No abstract

This paper studies antenna selection for an energyefficient millimeter-wave (mmWave) dual-functional radarcommunication (DFRC) system, communicating with multiple users while sensing a point target. To obtain a hardware efficient DFRC system, we propose an optimization problem that minimizes the Cramér-Rao bound (CRB) for tracking the target while guaranteeing the communication quality of service (QoS), and selects the best active antennas using 0 norm. To address the non-convexity of the proposed problem, we relax the radar and communication constraints using semidefinite programming (SDP) and employ an 1,∞ norm, which promotes group sparsity, instead of the 0 norm. Numerical results show that the proposed approach obtains better performance in terms of the CRB compared to the conventional approach while reducing the number of radio frequency (RF) chains and total power consumption at the transmitter with almost the same computational complexity.

In this paper, we address the problem of recovering point sources from two dimensional low-pass measurements, which is known as super-resolution problem. This is the fundamental concern of many applications such as electronic imaging, optics, microscopy, and line spectral estimation. We assume that the point sources are located in the square [0, 1] 2 with unknown locations and complex amplitudes. The only available information is low-pass Fourier measurements band-limited to integer square [−fc, fc] 2 . The signal is estimated by minimizing Total Variation (TV) norm, which leads to a convex optimization problem. It is shown that if the sources are separated by at least 1.68/fc, there exist a dual certificate that is sufficient for exact recovery.

In this paper, we propose a novel estimator for pilot-aided orthogonal frequency division multiplexing (OFDM) channels in an additive Gaussian and impulsive perturbation environment. Due to sensor failure which might happen because of man-made noise, a number of measurements in high rate communication systems is often corrupted by impulsive noise. High power impulsive noise is generally an obstacle for OFDM systems as valuable information will be completely lost. To overcome this concern, an objective function based on a penalized atomic norm minimization (PANM) is provided in order to promote the sparsity of time dispersive channels and impulsive noise. The corresponding dual problem of the PANM is then converted to tractable semidefinite programming. It has shown that one can simultaneously estimate the time dispersive channels in a continuous dictionary and the location of impulsive noise using the dual problem. Several numerical experiments are carried out to evaluate the performance of the proposed estimator.Index Terms-Pilot-aided OFDM systems, impulsive noise, atomic norm, semidefinite programming.

The aim of two-dimensional line spectral estimation is to super-resolve the spectral point sources of signal from time samples. In many associated applications such as radar and sonar, due to cut-off and saturation regions in electronic devices, some of the number of samples are corrupted by spiky noise. To overcome this problem, we present a new convex program to simultaneously estimate spectral point sources and spiky noise in two dimension. To prove uniqueness of the solution, it is sufficient to show that a dual certificate exists. Construction of the dual certificate imposes a mild condition on the separation of the spectral point sources. Also, the number of spikes and detectable sparse sources are shown to be a logarithmic function of the number of time samples. Simulation results confirm the conclusions of our general theory.