Split-RAN Massive MIMO Uplink Beamforming Against Beam Aging
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Solution Overview
Problem
In massive MIMO deployments, beam aging occurs due to outdated channel information in existing fronthaul split configurations, leading to suboptimal communication performance, as beams designed by the DU prior to RU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 using uplink signal information and combine them with SRS-based beams, leveraging AI/ML to dynamically optimize beam design responsibilities between the DU and RU, thereby updating beamforming matrices with the most up-to-date information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If beams are designed by the DU prior to RU reception in existing fronthaul split configurations, then the system can maintain a simpler processing architecture, but beam aging occurs due to outdated channel information leading to suboptimal communication performance
Solution Approach 1:
The beamforming process is segmented into two parts: SRS-based beam design performed by the DU and DMRS-based beamforming performed by the RU. This segmentation allows each entity to perform beamforming based on the most current channel information available at their respective locations, thereby eliminating beam aging while maintaining architectural simplicity through clear functional division.
Solution Approach 2:
The system implements feedback mechanisms where the RU uses DMRS (downlink reference signals) to obtain current channel information and update beamforming matrices locally. This feedback loop ensures that beamforming is continuously adapted to current channel conditions without requiring complex centralized processing, thus resolving the contradiction between simplicity and performance.
2Measurement precision
If the RU generates DMRS-based beams using uplink signal information, then beamforming accuracy is improved by using current channel conditions, but the processing load at the RU increases
Solution Approach 1:
Each entity (DU and RU) performs beamforming based on the quality and recency of local channel information. The RU uses locally available DMRS information to perform beamforming, while the DU uses SRS information. This local quality approach optimizes measurement precision at each location without requiring excessive processing load anywhere, as each entity only processes information relevant to its local conditions.
Solution Approach 2:
The system performs partial beamforming actions at the RU (using only DMRS-based beamforming for uplink) rather than requiring full beamforming processing at the RU. This partial action approach improves channel information accuracy for uplink communications while keeping the RU processing load manageable by limiting the scope of complex processing to only where necessary.
3Productivity
If combined beamforming is implemented with both SRS-based and DMRS-based beams, then uplink performance is enhanced, but the system complexity increases
Solution Approach 1:
The system merges SRS-based beamforming (performed by DU) and DMRS-based beamforming (performed by RU) into a unified combined beamforming approach. This merging leverages the complementary strengths of both methods: SRS provides accurate channel estimation while DMRS provides timely updates. The combination is achieved through straightforward signal processing at the RU where DMRS-based beams are applied to uplink signals, enhancing uplink performance without introducing complex system architecture.
Solution Approach 2:
The beamforming framework is designed with multi-functionality to handle both SRS-based and DMRS-based beamforming operations. The RU is configured to support both beamforming approaches and can dynamically apply the appropriate method based on the specific communication requirements. This universal design enhances uplink performance while avoiding increased system complexity by using a unified, flexible framework rather than separate specialized systems.
4Adaptability or versatility
If AI/ML is used to dynamically optimize beam design responsibilities, then load balancing across the fronthaul is optimized, but the computational requirements increase
Solution Approach 1:
The system dynamically adjusts beam design responsibilities between DU and RU based on real-time channel conditions and network requirements. AI/ML algorithms analyze current system state and allocate beamforming tasks dynamically, optimizing load balancing across the fronthaul. This dynamic approach improves adaptability by allowing flexible task distribution while managing computational energy consumption by activating complex optimization only when needed rather than continuously.
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.