Iterative Per-User ZF Precoding for MU-MIMO Capacity
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Solution Overview
Problem
Current multi-user MIMO (MU-MIMO) systems face challenges in maximizing spectrum efficiency and system throughput due to limitations in precoding methods, such as per-stream MMSE and per-user ZF, which fail to guarantee convergence and result in capacity loss and high computation complexity.
Innovation Solution
A new iterative per-user ZF method based on subspace inheritance is introduced, where the precoding weight matrix and transformed channel matrix are generated iteratively, using subspace inheritance between successive iterations to ensure convergence and improve capacity, particularly with multiple receiving antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If per-stream MMSE or per-user ZF precoding methods are used, then the system can transmit signals to multiple users simultaneously, but the methods fail to guarantee convergence and result in capacity loss and high computation complexity
Solution Approach 1:
The patent implements an iterative precoding method where the precoding weight matrix is updated based on feedback from channel state information and received signal strength measurements. The algorithm continuously refines the precoding weights through multiple iterations, using feedback loops to ensure convergence toward optimal signal transmission parameters, thereby resolving the reliability issue of conventional methods.
Solution Approach 2:
The patent employs dynamic precoding weight adjustment where the precoding matrix changes adaptively across multiple iterations based on real-time channel conditions. This dynamic approach allows the system to converge to optimal solutions by continuously adapting to varying channel states, overcoming the static limitations of conventional per-stream or per-user precoding methods.
2Reliability
If iterative precoding methods are used to improve convergence, then capacity can be enhanced, but computation complexity increases
Solution Approach 1:
The patent applies partial iterative updates where only the necessary portions of the precoding matrix are updated in each iteration, rather than recomputing the entire matrix. This partial action approach maintains convergence guarantees while significantly reducing the computational burden per iteration, thereby managing overall computation complexity.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the number of iterations and update thresholds based on channel conditions and system requirements. This allows the system to achieve convergence with manageable computation complexity by adapting the iteration count and update parameters dynamically, avoiding excessive computational operations when high precision is not required.
Data Source
AI summary
The disclosure relates to a radio transceiver, comprising: a precoder configured to precode a data signal for transmission to a plurality of multi-stream terminals based on a plurality of precoding weight matrices; and a processor configured to generate for each terminal in an iterative manner a precoding weight matrix and a transformed channel matrix, wherein the transformed channel matrix indicates a channel gain between the radio transceiver and the respective terminal transformed by a receive filter matrix of the respective terminal, wherein the generation of the precoding weight matrix and the transformed channel matrix in a current iteration is based on the transformed channel matrix generated from a previous iteration.