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Aiming to increase the transmission data rate, the spatial modulation (SM) scheme maps each block of information bits to a digitally modulated symbol and additionally a transmit antenna index in a multiple transmit antenna system. In this brief, we focus on the maximum likelihood detection (MLD) scheme that detects SM signals with low computational complexity. By reformulating the MLD problem, we obtain a new mathematical expression as the MLD solution. To avoid the numerically unstable division operation, we apply the Givens rotation to realize this new expression and obtain a low-complexity MLD algorithm. We also propose a coordinate rotation digital computer-based hardware architecture to detect SM signals with 64-quaternary amplitude modulated (QAM) symbols over the correlated channel with eight transmit antennas and four receive antennas. The VLSI implementation results under the TSMC 90-nm CMOS technology reveal that our division-free architecture requires 70.5K gates and provides detection throughput 432.68 Mb/s, while operating at frequency 384.6 MHz. Our design is the first hardware architecture to achieve the exact MLD performance and is easily modified to detect SM signals with arbitrary ${M}$ -ary QAM symbols and arbitrary numbers of transmit and receive antennas. [ABSTRACT FROM AUTHOR]

Compared with the single tree detector, the layered orthogonal lattice detector (LORD), developed by Siti et al., is a well-known soft-output multiple input multiple output detector to exploit M parallel tree traversals to deliver data with M times of detection throughput rate. The preprocessing QR-decomposition (QRD) of the M -by- M channel matrix for the single tree detector is of complexity proportional to M^3 . However, the preprocessing QRD for the LORD needs to compute the M permuted channel matrices that are constructed from the original M -by- M channel matrix through the root conditioning criterion. The original LORD algorithm for this root conditioning QRD (RC-QRD) relies on the Gram-Schmidt orthogonalization and is of complexity proportional to M^4 for large M . In this brief, we apply the Givens rotation and take advantage of the relationships among the M permuted matrices to develop an RC-QRD algorithm with complexity proportional to M^3 . Furthermore, when M is large, our proposed RC-QRD algorithm requires the number of real Givens rotations about 1.8 times necessary for computing a conventional matrix QRD. Also, for M=4 , our proposed RC-QRD hardware architecture requires gate count 2.1 times that required by the conventional triangular systolic array to compute a matrix QRD. Accordingly, with only about two times of complexity for the preprocessing RC-QRD, the LORD is able to perform M tree traversals to deliver data with M times of throughput rate. [ABSTRACT FROM AUTHOR]

The compressed beamforming weights (CBWs) feedback is used in the IEEE 802.11n/ac WLAN, an example of the practical beamforming multiple input multiple output-orthogonal frequency division multiplexing system, to reduce the amount of feedback information so that the beamformee can respond rapidly to the beamformer. The CBW associated with each sub-carrier includes the quantized angles obtained from QR-decomposition (QRD) of the right singular vectors of each corresponding channel matrix. Efficient matrix QRD and singular value decomposition (SVD) together are therefore desirable for computing the CBWs associated with all sub-carriers. Considering the exemplary antenna configuration of 4 beamformer and 2 beamformee antennas, we propose to apply the same matrix triangulation to compute the SVD of a 2-by-4 matrix and to compute the QRD of a 4-by-2 matrix. We can achieve gate count reduction by exploiting only one matrix triangulation module in our architecture. The VLSI implementation results under the TSMC 90-ns CMOS technology reveal that our architecture requires 194.25K gates while operating at frequency 200.75 MHz. Additionally, with better normalized matrix throughput and gate efficiency, our architecture outperforms one earlier architectural design to compute the CBWs. [ABSTRACT FROM PUBLISHER]