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In nonadaptive combinatorial group testing (CGT), it is desirable to identify a small set of up to d defectives from a large population of n items with as few tests (i.e. large rate) and efficient identifying algorithm as possible. In the literature, d -disjunct matrices ( d -DM) and d -separable matrices ( d -SM) are two classical combinatorial structures having been studied for several decades. It is well-known that a d -DM provides a more efficient identifying algorithm than a d -SM, while a d -SM could have a larger rate than a d -DM. In order to combine the advantages of these two structures, in this paper, we introduce a new notion of strongly d -separable matrix ( d -SSM) for nonadaptive CGT, which is sandwiched between d -DM and d -SM. We show that a d -SSM has the identifying algorithm more efficient than a d -SM, as well as the largest rate no less than a d -DM. In addition, the general bounds on the largest rate of d -SSM are established. Moreover, by the random coding method with expurgation, we derive an improved lower bound on the largest rate of 2-SSM which is much higher than the best known result of 2-DM.

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.

Cover-free families have been widely studied over recent decades due to their applications in numerous subjects. In this paper, we introduce the concept of ( s , t ; d ) -union–intersection-bounded families, which is a generalization of t -cover-free families. We provide a general upper bound on the maximum size of an ( s , t ; d ) -union–intersection-bounded family, and show a probabilistic lower bound for the case that the ground set is sufficiently large. They have the same order of magnitude for certain cases. We also discuss the applications of ( s , t ; d ) -union–intersection-bounded families in broadcast encryption, and derive a better upper bound for ( 1 , t ; d ) -union–intersection-bounded families (also known as superimposed distance codes).

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.

This paper is dedicated to providing a solution framework for the bi-objective vehicle routing problem with simultaneous delivery and pickup which aims to minimize the comprehensive cost as well as maximize the recycling revenue in each round of dispatching. Considering that the real weights of goods to be recycled from the customers are fixed but cannot be precisely given while making one-time routing scheme in the daily operations, the pickup demands are considered as fuzzy numbers, and accordingly a fuzzy chance-constraint programming model for obtaining a prior route solution from the perspective of risk is presented. Afterwards, by demonstrating the continuity and monotonicity of the objective functions, a two-phase approach based on the operational law of the inverse credibility distribution is introduced to solve the model, including translating it into an equivalent nonlinear programming model and resorting to the existing algorithms for obtaining optimal solutions afterwards. Subsequently, in order to validate the performance of the proposed approach and in consideration of the inherent complexity of the vehicle routing problem, a two-phase-based genetic algorithm and the conventional fuzzy simulation-based genetic algorithm are designed and compared by a clothes delivery and pickup problem. The computational results demonstrate that the proposed solution framework is competitive in effectiveness and efficiency, and the parameter analyses provide some suggestions for guidance.

Adaptive beamformers are sensitive to model mismatch, especially when the desired signal is present in the training data. In this paper, we reconstruct the interference-plus-noise covariance matrix in a sparse way, instead of searching for an optimal diagonal loading factor for the sample covariance matrix. Using sparsity, the interference covariance matrix can be reconstructed as a weighted sum of the outer products of the interference steering vectors, the coefficients of which can be estimated from a compressive sensing (CS) problem. In contrast to previous works, the proposed CS problem can be effectively solved by use of a priori information instead of using l"1-norm relaxation or other approximation algorithms. Simulation results demonstrate that the performance of the proposed adaptive beamformer is almost always equal to the optimal value.