In contrast to conventional fixed-position antennas (FPAs), movable antennas (MAs) are capable of actively exploiting the spatial channel variations to enhance the performance of wireless systems. In this paper, we investigate a movable antenna (MA) aided integrated sensing and covert communication (ISACC) system, where the MA movable regions are quantized into practical discrete positions. We aim to maximize the covert sum rate by jointly optimizing the BS transmit beamformers, the positions of both BS- and user-side MAs, and the radar receive equalizer, subject to constraints on radar echo signal-to-clutter-plus-noise ratio (SCNR) and covertness. To effectively tackle this problem, an efficient successive convex approximation (SCA) based alternating optimization (AO) algorithm is proposed, where the complicated log-fractional objective function is handled by fractional programming (FP) technique, and the discrete MA position variables are optimized by employing the penalty strategy. To obtain useful insights, we then focus on a simple single-user single-target (SUST) scenario, and demonstrate that the optimal Tx MA positions aim to de-correlate the BS-target and BS-Willie channels, whereas the optimal Tx MA positions can be flexibly chosen. Furthermore, we extend our work into the practical imperfect CSI scenario, in which a conservative approximation of the covertness constraint is derived, based on which the proposed AO algorithm is still applicable after some slight modifications. Numerical results demonstrate the superior performance of our proposed algorithms under both perfect CSI and imperfect CSI.

In this paper, we propose a general energy efficiency (EE) optimization framework for the hybrid analog-digital transceivers design in multi-hop communication systems. The analog and digital beamforming matrices are jointly optimized considering two kinds of practical power constraint models, i.e., sum power with box eigenvalue constraints (SPBECs) and multiple weighted power constraints (MWPCs), and unit-modulus constraints on analog beamforming matrices. For both the SPBECs and MWPCs cases, to tackle the challenging problem involving highly-coupled variables, an effective decoupling approach is first proposed. Specifically, a set of auxiliary variables are introduced to equivalently transform the original problem into a decoupled form with respect to the variables of each node. Then, for each node, we propose an efficient two-stage analog and digital beamforming optimization algorithm. To be specific, we optimize the analog beamforming matrices in the first stage by jointly exploiting the matrix-monotonic optimization framework and channel-alignment strategy. Then, we optimize the digital beamforming matrices in the second stage based on the multi-node water-filling methodology. Furthermore, in order to compute the parameters involved in the multi-node water-filling solutions for the SPBECs case, we propose two novel strategies, i.e., the Dinkelbach based strategy and the per-node penalty based strategy, which derive the parameters in closed-forms and offer clear physical interpretations. Moreover, the per-node penalty based strategy is effectively extended to the MWPCs case by additionally employing the Lagrangian duality theory. Simulation results demonstrate the superior performance and high efficiency of our proposed algorithms.

Movable antenna (MA) has emerged as a promising technology for enhancing communication performance over conventional fixed position antenna (FPA) by exploiting spatial channel variations. In this paper, we propose a general energy efficiency (EE) optimization framework for the MA-aided multi-user multiple-input multiple-output (MU-MIMO) downlink communications. We jointly optimize the precoding matrices and the positions of transmit and receive MAs considering two different types of power constraint models, i.e., the sum power constraint (SPC) and multiple weighted power constraints (MWPCs), and various physical constraints on MA positions. In both the SPC case and the MWPCs case, we optimize the MA positions by jointly employing the weighted minimum mean square error (WMMSE) and successive convex approximation (SCA) methodologies. As for the precoding matrices optimization, by exploiting the uplink-downlink duality of MU-MIMO systems, we transform the downlink EE optimization into their virtual uplink EE optimization counterparts. Then, we derive the optimal structures of the precoding matrices, where the involved optimal power allocations take the multi-user water-filling solutions. To compute the parameters of the multi-user water-filling solutions, by taking advantage of the underlying algebraic monotonicity of the problem, we propose three novel design strategies, i.e., the direct Dinkelbach based design, the modified Dinkelbach based design, and the bound-ware penalty based design. In contrast to conventional fractional programming (FP) based EE optimization methods, the proposed algorithms offer significantly lower computational complexities and explicit physical insights. Moreover, the simulation results demonstrate the superior performance and high efficiency of our proposed EE optimization algorithms.

