This letter addresses the problem of space-time adaptive processing (STAP) for airborne nonuniform linear array (NLA) radar using a generalized sidelobe canceller (GSC). Due to the difficulty of determining the spatial nulls for the NLAs, it is a problem to obtain a valid blocking matrix (BM) of the GSC directly. In order to solve this problem and improve the STAP performance, a BM modification method based on the modified Gram-Schmidt orthogonalization algorithm is proposed. The modified GSC processor can achieve the optimal STAP performance and as well a faster convergence rate than the orthogonal subspace projection method. Numerical simulations validate the effectiveness of the proposed methods.

Lattice-reduction (LR) technique has been adopted to improve the performance and reduce the complexity in MIMO data detection. This paper presents an improved quantization scheme for LR aided MIMO detection based on Gram-Schmidt orthogonalization. For the LR aided detection, the quantization step applies the simple rounding operation, which often leads to the quantization errors. Meanwhile, these errors may result in the detection errors. Hence the purpose of the proposed detection is to further solve the problem of degrading the performance due to the quantization errors in the signal estimation. In this paper, the proposed quantization scheme decreases the quantization errors using a simple tree search with a threshold function. Through the analysis and the simulation results, we observe that the proposed detection can achieve the nearly optimal performance with very low complexity, and require a little additional complexity compared to the conventional LR-MMSE detection in the high Eb/N0 region. Furthermore, this quantization error reduction scheme is also efficient even for the high modulation order.

PCA-L1 (principal component analysis based on L1-norm maximization) is an approximate solution of L1-PCA (PCA based on the L1-norm), and has robustness against outliers compared with traditional PCA. However, the more dimensions the feature space has, the more calculation time PCA-L1 consumes. This paper focuses on an initialization procedure of PCA-L1 algorithm, and proposes a fast method of PCA-L1 using Gram-Schmidt orthogonalization. Experimental results on face recognition show that the proposed method works faster than conventional PCA-L1 without decrease of recognition accuracy.

In multiuser multiple-input multiple-output (MU-MIMO) wireless downlink systems, block diagonalization (BD) is a technique, where the transmit precoding matrix of each user is designed such that its subspace lies in the null space of all the other remaining users, so that multiuser interference (MUI) is completely canceled. In low signal to noise power ratio (SNR) or low signal to interference plus noise power ratio (SINR) environments, regularized BD, that lets some MUI remain and maximizes the sum rate capacity of the BD MIMO channel, was also proposed. One of the problems of both the approaches is high complexity of computation due to a lot of singular value decomposition (SVD) processes. In this paper we propose new BD techniques utilizing QR decomposition (QRD) which can be practically achieved by Gram-Schmidt orthogonalization (GSO) with lower complexity compared to the conventional method employing SVD. We can show that the performance of the proposed approaches is close to the conventional approaches, while the proposed approaches have much lower complexity.

In multiple-input multiple-output (MIMO) systems, the multiuser MIMO (MU-MIMO) systems have the potential to provide higher channel capacity owing to multiuser and spatial diversity. Block diagonalization (BD) is one of the techniques to realize MU-MIMO systems, where multiuser interference can be completely cancelled and therefore several users can be supported simultaneously. When the number of multiantenna users is larger than the number of simultaneously receiving users, it is necessary to select the users that maximize the system capacity. However, computation complexity becomes prohibitive, especially when the number of multiantenna users is large. Thus simplified user scheduling algorithms are necessary for reducing the complexity of computation. This paper proposes a simplified capacity-based user scheduling algorithm, based on analysis of the capacity-based user selection criterion. We find a new criterion that is simplified by using the properties of Gram-Schmidt orthogonalization (GSO). In simulation results, the proposed algorithm provides higher sum rate capacity than the conventional simplified norm-based algorithm; and when signal-to-noise power ratio (SNR) is high, it provides performance similar to that of the conventional simplified capacity-based algorithm, which still requires high complexity. Fairness of the users is also taken into account. With the proportionally fair (PF) criterion, the proposed algorithm provides better performance (sum rate capacity or fairness of the users) than the conventional algorithms. Simulation results also shows that the proposed algorithm has lower complexity of computation than the conventional algorithms.

Multi-user MIMO (Multiple Input Multiple Output) systems, in which multiple Mobile Stations (MSs) equipped with multiple antennas simultaneously communicate with a Base Station (BS) equipped with multiple antennas, at the same frequency, are attracting attention because of their potential for improved transmission performance in wireless communications. In the uplink of Space Division Multiplexing based multi-user MIMO (multi-user MIMO/SDM) systems that do not require full Channel State Information (CSI) at the transmitters, selecting active MS antennas, which corresponds to scheduling transmit antennas, is an effective technique. The Full search Selection Algorithm based on exhaustive search (FSA) has been studied as an optimal active MS antenna selection algorithm for multi-user MIMO systems. Unfortunately, FSA suffers from extreme computational complexity given large numbers of MSs. To solve this problem, this paper introduces the Gram-Schmidt orthogonalization based Selection Algorithm (GSSA) to uplink multi-user MIMO/SDM systems. GSSA is a suboptimal active MS antenna selection algorithm that offers lower computational complexity than the optimal algorithm. This paper evaluates the transmission performance improvement of GSSA in uplink multi-user MIMO/SDM systems under realistic propagation conditions such as spatially correlated BS antennas and clarifies the effectiveness of GSSA.

We propose the Kernel MUSIC algorithm as an improvement over the conventional MUSIC algorithm. This algorithm is based on the orthogonality between the image and kernel space of an Hermitian mapping constructed from the received data. Spatial smoothing, needed to apply the MUSIC algorithm to coherent signals, is interpreted as constructing procedure of the Hermitian mapping into the subspace spanned by the constituent vectors of the received data. We also propose a new spatial smoothing technique which can remove the redundancy included in the image space of the mapping and discuss that the removal of redundancy is essential for improvement of resolution. By computer simulation, we show advantages of the Kernel MUSIC algorithm over the conventional one, that is, the reduction of processing time and improvement of resolution. Finally, we apply the Kernel MUSIC algorithm to the Laser Microvision, an optical misroscope we are developing, and verify that this algorithm has about two times higher resolution than that of the Fourier transform method.