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• W2887406393 abstract "Interference alignment (IA) is a well known technique to boost the system capacity of MIMO interfering broadcast channels (IBCs) by aligning interferences into spatial dimensions, which are almost orthogonal to the dimensions occupied by the desired signals. However, the conventional IA algorithms with iterations suffer from large convergence times. We propose a novel hybrid method of nonlinear precoding (NLP) and IA for more efficient interference cancellation. Firstly, we modify the minimum mean squared error IA scheme by ignoring some interference. Then, we selectively apply the Tomlinson-Harashima Precoding (THP) NLP on top of selected IA beams. It is shown through the simulations that the bit error rate performances for all MIMO links with or without NLP are improved dramatically. Meanwhile, the computational complexity in terms of number of iterations is reduced significantly. In addition, to maximize the capacity and overcome the unfairness inherent in THP, that is the latter coded stream has smaller diversity gain, we propose to repeat the low-cost modified IA with random initializations. The optimal precoders and decoders are selected among the repetitions with the largest determinant factor related to the effective channel over the selected IA beams." @default.
• W2887406393 created "2018-08-22" @default.
• W2887406393 creator A5004062241 @default.
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• W2887406393 date "2018-05-01" @default.
• W2887406393 modified "2023-09-25" @default.
• W2887406393 title "Hybrid Method of Selective Nonlinear Precoding and Interference Alignment" @default.
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• W2887406393 doi "https://doi.org/10.1109/icc.2018.8422925" @default.
• W2887406393 hasPublicationYear "2018" @default.
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