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The frequency diverse array (FDA) radar has been extensively studied due to its unique range-angle dependent beampattern. Time-invariant spatial patterns have been reported for FDAs proposed so far. However, some recent studies indicate that the patterns obtained by using these methodologies neglect the time-range or frequency-phase relationship, and the definition of time in some equations are misinterpreted which results in erroneous conclusions. Taking the time-variant property of FDA beampatterns into consideration, in this study, the authors propose a short range FDA radar to generate a time-variant focused range-angle dependent transmit beampattern, where chirp waveform with multi-carrier FDA architecture is used. Both the fixed and time-modulated frequency offsets are considered to analyse the proposed FDA radar. The frequency offset employed across each element is generated by chaos sequence and sine function. By compensating the propagation delays of signals, the transmitted signals sum up constructively to focus on the desired range-angle sector only at a specific instant of time. Numerical results are implemented to verify the validity of the proposed schemes.

Parent-identifying set system is a kind of combinatorial structures with applications to broadcast encryption. In this paper we investigate the maximum number of blocks $$I_2(n,4)$$ in a 2-parent-identifying set system with ground set size n and block size 4. The previous best-known lower bound states that $$I_2(n,4)=\varOmega (n^{4/3+o(1)})$$ . We improve this lower bound by showing that $$I_2(n,4)= \varOmega (n^{3/2-o(1)})$$ using techniques in additive number theory.

Separable codes and $B_{2}$ codes are combinatorial structures which could be applied to identify traitors in multimedia fingerprinting and to uniquely decode messages in multiple access communication respectively. In this letter we provide new lower and upper bounds for the largest code rates of $q$ -ary separable codes and $B_{2}$ codes, which improve the existing results when the alphabet size $q$ is relatively small.

In massive multiple-input multiple-output (MIMO) systems, efficiently estimating both the direction-of-arrival (DOA) and the source power with an increased number of degrees-of-freedoms (DOFs) is important but challenging. Aiming at this, we introduce the framework of coprime array signal processing into massive MIMO system and propose an efficient inverse discrete Fourier transform (IDFT)-based DOA estimation algorithm in this paper. By implementing IDFT on the second-order virtual array signals characterized by the equivalent spatial frequency, it is proved that the resulting spatial response enables to effectively estimate both DOA and source power with an increased number of DOFs. Meanwhile, the window method and the zero-padding technique are sequentially considered to alleviate the spectral leakage phenomenon and improve the DOA estimation accuracy. Compared with the existing coprime array DOA estimation algorithms, the implementation of IDFT indicates a remarkably reduced computational complexity as well as the hardware overhead. Simulation results show the effectiveness of the proposed algorithm.

As the most promising technology in wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge in practical implementation because of the high complexity and cost involved in deploying a separate front-end circuit for each antenna. In this paper, we apply the compressive sampling technique to reduce the number of required front-end circuits in the analog domain and the computational complexity in the digital domain. Unlike the commonly adopted random projections, we exploit the a priori probability distribution of the user positions to optimize the compressive sampling strategy, so as to maximize the mutual information between the compressed measurements and the direction-of-arrival (DOA) of user signals. With the optimized compressive sampling strategy, we further propose a compressive sampling Capon spatial spectrum estimator for DOA estimation. In addition, the user signal power is estimated by solving a compressed measurement covariance matrix fitting problem. Furthermore, the user signal waveforms are estimated from a robust adaptive beamformer through the reconstruction of an interference-plus-noise compressed covariance matrix. Simulation results clearly demonstrate the performance advantages of the proposed techniques for user signal parameter estimation as compared to existing techniques.

Joint-sparse signal reconstruction is a key issue in distributed compressed sensing based on the mixed support set model. In this letter, a novel joint-sparse signal reconstruction algorithm is proposed based on the common support set refinement. The common support set is first roughly estimated by greedy pursuit algorithms. The roughly estimated common support set is then refined by pruning the incorrect elements. With the refined common support set, greedy pursuit is utilized to reconstruct the joint-sparse signals. The complexity analysis and simulation results indicate that the proposed algorithm achieves better estimates of the support sets and finally reduces the reconstruction error for the joint-sparse signals with a moderate complexity compared with the state-of-the-art algorithms.

