Distributed MU-MIMO Precoding for Lower-Complexity Sum Rate Optimization

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Solution Overview

Problem

Existing wireless communication systems face challenges in efficiently maximizing total sum rate in downlink multi-user MIMO (MU-MIMO) scenarios due to high computational burdens and non-convex optimization problems in precoder design, particularly in low signal-to-noise regions.

Innovation Solution

Implementing a distributed computation methodology for determining optimal linear precoders using iterative algorithms and machine learning models, such as deep unfolding, to maximize total sum rate while reducing computational complexity.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If centralized precoding computation is used in MU-MIMO systems, then optimal precoders can be determined to maximize total sum rate, but computational burden becomes excessively high and complexity increases

Engineering Contradiction:

Improvetotal sum rateVSAvoidcomputational complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent divides the centralized precoding computation into distributed segments performed by multiple user equipments (UEs). Each UE performs local computations based on received channel state information and iteratively updates precoding parameters, which are then aggregated at the base station. This segmentation reduces the computational burden on any single device while maintaining the ability to determine optimal precoders for maximizing total sum rate.

Inventive Principle:

Principle #1Segmentation

2Manufacturing precision

If iterative algorithms are used for precoder design, then optimal solutions can be achieved, but computation time and complexity increase significantly

Engineering Contradiction:

Improveprecoder optimization accuracyVSAvoidcomputation time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by having the base station pre-compute and distribute channel state information to multiple UEs before the actual precoding computation. This allows UEs to perform local iterative computations in parallel with reduced initial processing requirements, thereby maintaining optimization accuracy while reducing overall computation time through distributed parallel processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent implements periodic iterative updates where UEs perform local computations in discrete iterations, exchanging updated precoding parameters with the base station at regular intervals. This periodic action allows the system to converge to optimal solutions while managing computation time through controlled iterative steps rather than continuous centralized processing.

Inventive Principle:

Principle #19Periodic action

3Device complexity

If distributed computation is implemented across multiple UEs, then computational complexity is reduced, but coordination and information exchange overhead increases

Engineering Contradiction:

Improvecomputational complexityVSAvoidsignaling overhead

Core Design Contradiction:

Device complexityVSLoss of information

Solution Approach 1:

The patent extracts only the essential channel state information and precoding parameters that need to be exchanged between the base station and UEs, rather than transmitting all raw data. By taking out only the necessary computational inputs and outputs, the system achieves distributed computation with minimized signaling overhead and information exchange requirements.

Inventive Principle:

Principle #2Taking out (Extraction)

4Productivity

If more UEs are served simultaneously in MU-MIMO, then system capacity increases, but precoding computation becomes more complex and difficult to manage

Engineering Contradiction:

Improvesystem capacityVSAvoidprecoding management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements self-service by enabling each UE to autonomously perform local precoding computations based on received channel state information. Each UE independently determines its local precoding parameters through iterative updates, reducing the need for complex centralized management. This self-service approach allows the system to serve more UEs simultaneously while keeping individual UE complexity manageable through autonomous local processing.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260039341A1Implementing distributed computation for precoding in MU-mimo

Publication Date: 2026.02.05 SAMSUNG ELECTRONICS CO LTD

AI summary

Distributed precoding computation involves receiving, from UEs served by the base station, UE precoding capability information for UE support of distributed iterative precoding computation or distributed artificial intelligence (AI)-based precoding computation. A precoding computation scheme is determined based on the UE precoding capability information and UE characteristics. Results of local precoding computations by each of the UEs are employed in determining precoders for the UEs. Distributed precoding computation parameters are transmitted to the UEs. The parameters may enable/disable distributed precoding computation, identify a precoding algorithm, or indicate a number of iterations or a maximum iteration time for precoding computation, and the results of local precoding computations may include an updated result after a specific iteration. For a transformer-based distributed precoding computation algorithm, the distributed precoding computation parameters may indicate trained neural network parameters and the results of local precoding computations may include an output of a neural network encoder.