A new reduced complexity ML detection scheme for MIMO systems

For multiple-input multiple-output (MIMO) systems, optimal maximum likelihood (ML) detection requires considerable complexity as the number of antennas or the modulation level increases. This paper proposes a new algorithm that can achieve ML performance with greatly reduced complexity. Based on the Minimum Mean Square Error (MMSE) criterion, the proposed scheme reduces the search space by eliminating unreliable candidate symbols in the data stream. Probabilistic metrics for reliability are evaluated using the normalized likelihood function for each symbol candidate and allow for near optimal ML detection. We also derived performance analysis to support the effectiveness of the proposed approach. Threshold parameters were introduced to balance the trade-off between complexity and performance. In addition, we propose an efficient method to generate log-likelihood ratio (LLR) values ​​that can be used in coding systems. The simulation results show that the proposed scheme achieves almost the same performance as ML detection when the bit error rate (BER) is 10 - 4 compared with the conventional QR decomposition M algorithm (QRD -). % And 15% M) are 4-QAM and 16-QAM respectively. Furthermore, we confirm that the proposed scheme achieves nearly optimal performance over all code rates, and that complexity is greatly reduced. For example, our scheme shows 74% and 46% multiplication reductions in 4 - QAM and 16 - QAM, respectively, compared to a sphere - based soft output scheme using a rate 1/2 convolutional code.

In this paper, we discuss three applications of QR decomposition algorithm for decoding in MIMO system. We propose a new lattice representation for spherical decoding. This new structure has the main effect of decrypting the real part and the imaginary part of each transmitted composite symbol independently of each other and enabling parallel detection. This reduces the number of calculations required by the receiver, which reduces the overall decoding complexity. On the other hand, in the other two applications, an effective ML decoding algorithm based on the QR matrix decomposition of the channel matrix is ​​proposed for quasi-orthogonal space-time block code and orthogonal space-time block code. While greatly reducing decoding complexity, performance is the best compared to traditional ML.

For multiple-input multiple-output (MIMO) systems, optimal maximum likelihood (ML) detection requires considerable complexity as the number of antennas or the modulation level increases. This paper proposes a new algorithm that can achieve ML performance with greatly reduced complexity. Based on the Minimum Mean Square Error (MMSE) criterion, the proposed scheme reduces the search space by eliminating unreliable candidate symbols in the data stream. Probabilistic metrics for reliability are evaluated using the normalized likelihood function for each symbol candidate and allow for near optimal ML detection. We also derived performance analysis to support the effectiveness of the proposed approach. Threshold parameters are introduced to balance the trade-off between complexity and performance. We also propose an efficient method for generating log likelihood ratio (LLR) values ​​that can be used in coding systems.

Spatial modulation (SM) is a recently developed low complexity multi-input multi-output scheme that uses antenna index and conventional signal set to communicate information. The Maximum Likelihood (ML) detector of the SM system includes simultaneous detection of the transmit antenna index and transmit symbols and thus it has been shown that the ML search complexity increases linearly with the number of transmit antennas and the transmit antenna size ing. Signal set To avoid this problem, we show that the complexity of the ML search of the SM system can be presented irrespective of the constellation size, as long as the signal set used is a square or rectangular QAM. Furthermore, we derive the limits of the SM system capacity by maximizing the worst-case capacity limit of the SM system operating with incomplete channel state information and estimate the optimal power allocation between the data and the training sequence derive.