Massive multiple-input multiple-output (MIMO) is one of the most promising techniques for next generation wireless communications due to its superior capability to provide high spectrum and energy efficiency. Considering the very large number of antennas employed at the base station, however, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD) multiuser massive MIMO systems. In this paper, we propose an information-theoretic metric to design the pilot for downlink channel estimation in FDD multiuser massive MIMO systems. By exploiting the low-rank nature of the channel covariance matrix, we first derive the minimum number of pilot symbols required to ensure perfect channel recovery, which is much less than the number of antennas at the base station. Further, under a general channel model that the channel vector of each user follows a Gaussian mixture distribution, the pilot symbols are designed by maximizing the weighted sum of the Shannon mutual information between the measurements of the users and their corresponding channel vectors on the complex Grassmannian manifold. Simulation results demonstrate the effectiveness of the proposed information-theoretic pilot design for the downlink channel estimation in FDD massive MIMO systems.

Sparse signal reconstruction algorithms in compressive sensing mainly focus on greedy algorithms, which are short-sighted. In this letter, an optimization-oriented algorithm using global optimization method is proposed to reconstruct the sparse signal. First, a pre-selection is made to estimate the most likely support of the sparse signal. Second, a backtracking strategy is introduced to avoid the over-fitting problem. Last, an optimization-oriented search strategy is designed to refine the pre-selected support toward the best estimate of the signal support. Numerical results demonstrate that the proposed algorithm performs better for sparse signal reconstruction with moderate computational complexity compared with the existing algorithms.

In the private information retrieval (PIR) problem, a user wants to retrieve a file from a database without revealing any information about the desired file's identity to the servers that store the database. In this paper, we study the PIR capacity of a graph-based replication system, in which each file is stored on two distinct servers according to an underlying graph. This paper aims to provide upper and lower bounds to the PIR capacity of graphs via various graph properties. In particular, we provide several upper bounds on the PIR capacity that apply to all graphs. We further improve the bounds for specific graph families (which turn out to be tight in certain cases) by utilizing the underlying graph structure. For the lower bounds, we establish optimal rate PIR retrieval schemes for star graphs via edge-coloring techniques. Lastly, we provide an improved PIR scheme for complete graphs, which implies an improved general lower bound on all graphs' PIR capacity., 24 pages

In this paper, we consider the problem of underdetermined direction-of-arrival (DOA) estimation using coprime arrays from a ont-bit perspective, where the coarray correlations of the quantized sparse measurements are explored for augmented covariance matrix reconstruction. To fully utilize the coarray signals calculated from the one-bit coprime array measurements for DOA estimation, a correlation reconstruction problem is formulated to obtain the quantized covariance matrix corresponding to a filled coarray containing the discontiguous one, where the one-bit quantization transforms the possibilities of correlations from an infinite to a finite number. The performance of the proposed method is validated from the aspects of degrees- of-freedom (DOFs), estimation accuracy, as well as the resolution performance. Simulation results demonstrate that the proposed method not only retains full achievable DOFs of the coprime array, but is also capable of presenting a better DOA estimation performance than the non-quantization approaches.

Complex codebooks with small inner-product correlation have many applications such as in code-division multiple access communications and compressed sensing. It is desirable but difficult to construct optimal codebooks achieving the well-known Welch bound. In this paper, complex codebooks are investigated from a graph theoretic perspective. A connection between codebooks and Cayley sum graphs is established. Based on this, many infinite families of complex codebooks are explicitly constructed, which are asymptotically optimal with respect to the Welch bound. These constructions not only include some known constructions as special cases but also provide flexible new parameters.

Coprime arrays can cope with the underdetermined case for direction-of-arrival (DOA) estimation. However, the popular matrix-based coarray signal processing approaches suffer performance loss on the underlying characteristics among the multi-dimensional signals. To address this problem, we propose a novel coarray tensor-based two-dimensional underdetermined DOA estimation method for coprime planar array in this paper, where both the multi-dimensional information of the received signals and the augmented coarray are effectively utilized. The received signal tensors of the coprime planar array are constructed by concatenating each snapshot, which are then transformed to an augmented uniform rectangular array statistics for extending the effective array aperture. Subsequently, the tensorization technique is adopted to increase the degrees-of-freedom of the proposed DOA estimation method, and a structured coarray tensor is optimized for super-resolution DOA estimation. The effectiveness of the proposed method is verified via simulation results.

