This paper introduces a spiking-aided wifi sensing network (SWS-Net), a novel hybrid neural architecture that integrates Spiking Neural Networks (SNNs) with conventional Artificial Neural Networks (ANNs) for robust WiFi-based indoor sensing. WiFi signals offer a low-cost and device-free solution for recognizing human activities, gestures, identities and etc. However, their susceptibility to multipath fading and environmental noise poses significant challenges. Inspired by the human brain’s capability to process noisy information, SWS-Net leverages the noise-resilient dynamics of spiking neurons alongside the feature extraction ability of ANNs. We present a theoretical analysis comparing the noise-handling capacities of SNNs and ANNs, and show how their combination yields both improved robustness and training efficiency. Experimental results across three WiFi sensing tasks demonstrate that SWS-Net consistently achieves higher accuracy and faster convergence compared to baseline models, validating its effectiveness in challenging indoor environments.

This paper presents WirelessGPT, a generative foundation model designed for multi-task learning in integrated sensing and communication (ISAC) systems. Built upon large-scale heterogeneous wireless datasets including Traciverse, Sensiverse, and DeepMIMO, WirelessGPT learns universal spatio-temporal-frequency representations through self-supervised pretraining with masked channel token prediction. The proposed architecture introduces a multi-scale patch embedding module to capture both local and global channel features, and a triple-axis attention encoder to jointly model temporal, spatial, and frequency-domain dependencies. After pretraining, the model can be efficiently fine-tuned via lightweight adapters for diverse downstream tasks such as channel estimation, channel prediction, human activity recognition, environment reconstruction, and object tracking. Experimental results show that WirelessGPT achieves superior accuracy and generalization under limited labeled data and dynamic ISAC conditions, outperforming traditional and task-specific models in low-SNR and high-mobility scenarios while maintaining efficient inference suitable for edge deployment. By unifying communication and sensing functionalities within a single generative backbone, WirelessGPT establishes a scalable paradigm for AI-native 6G systems, enabling shared representations that support heterogeneous wireless tasks.

Artificial intelligence is expected to play a central role in enabling future 6 G networks. Developing foundation models that support a wide range of downstream tasks is critical for advancing 6 G standardization. This paper proposes a general framework for compressing wireless channel state information (CSI) using pretrained wireless foundation models. The foundation model is pre-trained using self-supervised learning with a masked reconstruction objective, achieving a normalized mean square error on the order of$10^{-3}$during pretraining. The model is evaluated on a range of wireless communication and sensing tasks, including classification tasks where compressed CSI is directly used for prediction, and regression tasks that require full CSI reconstruction. For regression tasks such as massive MIMO CSI feedback, the pre-compressed output from the foundation model is used as an auxiliary input to the downstream compressor, effectively enhancing the reconstruction quality. Compared to the Type-I codebook with comparable number of feedback bits, our method improves SGCS by 16.22% and reduces NMSE by 93.24%. Additionally, it achieves comparable SGCS performance to the Type-II codebook while using only 29% of the feedback bits. Comparison with existing research further confirms the contribution of the foundation model's compressed output in improving CSI compression performance. For classification tasks such as WiFi-based human activity recognition and human identification, the compressed representations produced by the foundation model can be directly utilized without additional fine-tuning. These representations achieve over 97% accuracy, outperforming conventional AI-based methods even under higher compression ratios. These findings demonstrate that leveraging a pretrained wireless foundation model consistently enhances performance across both classification and regression tasks, underscoring its versatility and potential in wireless CSI processing.

Stance detection aims to determine the attitude expressed in text towards a given target. Zero-shot stance detection (ZSSD) has emerged to classify stances towards unseen targets during inference. Recent data augmentation techniques for ZSSD increase transferable knowledge between targets through text or target augmentation. However, these methods exhibit limitations. Target augmentation lacks logical connections between generated targets and source text, while text augmentation relies solely on training data, resulting in insufficient generalization. To address these issues, we propose an encoder-decoder data augmentation (EDDA) framework. The encoder leverages large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships. The decoder generates new samples based on these expressions using a semantic correlation word replacement strategy to increase syntactic diversity. We also analyze the generated expressions to develop a rationale-enhanced network that fully utilizes the augmented data. Experiments on benchmark datasets demonstrate our approach substantially improves over state-of-the-art ZSSD techniques. The proposed EDDA framework increases semantic relevance and syntactic variety in augmented texts while enabling interpretable rationale-based learning.

