This paper proposes a novel IRS-aided framework for secure sensing, which aims to minimize the worst-case Cram´er-Rao Bound (WC-CRB) within an entire surveillance area by optimizing the IRS reflecting beamforming, enabling reliable and secure localization of arbitrary and unknown targets. Specifically, we first establish a general IRS-aided localization coverage model and derive the closed-form expression for the CRB of an arbitrary point, which reveals the relationship between the localization error bound and the Fisher information of the angle of arrival (AOA), angle of departure (AOD) and delay. To solve this challenging min-max optimization problem, we design efficient algorithms for different area types. For sector area, we first represent the Fisher information as trigonometric polynomials, then construct the WC-CRB coverage constraint as a non-negativity problem of these polynomials, and finally approximate it as an efficiently solvable semidefinite program (SDP). For the more challenging case of arbitrarily shaped area, we propose a two-tiered solution comprising a low-complexity heuristic algorithm based on geometric approximation and a high-performance detailed design that accurately solves the problem by decomposing the irregular boundary into multiple continuous segments. Numerical simulations validate the superiority of the proposed framework, demonstrating that our designs significantly outperform various benchmark schemes in terms of robustness and performance uniformity. The results show that the framework not only effectively reduces the WC-CRB but also achieves a highly uniform performance coverage across the entire area, providing a reliable and efficient solution for practical localization security applications.

Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, together with the positions of MAs, while guaranteeing the individual requirement of each EHR. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs’ requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

Conventional antenna arrays rely primarily on digital beamforming for spatial control. While adding more elements can narrow beamwidth and suppress interference, such scaling incurs prohibitive hardware and power costs. Rotatable antennas (RAs), which allow mechanical or electronic adjustment of element orientations, introduce a new degree of freedom to exploit spatial flexibility without enlarging the array. By dynamically optimizing orientations, RAs can substantially improve desired link alignment and interference suppression. This paper investigates RA-enabled multiple-input single-output (MISO) interference channels under co-channel spectrum sharing and formulates a weighted sum-rate maximization problem that jointly optimizes transmit beamforming and antenna orientations. To tackle this nonconvex problem, we develop an alternating optimization (AO) framework that integrates weighted minimum mean-square error (WMMSE)-based beamforming with Frank-Wolfe-based orientation updates. To reduce complexity, we further study orientation optimization under maximum-ratio transmission (MRT) and zero-forcing (ZF) beamforming schemes. For finite-resolution actuators, we construct spherical Fibonacci codebooks and design a cross-entropy method (CEM)-based algorithm for discrete orientation selection. Simulations show that integrating RAs with conventional beamforming markedly increases weighted sum-rate, with gains rising with element directivity. Under discrete orientation control, the proposed CEM algorithm consistently outperforms the nearest-projection baseline.

In this paper, we study employing movable components on both base station (BS) and intelligent reflecting surface (IRS) in a wideband terahertz (THz) multiple-input-single-output (MISO) system, where the BS is equipped with a movable antenna (MA) array and the IRS consists of movable subarrays. To alleviate double beam squint effect caused by the coupling of beam squint at the BS and IRS, we propose to maximize the minimal received power across a wide THz spectrum by delicately configuring the positions of MAs and IRS subarrays, which is highly challenging. By adopting majorization-minimization (MM) methodology, we develop an algorithm to tackle the aforementioned optimization. Numerical results demonstrate the effectiveness of our proposed algorithm and the benefit of utilizing movable components on the BS and IRS to mitigate double beam squint effect in wideband THz communications.