© 2018 IEEE. Coprime arrays can achieve an increased number of degrees of freedom by deriving the equivalent signals of a virtual array. However, most existing methods fail to utilize all information received by the coprime array due to the non-uniformity of the derived virtual array, resulting in an inevitable estimation performance loss. To address this issue, we propose a novel virtual array interpolation-based algorithm for coprime array direction-of-arrival (DOA) estimation in this paper. The idea of array interpolation is employed to construct a virtual uniform linear array such that all virtual sensors in the non-uniform virtual array can be utilized, based on which the atomic norm of the second-order virtual array signals is defined. By investigating the properties of virtual domain atomic norm, it is proved that the covariance matrix of the interpolated virtual array is related to the virtual measurements under the Hermitian positive semi-definite Toeplitz condition. Accordingly, an atomic norm minimization problem with respect to the equivalent virtual measurement vector is formulated to reconstruct the interpolated virtual array covariance matrix in a gridless manner, where the reconstructed covariance matrix enables off-grid DOA estimation. Simulation results demonstrate the performance advantages of the proposed DOA estimation algorithm for coprime arrays.

In this letter, we propose a coprime array interpolation approach to provide an off-grid direction-of-arrival (DOA) estimation. Through array interpolation, a uniform linear array (ULA) with the same aperture is generated from the deterministic non-uniform coprime array. Taking the observed correlations calculated from the signals received at the coprime array, a gridless convex optimization problem is formulated to recover all the rows and columns of the unknown correlation matrix entries corresponding to the interpolated sensors. The optimized Hermitian positive semidefinite Toeplitz matrix functions as the covariance matrix of the interpolated ULA, which enables to resolve off-grid sources. Simulation results demonstrate that the proposed array interpolation-based DOA estimation algorithm achieves improved performance as compared to existing coarray-based DOA estimation algorithms in terms of the number of achievable degrees-of-freedom and estimation accuracy.

Spectrum sharing using a joint platform for radar and communication systems has attracted significant attention in recent years. In this paper, we propose a novel dual-function radar-communications (DFRC) strategy to embed quadrature amplitude modulation (QAM) based communication information in the radar waveforms by exploiting sidelobe control and waveform diversity. The proposed information embedding technique can support multiple communication receivers located in the sidelobe region. In addition to the information broadcasting, the developed approach enables multi-user access by allowing simultaneous transmission of distinct information streams to the communication receivers located in different directions. We prove that the proposed technique ensures a significant data rate enhancement compared to the existing techniques. Moreover, the developed DFRC strategy generalizes the mathematical framework of the existing sidelobe control-based information embedding techniques.

It is a promising solution for real-time multitarget tracking to implement particle cardinality balanced multitarget multi-Bernoulli (CBMeMBer) filters in hardware platforms. However, this solution is difficult to materialize since there is a contradiction between the time-varying number of Bernoulli intensity components in CBMeMBer filters and the limited number of particles in hardware platforms. Moreover, real-time hardware implementation requires a resampling procedure that is suitable for parallel processing, while the existing parallel resampling algorithms oversimplify this procedure, resulting in estimation performance degradation. In this paper, we propose an optimization algorithm of particle allocation to overcome the above-mentioned contradiction, and a parallel resampling algorithm to improve the estimation performance. Numerical experiments demonstrate the effectiveness of the proposed algorithms in multitarget tracking.

The house prices in China have been growing nearly twice as fast as national income over the last decade. Such an irrational soaring of house prices, not only puts the Chinese economy in danger, but also the country’s social interconnectedness and stability are at risk. Under this background, assuming that the affordable level of house prices from a consumer perspective is an uncertain parameter, which can be modelled, respectively, as symmetric and asymmetric triangular fuzzy number, several types of fuzzy linear regression models are introduced. A survey for the city of Shanghai was conducted, where three major policy and an equal number of non-policy variables have been selected to facilitate the analysis. The results derived show that the real estate tax policy had a key role for retaining the house prices in Shanghai in short run, whereas the two non-policies variables, annual household income and housing size, have even greater influence on consumers than policy variables. Additionally, it was observed that the family population and the affordable level of house prices are correlated negatively.

