In this letter, we propose an extended nested array (ENA) strategy, which relaxes the constraints on the two-side subarrays of the augmented nested array and allows the sensors further placed from the sparse uniform linear array. To guarantee the continuity of the difference co-array (DCA), several conditions are provided for specific design. Based on these rules, two ENAs, referred to as one-side extended nested array (OS-ENA) and two-side extended nested array (TS-ENA), are developed. The DCAs of both the OS-ENA and TS-ENA are hole-free and possess more consecutive lags than the existing nested-like arrays. Simulations verify the superiorities of the proposed ENAs with the spacial smoothing MUSIC algorithm used.

Mutual coupling effect and degree of freedom are two important factors to be considered in the planar sparse array (PSA) design. This paper presents two new planar sparse geometries, referred to as the half H array and ladder array, by introducing the effective difference co-array concept and redistributing the dense array elements of the open box array (OBA) to the outer diagonal straight line. The detection capacities of the new structures can be maintained at the same level as the OBAs. Furthermore, they possess much fewer sensor pairs closely spaced, leading to the mutual coupling between the adjacent sensors significantly reduced. Simulations of the direction-ofarrival estimation verify the superiorities of the proposed PSAs with the two-dimensional unitary ESPRIT algorithm.

With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), communication between users of different devices has become increasingly close. In practical scenarios, obstacles present between the transceiver may cause a deterioration in the quality of the received signals. Therefore, the reconfigurable intelligent surface (RIS) is employed to create virtual Line-of-Sight (LoS) channels in an IoT network. Specifically, this paper aims at maximizing the sum-rate of the RIS-assisted multi-user multiple input single output (MU-MISO) communication systems by jointly optimizing the phase shift matrix of the RIS and transmit beamforming. To solve the formulated non-convex problem, a piecewise-deep reinforcement learning (DRL) algorithm is proposed in this paper. Unlike the existing alternative optimization (AO) algorithms, the proposed algorithm avoids falling into the local optimal by using an exploration mechanism. Moreover, piecewise-DRL can reduce the action dimension, allowing the algorithm to obtain faster convergence. Simultaneously, this algorithm also ensures that the parameters of the two-part networks are updated to generate a larger system sum-rate by unsupervised joint optimization. Simulations in various circumstances reveal that the proposed approach is more robust and presents better stability and faster convergence than previous state-of-the-art algorithms while obtaining competitive performance.

This letter investigates the spectrum sharing between a multiple-input single-output (MISO) secure communication system and a multiple-input multiple-output (MIMO) radar system in the presence of one suspicious eavesdropper. We harness the detrimental radar interference, which traditionally impairs communication systems, as a strategic mechanism to jam the eavesdropper, thereby substantially enhancing the performance of secure communication. In particular, by considering the imperfect channel state information (CSI) for the user and eavesdropper, we present a robust joint design for the radar waveform and communication beamforming vector at the two systems, with the objective of maximizing the worst-case secrecy rate at the user, while ensuring the detection performance of the radar system. To tackle this challenging problem, we propose a two-layer robust cooperative algorithm based on the Charnes-Cooper transformation and the Cauchy-Schwarz inequality. Simulation results reveal the trade-off between secrecy rate and detection probability and highlight the pivotal role of such robust designs in facilitating secure coexistence systems.

L-shaped array (LA) is traditionally used as two one-dimensional uniform linear arrays (ULAs) for two-dimensional direction of arrival (DOA) estimations. With elevation and azimuth angles estimated separately, the detectable source number is strictly limited by the number of elements in the ULAs. To increase the detection ability, in this paper, we propose to exploit the diff-sum co-array (DSCA) concept on the L-shaped array. As such, a virtual element in the quadrants of DSCA can be generated by only one pair of sensors in the LA. Therefore, it can acquire much higher number of degrees of freedom for DOA estimation. To further reduce the redundancy of elements on the horizontal and vertical axes of DSCA, an element moving strategy is proposed. It is proved that the DSCA can remain intact with any one or more physical sensors relocated to the centrosymmetric positions. Based on this strategy, a generalized L-shape array concept and a new configuration referred to as generalized L-shaped array with odd-even locations (GLA-OEL) are developed. For GLA-OEL, the number of sensor pairs with small spacing is as small as one, which dramatically reduces the mutual coupling of adjacent elements. Besides, with the same number of sensors, the GLA-OEL can generate much larger virtual uniform rectangular array in the DSCA. Simulations verify the superiorities of the proposed array in terms of detection performance and estimation accuracy.

This paper presents a new two dimentional(2D) sparse array called the augmented L-shaped nested array based on the concept of fourth-order difference co-array. By properly choosing the relative positions of two L-shaped nested arrays, one could obtain a virtual planar array containing a large-scale hole-free uniform rectangular co-array. As a result, the degree of freedom could be greatly enhanced. Many theoretical properties are derived and simulations are included by considering the fourth-order cumulants of quasi-stationary signal to validate the superior performance of the proposed array in comparison with other 2D sparse arrays.