Cross-target stance detection (CTSD) is an important task, which infers the attitude of the destination target by utilizing annotated data derived from the source target. One important approach in CTSD is to extract domain-invariant features to bridge the knowledge gap between multiple targets. However, the analysis of informal and short text structure, and implicit expressions, complicate the extraction of domain-invariant knowledge. In this paper, we propose a Multi-Perspective Prompt-Tuning (MPPT) model for CTSD that uses the analysis perspective as a bridge to transfer knowledge. First, we develop a two-stage instruct-based chain-of-thought method (TsCoT) to elicit target analysis perspectives and provide natural language explanations (NLEs) from multiple viewpoints by formulating instructions based on large language model (LLM). Second, we propose a multi-perspective prompt-tuning framework (MultiPLN) to fuse the NLEs into the stance predictor. Extensive experiments results demonstrate the superiority of MPPT against the state-of-the-art baseline methods.

Aspect-based sentiment analysis is an important task in fine-grained sentiment analysis, which aims to infer the sentiment towards a given aspect. Previous studies have shown notable success when sufficient labeled training data is available. However, annotating adequate data is labor-intensive, which sets substantial barriers for generalizing the sentiment predictor to the new domain. Two main challenges exist in cross-domain aspect-based sentiment analysis. One challenge is acquiring the domain-invariant knowledge; the other challenge is mining the syntactic-related words towards the aspect-term. In this article, we propose a transformer-based semantic-primary knowledge transferring network (TSPKT) for cross-domain aspect-term sentiment analysis, which utilizes semantic-primary knowledge as a bridge to enable knowledge transfer across domains. Specifically, we first build an S-Graph from external semantic lexicons, and extract the semantic-primary knowledge from the S-Graph. Second, AoaGraphormer is proposed to learn the syntactically relevant words towards the aspect-term. Third, we extend the standard biLSTM classifier to fully integrate the semantic-primary knowledge by adding a novel knowledge-aware memory unit (KAMU) to the biLSTM cell. Extensive experiments on six cross-domain setups demonstrate the superiority of TSPKT against the state-of-the-art baseline methods.

Aspect-term sentiment analysis (ATSA) is an important task that aims to infer the sentiment towards the given aspect-terms. It is often required in the industry that ATSA should be performed with interpretability, computational efficiency and high accuracy. However, such an ATSA method has not yet been developed. This study aims to develop an ATSA method that fulfills all these requirements. To achieve the goal, we propose a novel Sentiment Interpretable Logic Tensor Network (SILTN). SILTN is interpretable because it is a neurosymbolic formalism and a computational model that supports learning and reasoning about data with a differentiable first-order logic language (FOL). To realize SILTN with high inferring accuracy, we propose a novel learning strategy called the two-stage syntax knowledge distillation (TSynKD). Using widely used datasets, we experimentally demonstrate that the proposed TSynKD is effective for improving the accuracy of SILTN, and the SILTN has both high interpretability and computational efficiency.

We propose the use of subcarrier delay spread based adaptive orthogonal frequency division multiplexing (OFDM) for communication through wideband waveguide channels. The technique has important communication applications when robots or vehicles are traveling inside waveguide channels such as tunnels or pipes. The delay spread in these mobile wideband waveguide channels varies significantly across subcarriers because propagation occurs in modes whose characteristics change significantly with frequency. Using knowledge of the delay spread in each subcarrier can then be used to adaptively select and avoid subcarriers with delay spread exceeding the cyclic prefix length. With this approach it is possible to create an OFDM system that is more efficient than systems satisfying the delay spread of all subcarriers. In this paper, we therefore propose subcarrier delay spread based adaptive OFDM to enhance the performance of OFDM systems operating in mobile wideband waveguide channels. To identify the delay spread of individual subcarriers we propose two methods. Using these methods, we provide simulation results to demonstrate the effectiveness of the technique. We also make use of experimental waveguide channel measurements to demonstrate the technique in realistic settings. Results show that performance can be significantly improved by using the proposed approach.