Split-RAN MIMO Uplink Beamforming with DMRS Beam Refinement
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Solution Overview
Problem
In massive MIMO deployments, existing fronthaul split configurations face issues with beam aging due to outdated channel information, leading to suboptimal communication performance, as beams designed by the DU prior to RU reception may not reflect current channel conditions.
Innovation Solution
Implement combined beamforming by configuring the RU to generate DMRS-based beams and combine them with SRS-based beams from the DU, leveraging AI/ML to dynamically allocate beam design responsibilities based on performance indicators and load balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If beams are designed by the DU prior to RU reception, then processing can be centralized and fronthaul throughput can be reduced, but beam aging occurs due to outdated channel information
Solution Approach 1:
The beamforming process is segmented into two parts: SRS-based beam design performed by the DU (centralized processing) and DMRS-based beam refinement performed by the RU (local processing). This segmentation allows the system to benefit from both centralized coordination and local adaptability to current channel conditions, resolving the beam aging issue while maintaining processing efficiency.
Solution Approach 2:
The DU performs preliminary beam design using SRS measurements and transmits these beams to the RU in advance. The RU then refines these preliminary beams using current DMRS measurements. This preliminary action approach allows centralized processing to occur while ensuring the final beams reflect current channel conditions.
2Reliability
If DMRS-based beamforming is implemented at the RU, then uplink performance is enhanced using current channel information, but fronthaul interface burden increases
Solution Approach 1:
The patent extracts and transmits only the essential beam information (beam indices or parameters) from the DU to the RU, rather than transmitting complete channel state information. This extraction approach reduces fronthaul bandwidth requirements while enabling the RU to perform accurate beamforming using local DMRS measurements.
Solution Approach 2:
The RU performs self-service beam refinement by locally measuring DMRS signals and adjusting beamforming weights without requiring continuous feedback or heavy fronthaul traffic. The RU uses its own local measurements to optimize beamforming, reducing the burden on the fronthaul interface.
3Reliability
If combined beamforming with AI/ML allocation is used, then beam design optimization is improved, but system complexity increases
Solution Approach 1:
The system dynamically allocates beam design responsibilities between DU and RU based on current channel conditions, load patterns, and performance indicators using AI/ML models. This dynamic allocation optimizes beamforming performance while adapting to changing network conditions, managing system complexity through intelligent automation rather than rigid fixed architecture.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, a system comprising a distributed unit (DU) configured to generate sounding reference signal (SRS)-based beams, and a remote unit (RU) communicatively coupled with the DU over a fronthaul, wherein the RU is configured to generate demodulation reference signal (DMRS)-based beams and perform digital beamforming for an uplink using the DMRS-based beams in combination with the SRS-based beams. Other embodiments are disclosed.