Iterative Precoder Coordination for Sidelink Uplink Coverage
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Solution Overview
Problem
Current wireless communication systems face challenges in optimizing sidelink and uplink coverages due to limitations in iterative precoder computation and coordination, particularly in multi-antenna systems where simultaneous signal maximization across multiple receive antennas is difficult, and coordination between relay nodes is suboptimal.
Innovation Solution
The method involves determining and sharing channel state feedback parameters between network entities to iteratively select and update beam coefficients and precoders, forming a virtual super node for improved beamforming, which enhances both sidelink and uplink communications by optimizing beam alignment and error minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative precoder computation and coordination is implemented between network entities, then sidelink and uplink coverage are improved, but system complexity and computational overhead increase
Solution Approach 1:
The system segments the precoder computation task by dividing network entities into relay nodes (first network entity) and destination nodes (second network entity). Each node performs localized channel state feedback parameter determination and beam coefficient selection based on its specific role, rather than all nodes performing complete precoder computations. This segmentation reduces individual node complexity while maintaining overall system performance through coordinated operation.
Solution Approach 2:
The system performs preliminary actions by having the first network entity determine channel state feedback parameters and select beam coefficients before actual data transmission. The precoder computation is performed in advance based on channel conditions, allowing the system to prepare optimal beamforming weights beforehand. This preliminary computation reduces real-time processing requirements during actual communication.
2Measurement precision
If beam coefficients are selected based on channel state feedback parameters from multiple network entities, then beam alignment accuracy is improved, but information exchange overhead and processing time increase
Solution Approach 1:
The system extracts only the essential channel state feedback parameters needed for precoder computation from the complete channel state information. Instead of exchanging and processing all available channel data, the system identifies and uses specific parameters (such as channel quality indicators and beam measurement results) that are most critical for beam coefficient selection. This extraction reduces information exchange overhead while maintaining beam alignment accuracy.
3Productivity
If virtual super node formation is implemented to maximize simultaneous signals at multiple receive antennas, then system capacity and coverage are improved, but coordination complexity between relay nodes increases
Solution Approach 1:
The system merges multiple relay nodes into a virtual super node by coordinating their precoder computations and beamforming operations. The first and second network entities work together to maximize simultaneous signals at multiple receive antennas, effectively combining their transmission capabilities. This merging approach increases system capacity and coverage by utilizing the combined resources of multiple nodes while maintaining manageable coordination through defined roles and information exchange protocols.
Data Source
AI summary
Aspects relate to mechanisms for improved uplink and sidelink coverages. A first network entity determines one or more channel state feedback (CSF) parameters of one or more beams associated with the first network entity, selects one or more beam coefficients based on the one or more CSF parameters, and transmits the one or more beam coefficients to at least a second network entity. The second network entity selects one or more beams for beaming forming based on the one or more beam coefficients, determines one or more CSF parameters of the one or more beams associated with the second network entity, and transmits the one or more CSF parameters of the one or more beams associated with the second network entity to the first network entity. The first network entity selects one or more new beam coefficients based on the one or more CSF parameters from the second network entity.