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Compared to the uniform linear array (ULA), the sparse linear array (SLA) has many advantages such as a greater degrees of freedom (DOF), a higher resolution, and robustness against mutual coupling. The optimal placement of elements to achieve the maximum DOF in SLA has been an old problem in array signal processing. Finding the optimum placement is limited to an exhaustive computer search as yet. Therefore, some familiar SLAs (such as ‘nested’ and ‘coprime’ arrays) have been suggested to improve DOF although none of them necessarily can achieve the maximum DOF with a certain number of sensors. In this paper, first we have proposed a novel hole-free SLA that yields the minimum number of sensors (MSA) for the desired DOF. The optimization problem associated with such an array is a nonlinear binary optimization problem. Then, the problem is transformed into a binary linear programming (BLP) problem which could be solved exactly. Using the proposed method, a fast and efficient solution to the minimum redundancy array(MRA) is found. Finally, the simulation results show that the proposed array has a higher DOF compared to the other competitive methods for a given number of sensors, which is the maximum possible DOF. This can lead to a better target resolution and DOA estimation. [ABSTRACT FROM AUTHOR]

This paper is focused on the design of phase sequences with good (aperiodic) autocorrelation properties in terms of peak sidelobe level and integrated sidelobe level. The problem is formulated as a biobjective Pareto optimization forcing either a continuous or a discrete phase constraint at the design stage. An iterative procedure based on the coordinate descent method is introduced to deal with the resulting optimization problems that are nonconvex and NP-hard in general. Each iteration of the devised method requires the solution of a nonconvex min–max problem. It is handled either through a novel bisection or an FFT-based method respectively for the continuous and the discrete phase constraint. Additionally, a heuristic approach to initialize the procedures employing the $l_p$-norm minimization technique is proposed. Simulation results illustrate that the proposed methodologies can outperform some counterparts providing sequences with good autocorrelation features especially in the discrete phase/binary case. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the power allocation issue for cooperative-sensing-based code-division multiple access (CDMA) cognitive radio (CR) networks. We consider a network consisting of multiple secondary users (SUs) and a secondary base station (BS) implementing a two-phase protocol. In the first phase, censor-based cooperative spectrum sensing is carried out to detect the PU's presence. When the channel is estimated to be free, SUs transmit data in the uplink to the BS in the second phase by using CDMA. We optimize the sensing parameters and transmit power of SUs jointly to minimize the total energy consumption with the constraints on SUs' quality of service (QoS) and detection probability of the PU. This is a nonconvex problem, which we represent as a monotonic optimization problem and solve by means of monotonic programming. Furthermore, we study the separate optimization problem in which sensing parameters and transmit power are optimized to minimize the energy consumption of the first phase and the second phase disjointly. Numerical results show that the proposed joint optimization method saves energy consumption significantly in lower signal-to-noise ratios (SNRs). It can introduce as high as 3.9 and 8.7 dB joule energy saving per time slot in zero-sensing SNR under the same data rate constraints of 0.5 and 1 bit/s/Hz, respectively. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of code design to improve the detection performance of multi-static radar in the presence of clutter (i.e., a signal-dependent interference). To this end, we briefly present a discrete-time formulation of the problem as well as the optimal detector in the presence of Gaussian clutter. Due to the lack of analytical expression for receiver operation characteristic (ROC), code design based on ROC is not feasible. Therefore, we consider several popular information-theoretic criteria including Bhattacharyya distance, Kullback-Leibler (KL) divergence, J-divergence, and mutual information (MI) as design metrics. The code optimization problems associated with different information-theoretic criteria are obtained and cast under a unified framework. We propose two general methods based on Majorization-Minimization to tackle the optimization problems in the framework. The first method provides optimal solutions via successive majorizations whereas the second one consists of a majorization step, a relaxation, and a synthesis stage. Moreover, derivations of the proposed methods are extended to tackle the code design problems with a peak-to-average ratio power (PAR) constraint. Using numerical investigations, a general analysis of the coded system performance, computational efficiency of the proposed methods, and the behavior of the information-theoretic criteria is provided. [ABSTRACT FROM AUTHOR]