In this letter, we propose a novel sparse Bayesian learning (SBL) algorithm using Generalized Double Pareto (GDP) prior to enhance the performance of direction of arrival (DOA) estimation for complex signals. Firstly, a novel hierarchical prior model is formulated for complex signals so that the marginal distribution of the complex signal is the GDP distribution, which promotes the sparsity more significantly than conventional priors used in SBL. Secondly, a novel fixed-point update rule of the hyperparameters is derived to speed up the convergence of the proposed SBL. Finally, a refined DOA searching method is also derived to tackle the grid-mismatch problem. Simulation results demonstrate the improved accuracy and efficiency of the proposed algorithm in low SNR and limited snapshots scenarios compared with other SBL-based DOA estimation methods.

In sparse signal recovery methods for direction of arrival (DOA) estimation, a set of uniform angular grid points is usually predefined. Dense grid points will improve the resolution and precision, but increase computational workload distinctly. To improve the efficiency and performance when using coarse initial grid points, an adaptive grid refinement (AGR) sparse Bayesian learning (SBL) method is proposed. The key idea of the proposed method is to adaptively insert new grid points based on the spatial spectrum learned from SBL iterations, as a result, grid points become denser and denser around the potential DOAs. The number of total grid points in the AGR process is much smaller than that of traditional uniform grid points, which enhances the computation efficiency. After the improved on-grid estimation of the AGR process, a post-processing DOA search procedure is implemented to reduce the off-grid DOA error. Furthermore, the proposed method is extended into the wideband case. Simulation results demonstrate that the proposed method has higher computation efficiency and precision than the classical off-grid SBL methods in scenarios of low SNR and limited snapshots. The effectiveness of the proposed method is also validated using the data of the SWellEx-96 ocean acoustic experiment.

Since the intra-group co-channel interference is introduced intentionally by the non-orthogonal multiple access (NOMA) technique, the reliability of NOMA based networks is limited by far users, while the sum-rate is mainly contributed by near users. In this paper, a NOMA based multiband scheduling policy is proposed to overcome this problem. Unlike conventional NOMA where all users are served in a single frequency band, the proposed scheme divides the entire band into multiple sub-bands, and accommodates the near and far users in different manners by exploring the sub-bands. Particularly, the far users employ repetition-based transmission in all sub-bands, and the maximum ratio combination (MRC) is integrated with successive interference cancellation (SIC) to jointly decode their intended symbols. Closed-form expression of the achievable sum-rate of the proposed scheme is derived, and the outage performance is characterized by the derived upper-bounded outage probability. Numerical results show that the proposed scheme enables NOMA systems to achieve both high reliability and high sum-rate.

Three-dimensional (3-D) source localization based on one-dimensional (1-D) angles of arrival (AOA) measurements from multiple linear arrays have attracted much attention due to the low cost of linear arrays and the easy access of 1-D AOAs. However, most existing methods are restricted to specific array deployments, and convex optimization method applicable to linear array with arbitray deployments have drawbacks such as high computational complexity and non-portability to small industrial equipment. In this paper, we propose two 1-D AOA-based source localization methods. The first method uses the iterative reweighted least square (IRLS) to obtain a coarse solution and a deviation refinement (DR) procedure to refine the estimation. Each step in IRLS-DR has a closed-form solution; thus, it is computationally efficient. Moreover, it applies to arbitrary inter-array deployments, and theoretical analysis shows that its performance can approach the Cramér-Rao lower bound (CRLB) at small noise levels. The second method addresses the maximum likelihood estimation (MLE). A modified Levenberg Marquardt (MLM) method is proposed, which focuses on designing a new selection strategy of the regularization parameter to realize a high convergence probability and localization accuracy. Experiments based on computer simulations and SWellEx96-S59 ocean acoustic measurements are carried out to verify the performance and advantages of the proposed methods.