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The distributed source parameter estimator (DSPE) is one of the well-known angular parameter estimation techniques for coherently distributed sources. However, the computational cost of DSPE is not attractive due to the two-dimensional spectrum peak search. To overcome this problem, this letter proposes a novel DSPE algorithm based on compressive sensing theory, named as LP-DSPE algorithm. In the proposed method, at first, the nominal direction of arrival (DOA) estimation can be transformed into a linear programming problem through sparse recovery theory, whose feasible solution set is further expanded to improve the estimation accuracy. Then the angular spread can be obtained via a one-dimensional peak search utilizing the estimated nominal DOA. Compared with the previous works, the proposed algorithm can offer more accurate parameter estimation performance with lower computational complexity, even if in large angular spread scenarios. Theoretical analysis and simulation results demonstrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

This letter presents a two-stage (Ts) hybrid precoding algorithm based on convolutional neural network (CNN) with minimum hybrid precoding residual, named as Ts-CNN algorithm. Firstly, using the optimal precoding matrix as a label, a new CNN architecture is constructed, which can be trained to learn how to predict the vectorized hybrid precoder. Furthermore, a two-stage hybrid precoding scheme is proposed. In the first stage, the proposed CNN learns how to minimize the residual between the hybrid precoder and the optimal precoder when it only has the estimated channel as its input. Specifically, two special network layers are designed to satisfy the constraint conditions of the output. In the second stage, the estimated channel is fed into the well-trained CNN, correspondingly, the analog precoding matrix and the digital precoding matrix are output. Simulation results and complexity analysis show that under certain conditions, the proposed method offers better spectral efficiency than the other related methods with acceptable complexity. [ABSTRACT FROM AUTHOR]

This letter proposes a joint-sparse index removal (JSIR) based algorithm to detect the index of space-frequency index modulation (SFIM) signal for MIMO-OFDM cognitive radio (MIMO-OFDM-CR) system. At first, the proposed algorithm calculates the inner-product matrix of the received signal and channel gain to measure the index information of each subcarrier on each antenna. Then, an antenna index metric is computed through a definition which is given by summing the metrics of all subcarriers on the antenna. According to the metric, the silent antenna indices are acquired by comparing the metric with a threshold derived from statistical distribution. Next, the index detection problem is simplified by removing the silent antenna indices, i.e., the joint-sparse indices of SFIM signal matrix. At last, the indices of active subcarriers on active antennas are obtained through solving the simplified problem via a compressed sensing reconstruction algorithm. The complexity analysis shows that the proposed JSIR algorithm has lower complexity under certain conditions, compared with other related methods. The simulation results verify the accuracy of the proposed method in terms of bit error rate (BER). [ABSTRACT FROM AUTHOR]

