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Minimum-mean-square-error (MMSE) detection is becoming increasingly relevant in signal detection for massive multiple-input-multiple-output systems because of the increasing numbers of both users and antennas. This paper proposes a signal detection method called parallelizable Chebyshev iteration (PCI) that reduces the computing load and explores the potential parallelism of matrix inversions and...
Minimum mean square error hometd has proved its superiority for signal detection in massive multiple-input multiple-output (MIMO) systems for its near-optimal performance. However, the detection efficiency is restrained by a high computation complexity and low parallelism operation of matrix inversion. This paper presented a hardware efficient signal detector based on low complexity Lanczos Method,...
For large-scale multiple-input multiple-output (MIMO) systems, linear minimum mean square error (MMSE) method is one of the most near-optimal ways for signal detection. However, MMSE involves matrix inversion which is of high complexity for computation. In this paper, a Lanczos-based method is proposed to solve the problem by transferring the matrix inversion computation into an iteration process...
In this paper, we propose a very large scale integration design method for a large-scale multiple-input multiple-output detection algorithm. Our design uses a modified version of the Successive Over Relaxation (SOR) method, which substantially reduces the highly computational complexity of data detection and achieves the near-optimal performance. We use a reconfigurable Processing Elements Array (PEA)...
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