Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance indoor wireless communication and sensing performance. However, the construction of reliable received signal strength (RSS)-based fingerprint databases for RIS-assisted indoor positioning remains an open challenge due to the lack of realistic and spatially consistent channel modeling methods. In this paper, we propose a novel method with open-source code for generating RIS-assisted RSS fingerprint databases. Our method captures the complex RIS-assisted multipath behaviors by extended cluster-based channel modeling and the physical and electromagnetic properties of RIS and transmitter (Tx). And the spatial consistency is incorporated when simulating the fingerprint data collection across neighboring positions. Moreover, an effective sorting algorithm is proposed to solve the online synchronization issue, a closed-form RIS phase configuration strategy is proposed to improve the localization accuracy, and the modeling method of mutual coupling (MC) effect is provided. Extensive simulations are conducted to evaluate the fingerprint database generated by the proposed method. And the positioning performance on the database using different algorithms is analyzed, providing valuable insights for the system design.

Polarization adds an additional dimension to the radar signals, contributing to waveform diversity. Codesign of unimodular waveforms and filters with polarimetric power allocation for maximizing the signal-to-interference-plus-noise ratio (SINR) plays a key role in the polarimetric radar system. The problem is challenging to solve due to the nonconvex nature of the objective function and constraints, coupled with the interdependence of multiple variables. Existing methods mainly solve this problem by fixing the power allocation or relaxing the objective function and obtaining the receive filters with matrix inversion. We directly address this problem without matrix inversion by using the proposed adaptive unified manifold optimization (AUMO) framework. Specifically, a unified manifold space (UMS) is constructed to satisfy the constraints of unimodular waveform, filters, and power, transforming the problem to an unconstrained optimization problem over the manifold. To solve this problem, a parallel conjugate gradient (PCG) algorithm is derived. This algorithm can adaptively change the step size by exploring the local features of the manifold space. The experimental results based on the measured data show that the proposed method outperforms existing methods in terms of SINR gain and execution time.

The underwater acoustic sensor network (UASN) is a pivotal paradigm within the underwater Internet of Things, where multi-hop forwarding-based underwater data routing is essential for information acquisition. However, the dynamic nature of underwater network topology and the instability of underwater acoustic communication pose significant challenges to achieving efficient and reliable data transmission. In light of unreliable underwater environments and potential malicious attacks, studying trusted routing strategies for UASNs is crucial. This study introduces a context-aware trust routing scheme (CADTR) based on deep reinforcement learning (DRL), which integrates real-time environmental state perception with AI-driven routing decisions, thereby enhancing the reliability and robustness of data routing in dynamic and potentially hostile underwater scenarios. Firstly, a unified trust evidence framework is developed to strengthen the support of evidence experience for subsequent trust decisions by mapping multi-dimensional trust evidence to a unified scale. This framework is tightly coupled with the DRL agent, allowing the agent to evaluate and update trust levels based on real-time evidence. Secondly, a dynamic topology perception model and an underwater acoustic communication perception model are constructed to enable real-time perception of the interactive experience context. These models provide continuous input to the DRL agent, enabling it to adapt to topological changes and communication conditions dynamically. This facilitates priority experience sampling during the training process of the routing decision model, indirectly boosting model training efficiency and decision accuracy. Finally, the DRL agent learns optimal routing policies by interacting with the environment, leveraging the trust evidence and perception models to make informed decisions. Experimental results demonstrate that the proposed CADTR algorithm significantly improves the overall performance of the routing strategy in terms of packet delivery rate, energy utilization efficiency, and data transmission delay compared to the benchmark algorithms.

Joint design of waveforms and filters with adaptive power alloction has important applications in improving the target detection performance of polarization radar. The resulting problem is maximizing polarimetric radar target detection performance through unimodular waveform and filter design with power allocation, which is a non-convex and NP-hard problem. Existing methods mainly solve this problem by relaxing the objective function and obtaining filters with matrix inversion, which may introduce relaxation errors and high complexity. We construct a method based on unified product manifold to directly address this problem without matrix inversion. Specifically, a product manifold space is constructed to satisfy the constraints of unimodular waveform, filters and power. Then, an unconstrained problem is obtained by projecting the problem onto the unified product manifold. To solve this problem, a parallel conjugate gradient algorithm is derived. This algorithm can adaptively change the step size and fully explore the manifold space. Simulation results show that the proposed method can achieve better performance in shorter time than existing methods.

