Minjian Zhao

Adaptive Reduced-Rank Receive Processing Based on Minimum Symbol-Error-Rate Criterion for Large-Scale Multiple-Antenna Systems

In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and M-ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.

Robust Multibranch Tomlinson–Harashima Precoding Design in Amplify-and-Forward MIMO Relay Systems

This paper proposes the design of robust transceivers with Tomlinson-Harashima precoding (THP) for multiple-input-multiple-output relay systems with amplify-and-forward protocols based on a multibranch (MB) strategy. The MB strategy employs successive interference cancellation on several parallel branches, which are equipped with different ordering patterns so that each branch produces transmit signals by exploiting a certain ordering pattern. For each parallel branch, the proposed robust nonlinear transceiver design consists of THP at the source along with a linear precoder at the relay and a linear minimum-mean-square-error receiver at the destination. By taking the channel uncertainties into account, the source and relay precoders are jointly optimized to minimize the mean square error. We then employ a diagonalization method along with some attributes of matrix-monotone functions to convert the optimization problem with matrix variables into an optimization problem with scalar variables. We resort to an iterative method to obtain the solution for the relay and the source precoders via Karush-Kuhn-Tucker conditions. An appropriate selection rule is developed to choose the nonlinear transceiver corresponding to the best branch for data transmission. Simulation results demonstrate that the proposed MB-THP scheme is capable of alleviating the effects of channel state information errors and improving the robustness of the system.

A Precoding Scheme for N-Continuous OFDM

In this letter, we present a novel, with-memory precoding scheme for N-continuous OFDM, which is based on the decomposition of a relevant matrix into its null space and orthogonal complement subspace. The transmitted data can be fully recovered by the proposed scheme, while the out-of-band emission performance is retained as other N-continuous OFDM signals. The data rate loss is reduced to half compared to the memory-less scheme. Bit-error-rate performance can be improved by exploiting the redundant information in the subsequent OFDM symbol.

Robust Transceiver Design for MISO Interference Channel With Energy Harvesting

In this paper, we consider the multiuser multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. The transmit beamforming vectors and receive power splitting (PS) ratios are jointly designed in order to minimize the total transmission power subject to both signal-to-interference-plus-noise ratio (SINR) and EH constraints. Most joint beamforming and power splitting (JBPS) designs assume that perfect channel state information (CSI) is available; however CSI errors are inevitable in practice. To overcome this limitation, we study the robust JBPS design problem assuming a norm-bounded error (NBE) model for the CSI. Three different solution approaches are proposed for the robust JBPS problem, each one leading to a different computational algorithm. Firstly, an efficient semidefinite relaxation (SDR)-based approach is presented to solve the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. A rank-one recovery method is provided to recover a robust feasible solution to the original problem. Secondly, based on second order cone programming (SOCP) relaxation, we propose a low complexity approach with the aid of a closed-form robust solution recovery method. Thirdly, a new iterative method is also provided which can achieve near-optimal performance when the SDR-based algorithm results in a higher-rank solution. We prove that this iterative algorithm monotonically converges to a Karush-Kuhn-Tucker (KKT) solution of the robust JBPS problem. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.

Robust MMSE Precoding Based on Switched Relaying and Side Information for Multiuser MIMO Relay Systems

This paper proposes a novel precoding scheme for multiuser multiple-input-multiple-output (MIMO) relay systems in the presence of imperfect channel state information (CSI). The base station (BS) and the MIMO relay station (RS) are both equipped with the same codebook of unitary matrices. According to each element of the codebook, we create a latent precoding matrix pair, namely, a BS precoding matrix and an RS precoding matrix. The RS precoding matrix is formed by multiplying the appropriate unitary matrix from the codebook by a power scaling factor. Based on the given CSI and a block of transmit symbols, the optimum precoding matrix pair, within the class of all possible latent precoding matrix pairs derived from the various unitary matrices, is selected by a suitable selection mechanism for transmission, which is designed to minimize the squared Euclidean distance between the pre-estimated received vector and the true transmit symbol vector. We develop a minimum mean-square-error (MMSE) design algorithm to construct the latent precoding matrix pairs. In the proposed scheme, rather than sending the complete processing matrix, only the index of the unitary matrix and its power scaling factor are sent by the BS to the RS. This significantly reduces the overhead. Simulation results show that, compared with other recently reported precoding algorithms, the proposed precoding scheme is capable of providing improved robustness against the effects of CSI estimation errors and multiuser interference.

