MU-MIMO User Pairing and Rank Balancing for LTE-Advanced
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Solution Overview
Problem
Current methods for MU-MIMO scheduling with rank restrictions fail to optimize throughput performance, particularly in wideband MIMO-OFDM systems, due to the NP-hard nature of the optimization problem and limitations in existing algorithms that do not account for common rank constraints across all resource blocks.
Innovation Solution
A method that performs user pairing and resource allocation by maximizing an objective metric without common rank restrictions, followed by a rank balancing process to ensure uniform transmission ranks across all users, allowing for optimal pairing and allocation on each resource block, incorporating improved greedy and stochastic updating techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If rank restriction is applied to MU-MIMO scheduling, then device complexity and computational burden are reduced, but throughput performance deteriorates due to the NP-hard nature of the optimization problem
Solution Approach 1:
The patent segments the MU-MIMO scheduling problem into two independent stages: (1) user pairing optimization without rank restriction to maximize throughput, and (2) rank assignment optimization to satisfy common rank constraint. This segmentation allows each sub-problem to be solved optimally without the computational burden of solving the joint problem as a single NP-hard optimization, thereby resolving the contradiction between complexity and performance.
2Device complexity
If common rank constraint is enforced across all resource blocks, then device complexity is reduced, but throughput performance is lost due to suboptimal user pairing
Solution Approach 1:
The patent applies dynamics by making the rank parameter adaptive rather than static. The common rank constraint is not fixed but is dynamically optimized in the second stage based on channel conditions and user pairing results from stage one. This allows the system to adapt the rank value to maximize throughput while still maintaining the common rank constraint across resource blocks, resolving the contradiction between complexity reduction and performance maintenance.
3Productivity
If optimal user pairing is performed without rank restriction, then throughput performance is improved, but the problem becomes NP-hard and computationally intractable
Solution Approach 1:
The patent segments the joint user pairing and rank optimization problem into two separate optimization stages. Stage one performs user pairing optimization without rank restriction, achieving high throughput performance. Stage two optimizes the common rank constraint based on the pairing results. This segmentation transforms the NP-hard joint problem into two more tractable sub-problems that can be solved sequentially, resolving the contradiction between achieving optimal pairing and managing computational complexity.
Data Source
AI summary
A method for user pairing and resource allocation, includes performing a multiuser multi-input-multi-output (MU-MIMO) user pairing process to maximize an objective metric without common rank restriction; performing a rank balancing process to determine a uniform transmission user rank along all allocated resource blocks for each user; and with the uniform transmission user rank fixed for all the users, determining optimal user pairing and allocation for each of the resource blocks for each user.