A new reduced complexity ML detection scheme for MIMO systems - IEEE Journals & Magazine

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For multi-input multiple-output (MIMO) systems, optimum maximum likelihood (ML) detection as antenna number and modulation level requires great complexity. In this paper, we can drastically reduce new algorithms and obtain ML performance in complex case. The minimum mean square error (MMSE) criterion streams candidate unreliable symbols, proposed solutions to reduce search space by excluding data. In order to evaluate the measure of reliability of probability, we have each candidate symbol using normalized likelihood function so that ML detection close to optimum is possible. Also, it supports the effectiveness of the proposed method and becomes a performance analysis. We introduce threshold parameters to balance the trade-off between complexity and performance. In addition, we propose a way to generate a Log Likelihood Ratio (LLR) value of an effective way, in which the value can use the coding scheme

In this paper, we discuss three application QR decomposition algorithms for MIMO system decoding. We propose that a new grid is represented as a sphere decode. This new structure has a major impact on the decoding performed independently of each other on each complex symbol transmitted to enable parallel detection of real and imaginary parts. This in turn reduces the number of calculations required for the receiver, thereby reducing the overall complexity of the decoder. On the other hand, it is proposed based on QR decomposition of channel matrix effective for two quadrature block codes and quasi - orthogonal STBC empty, ML decoding algorithm in two other applications. Compared with traditional ML, it shows the best performance while reducing the complexity of great decoding,

Abstract - In this paper, we discuss the QR decomposition algorithm of three applications for decoding multiple of multiple input multiple output (MIMO) systems. In the first application, we show that in a 4 x 4, conventional SD, 2 x 2 system, compared with the new way to reduce the overall complexity of 80%, MIMO intra-sphere decoding (SD) 6 × 6 case will be reduced by about 50%. In the second application, we propose the block code (QOSTBCs) of the low complexity maximum likelihood decoding (MLD) algorithm for quasi - orthogonal space. We show that for N = 8 transmit antennas and 16-QAM modulation scheme, the new method achieves a reduction of> 97%. Compared with MLD, the overall complexity of reporting algorithms and most competitive literature reduced by 89% compared to conventional. Increasing the number of transmit antennas (N) or constellation sizes (L) increases the complexity of the gain