Mining Limited Data Sufficiently: A BERT-inspired Approach for CSI Time Series Application in Wireless Communication and Sensing

Channel State Information (CSI) is the cornerstone in both wireless communication and sensing systems. In wireless communication systems, CSI provides essential insights into channel conditions, enabling system optimizations like channel compensation and dynamic resource allocation. However, the high computational complexity of CSI estimation algorithms necessitates the development of fast deep learning methods for CSI prediction. In wireless sensing systems, CSI can be leveraged to infer environmental changes, facilitating various functions, including gesture recognition and people identification. Deep learning methods have demonstrated significant advantages over model-based approaches in these fine-grained CSI classification tasks, particularly when classes vary across different scenarios. However, a major challenge in training deep learning networks for wireless systems is the limited availability of data, further complicated by the diverse formats of many public datasets, which hinder integration. Additionally, collecting CSI data can be resource-intensive, requiring considerable time and manpower. To address these challenges, we propose CSI-BERT2 for CSI prediction and classification tasks, effectively utilizing limited data through a pre-training and fine-tuning approach. Building on CSI-BERT1, we enhance the model architecture by introducing an Adaptive Re-Weighting Layer (ARL) and a Multi-Layer Perceptron (MLP) to better capture sub-carrier and timestamp information, effectively addressing the permutation-invariance problem. Furthermore, we propose a Mask Prediction Model (MPM) fine-tuning method to improve the model's adaptability for CSI prediction tasks. Experimental results demonstrate that CSI-BERT2 achieves state-of-the-art performance across all tasks.

LoFi: Vision-Aided Label Generator for Wi-Fi Localization and Tracking

Wi-Fi localization and tracking has shown immense potential due to its privacy-friendliness, wide coverage, permeability, independence from lighting conditions, and low cost. Current methods can be broadly categorized as model-based and data-driven approaches, where data-driven methods show better performance and have less requirement for specialized devices, but struggle with limited datasets for training. Due to limitations in current data collection methods, most datasets only provide coarse-grained ground truth (GT) or limited amount of label points, which greatly hinders the development of data-driven methods. Even though lidar can provide accurate GT, their high cost makes them inaccessible to many users. To address these challenges, we propose LoFi, a vision-aided label generator for Wi-Fi localization and tracking, which can generate ground truth position coordinates solely based on 2D images. The easy and quick data collection method also helps data-driven based methods deploy in practice, since Wi-Fi is a low-generalization modality and when using relevant methods, it always requires fine-tuning the model using newly collected data. Based on our method, we also collect a Wi-Fi tracking and localization dataset using ESP32-S3 and a webcam. To facilitate future research, we will make our code and dataset publicly available upon publication.

KNN-MMD: Cross Domain Wi-Fi Sensing Based on Local Distribution Alignment

As a key technology in Integrated Sensing and Communications (ISAC), Wi-Fi sensing has gained widespread application in various settings such as homes, offices, and public spaces. By analyzing the patterns of Channel State Information (CSI), we can obtain information about people's actions for tasks like person identification, gesture recognition, and fall detection. However, the CSI is heavily influenced by the environment, such that even minor environmental changes can significantly alter the CSI patterns. This will cause the performance deterioration and even failure when applying the Wi-Fi sensing model trained in one environment to another. To address this problem, we introduce a K-Nearest Neighbors Maximum Mean Discrepancy (KNN-MMD) model, a few-shot method for cross-domain Wi-Fi sensing. We propose a local distribution alignment method within each category, which outperforms traditional Domain Adaptation (DA) methods based on global alignment. Besides, our method can determine when to stop training, which cannot be realized by most DA methods. As a result, our method is more stable and can be better used in practice. The effectiveness of our method are evaluated in several cross-domain Wi-Fi sensing tasks, including gesture recognition, person identification, fall detection, and action recognition, using both a public dataset and a self-collected dataset. In one-shot scenario, our method achieves accuracy of 93.26%, 81.84%, 77.62%, and 75.30% in the four tasks respectively. To facilitate future research, we will make our code and dataset publicly available upon publication.

Invar-RAG: Invariant LLM-aligned Retrieval for Better Generation

Retrieval-augmented generation (RAG) has shown impressive capability in providing reliable answer predictions and addressing hallucination problems. A typical RAG implementation uses powerful retrieval models to extract external information and large language models (LLMs) to generate answers. In contrast, recent LLM-based retrieval has gained attention for its substantial improvements in information retrieval (IR) due to the LLMs' semantic understanding capability. However, directly applying LLM to RAG systems presents challenges. This may cause feature locality problems as massive parametric knowledge can hinder effective usage of global information across the corpus; for example, an LLM-based retriever often inputs document summaries instead of full documents. Moreover, various pre-trained tasks in LLMs introduce variance, further weakening performance as a retriever. To address these issues, we propose a novel two-stage fine-tuning architecture called Invar-RAG. In the retrieval stage, an LLM-based retriever is constructed by integrating LoRA-based representation learning to tackle feature locality issues. To enhance retrieval performance, we develop two patterns (invariant and variant patterns) and an invariance loss to reduce LLM variance. In the generation stage, a refined fine-tuning method is employed to improve LLM accuracy in generating answers based on retrieved information. Experimental results show that Invar-RAG significantly outperforms existing baselines across three open-domain question answering (ODQA) datasets. Code is available in the Supplementary Material for reproducibility.

Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning

To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantification is particularly important in personalized federated learning (PFL), as participating clients typically have small local datasets, making it difficult to unambiguously determine optimal model parameters. Bayesian PFL (BPFL) methods can potentially enhance calibration, but they often come with considerable computational and memory requirements due to the need to track the variances of all the individual model parameters. Furthermore, different clients may exhibit heterogeneous uncertainty levels owing to varying local dataset sizes and distributions. To address these challenges, we propose LR-BPFL, a novel BPFL method that learns a global deterministic model along with personalized low-rank Bayesian corrections. To tailor the local model to each client's inherent uncertainty level, LR-BPFL incorporates an adaptive rank selection mechanism. We evaluate LR-BPFL across a variety of datasets, demonstrating its advantages in terms of calibration, accuracy, as well as computational and memory requirements.

Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving

Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous driving (AD). However, inference model trained from data in a particular geographical region faces poor generalization when applied in other regions due to inter-city data domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization by collaborative privacy-preserving training over distributed datasets from different cities. Unfortunately, it suffers from slow convergence because data from different cities are with disparate statistical properties. Going beyond existing HFL methods, we propose a Gaussian heterogeneous HFL algorithm (FedGau) to address inter-city data heterogeneity so that convergence can be accelerated. In the proposed FedGau algorithm, both single RGB image and RGB dataset are modelled as Gaussian distributions for aggregation weight design. This approach not only differentiates each RGB image by respective statistical distribution, but also exploits the statistics of dataset from each city in addition to the conventionally considered data volume. With the proposed approach, the convergence is accelerated by 35.5\%-40.6\% compared to existing state-of-the-art (SOTA) HFL methods. On the other hand, to reduce the involved communication resource, we further introduce a novel performance-aware adaptive resource scheduling (AdapRS) policy. Unlike the traditional static resource scheduling policy that exchanges a fixed number of models between two adjacent aggregations, AdapRS adjusts the number of model aggregation at different levels of HFL so that unnecessary communications are minimized. Extensive experiments demonstrate that AdapRS saves 29.65\% communication overhead compared to conventional static resource scheduling policy while maintaining almost the same performance.

An overview of domain-specific foundation model: key technologies, applications and challenges

The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models, addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific foundation models, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized foundation models.

CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network

In recent years, Wi-Fi sensing has garnered significant attention due to its numerous benefits, such as privacy protection, low cost, and penetration ability. Extensive research has been conducted in this field, focusing on areas such as gesture recognition, people identification, and fall detection. However, many data-driven methods encounter challenges related to domain shift, where the model fails to perform well in environments different from the training data. One major factor contributing to this issue is the limited availability of Wi-Fi sensing datasets, which makes models learn excessive irrelevant information and over-fit to the training set. Unfortunately, collecting large-scale Wi-Fi sensing datasets across diverse scenarios is a challenging task. To address this problem, we propose CrossFi, a siamese network-based approach that excels in both in-domain scenario and cross-domain scenario, including few-shot, zero-shot scenarios, and even works in few-shot new-class scenario where testing set contains new categories. The core component of CrossFi is a sample-similarity calculation network called CSi-Net, which improves the structure of the siamese network by using an attention mechanism to capture similarity information, instead of simply calculating the distance or cosine similarity. Based on it, we develop an extra Weight-Net that can generate a template for each class, so that our CrossFi can work in different scenarios. Experimental results demonstrate that our CrossFi achieves state-of-the-art performance across various scenarios. In gesture recognition task, our CrossFi achieves an accuracy of 98.17% in in-domain scenario, 91.72% in one-shot cross-domain scenario, 64.81% in zero-shot cross-domain scenario, and 84.75% in one-shot new-class scenario. To facilitate future research, we will release the code for our model upon publication.

FedRC: A Rapid-Converged Hierarchical Federated Learning Framework in Street Scene Semantic Understanding

Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for world-wide distributed autonomous driving (AD) vehicles (e.g., Tesla). Its inference model faces poor generalization issue due to inter-city domain-shift. Hierarchical Federated Learning (HFL) offers a potential solution for improving TriSU model generalization, but suffers from slow convergence rate because of vehicles' surrounding heterogeneity across cities. Going beyond existing HFL works that have deficient capabilities in complex tasks, we propose a rapid-converged heterogeneous HFL framework (FedRC) to address the inter-city data heterogeneity and accelerate HFL model convergence rate. In our proposed FedRC framework, both single RGB image and RGB dataset are modelled as Gaussian distributions in HFL aggregation weight design. This approach not only differentiates each RGB sample instead of typically equalizing them, but also considers both data volume and statistical properties rather than simply taking data quantity into consideration. Extensive experiments on the TriSU task using across-city datasets demonstrate that FedRC converges faster than the state-of-the-art benchmark by 38.7%, 37.5%, 35.5%, and 40.6% in terms of mIoU, mPrecision, mRecall, and mF1, respectively. Furthermore, qualitative evaluations in the CARLA simulation environment confirm that the proposed FedRC framework delivers top-tier performance.

Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need

The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications. However, this technological leap comes with a substantial increase in energy consumption, presenting a significant challenge. To improve the energy efficiency of 5G networks, it is imperative to develop sophisticated models that accurately reflect the influence of base station (BS) attributes and operational conditions on energy usage.Importantly, addressing the complexity and interdependencies of these diverse features is particularly challenging, both in terms of data processing and model architecture design. This paper proposes a novel 5G base stations energy consumption modelling method by learning from a real-world dataset used in the ITU 5G Base Station Energy Consumption Modelling Challenge in which our model ranked second. Unlike existing methods that omit the Base Station Identifier (BSID) information and thus fail to capture the unique energy fingerprint in different base stations, we incorporate the BSID into the input features and encoding it with an embedding layer for precise representation. Additionally, we introduce a novel masked training method alongside an attention mechanism to further boost the model's generalization capabilities and accuracy. After evaluation, our method demonstrates significant improvements over existing models, reducing Mean Absolute Percentage Error (MAPE) from 12.75% to 4.98%, leading to a performance gain of more than 60%.