Coprime array offers a larger array aperture than uniform linear array with the same number of physical sensors, and has a better spatial resolution with increased degrees of freedom. However, when it comes to the problem of adaptive beamforming, the existing adaptive beamforming algorithms designed for the general array cannot take full advantage of coprime feature offered by the coprime array. In this paper, we propose a novel coprime array adaptive beamforming algorithm, where both robustness and efficiency are well balanced. Specifically, we first decompose the coprime array into a pair of sparse uniform linear subarrays and process their received signals separately. According to the property of coprime integers, the direction-of-arrival (DOA) can be uniquely estimated for each source by matching the super-resolution spatial spectra of the pair of sparse uniform linear subarrays. Further, a joint covariance matrix optimization problem is formulated to estimate the power of each source. The estimated DOAs and their corresponding power are utilized to reconstruct the interference-plus-noise covariance matrix and estimate the signal steering vector. Theoretical analyses are presented in terms of robustness and efficiency, and simulation results demonstrate the effectiveness of the proposed coprime array adaptive beamforming algorithm.

With the adoption of arbitrary and increasingly wideband signals, the design of modern radar systems continues to be limited by analog-to-digital converter technology and data throughput bottlenecks. Meanwhile, compressive sensing (CS) promises to reduce sampling rates below the Nyquist rate for some applications by constraining the set of possible signals. In many practical applications, detailed prior knowledge on the signals of interest can be learned from training data, existing track information, and/or other sources, which can be used to design better compressive measurement kernels. In this paper, we use an information-theoretic approach to optimize CS kernels for time delay estimation. The measurements are modeled via a Gaussian mixture model by discretizing the a priori probability distribution of the time delay. The optimal CS kernel that approximately maximizes the Shannon mutual information between the measurements and the time delay is then found by a gradient-based search. Furthermore, we also derive the Bayesian Cramer–Rao bound (CRB) for time delay estimation as a function of the CS kernel. In numerical simulations, we compare the performance of the proposed optimal sensing kernels to random projections and the Bayesian CRB. Simulation results demonstrate that the proposed technique for sensing kernel optimization can significantly improve performance, which is consistent with the Bayesian CRB versus signal-to-noise ratio (SNR). Finally, we use the Bayesian CRB expressions and simulation results to make conclusions about the usefulness of CS in radar applications. Specifically, we discuss CS SNR loss versus resolution improvement in SNR- and resolution-limited scenarios.

A coprime array has a larger array aperture as well as increased degrees-of-freedom (DOFs), compared with a uniform linear array with the same number of physical sensors. Therefore, in a practical wireless communication system, it is capable to provide desirable performance with a low-computational complexity. In this study, the authors focus on the problem of efficient direction-of-arrival (DOA) estimation, where a coprime array is incorporated with the idea of compressive sensing. Specifically, the authors first generate a random compressive sensing kernel to compress the received signals of coprime array to lower-dimensional measurements, which can be viewed as a sketch of the original received signals. The compressed measurements are subsequently utilised to perform high-resolution DOA estimation, where the large array aperture of the coprime array is maintained. Moreover, the authors also utilise the derived equivalent virtual array signal of the compressed measurements for DOA estimation, where the superiority of coprime array in achieving a higher number of DOFs can be retained. Theoretical analyses and simulation results verify the effectiveness of the proposed methods in terms of computational complexity, resolution, and the number of DOFs.

In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicati...

Direction-of-arrival (DOA), power, and achievable degrees-of-freedom (DOFs) are fundamental parameters for source estimation. In this paper, we propose a novel sparse reconstruction-based source estimation algorithm by using a coprime array. Specifically, a difference coarray is derived from a coprime array as the foundation for increasing the number of DOFs, and a virtual uniform linear subarray covariance matrix sparse reconstruction-based optimization problem is formulated for DOA estimation. Meanwhile, a modified sliding window scheme is devised to remove the spurious peaks from the reconstructed sparse spatial spectrum, and the power estimation is enhanced through a least squares problem. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of DOA estimation and power estimation as well as the achievable DOFs.

In this paper, we propose a robust adaptive beam-forming algorithm, where the interference-plus-noise covariance matrix is estimated by identifying and removing the desired signal component from the sample covariance matrix. For this purpose, we construct a desired signal subspace and its orthogonal subspace to identify the eigenvector of the sample covariance matrix corresponding to the desired signal. The adaptive beam-former is then designed using the estimated interference-plus-noise covariance matrix and the identified signal eigenvector. Because both are independent of the knowledge of the array geometry, the proposed adaptive beamformer is robust to array model mismatch. Simulation results demonstrate the effectiveness of the proposed robust adaptive beamforming algorithm.

