Massive MIMO Pre-Coding Algorithm Based on Improved Newton Iteration

In this paper, rapidly converging low-complexity iterative transmit precoding (TPC) techniques are proposed for the massive multiple-input multiple-output (MIMO) downlink. First of all, the proposed random block-based iterative TPC (RBI-TPC) algorithm performs its iterations by updating multiple rather than a single component at each instant, where the updating order of each block containing multiple components relies on the samples randomly sampled from a discrete distribution. Based on the analytically derived convergence rate, we demonstrate that improved convergence is achieved by the block-based update mechanism conceived since the correlation between multiple components can be beneficially exploited. Then, the random sampling that determines the updating order is studied. By applying conditional random sampling, the updating order is optimized based on the latest updates for attaining more rapid convergence. We also demonstrate that the associated updating order may become deterministic under specific conditions so that a fixed but optimized updating order can be used for facilitating the practical implementations, which paves the way for conceiving the ordered block-based iterative TPC (OBI-TPC) algorithm. Finally, the concept of successive over-relaxation (SOR) is adopted for further convergence improvement and simulations are presented to illustrate the performance improvements of the proposed RBI and OBI

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Massive MIMO Pre-Coding Algorithm Based on Improved Newton Iteration

Cited by 7 publications

References 10 publications

A Low Complexity Precoding Algorithm Based on Parallel Conjugate Gradient for Massive MIMO Systems

A Low Complexity Precoding Algorithm Based on Parallel Conjugate Gradient for Massive MIMO Systems

An Eigen-based Matrix Inverse Approximation Scheme with Stair Matrix Splitting for Massive MIMO Systems

Rapidly Converging Low-Complexity Iterative Transmit Precoders for Massive MIMO Downlink

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