing correlation matrices across these groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a single wideband correlation metric is used for user pairing, then the system is simple to implement, but the MU MIMO performance becomes sub-optimal due to inaccurate user correlation profiles
Solution Approach 1:
The patent divides the wideband correlation metric into multiple sub-band correlation metrics. Instead of using a single correlation value across the entire bandwidth, the system computes separate correlation metrics for different frequency sub-bands. This segmentation allows for more accurate user pairing decisions by capturing frequency-selective fading effects, thereby improving MU MIMO performance while maintaining reasonable system complexity.
2Reliability
If sub-band correlation computation is implemented for accurate user pairing, then MU MIMO performance improves, but computational complexity increases with more RRC connected users
Solution Approach 1:
The patent segments the set of RRC connected users into multiple SRS groups. Each group is assigned to a specific SRS resource, and correlation matrices are computed separately for each group rather than for all users simultaneously. This segmentation reduces the computational burden by breaking down the large-scale correlation computation into smaller, manageable tasks, while still providing accurate sub-band correlation information for improved user pairing.
Solution Approach 2:
The patent computes correlation matrices only for scheduled users in each SRS group rather than for all RRC connected users. By focusing computational resources on the subset of users who are currently scheduled and active, the system achieves the necessary correlation accuracy for MU MIMO pairing without the excessive computational cost of processing all connected users, thus balancing performance and complexity.
3Loss of information
If correlation matrices are computed for all RRC connected users, then complete user pairing information is available, but the system cannot accommodate a large number of users due to resource constraints
Solution Approach 1:
The patent segments the user population into multiple SRS groups, where each group is associated with a specific SRS resource. This segmentation enables the system to manage correlation information for a large number of users by organizing them into manageable groups. Each group maintains its own correlation matrix, allowing the system to scale to accommodate more users without requiring a single large correlation matrix that would consume excessive resources.
Solution Approach 2:
The patent creates multiple SRS groups that can be reused across different scheduling instances. Each SRS group serves multiple functions: it provides correlation information for user pairing, enables efficient resource allocation, and supports scalable user accommodation. This multi-functional approach allows the system to handle a large number of RRC connected users while maintaining complete correlation information for scheduled users within each group.
Data Source
AI summary
System and methods for implementations of sub-band correlation computation among all scheduled users to efficiently pair users across any group of Physical Resource Blocks.