Chongtao Guo

Iterative Methods for Subspace and DOA Estimation in Nonuniform Noise

Usually, direction-of-arrival (DOA) estimators are derived under the assumption of uniform white noise, whose covariance matrix is a scaled identity matrix. However, in practice, the noise can be nonuniform with an arbitrary unknown diagonal covariance matrix. In this situation, the performance of DOA estimators may be deteriorated considerably if the noise nonuniformity is ignored. To tackle this problem, iterative approaches to subspace estimation are developed and the corresponding subspace-based DOA estimators are addressed. In our proposed methods, the signal subspace and noise covariance matrix are first determined by maximizing the log-likelihood (LL) function or solving a least-squares (LS) minimization problem, both of which are accomplished in an iterative manner. Then, the DOAs are determined from the subspace estimate and/or noise covariance matrix estimate with the help of traditional DOA estimators. As the signal subspace and noise covariance matrix can be computed in closed-form in each iteration, the proposals are computationally attractive. Furthermore, the signal subspace is directly calculated without the requirement of the exact knowledge of the array manifold, enabling us to handle array uncertainties by incorporating conventional subspace-based calibration algorithms. Simulations and experimental results are included to demonstrate the superiority of the proposed approaches.

Convexity of Fairness-Aware Resource Allocation in Wireless Powered Communication Networks

This letter focuses on fairness-aware power and time allocation in wireless powered communication networks under the “harvest-then-transmit” protocol, where downlink wireless energy transfer is implemented at first and then uplink wireless information transfer takes place in a spectrum-sharing fashion. We aim to achieve the rate fairness of all users under three fairness criteria named max-min, proportional, and harmonic fairness. Although these problems are intractable in their original nonconvex formulations, all of them can be equivalently transformed to convex programming ones. As a result, the global optimal solution can be efficiently obtained via mature convex solvers. Simulation results verify the effectiveness of the proposed approach in terms of minimum-rate, geometric mean rate, and mean delay.

Direction Finding With Partly Calibrated Uniform Linear Arrays in Nonuniform Noise

We recently proposed an ESPRIT-like method for direction finding with partly calibrated uniform linear arrays. It has been shown that the unknown gains/phases and directionof-arrivals (DOAs) can be estimated jointly. However, this approach relies on the assumption of uniform white noise, i.e., all sensor noise powers are identical. Besides, at most M -2 sources can be handled for an M-element array. This motives us to develop enhanced methods for circumventing the limitations above. This paper extends the uniform white noise to nonuniform noise on one hand, and, on the other hand, allows us to estimate up to M - 1 DOAs of uncorrelated signals. More exactly, for uncorrelated signals, the array can be calibrated according to the specific structure of the array covariance matrix, and the nonuniformity of sensor noises can then be simply eliminated by reformulating the calibrated array covariance matrix. For correlated signals, the nonuniformity of sensor noises is mitigated by solving a least squares minimization problem, such that the signal/noise subspace can be properly determined. Thus, our previously developed ESPRIT-like approach can be adopted to determine the DOAs. The effectiveness of the proposed methods is confirmed by numerical examples.

A robust STAP method for airborne radar with array steering vector mismatch

In this paper, we consider the problem of space-time adaptive processing (STAP) for airborne radar in the presence of direction-of-arrival (DOA) and Doppler frequency uncertainties, either of which would result in steering vector mismatch. A robust STAP method is devised by introducing an accurate steering vector estimator. In particular, by considering the mismatched DOA and Doppler frequency, a spatial-temporal integral covariance matrix including the actual steering vector component is first constructed. The subspace corresponding to the clutter-plus-noise is then extracted from the so-obtained matrix and used to impose an appropriate constraint to estimate the actual steering vector. The resultant problem is a non-convex quadratically constrained quadratic program (QCQP), which is solved using the semidefinite programming (SDP) relaxation technique. Numerical examples are presented to demonstrate the performance of the proposed approach in different hypothetical scenarios. HighlightsA spatial-temporal integral covariance matrix is constructed by considering the mismatched DOA and Doppler frequency.The subspace corresponding to the clutter-plus-noise is extracted from the spatial-temporal integral covariance matrix.Clutter-plus-noise subspace is used for impose an appropriate constraint to estimate the actual steering vector.

Robust adaptive beamforming with random steering vector mismatch

In this paper, random steering vector mismatches in sensor arrays are considered and probability constraints are imposed for designing a robust minimum variance beamformer (RMVB). To solve the resultant design problem, a Bernstein-type inequality for stochastic processes of quadratic forms of Gaussian variables is employed to transform the probabilistic constraint to a deterministic form. With the use of convex optimization techniques, the deterministic problem is reformulated to a semidefinite programming (SDP) problem which can be efficiently solved. In order to overcome the degradation caused by the presence of the signal-of-interest (SOI) in the training snapshots, two methods with different application conditions to interference-plus-noise covariance matrix (INCM) construction are also introduced. Additionally, the uncertainty of the sample covariance matrix is taken into account to improve the robustness when the INCM-based approaches are not feasible. Numerical examples are presented to demonstrate the performances of the proposed robust beamformers in different scenarios. HighlightsBernstein-type inequality is used to simplify probability-constrained beamforming.Approaches to INCM construction are developed to overcome performance degradation.Uncertainty of the sample covariance matrix is considered for robustness improvement.