Integrated sensing and communication (ISAC) has been envisioned as a prospective technology to enable ubiquitous sensing and communications in next-generation wireless networks. In contrast to existing works on reconfigurable intelligent surface (RIS) aided ISAC systems using conventional phased arrays (PAs), this paper investigates a frequency diverse array (FDA)-enabled RIS-aided ISAC system, where the FDA aims to provide a distance-angle-dependent beampattern to effectively suppress the clutter, and RIS is employed to establish high-quality links between the BS and users/target. We aim to maximize sum rate by jointly optimizing the BS transmit beamforming vectors, the covariance matrix of the dedicated radar signal, the RIS phase shift matrix, the FDA frequency offsets and the radar receive equalizer, while guaranteeing the required signal-to-clutter-plus-noise ratio (SCNR) of the radar echo signal. To tackle this challenging problem, we first theoretically prove that the dedicated radar signal is unnecessary for enhancing target sensing performance, based on which the original problem is much simplified. Then, we turn our attention to the single-user single-target (SUST) scenario to demonstrate that the FDA-RIS-aided ISAC system always achieves a higher SCNR than its PA-RIS-aided counterpart. Moreover, it is revealed that the SCNR increment exhibits linear growth with the BS transmit power and the number of BS receive antennas. In order to effectively solve this simplified problem, we leverage the fractional programming (FP) theory and subsequently develop an efficient alternating optimization (AO) algorithm based on symmetric alternating direction method of multipliers (SADMM) and successive convex approximation (SCA) techniques. Numerical results demonstrate the superior performance of our proposed algorithm in terms of sum rate and radar SCNR.

Both the reconfigurable intelligent surface (RIS) and the hybrid analog-digital antenna array have been envisioned as two cost-effective and promising technologies for achieving various types of functionality enhancement of future wireless systems. In this paper, we develop a framework for the multi-functional optimization in the RIS-aided hybrid analog-digital multiple-input multiple-output (MIMO) system, where a board of performance metrics related to diverse system functionalities are considered, such as capacity and mean square error (MSE) for information transmission (IT), Cramer-Rao bound (CRB) for radar sensing, harvested energy for energy harvesting (EH) and so on. Under this framework, we focus on two types of multi-functional optimization problems, namely, the multi-objective multi-functional optimization and the single-objective optimization subject to multi-functional constraints, and propose a unified low-complexity algorithm by separately optimizing analog and digital matrix variables. Specifically, for the multi-objective optimization, we firstly propose the numerical quadratic optimization based (QuaOpt-based) algorithm and the low-complexity channel alignment based algorithm to separately optimize analog matrices, including the RIS reflecting matrix, the analog precoder and the analog equalizer. Then, for the optimization of digital precoder, the numerical semidefinite programming (SDP)-based algorithm and the QuaOpt-based algorithm are proposed to iteratively solve the digital precoder optimization problem, while the matrix-monotonic optimization based algorithm derives the optimal closed-form solution in low computational complexity. Whereas for the single-objective optimization, the above proposed algorithms are still applicable by applying the Lagrangian duality theory to tackle the multi-functional constraints. Numerical simulation results reveal that the proposed low-complexity algorithm can achieve comparable performance to numerical algorithms.

The general notion of a metric space encompasses a diverse range of data types and accompanying similarity measures. Hence, metric search plays an important role in a wide range of settings, including multimedia retrieval, data mining, and data integration. With the aim of accelerating metric search, a collection of pivot-based indexing techniques for metric data has been proposed, which reduces the number of potentially expensive similarity comparisons by exploiting the triangle inequality for pruning and validation. However, no comprehensive empirical study of those techniques exists. Existing studies each offers only a narrower coverage, and they use different pivot selection strategies that affect performance substantially and thus render cross-study comparisons difficult or impossible. We offer a survey of existing pivot-based indexing techniques, and report a comprehensive empirical comparison of their construction costs, update efficiency, storage sizes, and similarity search performance. As part of the study, we provide modifications for two existing indexing techniques to make them more competitive. The findings and insights obtained from the study reveal different strengths and weaknesses of different indexing techniques, and offer guidance on selecting an appropriate indexing technique for a given setting.