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Hybrid precoding based on the sub-connected structure is one of promising technologies for millimeter-wave communications. Whereas, the existing hybrid designs of the sub-connected structure are all based on the linear precoding, which may suffer from a noise enhancement and performance deterioration. To tackle this issue, this letter proposes a novel nonlinear hybrid Tomlinson-Harashima (TH) precoding algorithm. In the proposed algorithm, a column-wise refinement procedure is firstly developed to update the radio frequency (RF) precoding and combining matrices column by column via Schur complement. Such a refinement procedure can effectively obtain the optimal solutions of each columns of the RF precoding and combining matrices, however, it requires matrix inversions with high complexity. Thus a recurrence relation of the matrix inversion is further established, which can reduce the complexity of the aforementioned refinement procedure greatly. Finally, the digital precoding and combining matrices are given by QR decomposition. Simulations show that the proposed algorithm can effectively improve the spectral efficiency with lower complexity.

It is well known that the singular value decomposition (SVD), as the best rank-revealing factorization, furnishes the best rank- $k$ approximation to a dense matrix with expensive computational cost of $\mathcal O(M^{2}N+N^{2}M+\min (M,N)^{3})$ . Moreover, it is hard to implement in parallel, which challenges the memory storage in wireless communications data-driven system. In this letter, a fast rank-revealing technique, namely, bilateral random projections (BRP) with $\mathcal O(MN)$ operations, is exploited for two-dimensional (2-D) frequency estimation of a complex sinusoid in noisy environment. Based on the resulting data matrix, whereafter, two-stage QR factorization frequency estimation method with the weighted least squares (WLS) as solver, is proposed to reduce the computational complexity of those SVD-based frequency estimators. Simulation results demonstrate the efficiency of the proposed algorithm in comparison with several frequency estimation approaches and the Cramer-Rao lower bound (CRLB) as benchmark.

We consider vertical Bell laboratories layered space-time architecture for offset quadrature amplitude modulation based filter bank multicarrier (FBMC) systems, and propose two different receivers. We first investigate a low-complexity QR decomposition (QRD) based conventional minimum mean square error (MMSE) successive interference cancellation (C-MMSE-SIC) receiver. The intrinsic interference in FBMC systems severely degrades the symbol error rate (SER) of the C-MMSE-SIC receiver. By exploiting the overlapping nature of neighboring symbols in an FBMC system, and the fact that the desired symbol in an FBMC system interferes with the symbols in a small neighborhood, we propose novel low-complexity intrinsic interference estimation (IIE) based sequential MMSE-SIC (IIE-SMMSE-SIC) receivers with QRD and its sorted version. We show that the IIE-SMMSE-SIC receivers, with similar complexity order as that of the C-MMSE-SIC, have lower SER.

Compared with the well-known minimum mean-square error (mmse)-based turbo receivers, sorted QR decomposition (SQRD)-based data detection scheme has the advantages of lower complexity and better bit error rate (BER) due to the avoidance of matrix inversion and the use of successive interference cancelation. In this letter, we investigate SQRD-based data detection and soft interference cancelation in multi-user multiple-input multiple-output single-carrier frequency division multiple access system. We propose a new SQRD-based multi-user detection scheme by utilizing the a priori information from the channel decoder in the decomposition process through the use of a new extended channel matrix. The received signal vector for the current user’s data detection can be categorized into the expected signal, the interference, and the noise components, where the interference includes those from both the detected and the undetected users. Soft interference cancelation is then performed on the data. The residual interference from the detected users can be easily obtained due to the use of successive interference cancelation, whereas the interference from the undetected users can be obtained by using the a priori information from channel decoder. We further consider on how to extract the expected signal from the interference component, thus further improve the signal-to-interference-plus-noise ratio condition at the receiver. Simulation results show that the proposed scheme significantly improves the BER performance compared with the original SQRD-based multi-user detection scheme and the mmse-based turbo receivers.