In this paper, we study the portfolio selection problem considering transaction costs under multiple periods. For non-professional investors, it is a critical factor to choose an appropriate model among multiple portfolio selection models in investment. Based on the credibility measure, we formulate a multi-period polynomial portfolio selection model to gather the risk indicators involving variance, semi-variance, entropy, and semi-entropy, helping investors bet on assets. According to the polynomial goal programming (PGP) approach, investors can conquer the fields by combining apposite indicators to build appropriate models. Subsequently, an adjusted genetic algorithm on the foundation of the penalty function is designed to obtain the optimal solution of this multi-period model. The results indicate that the PGP method is suitable for investors to choose the model and assigns the proper models to investors with different risk preferences.

Radio astronomical observations are increasingly contaminated by radio frequency interference (RFI), rendering the development of effective RFI suppression techniques a pressing task. In practice, the existence of model mismatch makes the observing environment more challenging. In this paper, we develop a robust astronomical imaging method in the presence of RFI and model mismatch. The key contribution of the proposed method is the accurate estimation of the actual signal steering vector by maximizing the beamformer output power subject to a constraint that prevents the estimated steering vector from converging to the interference steering vectors. The proposed method is formulated as a quadratically constrained quadratic programming problem that can be solved using efficient numerical approaches. Simulation results demonstrate the effectiveness of the proposed method.

Parent-identifying schemes provide a way to identify causes from effects for some information systems such as digital fingerprinting and group testing. In this paper, we consider combinatorial structures for parent-identifying schemes. First, we establish an equivalent relationship between parent-identifying schemes and forbidden configurations. Based on this relationship, we derive probabilistic existence lower bounds for two related combinatorial structures, that is, $t$-parent-identifying set systems ($t$-IPPS) and $t$-multimedia parent-identifying codes ($t$-MIPPC), which are used in broadcast encryption and multimedia fingerprinting respectively. The probabilistic lower bound for the maximum size of a $t$-IPPS has the asymptotically optimal order of magnitude in many cases, and that for $t$-MIPPC provides the asymptotically optimal code rate when $t=2$ and the best known asymptotic code rate when $t\geq 3$. Furthermore, we analyze the structure of $2$-IPPS and prove some bounds for certain cases., Comment: 14 pages

In this paper, we study binary signature codes for the weighted binary adder channel (WbAC) and collusion-resistant multimedia fingerprinting. Let $A(n, t)$ denote the maximum size of a $t$ -signature code of length $n$ , and $A(n, w, t)$ denote the maximum size of a $t$ -signature code of length $n$ and constant-weight $w$ . First, we derive asymptotic and general upper bounds on $A(n,t)$ by relating signature codes to $B_{t}$ codes and bipartite graphs with large girth respectively, and also show the upper bounds are tight for certain cases. Second, we determine the exact values of $A(n,2,2)$ and $A(n,3,2)$ for infinitely many $n$ by connecting signature codes with $C_{4}$ -free graphs and union-free families, respectively. Third, we provide two explicit constructions for $t$ -signature codes which have efficient decoding algorithms and applications to two-level signature codes. Furthermore, we show from a geometric viewpoint that there does not exist any binary code with complete traceability for noisy WbAC and multimedia fingerprinting. A new type of signature codes with a weaker requirement than complete traceability is introduced for the noisy scenario.

In this paper, we present a fast Fourier transform (FFT)-based direction-of-arrival (DOA) estimation algorithm for coprime multiple-input multiple-output (MIMO) radar, where both the performance and the system overhead are well balanced. The coprime arrays are deployed in the framework of MIMO radar for sparse sensing, where an augmented virtual array can be generated from the difference coarray of sum coarray (DCSC) perspective. By implementing FFT on the second-order DCSC signals, it is shown that the DOAs can be retrieved from the resulting spatial response with an improved resolution. Moreover, neither the complex matrix operations nor the optimization problems are involved, indicating the efficient and hardware-friendly characteristic. Simulation results demonstrate the performance superiorities of the proposed algorithm.

