Robust MSE-Balancing Hierarchical Linear/Tomlinson-Harashima Precoding for Downlink Massive MU-MIMO Systems

Zarei, Shahram; Gerstacker, Wolfgang H.; Weigel, Robert; Vossiek, Martin; Schober, Robert

doi:10.1109/twc.2018.2866112

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…In sharp contrast to the Jacobi and Richardson iterations and so on, the GS iterations always results in exponential convergence, since the associated iteration matrix B gs satisfies λ max (B gs ) < 1 without any extra requirements. This also implies the convergence condition in (11) is always fulfilled, which is also known as global convergence [37].…”

Section: B Traditional Low-complexity Iterative Transmit Precodingmentioning

confidence: 95%

“…Similarly, for the Richardson iteration, P and Q are set as P = 1 ω I and Q = A − 1 ω I respectively, where ω > 0 is the relaxation factor [29]. However, due to the convergence requirement in (11), the Jacobi iteration works when the matrix A is strictly diagonally dominant (SDD) 1 while the Richardson iteration is convergent if 0 < ω < 2 λmax(A) , where λ max (•) denotes the largest eigenvalue of a matrix [45].…”

Section: B Traditional Low-complexity Iterative Transmit Precodingmentioning

confidence: 99%

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

Wang

Gao

et al. 2023

IEEE Trans. Commun.

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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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Section: B Traditional Low-complexity Iterative Transmit Precodingmentioning

confidence: 95%

Section: B Traditional Low-complexity Iterative Transmit Precodingmentioning

confidence: 99%

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

Wang

Gao

et al. 2023

IEEE Trans. Commun.

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“…Most of them focused on three aspects: hierarchical hybrid design, successive interference cancellation (SIC), and hardware architecture. For the hierarchical hybrid design, the authors in [11], [12] proposed a robust minimum maximum mean square error (MMSE) hybrid linear and THP precoder, which combines the low complexity of linear precoding with the high performance of THP. In this scheme, the UEs are divided into different groups, where linear precoding is used to mitigate the interference between groups, while THP is used to mitigate the interference between users within the same group.…”

Section: Introductionmentioning

confidence: 99%

“…In this scheme, the UEs are divided into different groups, where linear precoding is used to mitigate the interference between groups, while THP is used to mitigate the interference between users within the same group. In [13], the channel model was extended to Line-Of-Sight (LOS) environments from [11], [12] and a novel low-complexity hybrid linear and THP precoder with max-min power control was proposed. A hybrid VP and THP precoder was proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Tomlinson-Harashima Precoding Update Algorithm for Massive MIMO System

Fang,

Chen,

Chen

et al. 2024

IEEE Trans. Commun.

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Efficient implementation of Tomlinson-Harashima precoding (THP) is crucial in massive multiple-input-multipleoutput (MIMO) systems with a large number of antennas at the base station (BS) serving many user equipments (UEs). To address the high computational complexity of THP, in this paper, we first propose novel THP update algorithms that can avoid recomputing the THP filters when a new UE arrives or departs. Specifically, by using the Gram-Schmidt process and a series of Givens matrices, the THP filters are computed without full matrix operations. Then we extend the THP update algorithms to a more general scenario when multiple multi-antenna UEs arrive or depart. In this case, the proposed algorithms use both direct and iterative approaches. Moreover, the computational complexity of the proposed algorithms is derived and compared with that of the conventional THP. Finally, to further align with the practical scenario, we analyze and derive the approximate closeform expressions for the sum achievable rate of the proposed algorithms under imperfect channel state information (CSI). Simulation results are provided to illustrate the effectiveness of the proposed algorithms. The impact of quasi-static fading and slow time-varying scenarios with imperfect CSI on the communication performance of the proposed algorithms is also evaluated.

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Optimization of number of base station antennas in downlink massive MIMO and analysis of imperfect channel state information by perfection factor

Ramisetty

Chennupati

2020

Engineering Science and Technology, an International Journal

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Robust MSE-Balancing Hierarchical Linear/Tomlinson-Harashima Precoding for Downlink Massive MU-MIMO Systems

Cited by 13 publications

References 29 publications

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

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

Low-Complexity Tomlinson-Harashima Precoding Update Algorithm for Massive MIMO System

Optimization of number of base station antennas in downlink massive MIMO and analysis of imperfect channel state information by perfection factor

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