Multi-User MIMO Scheduling for Fixed Wireless Access
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Solution Overview
Problem
Existing multi-user multiple input multiple output (MIMO) scheduling systems in fixed wireless access (FWA) networks face challenges due to significant interference between users and small but non-negligible time variations in wireless channels, which complicates the scheduling process and requires efficient methods to optimize latency and transmit power while managing channel metrics.
Innovation Solution
The proposed solution involves a method for scheduling multi-user MIMO transmissions by preselecting user devices based on the time-invariant part of the wireless channel, followed by scheduling a subset of these devices using a scheduling algorithm that optimizes metrics such as maximum latency and transmit power, and employing fractional beam scheduling to minimize interference by grouping and partitioning user devices into sub-groups based on transmission metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-user MIMO scheduling is implemented to accommodate more users, then system capacity increases, but interference between users worsens and scheduling complexity increases
Solution Approach 1:
The patent segments users into different groups based on channel characteristics (time-invariant and time-varying components). By dividing the user population into distinct segments with similar channel properties, the system can apply targeted scheduling strategies to each group, reducing inter-user interference while maintaining high system capacity.
Solution Approach 2:
The patent performs preliminary user selection based on channel metrics before actual scheduling. Users are pre-filtered and pre-grouped according to their channel characteristics, which simplifies the subsequent scheduling process and reduces interference by ensuring that only compatible users are considered for simultaneous transmission.
2Productivity
If scheduling algorithms optimize latency and power metrics, then transmission efficiency improves, but computational complexity increases
Solution Approach 1:
The scheduling problem is segmented into multiple stages: user selection based on channel metrics, grouping users by characteristics, and then applying optimization algorithms to each group. This segmentation reduces the overall computational complexity by breaking down the large-scale optimization problem into smaller, more manageable sub-problems.
Solution Approach 2:
The patent applies partial optimization by focusing computational resources on optimizing key metrics (latency and power) for selected user subsets rather than attempting to optimize all possible user combinations. This approach achieves sufficient transmission efficiency without requiring exhaustive computational search.
3Measurement precision
If fractional beam scheduling is used to minimize interference, then signal quality improves, but scheduling overhead increases
Solution Approach 1:
The patent performs preliminary grouping of users based on channel characteristics before applying fractional beam scheduling. This pre-grouping reduces the number of possible scheduling combinations that need to be evaluated, thereby reducing scheduling overhead while still enabling the system to achieve high SINR through careful user selection and beam assignment.
Data Source
AI summary
Described are devices, systems and methods for scheduling multi-user (MU) multiple input multiple output (MIMO) transmissions in a fixed wireless access (FWA) system. One method for scheduling a large number of user devices in a wireless communication system includes a preselection process to pare down the number of user devices to be simultaneously scheduled, and then scheduling that subset of users. In an example, and assuming each user device communicates over a corresponding wireless channel, the preselection process includes determining a number of sets based on a first characteristic of the wireless channels, where each set includes at least one user device, and then determining a subset of user devices by selecting at most one user device from each of the sets. The scheduling of the selected subset of users is based on a scheduling algorithm and a second characteristic of the wireless channels.