This paper investigates secure hybrid beamforming with subarray architectures in a millimeter-wave (mmWave) integrated sensing and communication (ISAC) system. To achieve secure communication against a potential eavesdropper, we design the sensing signal in a dual-functional form, serving both to detect the target and to generate artificial noise (AN) that disrupts the eavesdropper. With fixed subarray architectures, the security and sensing performance are analyzed, including the impact of imperfect channel state information (CSI). Moreover, the design of analog and digital beamformers is formulated as the problem of minimizing transmit power while satisfying the constraints of sensing beampattern and secrecy rate. To solve the non-convex problem, semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques are employed to transform the original problem and obtain the solution. For the scenario with imperfect CSI, the robust hybrid beamforming is designed based on S-procedure. Additionally, to optimally utilize spatial domain resources provided by multiple antennas, a dynamic subarray architecture is introduced, along with two dynamic subarray partitioning algorithms. This flexible architecture can be dynamically adjusted according to the CSI, ensuring a bidirectional alignment with the actual channels and the proposed scheme. Specifically, the greedy algorithm and the secrecy rate increment maximization (SRIM) algorithm are designed to partition the dynamic subarray, and the alternating optimization is utilized to further improve the quality of the solution. Numerical results demonstrate the effectiveness of the proposed schemes.

Orthogonal time frequency space (OTFS) modulation has been proposed to provide users with stable and reliable services in high-mobility scenarios. The sparse representation of channels in OTFS makes it possible to obtain accurate channel state information with a small number of pilots by compressed sensing (CS) algorithms. However, conventional CS algorithms in MIMO-OTFS channel estimation schemes assume that channel sparsity K is known, which is often not available in practical scenarios. In this letter, we propose a structured sparsity adaptive matching pursuit (SSAMP) algorithm for MIMO-OTFS channel estimation without the prior information of the channel sparsity K. On this basis, we further propose a modified structured sparsity adaptive matching pursuit algorithm to improve both the channel estimation accuracy and reconstruction speed. Simulation results show that the proposed algorithms are effective.

The combination of orthogonal-time-frequency-space (OTFS) modulation and millimeter wave (mmWave) hybrid beamforming can improve communication performance in air-to-ground high mobility systems. This letter studies a cross-domain channel estimation scheme in OTFS-based mmWave hybrid beamforming systems with off-grid parameters. First, time-domain and delay-Doppler domain signal models are established. Then, channel estimation problems are decomposed into two parallel sparse recovery problems. Finally, a sparse Bayesian learning-based block expectation-maximization algorithm is proposed to obtain the off-grid parameters. Simulation results show the preferable and robust performance of the proposed algorithm compared with existing channel estimation algorithms.

In order to minimize the weight and volume of the radio frequency equipment while alleviating the shortage of spectrum resources, electronic system integration has become a topical area in both civil and military fields, and the radar-communications integrated technology based on the time-modulated array (TMA) has also been proposed due to its low complexity and easy reconfiguration property. The TMA has power losses caused by sideband signals radiation, which is inherent drawback. However, existing TMA power loss optimization methods are not suitable for radar-communication integration, because of the existence of multiple harmonic components. In this paper, a closed-form expression of TMA power losses for radar-communications integration is derived and the sideband radiation is optimized. Firstly, some preliminaries about TMA are shown. Then, the radar-communications integration strategy based on TMA is introduced. Moreover, a closed-form expression of TMA power losses caused by sideband signals radiation is derived, and the direct power losses optimization method is proposed based on the derived results. Finally, the theoretical results are validated by simulations.

To alleviate the shortage of spectral resources as well as to reduce the weight, volume, and power consumption of wireless systems, joint communication-radar (JCR) systems have become a focus of interest in both civil and military fields. JCR systems based on time-modulated arrays (TMAs) constitute an attractive solution as a benefit of their high degree of beam steering freedom, low cost, and high accuracy. However, their sideband radiation results in energy loss, which is an inherent drawback. Hence the energy-efficiency optimization of TMA-based JCR systems is of salient importance, but most of the existing TMA energy-efficiency optimization methods do not apply to JCR systems. To circumvent their problems, a single-sideband structure is designed for flexibly reconfigurable energy-efficient TMA beam steering. First, some preliminaries on single-sideband TMAs are introduced. Then, a closed-form expression is derived for characterizing the energy efficiency. Finally, the theoretical results are validated by simulations.

Millimeter-wave (mmWave) integrated sensing and communications (ISAC) equipped with multiple-input multiple-output (MIMO) antenna arrays can simultaneously deliver high data rates and sensing performance. However, the angular resolution and estimation accuracy of existing mmWave MIMO-orthogonal frequency division multiplexing (OFDM) ISAC schemes remain constrained by the limited physical aperture. In this letter, we propose an enhanced target sensing framework that fuses virtual array synthesis and tensor processing for mmWave MIMO-OFDM ISAC systems. Firstly, we model the target echo of the OFDM ISAC signal as a third-order tensor, which includes the angle, delay, and Doppler parameters of the targets. Then, we apply CANDECOMP/PARAFAC decomposition to the third-order tensor to obtain factor matrices containing the target parameters. By exploiting the fact that the target shares the same physical angle relative to both transmit and receive arrays in a monostatic sensing system, the Kronecker product is employed to combine the transmit and receive factor matrices, which enables the construction of a virtual array for extending the array aperture. Numerical results demonstrate that the estimation accuracy and spatial resolution of the proposed method are superior to state-of-the-art baselines, due to our specially-tailored algorithm design and virtual aperture construction.