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- W4205687191 abstract "The neighborhood search algorithms recently emerged as a promising low-complexity detection algorithm for massive multiple-input, multiple-output (MIMO) wireless systems have fascinated recent research attention. They iteratively perform searches in a constrained Maximum-likelihood (ML) space for the solution vector. In addition, they execute with a complex matrix inversion an immense number of computations, resulting in higher complexity within a massive MIMO configuration. Motivational by these, we focus on unconstrained minimization problems to quickly decline in an ML cost function and devise for updating rules to improve performance. Using this robust approach and the reduction of the ML cost function concerning the descent direction, we propose a computationally efficient Conjugate Gradient-based local neighborhood search (CGLS) detection algorithm. The proposed CGLS detection algorithm is employed to give the most reduction in the ML cost metric by determining in a decent direction update within an unconstrained neighborhood. It gains acceleration for convergence with few iterations. Simulation results show that the proposed CGLS detector outperforms the counterpart detector." @default.
- W4205687191 created "2022-01-25" @default.
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- W4205687191 date "2022-01-01" @default.
- W4205687191 modified "2023-09-26" @default.
- W4205687191 title "A Robust Massive MIMO Detection Based on Conjugate Gradient Approach" @default.
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- W4205687191 doi "https://doi.org/10.1007/978-981-16-6246-1_49" @default.
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