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Channel estimation (CE) of RIS device is a critical but also challenging issue for its development. The mainstream of existing CE methods is confined to the so-called cascaded channel (CscdChn) estimation scheme, which treats the multiplicative two-hop RIS channels as an effective one and measures it as a whole. This CscdChn training method suffers from severe double-fading attenuation loss, which significantly degrades the CE accuracy. In this paper, we propose a novel RIS-transmitting (RIS-TX) based CE scheme, which has lower pilot overhead than CscdChn scheme and effectively overcomes the double-fading curse via incorporating only one single transmit radio frequency (RF)-chain into RIS. We develop highly efficient gradient descent (GD) and penalty duality decomposition (PDD)-based solutions to resolve the pilot design task for the RIS-TX CE scheme, which is a difficult quartic optimization problem. Our designed pilot signal outperforms the discrete Fourier transform (DFT) sequence, which is reported to be optimal for CscdChn scheme. Besides, both theoretical analysis and numerical results demonstrate that our proposed RIS-TX scheme exhibits distinct performance characteristics as opposed to its CscdChn counterpart and yields superior accuracy when RIS device is not extremely large.

The rising reconfigurable intelligent surface (RIS) is a promising technology and a multitude of literature focuses on its channel estimation (CE), which is a critical and challenging task. Most existing works adopt a type of “cascaded channel” training scheme, where the “two-hop” channel cascaded by RIS is treated as one and measured by one shot. As unveiled by the latest researches, however, the concatenated channel suffers from severe fading loss and hence seriously degrades the CE precision. To resolve this difficulty, this paper proposes a novel training scheme. Specifically, being equipped with one transmit RF chain, the RIS broadcasts pilot signals to all other devices during the training period. This novel scheme can overcome double fading loss at a low hardware cost. The pilot design of the newly proposed training scheme is a difficult quartic optimization problem and we develop a gradient descent (GD) based solution to resolve it. Numerical results verify the effectiveness of our solution and demonstrate our training scheme can significantly outperform the traditional cascaded channel training method.

Reconfigurable intelligent surface (RIS) is envisioned as a promising technology to enhance future wireless communication systems. Very recently, a novel active RIS architecture has been proposed via introducing amplifiers into the reflecting elements. Although these embedded amplifiers can effectively extend the RIS coverage, they also bring non-negligible energy expenditure. To overcome this drawback, this paper proposes a novel sub-array based structure, which divides the entire RIS array into multiple sub-arrays with each being flexibly turned on/off. We aim to minimize the power consumption of the whole system via jointly activating sub-arrays and designing beamforming, which is highly challenging due to its combinatorial nature. Via inducing the group sparsity and leveraging the majorization-minimization (MM) approach, we develop an efficient solution to resolve this challenge. Numerical results demonstrate that our proposed sub-array structure can significantly reduce the power consumption compared to the conventional “all-on” scheme.

Internet of things (IoT) technology is an essential enabler to realize ubiquitous connections and pervasive intelli-gence for the future wireless communication system. The energy self-sustainability based on the wireless power transfer technique and the coexistence with heterogeneous networks will become two predominant attributes of IoT networks. In this paper we consider the system design in a context of coexistence of a wireless powered sensor network and a cellular system, both of which share common spectrum bandwidth and are assisted by intelligent reflecting surface (IRS). Specifically, the wireless sensors exploit the harvested energy from the cellular base station (BS) to transfer information to a data sink. We aim to design a cooperation scheme via jointly optimizing the time allocation of channel use, collaborative beamforming across networks and IRS phase-shifting control to improve the sensing network's throughput while guaranteeing the cellular users' quality of service. This design problem leads to a highly nonconvex and difficult mathematical optimization problem. Via utilizing the penalty-duality-decomposition (PDD) and successive convex approximation (SCA) methods, we have managed to develop an alternative optimization solution. Nu-merical results verify the effectiveness of our algorithm and demonstrate the benefits that come from the cooperative network design.

In this paper, Ant Colony Method is combined with Artificial Potential Field Method, to propose a new optimization method to enhance the robustness of t original algorithm. The core of new algorithm is aim to improve original path planning of ant colony method by using artificial potential field method, and to complete the allocation of primary data of ant colony algorithm. And the optimization method is applied to 3D track planning of UAV, and it is proved to be effective by simulation.