In this paper, we propose an inverse discrete Fourier transform (IDFT)-based direction-of-arrival (DOA) estimation algorithm for coprime array, where both DOAs and power of the sources can be efficiently estimated with an increased number of degrees-of-freedom. Specifically, the IDFT is generalized to realize the transformation between the defined angular-spatial domain and the spatial domain. With such a relationship, the IDFT is directly implemented on the second-order virtual signals characterized by the angular-spatial frequencies, and it is proved that both the DOAs and the sources power can be estimated from the resulting spatial response. Meanwhile, the window method and the zero-padding technique are sequentially incorporated to alleviate the spectral leakage and improve the estimation accuracy, respectively. The direct IDFT solution presents a remarkably reduced computational complexity as compared to the existing algorithms exploiting coprime array, and the simulation results validate the effectiveness of the proposed DOA estimation algorithm.

The requirement for a high number of training samples is a key factor that limits the application of space-time adaptive processing (STAP) technique in practice. In this paper, by exploiting the low-rank property of the clutter covariance matrix, we formulate an atomic norm minimization problem for the sparse reconstruction of the clutter-plus-noise covariance matrix. By defining the corresponding atomic norm in the continuous angle-Doppler domain, the proposed technique not only substantially reduces the number of required training samples, but also avoids the intrinsic basis mismatch problem that is encountered in conventional sparse reconstruction methods. Simulation results verify the effectiveness of the proposed STAP method and its superiority over existing techniques.

Adaptive beamformers are sensitive to model mismatch, especially when the number of training samples is small or the training samples are contaminated by the signal component. In this paper, we consider an extreme scenario where only a single signal-contaminated snapshot is available for adaptive beamformer design. In such a case, we cannot perform direct inversion or eigen-decomposition of the rank-one sample covariance matrix required in adaptive beamformer design. To address this issue, we formulate a sparsity-constrained covariance matrix fitting problem to estimate the spatial spectrum distribution over the observed spatial domain, which is then used for adaptive beamformer design via the sparse reconstruction of the interference-plus-noise covariance matrix. Simulation results demonstrate the performance advantage of the proposed adaptive beamforming algorithm over other beamforming algorithms suitable for the single-snapshot scenario.

Modern radio telescopes commonly use antenna arrays to achieve high-resolution imaging, and various beamforming techniques have been developed in radio astronomy to generate dirty images. Because the manifold of a radio telescope array varies over time due to Earth rotation, beamformers are separately designed and implemented at each time epoch, and the resulting images are averaged over multiple epochs to form enhanced dirty images. Because astronomical scenes are typically sparse, we propose a new method through sparse reconstruction to obtain clean astronomical images. To reduce the computational complexity, a singular value decomposition based compressive sensing scheme is applied. The proposed method offers reduced computational complexity while maintaining the high quality of the sparse reconstruction. Unlike traditional beamforming techniques which require an additional deconvolution procedure for clean image formation, the proposed technique provides clean astronomical images directly with accurate estimation of the source position and intensity.

Dual-function radar-communications (DFRC) systems have emerged as a promising solution for spectrum sharing problem in recent years. In this paper, we propose a novel DFRC strategy by exploiting directional power control and waveform diversity. The proposed technique ensures the highest possible magnitude of the radar main beam resulting in an improved signal-to-noise ratio for the radar operation. This maximization objective is achieved while considering the pre-allocated or adjustable transmit energy requirement for radar and communication operations. The secondary communication objective enabling multi-user access is realized by transmitting distinct amplitude levels and phases towards different communication receivers located in the sidelobe region of radar. As an example, power allocation for different orthogonal frequency-division multiplexing (OFDM) subcarriers projected towards the radar main beam and the communication receivers is discussed by considering the frequency response of target returns. Simulation results illustrate the performance of the proposed technique.

Massive multiple-input multiple-output (MIMO) is a promising technique for 5G communications due to its superior spectrum and energy efficiencies. Despite its many advantages, the high number of antennas used in massive MIMO brings many challenges in practical implementations. Among them, the pilot overhead for downlink channel estimation becomes unaffordable in frequency division duplex (FDD) massive MIMO systems. In this paper, we exploit the available a priori knowledge of the channel to optimize the pilot design. By utilizing the low-rank nature of the channel matrix, we first derive the minimum number of pilot symbols required for perfect channel recovery. Further, under the general Gaussian mixture model for the channel vector, the pilot symbols are optimized to maximize the mutual information between the measurements of the user and the corresponding channel vector. Simulation results demonstrate the effectiveness of the proposed optimal pilot design for the downlink channel estimation in FDD massive MIMO systems.