All digital tracking loop for OFDM symbol timing

A fine symbol timing tracking loop combined with sampling rate conversion for orthogonal frequency division multiplexing (OFDM) software receiver is investigated in this paper. Generally, symbol timing for OFDM receiver can be achieved by adjusting ADCs sampling frequency and phase with a voltage controlled oscillator. On a software radio platform, intermediate frequency OFDM waveform is digitized with a free running clock. So, the symbol timing method of OFDM receiver for software radio is different from that of generally implemented receivers. To implement such an OFDM receiver, an interpolation loop is used to realize sampling rate to symbol rate conversion. Simultaneously, the residual coarse timing error and the sampling frequency error can be tracked by a NCO controlled loop. According to the proposed loop structure, simulation results prove it is quite effective.

Low-complexity variable forgetting factor mechanisms for adaptive linearly constrained minimum variance beamforming algorithms

In this work, the authors propose two low-complexity variable forgetting factor (VFF) mechanisms for recursive least squares-based adaptive beamforming algorithms. The proposed algorithms are designed according to the linearly constrained minimum variance (LCMV) criterion and operate in the generalised sidelobe canceller structure. To obtain a better performance of convergence and tracking, the proposed VFF mechanisms adjust the forgetting factor by employing updated components related to the time-averaged LCMV cost function. They carry out the analyses of the proposed algorithms in terms of the computational complexity and the convergence properties and derive an analytical expression of the steady-state mean-square-error. Simulation results in non-stationary environments are presented, showing that the adaptive beamforming algorithms with the proposed VFF mechanisms outperform the existing methods at a significantly reduced complexity.

A new method to simultaneously estimate TX/RX IQ imbalance and channel for OFDM systems

In-phase and Quadrature Imbalance (IQI) exists in both analog IQ modulator and demodulator. When carrier frequency is ultra-high and/or bandwidth is ultra-wide, the impact of IQI on system performance is not negligible. While in most of the literatures, the combined effect of channel, TX, and RX IQI is discussed, in this paper, a method that can separate and simultaneously estimate the three is proposed. The complexity of the proposed method is low, and with estimated TX and RX IQI, simple symbol-by-symbol detection can be used to detect data symbols with negligible performance loss. Compared with the calibration methods, where auxiliary circuits are needed to separate the TX and RX IQI belonging to different communication links inside a transceiver, by exploiting the fact that the wireless channel changes much faster than the IQI, the method proposed here can separate not only the TX and RX IQI but also the channel belonging to the same communication link without any auxiliary circuits. Simulations are carried out to verify the performance of the proposed method.

Reduced-Rank DOA Estimation Algorithms Based on Alternating Low-Rank Decomposition

In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for direction-of-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction consists of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recursive least squares (RLS), denoted as ALRD-RLS and modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the auxiliary parameter vector. Simulations for large sensor arrays with both uncorrelated and correlated sources are presented, showing that the proposed algorithms are superior to existing techniques.

Doppler-Aided GNSS Position Estimation With Weighted Least Squares

In contrast to carrier-phase measurements, receiver-generated Doppler measurements have much better availability, even in severe urban canyons or high-dynamic movement applications, while providing centimeter-level precision. Thus, the Doppler-aided approach may improve the positioning accuracy of the receiver. Some Kalman-filter-based applications have been developed in recent years, but the theoretical error analysis is still missing. In this paper, we propose a model-free Doppler-aided position estimation approach. The Doppler measurements are modeled together with the code pseudoranges and then solved as a weighted least square (WLS) problem. In addition, we analyze the lower bound of errors of the approach based on the Cramér-Rao bound (CRB). Simulation results and the error analysis show that the proposed approach may improve accuracy in position estimation.