Recent years have witnessed growing interest in leveraging the bidirectional wave control of intelligent omni-surfaces (IOS) for integrated sensing and communication (ISAC) systems. Nevertheless, acquiring precise channel state information (CSI) is particularly challenging due to the inherent interplay between the electromagnetic properties of IOS and the dual functions of ISAC. In this paper, we propose a robust beamforming design for IOS-enabled ISAC systems. We jointly optimize the transmit beamforming, sensing waveform and IOS phase shifts to minimize the Cram´er-Rao bound (CRB) for sensing while ensuring communication reliability under an outage probability constraint. The resulting mixed-integer non-convex problem is tackled via a dual-loop penalty dual decomposition (PDD) algorithm. This framework solves the augmented Lagrangian (AL) subproblem in the inner loop, while the outer loop adjusts dual variables and penalty parameters to enforce constraint satisfaction. Simulation results demonstrate that our design substantially enhances sensing accuracy and communication reliability in scenarios with large CSI errors or fluctuating service requirements. Furthermore, it is shown that an optimal ratio between sensing and passive IOS elements must be maintained to balance energy utilization and spatial sampling capability in ISAC systems.

In this paper, we introduce a wireless-powered communication network (WPCN) which is composed of a base station (BS), an access point (AP), and an N-element intelligent reflecting surface (IRS). Specifically, several groups of internet of things (IoT) users will collect energy radiated from BS in the wireless energy transmission (WET) time scheduling, and transfer their collected information for AP in the wireless information transmission (WIT) time scheduling in the direct/reflecting way. Hybrid frequency-time division multiple access (HFTDMA) transmission protocol is adopted, and an optimization problem is formulated to maximize the sum throughput of the system. Since the optimization variables are strongly coupled together, the optimization problem is non-convex. Therefore, we utilize the Lagrangian function and Karush-Kuhn-Tucker (KKT) conditions to derive the optimal bandwidth allocation and the optimal WIT time scheduling. Then, the Lambert W function is applied to obtain the optimal WET time scheduling. Finally, we propose alternating direction method of multipliers based alternating optimization (AO) algorithm to acquire the optimal WET/WIT phase-shift matrices. Numerical results corroborate that the proposed algorithm significantly outperforms the benchmark schemes, and the trade-off between energy harvesting and information transmission plays the pivotal role in the IRS-assisted multigroup WPCNs.

Intelligent omni-surface (IOS) has been identified as an innovative technology to achieve omnidirectional wireless coverage for mobile users. However, due to the passive characteristics of the IOS, accurate channel state information (CSI) is difficult to acquire in IOS-assisted communication systems. In this article, we investigate a novel IOS-assisted multiuser multiple-input-single-output (MISO) downlink communication system. Specifically, the cascaded channel errors on both sides of the IOS are modeled separately to improve the flexibility and stability of the robust beamforming schemes. Considering the diverse practical communication requirements posed by the bounded and statistical CSI error models, we formulated the system sum-rate maximization and transmission power minimization problems for the worst-case and outage-constrained robust beamforming, respectively.$\boldsymbol {S}$-Procedure and Bernstein-type inequality are introduced to approximate the original nonconvex problems. Finally, we decompose the transformed problem into two subproblems and present an alternate optimization (AO) algorithm based on the success convex approximation (SCA) technique and the branch and bound method. Simulation results demonstrate that our robust beamforming schemes can effectively mitigate the system performance degradation caused by CSI error and enhance the downlink transmission robustness of the IOS-assisted communication system.

Reasonable robust beamforming design has been identified as a promising approach to enhance the adaptability, transmission efficiency and anti-interference capacity of the communication system. In this paper, we propose a beamforming design aimed at maximizing the sum-rate of communication in an intelligent omni-surface (IOS)-assisted multi-user multiple-input single-output (MISO) downlink communication system. Specifically, the presented scheme resolves the transmission rate optimization problem while considering constraints such as transmission power limitation at the base station (BS) and discrete phase shifts at the IOS. Additionally, we investigate S-Procedure to transform the produced semi-infinite objective function for addressing the non-convex problem due to the infinite inequality constraints. Then, we decompose the transformed problem into two subproblems to tackle the deep coupling between variables. Finally, a novel success convex approximation (SCA) algorithm is presented based on semi-definite programming (SDP) technology, branch and bound method to solve two subproblems iteratively until convergence. Simulation results demonstrate that our robust beamforming scheme can effectively mitigate the communication sum-rate degradation caused by channel state information (CSI) error and enhance the downlink transmission robustness of the IOS-assisted communication system.

Moving ground-target detection system is widely used to monitor illegal activities of pedestrians and vehicles. However, existing detection methods are restricted by the power consumption in hardware and are usually based on some single feature of the seismic signal, which leads to low detection accuracy and false alarms. To address these issues, we propose a new moving ground-target detection method for detecting the weak seismic signals generated by distant moving ground targets. This method combines an adaptive strategy and support vector machines (SVMs). Both time- and frequency-domain features of seismic signals are considered in the detection method. Additionally, we carry out field experiments to evaluate the performance of the proposed method. The results show that the proposed moving ground-target detection method can detect distant moving ground targets and avoid false alarms as many as possible, which indicates good performance.