Stacked intelligent metasurface (SIM) and dual-polarized SIM (DPSIM) enabled wave-domain signal processing have emerged as promising research directions for offloading baseband digital processing tasks and efficiently simplifying transceiver design. However, existing architectures are limited to employing SIM (DPSIM) for a single communication function, such as precoding or combining. To further enhance the overall performance of SIM (DPSIM)-assisted systems and achieve end-to-end (E2E) joint optimization from the transmitted bitstream to the received bitstream, we propose an SIM (DPSIM)-assisted E2E orthogonal frequency division multiplexing (OFDM) system, in which traditional communication tasks such as channel coding, modulation, precoding, combining, demodulation, and channel decoding are performed synchronously within the electromagnetic (EM) forward propagation. Furthermore, inspired by the idea of abstracting real metasurfaces as hidden layers of a neural network, we propose the EM neural network (EMNN) to enable the control of the E2E OFDM communication system. In addition, transfer learning is introduced into the model training, and a training and deployment framework for the EMNN is designed. Simulation results demonstrate that both SIM-assisted E2E OFDM systems and DPSIM-assisted E2E OFDM systems can achieve robust bitstream transmission under complex channel conditions. Our study highlights the application potential of EMNN and SIM (DPSIM)-assisted E2E OFDM systems in the design of next-generation transceivers.

To mitigate the issue of limited base station coverage caused by severe high-frequency electromagnetic wave attenuation, Extremely Large Reconfigurable Intelligent Surface (XL-RIS) has garnered significant attention due to its high beam gain. However, XL-RIS exhibits a narrower beam width compared to traditional RIS, which increases the complexity of beam alignment and broadcast. To address this problem, we propose a variable-width beam generation algorithm under the near-field assumption and apply it to the near-field codebook design for XL-RIS. Our algorithm can achieve beam coverage for arbitrarily shaped codeword regions and generate a joint codebook for the multi-XL-RIS system. The simulation results demonstrate that our proposed scheme enables user equipment (UE) to achieve higher spectral efficiency and lower communication outage probability within the codeword region compared to existing works. Furthermore, our scheme exhibits better robustness to codeword region location and area variations.

Scene understanding in adverse conditions, such as fog, snow, and night, is challenging due to the visual appearance degeneration. In this context, we propose a Cross-modal Semantic Compensation Adaptation method (CroPe) for scene understanding. Distinct from the existing methods, which only use the visual information to learn the domain-invariant features, CroPe establishes a visual-textual paradigm which provides textual semantic compensation for visual features, enabling the model to learn more consistent representations. We propose the Complementary Perceptual Text Generation (CPTG) module which generates a set of multi-level complementary-perceptive text embeddings incorporating both generalization and domain awareness. To achieve cross-modal semantic compensation, the Reverse Chain Text-Visual Fusion (RCTVF) module is developed. By the unified attention and reverse decoding chain, compensation information is successively fused to the visual features from the deep (semantic dense) to shallow (semantic sparse) features, maximizing compensation gain. CroPe yields competitive results under all adverse conditions and significantly improves the state-of-the-art performance by 6.5 mIoU for ACDC-Night dataset and 1.2 mIoU for ACDC-All dataset, respectively.

In this study, we investigate a two-user multiple-input multiple-output (MIMO) multiple access wiretap channel, in which the channel matrices of users exhibit rank deficiency. In this model, the legitimate transmitters and receiver are equipped with$M$and$N$antennas, respectively, while the eavesdropper has$K$antennas. The channel matrices of legitimate users are rank-deficient, whereas those between transmitters and the eavesdropper are full-rank, representing a worst-case scenario. We derive the optimal secure degrees of freedom (SDoF) for this model by combining two separate outer bounds. Considering the variations in the number of antennas at each node and the rank of user channels, we categorize our analysis into several regimes. We then present achievable schemes for each regime, grounded in spatial interference alignment, symbol extension and zero-forcing techniques.

The IETF 6TiSCH Working Group proposed a highly reliable and low-power industrial wireless network protocol stack, which integrates IEEE 802.15.4e Time Slotted Channel Hopping(TSCH) with IPv6 protocols. Scheduling is crucial in the 6TiSCH protocol stack, as it determines when a node sends or receives network packets on specific timeslots and channels. However, the current resource scheduling suffers from random timeslot selection, channel collisions, and high energy consumption, all of which degrade network performance. In this article, we propose an innovative scheduling function consisting of three core components. First, a multi-weight quality evaluation is proposed to enable precise timeslot selection, improving resource allocation efficiency. Second, an improved Q-Learning-based scheduling mechanism is proposed to generate optimal parameters for multi-weight evaluations, which is based on the global network state implemented at the edge router. Third, a pseudo-random channel selection strategy is proposed to effectively reduce channel congestion and interference. Extensive experimental results demonstrate that our function achieves a latency improvement of up to 55% compared to state-of-the-art low-latency scheduling, and along with an enhancement of up to 6% in the packet delivery ratio.

Elevator maintenance plays an important role in the safety and economic aspects of daily life, and the location of elevator maintenance sites is crucial in this scenario. A reasonable location can help reduce economic expenses and ensure the healthy operation of the system of maintenance sites. However, due to the lack of candidate location sets and unevenly distributed data layers, existing coverage siting methods have difficulties and challenges in solving this problem, and ignore the fairness of load among different sites. To address above problems, We propose a heuristic coverage siting method called MRM, which is based on multi-round MeanShift clustering with coverage radius variation. Experiments on a real elevator dataset with noise show that MRM not only achieves 99.21 % coverage with fewer sites but also achieves fairly good global fairness.