Resource allocation for enhancing the target capacity of multiple target tracking (MTT) with desired accuracies for given transmit power is the key issue in radar networks. Most existing methods solve this problem with heuristic evolutionary methods or convex relaxation methods with high computational cost, which lack the real-time adaptability for dynamic threat scenarios. To overcome this issue, we propose a real-time Adaptive Manifold Optimization (AMO) framework. This is achieved by utilizing the inherent real oblique characteristic of the power matrix constraints. Specifically, we construct a real oblique manifold that satisfies the constraints, enabling the problem to be rephrased as an unconstrained problem over the manifold space. Then, we derive a conjugate gradient algorithm for direct optimization of the problem. Simulation results demonstrate that the proposed method outperforms existing approaches in terms of target capacity, MTT accuracy and computational cost.

The joint design of waveforms and filters has key applications in polarimetric radar target detection. This letter studies the joint design of waveforms and filters to maximize the signal-to-interference-to-noise ratio (SINR) of polarimetric radar, which is a nonconvex and NP-hard problem. Most existing works solve it based on matrix inversion and problem relaxation, which inevitably introduce high complexity and relaxation errors. We notice that a unified manifold space naturally satisfies the constant modulus constraint (CMC) and the norm constraint. Based on this characteristic, we proposed a parallel manifold joint optimization (PMJO) method to solve it without relaxing the objective function. Specifically, the unified product manifold is constructed to satisfy both waveform and filter constraints. Subsequently, the problem is transformed into an unconstrained one by projecting it onto the product manifold space. Finally, a parallel conjugate gradient method is proposed to simultaneously optimize waveforms and filters, which can adaptively adjust the step size and fully explore the product manifold space. Simulation results show that our method can obtain a 2-dB performance advantage compared with the existing methods, while having a half-order of magnitude advantage in time complexity.

Intelligent reflecting surface (IRS) is a key technique for enhancing the performance of wireless communications. In this letter, we focus on the sum-path-gain maximization (SPGM) problem in an IRS-aided MIMO communication system, which is non-convex due to the constant modulus constraint. The existing works mainly include the relaxation method with relaxation error and the non-relaxation methods with high complexity. Different from the existing methods, we notice that constant modulus constraint can naturally satisfy the Riemannian manifold, and the deep learning method has strong non-convex learning ability. By exploiting these characteristics, the Riemannian gradient descent network (RGD-Net) is proposed. In the proposed method, we first project the non-convex SPGM problem to the Riemannian manifold. Then, the Riemannian gradient descent iterations are unfolded as the network layers. Finally, the step sizes of each layer are learned in unsupervised manner to ensure converged performance. Compared with the existing methods, the proposed method achieves higher spectral efficiency with lower computational cost.

Reconfigurable intelligent surfaces (RISs) are envisioned to be a disruptive wireless communication technique that is capable of reconfiguring the wireless propagation environment. In this paper, we study a free-space RIS-assisted multiple-input single-output (MISO) communication system in far-field operation. To maximize the received power from the physical and electromagnetic nature point of view, a comprehensive optimization, including beamforming of the transmitter, phase shifts of the RIS, orientation and position of the RIS is formulated and addressed. After exploiting the property of line-of-sight (LoS) links, we derive closed-form solutions of beamforming and phase shifts. For the non-trivial RIS position optimization problem in arbitrary three-dimensional space, a dimensional-reducing theory is proved. The simulation results show that the proposed closed-form beamforming and phase shifts approach the upper bound of the received power. The robustness of our proposed solutions in terms of the perturbation is also verified. Moreover, the RIS significantly enhances the performance of the mmWave/THz communication system.