Coprime arrays are capable of achieving an increased number of degrees-of-freedom by operating the coarray signals. However, their non-uniform coarrays prevent the full utilization of the available signals. To address this problem, a novel coarray interpolation-based direction-of-arrival (DOA) estimation algorithm via covariance matrix reconstruction is proposed in this paper. In particular, we formulate a gridless optimization problem to reconstruct the covariance matrix of the interpolated coarray, such that all the coarray observations are fully utilized. We also investigate the rotational invariance in the coarray domain to retrieve the DOAs. Neither spatial sampling nor spectrum searching is required in the proposed algorithm, indicating the capability of resolving off-grid DOAs. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

In previous work, we have exploited prior knowledge of target signals to design compressive measurement kernels for fast-time sub-Nyquist sampling. The kernels were designed to maximize the Shannon mutual information between the measurement and the target signals to be estimated. We showed that in cases where the radar system is resolution-limited rather than noise-limited, compressive sensing (CS) could provide a performance benefit for these applications, despite the signal-to-noise ratio (SNR) loss inherent in radio frequency compressive sensing. Hence, the largest performance gains were seen at high SNR. On the other hand, both the kernel optimization and the signal reconstruction are model based, meaning they are highly dependent on an accurate forward model of the sensing process. Because sub-Nyquist analog-to-digital conversion requires analog multiplication and lowpass filtering, the forward model must accurately quantify the signal operations in order for CS-based techniques to reach their full utility. In this paper, we model inaccuracies in the analog multiplier via third-order non-linear terms in the sensing model and quantify their effect on overall performance.

In this paper, we propose an information-theoretic compressive measurement scheme for the frequency hopping pattern recognition of frequency-hopping spread spectrum (FHSS) signals. We model the FHSS signals as a Gaussian mixture distribution, where each component is corresponding to one candidate frequency hopping channel. The compressive measurement scheme is then designed to maximize the Shannon mutual information between the compressive measurements and the frequency hopping pattern to be recognized. The approximated mutual information gradient with respect to the compressive measurement matrix is used in a gradient-based approach to search for the optimal compressive measurement scheme. Simulation results demonstrate that the proposed information-theoretic compressive measurement scheme can significantly improve the recognition performance of the frequency hopping pattern than random projections typically used in the classical compressive sensing theory.

Rough random theory, generally applied to statistics, decision-making, and so on, is an extension of rough set theory and probability theory, in which a rough random variable is described as a random variable taking “rough variable” values. In order to extend and enrich the research area of rough random theory, in this paper, the well-known probabilistic inequalities (Markov inequality, Chebyshev inequality, Holder’s inequality, Minkowski inequality and Jensen’s inequality) are proven for rough random variables, which gives a firm theoretical support to the further development of rough random theory. Besides, considering that the critical values always act as a vital tool in engineering, science and other application fields, some significant properties of the critical values of rough random variables involving the continuity and the monotonicity are investigated deeply to provide a novel analytical approach for dealing with the rough random optimization problems.

Astronomical signals are extremely weak and vulnerable to radio frequency interference. To meet the increasing demand for spectrum resources, development of spectrum-sharing and interference-immune radio astronomical technologies become very important and emerging. In this paper, we develop a robust astronomical imaging technique in the presence of wireless communication interference signals. The proposed technique effectively mitigates interference signals through robust adaptive beamforming which accounts for steering vector mismatching. Simulation results demonstrate the effectiveness of the proposed method.

In this paper, we propose a novel Vandermonde decomposition-based direction-of-arrival (DOA) estimation algorithm by using a coprime array, where increased number of degrees-of-freedom (DOFs) can be achieved in an off-grid manner. Specifically, the equivalent statistics corresponding to an augmented virtual uniform linear array are first derived from the coprime array received signals, and the resulting coprime coarray covariance matrix is capable to increase the DOFs. While the obtained coprime coarray covariance matrix follows a positive semi-definite Hermitian Toeplitz structure, Vandermonde decomposition can be incorporated to perform unique decomposition in the virtual domain. By matching the Vandermonde decomposition result and its theoretical version, closed-form solutions are formulated for estimating the DOA and power of each source. Simulation results demonstrate the effectiveness of the proposed DOA estimation algorithm.