Paired carrier multiple access (PCMA) signal is one of the most common single-channel mixtures. It is still a great challenge to recover the component signals from non-cooperative received PCMA signals due to the high complexity of existing single-channel blind source separation algorithms. Hence, a Markov chain Monte Carlo (MCMC) procedure, called the QR Decomposition with M algorithm-based Gibbs sampler, is employed to realize the separation of PCMA signals. The method is robust against modulation parameter estimation error, since a data-aided adaptive filter that can update the channel response is embedded into the QRD-M-based Gibbs sampler. Simulation results show that the MCMC estimator efficiently achieves approximately joint likelihood sequence estimation.

In this letter, we propose two improved decoding algorithms based on traditional low-complexity maximum likelihood (LC-ML) detection algorithm for the quasi-orthogonal space-time block code (QOSTBC) with four-transmit-antennas. The decoding complexities of the proposed algorithms are both lower than LC-ML decoder. By decomposing the channel matrix and its elementary transformation form with QR decomposition, the first algorithm can detect the four signals independently at the cost of some BER performance penalty whereas the second algorithm achieves almost the same BER performance with LC-ML decoder by using the interference cancellation method twice.

QR decomposition (QRD) is widely used in various engineering applications and its implementation has a significant impact on the system performance and complexity. This paper develops a low-complexity QRD algorithm based on fast plane rotations, which does not require square-root operations for decomposing a complex-valued matrix. Furthermore, an update-based implementation is presented where computations are performed incrementally as the data arrives sequentially in time to drastically reduce the overall latency and hardware resources. Practical results for QRD-based spatial correlation estimator are provided to demonstrate the effectiveness of our solution for multiple-input multiple-output (MIMO) systems with complex-valued signals.

By employing the regularized block diagonalization (RBD) preprocessing technique, the MU-MIMO broadcast channel is decomposed into multiple parallel independent SU-MIMO channels and achieves the maximum diversity order at high data rates. The computational complexity of RBD, however, is relatively high due to two singular value decomposition (SVD) operations. In this letter, a low-complexity lattice reduction-aided RBD is proposed. The first SVD is replaced by a QR decomposition, and the orthogonalization procedure provided by the second SVD is substituted by a lattice-reduction whose complexity is mainly contributed by a QR decomposition. Simulation results show that the proposed algorithm can achieve almost the same sum-rate as RBD, substantial bit error rate (BER) performance gains and a simplified receiver structure, while requiring a lower complexity.

In multiuser TDD MIMO downlink communications, system performance suffers from multiuser interference(MUI) and other-cell interference(OCI). This paper proposes designing a precoder to jointly suppress MUI and OCI. Firstly, a precoding matrix is designed for multiuser interference suppression, with the help of the QR decomposition of generalized MMSE channel inversion. And then, using a signal leakage criterion, the second precoding matrix is designed to suppress OCI and residual MUI. The proposed algorithm can avoid user feedback in multiuser TDD MIMO systems. Simulation results demonstrate the effectiveness of the proposed scheme.

We present a new parallel algorithm for computing the optimum coefficients of the fractionally-spaced FIR MMSE-DFE based on the Householder orthogonal transformation which avoids the back-substitution computational bottleneck. The resulting significant reductions in the algorithm's execution time are demonstrated through a real-time FPGA implementation.

A multiple space-time transmit diversity (MSTTD) detection scheme on quasi-static Rayleigh flat-fading channels is proposed. The proposed detection algorithm first determines the candidates for M units each using a QR decomposition, and computes a reference metric for a full-length vector consisting of the first candidates at each unit. Then it performs a more likely searching simply by comparing the reference metric with other candidates such that an inter-unit interference can be effectively eliminated. Through these procedures it can approach the ML performance with less complexity even compared to an improved Schnorr-Euchner (ISE) scheme.

We propose an efficient 2Ntimes2N MIMO detection algorithm where the transmit signals are grouped in pairs and separately coded using the standard Alamouti space-time code. At the receiver, one or more QR decompositions are performed and the upper triangular property of the R matrices so obtained is exploited in order to successively decode the transmitted symbols starting with those interference-free symbols corresponding to the last two rows and columns of R. Bit-error-rate simulation results, for a 4times4 MIMO system and a bandwidth efficiency of 8 bits/s/Hz, show that the proposed technique, while less complex than ordered MMSE V-BLAST, outperforms the latter by 2-6 dB at a BER of 10-4