Robust Adaptive Beamforming With Precise Main Beam Control

Many efforts have been recently devoted to robust adaptive beamforming with main beam control such that sufficient robustness against large look direction mismatches can be achieved by flexibly adjusting the beamwidth and response ripple. However, most of the existing approaches inherently rely on assuming an exactly known array manifold, but have not yet addressed the issue of robust beamforming with precise main beam control in the presence of arbitrary steering vector uncertainties. This motivates us to develop a robust beamforming approach that is capable of accurately controlling the array main beam. Unlike the conventional methods, steering vector uncertainties are taken into account in the magnitude response constraints of the adaptive beamformer. This allows us to control the main beam as prescribed. As the resultant nonconvex problem has a more complicated formulation than that of the existing methods, techniques developed for solving the problem of robust beamforming with magnitude response constraints cannot be employed directly. To tackle this problem, the lower and upper norm bounds of the beamformer weight vector are derived. The semidefinite relaxation technique is then employed as approximate solver, ending up with iterative, grid search, and linearization solutions. Simulation results show that the proposed methods are able to precisely control the main beam magnitude response in the presence of steering vector uncertainties.

Energy-Efficient Resource Allocation in TDMS-Based Wireless Powered Communication Networks

This letter considers a wireless powered communication network (WPCN) where users first harvest energy in downlink and then utilize the energy to transmit information signal in uplink simultaneously. Our goal is to maximize the energy efficiency (EE) of the network via joint time allocation and power control. However, directly solving this issue is of significant challenge due to its nonconvex property. By exploiting the nonlinear fractional programming, an iterative resource allocation method based on the Dinkelbach structure is proposed to solve the considered nonconvex optimization problem. In each iteration, we optimize power with fixed time allocation and then optimize time with a given power allocation. Simulation results are presented to show that the system EE is greatly improved by the proposed approach.

New Approaches to Direction-of-Arrival Estimation With Sensor Arrays in Unknown Nonuniform Noise

It is known that classical subspace-based direction-of-arrival (DOA) estimation algorithms are not straightforwardly applicable to scenarios with unknown spatially nonuniform noise. Among the state-of-the-art solutions, this problem is tackled by iterative subspace estimation algorithms to incorporate subspace-based approaches or nonlinear optimization routines to bypass the direct identification of subspaces. In this paper, the problem of DOA estimation in nonuniform noise is revisited by devising two computationally efficient proposals. It is proved herein that, if the signals are uncorrelated, the signal and noise subspaces can be directly obtained from the eigendecomposition of a reduced array covariance matrix. On the other hand, when the signals are correlated, the estimation of the noise covariance matrix is formulated into a rank minimization problem which can be approximately solved by semidefinite programming. In both cases, the signal and noise subspaces are easy to compute without iterations. Consequently, classical subspace-based algorithms can be employed to determine the DOAs. Numerical examples are provided to demonstrate the performance and applicability of the proposed methods.

A robust beamformer with main beam control

In this paper, a new robust beamforming approach which is capable of accurately controlling the main beam response is proposed. In this method, steering vector uncertainties are taken into account in the beamformer design problem with array magnitude response constraints. This allows us to control the main beam as prescribed. However, the resultant non-convex problem has a different formulation from that of the existing methods for robust beamforming with magnitude response constraints. Thus, existing techniques cannot be applied directly. To cope with this problem, the lower and upper norm bounds of the beamformer weight vector are first derived. The semidefinite relaxation technique is then employed as an approximate solver ending up with a grid search solution. Simulation results show that the proposed method is able to accurately control the main beam magnitude response in the presence of steering vector uncertainties.

Matrix completion based direction-of-arrival estimation in nonuniform noise

It is known that eigenstructure-based direction-of-arrival (DOA) estimation algorithms are vulnerable to nonuniform noise. In order to tackle this problem, recently we proposed an approach for joint estimasion of the signal subspace and noise covariance matrix. However, an iterative procedure is involved in this method. This motivates us to present an new approach which is free of iteration in this paper. More precisely, the problem of noise-free covariance matrix estimation is first formulated as matrix completion. The signal and noise subspaces are then achieved by eigendecomposing the noise-free covariance matrix estimate and, therefore, traditional subspace-based DOA estimation algorithms can be applied directly. Numerical simulation results are provided to illustrate the effectiveness of the proposed method.