Channel State Information (CSI) provides a detailed description of the wireless channel and has been widely adopted for Wi-Fi sensing, particularly for high-precision indoor positioning. However, complete CSI is rarely available in real-world deployments due to hardware constraints and the high communication overhead required for feedback. Moreover, existing positioning models lack mechanisms to detect when users move outside their trained regions, leading to unreliable estimates in dynamic environments. In this paper, we present FPNet, a unified deep learning framework that jointly addresses channel feedback compression, accurate indoor positioning, and robust anomaly detection (AD). FPNet leverages the beamforming feedback matrix (BFM), a compressed CSI representation natively supported by IEEE 802.11ac/ax/be protocols, to minimize feedback overhead while preserving critical positioning features. To enhance reliability, we integrate ADBlock, a lightweight AD module trained on normal BFM samples, which identifies out-of-distribution scenarios when users exit predefined spatial regions. Experimental results using standard 2.4 GHz Wi-Fi hardware show that FPNet achieves positioning accuracy above 97% with only 100 feedback bits, boosts net throughput by up to 22.92%, and attains AD accuracy over 99% with a false alarm rate below 1.5%. These results demonstrate FPNet’s ability to deliver efficient, accurate, and reliable indoor positioning on commodity Wi-Fi devices.

T-cell receptors (TCRs) recognize peptide-MHC (pMHC) complexes through intricate structural interactions, which is a core component of adaptive immunity. However, the diverse and cross-reactive nature of TCRs poses great challenges for accurate prediction of TCR-epitope interactions, hampering the advancement and broad application of TCR-related therapies. Here, we present SageTCR, a bi-level graph neural network (GNN) framework that leverages structural data to predict TCR-pMHC binding possibilities. Harnessing the pretrained language models, SageTCR encodes detailed structural arrangement at both residue-level and atomic-level and effectively integrates the bimodal representations via attention mechanisms. To tackle the deficiency of experimental structures, we explore comprehensive data augmentation strategies to enrich the training and increase the generalizability while concurrently preserving the characteristic TCR-pMHC diagonal binding mode. SageTCR demonstrates superior performance compared to six methods with different deep learning architectures. Furthermore, SageTCR offers the interpretability by identifying and focusing on the conformational features of pivotal contact residues on the interface, which can provide valuable insights for TCR engineering and immunotherapy design.

Recent advances in artificial intelligence offer groundbreaking alternatives to conventional codebook-based channel state information (CSI) feedback techniques. Confronted with the influx of CSI data from simulations and real-world environments, leveraging neural networks to mine valuable insights poses significant training costs and technical challenges for base station (BS) manufacturers. To address this, we propose a third-party platform serving as a CSI knowledge repository and feedback model hub, reducing training expenses and addressing technical issues for various BS manufacturers. However, tailoring training for each manufacturer’s model may lead to proprietary information leaks and inefficient resource utilization. In response, we present “CSI Meta-knowledge Support”, a cutting-edge CSI feedback network deployment strategy using Learngene, enabling seamless transfer of CSI meta-knowledge across heterogeneous networks. This method captures a Learngene unit enriched with vital CSI meta-knowledge during comprehensive training sessions, serving as a plug-and-play prior to facilitate swift convergence and efficient local fine-tuning for manufacturers. The approach introduces adaptable and scalable CSI feedback network configurations, emphasizing reusability, cost-effectiveness, and resource management while safeguarding intellectual property. Our tests demonstrate enhanced performance, reduced training sample demands, and faster convergence relative to conventional techniques.

Deep learning has brought about a revolution in the design of the channel state information (CSI) feedback module in wireless communications. However, designing the optimal neural network (NN) architecture for CSI feedback can be a laborious and time-consuming process, and manual design can be prohibitively expensive for customized NNs tailored to different scenarios. To tackle this challenge, this paper proposes the use of neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures. By employing automated machine learning and gradient-descent-based NAS, an efficient and cost-effective architecture design process is achieved, requiring less expert experience and design time, thus lowering the design threshold. The proposed approach leverages implicit scene knowledge and integrates it into the scenario customization process in a data-driven manner, fully exploiting the potential of deep learning in a given scenario. To address the issue of excessive search, early stopping and elastic selection mechanisms are employed, further enhancing the proposed scheme. The experimental results demonstrate that the generated architecture, known as Auto-CsiNet, outperforms manually-designed models in terms of reconstruction performance (achieving approximately 14% improvement) and complexity (reducing by approximately 50%), highlighting the effectiveness of the NAS-based automatic scheme. Furthermore, the paper analyzes the impact of the scenario on the NN architecture and capacity.