In cognitive radio (CR), the problem of spectrum hole detection has been extensively studied in single dimension, such as frequency domain, spatial domain, and so on. Recently, a class of two dimension spectrum hole detection methods, named as joint angle-frequency estimation (JAFE), has attracted much attention. Nevertheless, most of the existing approaches are only suitable for the scenario with matching frequency-angle pairs, rather than the non-matching scenario between the two parameters, like space division multiple access (SDMA) or frequency division multiplexing access (FDMA) communication mode which allows the same frequency band (or angle) to be reused in different angles (or frequencies). For the above two cases, including matching and non-matching scenarios, this paper develops an effective frequency-angle spectrum hole detection algorithm with Taylor expansion based focusing transformation (TFT-FASHD), on the basis of signal sparse representation by extending the array manifold from angle domain to frequency-angle domain. In the proposed method, for Fourier transform representation of the sparse model, a focusing transformation based on Taylor expansion is first performed to focus the signal subspaces at different frequencies to a single frequency, so as to carry out dimension reduction of dictionary in angular domain. TFT is derived by decomposing the array manifold with Taylor expansion, and further the optimum focusing frequency of focusing transform is discussed theoretically. Second, atoms with high representative performance are chosen by the presented TFT and compressed sensing (CS). Third, according to the low dimension dictionary, the TFT-FASHD is implemented by CS under multiple measurement vector (MMV) circumstances. The accuracy of the algorithm in non-matching scenario is verified by simulation results. For the matching scenario, compared with the related JAFE methods, the proposed algorithm has a lower computational complexity, smaller detection error, and higher energy efficiency, which are validated through simulation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents an effective adaptive beamforming method based on an $\ell _{2}$ -norm minimization problem with multiple constraints, named as MC- $\ell _{2}$ -M algorithm. In the proposed method, the output power minimization problem can be formulated as an $\ell _{2}$ -norm minimization problem of the weight vector, with multiple constraints including distortionless response constraint and zero-forcing response constraints toward the desired signal and the interference signals, respectively. Furthermore, via an interference subspace estimation, the presented $\ell _{2}$ -norm minimization problem can be rewritten as an equivalent constrained convex optimization problem of the weight vector, which is readily to be solved. Finally, the closed-form solution of the proposed optimization problem is given. Compared with the previous works, the proposed MC- $\ell _{2}$ -M approach provides better beamforming performance, for example: 1) it reduces the sensitivity to the variation of signal-to-noise ratio (SNR) so that it has better output array gain, even if in the large dynamic SNR changing circumstances; 2) the better sidelobe and nulling levels are maintained, since it weakens the component of the desired signal in the covariance matrix of the array snapshot vector by a new defined blocking matrix; and 3) it can not only effectively suppress the interference signals but also ensure that the response of the desired signal is distortionless. Simulation results demonstrate the efficiency of the presented approach. [ABSTRACT FROM AUTHOR]

This paper presents an effective direction-of-arrival (DOA) estimation method based on a new computation method of the \ell \infty -norm for a complex matrix. Making use of the new computation method of the \ell \infty -norm, the DOA estimation problem can be efficiently formulated as a linear programming (LP) problem. Thus, the proposed method can effectively give the DOA estimation by finding the sparse coefficients which are obtained by solving the LP problem. Except for the first singular value decomposition of the data matrix, the proposed method is effectively implemented in the light of the LP theory based on real-valued computation. It avoids solving the complicated second-order cone programming problem. Furthermore, it can primely suppress spurious peaks in DOA estimation. Simulation results demonstrate the efficiency of the presented approach. [ABSTRACT FROM PUBLISHER]

In most of compressed sensing (CS) contributions, the sparsity of interesting signals is usually assumed to be prior. It may incur performance degradation of CS in such environments, where the sparsity order of channel occupancy changes dynamically. Therefore, sparsity order estimation (SOE) is vital, so that the sampling rate can be adjusted adaptively to exploit fully CS techniques. This paper studies the SOE problem for wideband cognitive radio. To investigate this issue, at first, the theoretical expression of sparsity order of the considered signals is derived via random matrix theory. The theoretical analysis shows that the sparsity order can be expressed with the ratio, which consists of the trace of measured signals’ covariance matrix, compression sampling ratio, signal-to-noise ratio, and so on. And then, according to the theoretical expressions, a novel SOE approach is presented. In the proposed method, the trace of measured signals’ covariance matrix is first calculated, and second, the sparsity order is estimated based on the trace. Compared with the previous work, the proposed algorithm, besides the lower computational complexity, has some superior performance, such as better robustness, smaller SOE error, and so on. Theoretical analysis and simulation results demonstrate the performance of the proposed approach. [ABSTRACT FROM PUBLISHER]

Direction-of-arrival (DOA) estimation is an important problem in array signal processing. An effective spatial differencing method is addressed, for the DOA estimation problem of a number of uncorrelated and coherent narrowband signals simultaneously impinging on the far field of a uniform linear array (ULA). In the proposed method, the uncorrelated sources are firstly estimated using conventional subspace methods, and then they are eliminated by exploiting the spatial differencing technique, that is, only coherent components remain in the spatial differencing matrix. Finally, the remaining coherent signals are estimated by utilizing the spatial differencing matrix. Compared with the previous works, the presented method can improve the DOA estimation accuracy, as well as increase the maximal number of detectable signals. The theoretical analysis and simulation results confirm the effectiveness of the method. [ABSTRACT FROM AUTHOR]