A coprime array enables an increased number of degrees-of-freedom by deriving a non-uniform virtual array. However, existing work such as spatial smoothing fails to utilize all of the information provided by the coprime array, which results in performance loss. In this paper, we propose a novel coprime virtual array interpolation-based direction- of-arrival (DOA) estimation algorithm by Toeplitz matrix reconstruction. After investigating the challenges caused by the non-uniformity, we introduce the idea of array interpolation to construct a uniform linear virtual array, such that the information contained in the coprime virtual array can be fully utilized. According to the statistics of non-uniform coprime virtual array signal, we formulate a convex optimization problem for DOA estimation by reconstructing the covariance matrix of the equivalent received signals of the interpolated coprime virtual array under the Toeplitz constraint. Simulation results demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a novel coprime array adaptive beamforming algorithm based on virtual array spatial spectrum estimation to enhance the degrees-of-freedom (DOFs) capability. Specifically, the coprime array received signals are derived to virtual domain, where the spectrum estimation is performed on an equivalent virtual uniform linear array. Since the virtual array contains more virtual sensors than physical sensors, the DOFs capability is effectively enhanced. Meanwhile, the large array aperture offered by coprime array provides a higher resolution than uniform linear array. With the estimated sources' directions and interferences' power, the coprime array adaptive beamformer is designed by reconstructing the interference-plus-noise covariance matrix and desired signal steering vector. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

The Stinson-Wei traceability scheme (known as traceability scheme) was proposed for broadcast encryption as a generalization of the Chor-Fiat-Naor traceability scheme (known as traceability code). Cover-free family was introduced by Kautz and Singleton in the context of binary superimposed code. In this paper, we find a new relationship between a traceability scheme and a cover-free family, which strengthens the anti-collusion strength from $t$ to $t^2$, that is, a $t$-traceability scheme is a $t^2$-cover-free family. Based on this interesting discovery, we derive new upper bounds for traceability schemes. By using combinatorial structures, we construct several infinite families of optimal traceability schemes which attain our new upper bounds. We also provide a constructive lower bound for traceability schemes, the size of which has the same order with our general upper bound. Meanwhile, we consider parent-identifying set system, an anti-collusion key-distributing scheme requiring weaker conditions than traceability scheme but stronger conditions than cover-free family. A new upper bound is also given for parent-identifying set systems., 14 pages

In this paper, we propose a novel robust adaptive beamforming algorithm with direction-of-arrival (DOA) support for the coprirne array. Specifically, by using the property of coprirne number, we may estimate the DOAs of sources by matching two super-resolution spatial spectra of the pair of decomposed coprime subarrays. After that, the power of each source can be estimated via a covariance matrix joint estimation problem corresponding to the pair of decomposed coprime sub-arrays. Taking the estimated DOAs and their corresponding power as the support information, the interference-plus-noise covariance matrix for the coprime array can be reconstructed, from which the minimum variance distortionless response beamformer weight vector can be calculated. Simulation results show that the proposed adaptive beamforming algorithm is more robust to signal look direction mismatch than the existing algorithms.

In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

Quasi-stationary signals have been widely found in practical applications, which have time-varying second-order statistics while staying static within local time frames. In this paper, we develop a robust direction-of-arrival (DOA) estimation algorithm for quasi-stationary signals based on the Khatri–Rao (KR) subspace approach. A partly-calibrated array is considered, in which some of the sensors have an inaccurate knowledge of the gain and phase. In detail, we first develop a closed-form solution to estimate the unknown sensor gains and phases. The array is then calibrated using the estimated sensor gains and phases which enables the improved DOA estimation. To reduce the computational complexity, we also proposed a reduced-dimensional method for DOA estimation. The exploitation of the KR subspace approach enables the proposed method to achieve a larger number of degrees-of-freedom, i.e., more sources than sensors can be estimated. The unique identification condition for the proposed method is also derived. Simulation results demonstrate the effectiveness of the proposed underdetermined DOA estimation algorithm for quasi-stationary signals.

We study the problem of communicating over a discrete memoryless two-way channel using non-adaptive schemes, under a zero probability of error criterion. We derive single-letter inner and outer bounds for the zero-error capacity region, based on random coding, linear programming, linear codes, and the asymptotic spectrum of graphs. Among others, we provide a single-letter outer bound based on a combination of Shannon's vanishing-error capacity region and a two-way analogue of the linear programming bound for point-to-point channels, which in contrast to the one-way case, is generally better than both. Moreover, we establish an outer bound for the zero-error capacity region of a two-way channel via the asymptotic spectrum of graphs and show that this bound could be achieved for certain cases., 27 pages