Deep learning has revolutionized the design of channel state information (CSI) feedback modules in wireless communication. However, designing an optimal neural network (NN) architecture for CSI feedback can be laborious and time-consuming, especially for customized networks targeting different scenarios. To address this challenge, this paper proposes the use of Neural Architecture Search (NAS) to automatically generate scenario-specific CSI feedback neural network architectures. By employing automated machine learning and gradient-based NAS, an efficient and cost-effective architecture design process is achieved with reduced reliance on expert knowledge and design time, thus lowering the design threshold. This approach leverages implicit scenario knowledge and integrates it into the scenario customization process in a data-driven manner, fully harnessing the potential of deep learning in a given scenario. Experimental results demonstrate that the generated architecture called Auto-CsiNet outperforms manually designed models in terms of reconstruction performance (improvement by approximately 14%) and complexity reduction (approximately 50%), highlighting the effectiveness of NAS-based automated solutions.

For frequency division duplex (FDD) systems, downlink channel state information (CSI) feedback is essential. Deep learning-based auto-encoder (AE) structures have shown promise in reducing feedback overhead. However, designing a super-large AE network to handle the CSI of all scenarios is not practical. A more practical approach is to divide the CSI dataset by region/scenario and use multiple simple AE networks. However, this method requires high memory capacity, making it unsuitable for low-end user equipment (UE). In this paper, we propose a new UE-friendly framework based on multi-tasking mode. Our framework, called single-encoder-to-multiple-decoders (S-to-M), uses multi-task-learning to design multiple independent AEs into a joint architecture with a shared encoder that corresponds to multiple task-specific decoders. We also integrate GateNet as a classifier to enable the base station to autonomously select the right task-specific decoder for the subregion. Our experiments on a simulated multi-scenario CSI dataset show that our proposed S-to-M framework outperforms other benchmark modes by significantly reducing model complexity and UE memory consumption.

In the intelligent monitoring of the hypertensive patients, it's necessary to assess their treatment effect and give corresponding diagnostic feedback automatically. This paper proposed a hybrid decision support system (DSS) combining several data mining techniques using an improved weighted majority voting scheme (iWMV). The mass health data of hypertensive patients were used as data source of the data mining techniques, and iWMV was used to produce a proper final judgement on patients' control condition on the basis of the individual classifier results. The proposed system was trained and evaluated using data from 167 hypertensive patients. Performance analysis showed that the hybrid system could reach classification rate (CR) of 95.34% and kappa coefficient (KC) of 92.54%, much better than systems with a single classification algorithm or combining using the simple weighted majority voting scheme (WMV). Moreover, the proposed DSS showed high stability.

With the unprecedented aging of population, chronic disease become a serious problem in modern medical area. Recent advances of wireless technology make the health monitoring in home to be more convenient for the chronic disease. Reducing the influence of the interference from the other wireless equipment is one of the most important problems in wireless physical parameter monitoring device in healthcare field, as reliable data transmitting is vital in medical care. In this paper, we propose a novel interference avoidance strategy that can greatly reduce the influence of the interference from other wireless equipment to wireless physical parameter monitoring devices in the hospital or home environment. Experimental results show that the strategy has good effect on interference avoidance and reduce the package loss rate.

Recent advances of hardware and integrated chip have made the body-implanted medical device reality. In this paper, a chronic disease management system with implanted devices based on Wireless Sensor Networks (WSN) was proposed for people with chronic illnesses. We first described the architecture of the chronic disease management system. Then through analyzing the system architecture, we explained the advantages of our new system and pointed out the main issue of the short lifetime of the implanted medical devices. Then a cooperative strategy was proposed in the particular system to decrease the energy consumption, which extended the entire lifetime of all implanted devices. Finally, we evaluated the performance of our strategy with simulations.