MU-MIMO User Grouping via Beam SNR Selection
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Solution Overview
Problem
Current Multiple-Input Multiple-Output (MIMO) technologies face challenges in maximizing downlink MU-MIMO capacity due to high computational complexity and data transfer limitations between Distributed Units (DUs) and Radio Units (RUs) in wireless networks, particularly in selecting appropriate users for parallel data transmission and performing precoding/beamforming efficiently.
Innovation Solution
The system employs a method where the DU associated with a gNB computes SRS-based channel matrices, selects users based on SNRs and CQIs, and calculates a precoding matrix using Regularized Zero Forcing (RZF) or 2D-DFT to optimize user grouping and beamforming, reducing inter-layer interference and computational load, while minimizing data transfer over the fronthaul.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional RZF-based user grouping is used to maximize MU-MIMO capacity, then downlink capacity is improved, but computational complexity increases significantly
Solution Approach 1:
The patent segments the user selection process into two stages: first selecting users based on beam SNR thresholds to create a candidate set, then applying a simplified grouping algorithm only to this reduced set. This segmentation reduces computational complexity while maintaining capacity performance by avoiding exhaustive evaluation of all possible user combinations.
Solution Approach 2:
The patent changes the parameter used for user selection from traditional RZF-based channel matrices to beam SNR values derived from channel matrices. This parameter change enables simpler computations while achieving comparable MU-MIMO capacity, as SNR-based selection is computationally less intensive than full RZF optimization.
2Measurement precision
If full channel state information is transferred between DU and RU for precise user selection, then user grouping accuracy is improved, but fronthaul data transfer requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for user selection (beam SNR values and CQI) from the full channel state information and transfers only these reduced representations over the fronthaul. This extraction maintains sufficient accuracy for user grouping while dramatically reducing the data transfer volume required between DU and RU.
Solution Approach 2:
The patent creates simplified copies of channel information in the form of beam SNR values and CQI reports, which serve as substitutes for full channel state information. These copies contain the essential selection criteria while being much more compact, enabling accurate user selection with reduced fronthaul requirements.
3Productivity
If L1/L2 cross-layer optimization is implemented for user grouping, then scheduling efficiency is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary user selection based on beam SNR thresholds and CQI values before the actual data transmission. This preliminary action in L2 enables L1/L2 cross-layer optimization by pre-identifying suitable users, thereby improving scheduling efficiency while managing complexity through structured preparation rather than real-time complex optimization.
Data Source
AI summary
In one embodiment, a method includes sending SRS received from a plurality of UEs associated with the base station to a DU associated with the base station, receiving information regarding a subset of the plurality of UEs selected for downlink data transmissions for an RBG, multi-user data to be transmitted to UEs in the subset, and identities of selected beams among a plurality of pre-determined beams to be associated with the UEs in the subset from the DU, where each of the plurality of pre-determined beams corresponds to a DFT vector, computing a precoding matrix for the RBG based on IDFT vectors corresponding to the selected beams, preparing pre-coded multi-user data by applying the precoding matrix to the multi-user data, and transmitting the pre-coded multi-user data to the UEs in the subset for the RBG using MIMO technologies.