MIMO Layer Selection for Latency and Spectral Efficiency
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Solution Overview
Problem
Existing wireless networks face challenges in efficiently selecting between SU-MIMO and MU-MIMO configurations to optimize performance metrics such as latency, throughput, and spectral efficiency for diverse end devices in Next Generation (NG) wireless networks like 5G NR.
Innovation Solution
A MIMO selection service that identifies performance metric-sensitive end devices and selects the optimal MIMO configuration (SU-MIMO or MU-MIMO) based on layer comparisons and spectral efficiency calculations, recalculating configurations to maximize performance and spectral efficiency for both latency-sensitive and non-latency-sensitive devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If SU-MIMO configuration is used, then spectral efficiency is improved, but network capacity and user throughput are limited
Solution Approach 1:
The system dynamically switches between SU-MIMO and MU-MIMO configurations based on real-time channel conditions, device capabilities, and network load. The gNodeB evaluates channel state information, device mobility, and QoS requirements to adaptively select the optimal MIMO mode, resolving the contradiction between spectral efficiency and network capacity through dynamic adaptation rather than fixed configuration
Solution Approach 2:
The system changes operational parameters by adjusting MIMO configuration settings based on channel conditions and device requirements. This includes modifying the number of active antennas, transmission modes, and resource allocation parameters to optimize both spectral efficiency and network capacity under different operating scenarios
2Productivity
If MU-MIMO configuration is used, then network capacity is improved, but latency increases for performance-sensitive devices
Solution Approach 1:
The system applies different MIMO configurations to different devices or device groups based on their specific requirements. Performance-sensitive devices receive SU-MIMO service with optimized latency parameters, while other devices benefit from MU-MIMO capacity enhancement, allowing each device to receive tailored service quality matching its needs
Solution Approach 2:
The system dynamically adjusts MIMO configuration based on real-time channel conditions and device priorities. When channel conditions are favorable and devices are stationary, MU-MIMO is activated for capacity enhancement. When mobility increases or latency becomes critical, the system switches to SU-MIMO to reduce processing overhead and latency
3Loss of energy
If MIMO configuration is recalculated frequently, then spectral efficiency is optimized, but system complexity and processing overhead increase
Solution Approach 1:
The system performs MIMO configuration calculations periodically rather than continuously, evaluating channel conditions at scheduled intervals and triggering recalculation only when significant changes occur. This periodic evaluation approach maintains spectral efficiency optimization while reducing processing overhead and system complexity compared to continuous recalculation
Data Source
AI summary
A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an MIMO selection service. The MIMO selection service may include calculating MIMO configurations that include a number of layers for each MIMO configuration pertaining to a group of end devices; and identifying performance metric sensitive end devices of the group. The MIMO selection service may further include selecting for each performance metric sensitive end device, a first MIMO configuration from the MIMO configurations that has a higher number of layers relative to one or more other MIMO configurations of the MIMO configurations; and selecting for each non-performance metric sensitive end device of the group, a second MIMO configuration from the MIMO configurations that combined with the first MIMO configuration yield a highest spectral efficiency.