相关论文: Multi-Station WiFi CSI Sensing Framework Robust to…
Wireless indoor localization using predictive models with received signal strength information (RSSI) requires proper calibration for reliable position estimates. One remedy is to employ synthetic labels produced by a (generally different)…
Accurately mapping large-scale cropland is crucial for agricultural production management and planning. Currently, the combination of remote sensing data and deep learning techniques has shown outstanding performance in cropland mapping.…
Clustered Federated Multi-task Learning (CFL) has emerged as a promising technique to address statistical challenges, particularly with non-independent and identically distributed (non-IID) data across users. However, existing CFL studies…
Massive MIMO has been regarded as one of the key technologies for 5G wireless networks, as it can significantly improve both the spectral efficiency and energy efficiency. The availability of high-dimensional channel side information (CSI)…
Indoor localization becomes a raising demand in our daily lives. Due to the massive deployment in the indoor environment nowadays, WiFi systems have been applied to high accurate localization recently. Although the traditional model based…
Intelligent reflecting surface (IRS) is a promising technology for enhancing wireless communication systems. It adaptively configures massive passive reflecting elements to control wireless channel in a desirable way. Due to hardware…
Massive multiple-input multiple-output (MIMO) systems offer significant potential to enhance wireless communication performance, yet accurate and timely channel state information (CSI) acquisition remains a key challenge. Existing works on…
Semantic segmentation is a core computer vision problem, but the high costs of data annotation have hindered its wide application. Weakly-Supervised Semantic Segmentation (WSSS) offers a cost-efficient workaround to extensive labeling in…
Deep learning (DL)-based channel state information (CSI) feedback has shown promising potential to improve spectrum efficiency in massive MIMO systems. However, practical DL approaches require a sizeable CSI dataset for each scenario, and…
In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned…
The paper presents a novel Wi-Fi fingerprinting system that uses Channel State Information (CSI) data for fine-grained pedestrian localization. The proposed system exploits the frequency diversity and spatial diversity of the features…
Multi-Label Remote Sensing Image Classification (MLRSIC) has received increasing research interest. Taking the cooccurrence relationship of multiple labels as additional information helps to improve the performance of this task. Current…
Wireless sensing is a promising technology for future wireless communication networks to realize various application services. Wireless local area network (WLAN)-based localization approaches using channel state information (CSI) have been…
Massive MIMO basestations, operating with frequency-division duplexing (FDD), require the users to feedback their channel state information (CSI) in order to design the precoding matrices. Given the powerful capabilities of deep neural…
Radio channel state information (CSI) measured with many receivers is a good resource for localizing a transmit device with machine learning with a discriminative model. However, CSI localization is nontrivial when the radio map is…
Scarcity of pixel-level labels is a significant challenge in practical scenarios. In specific domains like industrial smoke, acquiring such detailed annotations is particularly difficult and often requires expert knowledge. To alleviate…
In the era of 5G communication, the knowledge of channel state information (CSI) is crucial for enhancing network performance. This paper explores the utilization of language models for spatial CSI prediction within MIMO-OFDM systems. We…
This paper explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using WiFi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver…
The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands. However, the dynamic spectrum-sharing mechanisms often…
We investigate model based classification with partially labelled training data. In many biostatistical applications, labels are manually assigned by experts, who may leave some observations unlabelled due to class uncertainty. We analyse…