Constructing radio maps traditionally requires extensive site surveys with precise location labels, resulting in costly and time-consuming calibration. Conventional approaches derive labels from inertial measurement units (IMUs), but are constrained by device-level access permissions and the need for pre-installed IMU hardware. In this paper, we present a calibration-free radio map construction method that relies solely on Channel State Information (CSI) measurements, thereby obviating the need for location labels. Our key insight is to embed CSI data into a two-dimensional geographic space using a neural network, without any label information. The primary challenge is to preserve the real-world topology in the embedded space. To address this issue, we propose a novelTopology-Guided Manifold Learning (TGML)approach that learns a low-dimensional embedding through self-supervision based on its mapping to a topological map in the geographic space. Specifically, we introduce an embedding network that learns locally smoothed proximity, thus creating a compact low-dimensional representation in the latent space. We further employ a regularized transport method with a differentiable Sinkhorn distance to establish an optimal mapping between the latent and geographic spaces. We use the resulting mapping deviation to supervise the training of the embedding network, enabling continuous refinement through self-supervision. Experiments in an office environment show that TGML achieves an average localization error of 2.37m and a relative beam estimation error of 6.68%, outperforming state-of-the-art methods.

Unsupervised human motion segmentation (HMS) can be effectively achieved using subspace clustering techniques. However, traditional methods overlook the role of temporal semantic exploration in HMS. This paper explores the use of temporal vision semantics (TVS) derived from human motion sequences, leveraging the image-to-text capabilities of a large language model (LLM) to enhance subspace clustering performance. The core idea is to extract textual motion information from consecutive frames via LLM and incorporate this learned information into the subspace clustering framework. The primary challenge lies in learning TVS from human motion sequences using LLM and incorporating this information into subspace clustering. To address this, we determine whether consecutive frames depict the same motion by querying the LLM and subsequently learn temporal neighboring information based on its response. We then develop a TVS-integrated subspace clustering approach, incorporating subspace embedding with a temporal regularizer that induces each frame to share similar subspace embeddings with its temporal neighbors. Additionally, segmentation is performed based on subspace embedding with a temporal constraint that induces the grouping of each frame with its temporal neighbors. We also introduce a feedback-enabled framework that continuously optimizes subspace embedding based on the segmentation output. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art approaches on four benchmark human motion datasets.

Online Channel state information (CSI) acquisition is a challenging problem in massive multiple-input multipleoutput (MIMO) networks. Although the current 5G massive networks collect a large amount of CSI data for radio resource management, such data is discarded immediately after use, because the collected CSI data is not associated with location labels. Radio maps provide a promising solution for radio resource management by reducing online CSI acquisition. However, conventional approaches for radio map construction require location-labeled CSI data, which is challenging in practice. This paper investigates unsupervised angular power map construction based on large-scale CSI data collected in a massive MIMO network without location labels. A hidden Markov model (HMM) model is built to connect the hidden trajectory of a mobile with the CSI evolution of a massive MIMO channel. As a result, the mobile location can be estimated, enabling the construction of an angular power map. Based on reference signal received power (RSRP) data collected in a real 5G network, an average localization error of 17.9 meters can be achieved for location labeling of CSI. By using the angular power map constructed from the estimated locations, the proposed method achieves promising CSI prediction performance, demonstrating at least a 20% improvement compared to the traditional approach.

The traditional construction of radio maps demands extensive radio measurement data accompanied by precise location labels, thus necessitating considerable calibration. This article presents a calibration-free radio map construction method that relies solely on received signal strength (RSS) measurements, obviating the need for location labels. In this method, multiple mobile users traverse an indoor environment equipped with WiFi sensors, generating RSS measurement sequences. We estimate RSS collection locations along trajectories using prior knowledge of the layout, principles of signal propagation, and user mobility patterns. The challenge of estimating these locations based purely on RSS measurements is met by conforming to the intricate indoor layout and adhering to signal propagation models. Traditional calibration-free approaches often depend on inertial measurement units (IMUs) for real-time location estimation, but such reliance on IMUs is both cumbersome and encumbered by privacy concerns. We introduce a regional channel tracing (RCT) method, employing a signal subspace model and a subspace segmental clustering algorithm to classify RSS measurements and map them to regions. This method further includes a location tagging technique that, given the region labels, estimates RSS collection locations and regional path-loss models. Empirical results in an office setting demonstrate that our RCT-based radio map achieves performance comparable to advanced IMU-assisted methods.

The widespread deployment of wireless communication networks has catalyzed significant advancements in utilizing signal channs to address real-world challenges, such as vehicle trajectory reconstruction (VTR), drone trajectory planning, and network optimization. Existing methods primarily utilize time-difference-of-arrival (TDoA) measurements for vehicle localization. However, these methods require specialized decoding receivers capable of deciphering communication protocols, leading to increased application costs. received signal strength (RSS), a measure of wireless signal strength, can be recorded by any standard communication device, thus allowing RSS-based VTR to benefit from cost-effectiveness. Nevertheless, the inherently noisy and sporadic nature of RSS poses significant challenges for accurately reconstructing vehicle trajectories. This paper aims to utilize RSS measurements to reconstruct vehicle trajectories within a road network. We constrain the trajectories to comply with signal propagation rules and vehicle mobility constraints, thereby mitigating the impact of the noisy and sporadic nature of RSS data on the accuracy of trajectory reconstruction. The primary challenge involves exploiting latent spatial-temporal correlations within the noisy and sporadic RSS data while navigating the complex road network. To overcome these challenges, we develop an hidden Markov model (HMM)-based RSS embedding (HRE) technique that utilizes alternating optimization to search for the vehicle trajectory based on RSS measurements. This model effectively captures the spatial-temporal relationships among RSS measurements, while a road graph model ensures compliance with network pathways. Additionally, we introduce a maximum speed-constrained rough trajectory estimation (MSR) method to effectively guide the proposed alternating optimization procedure, ensuring that the proposed HRE method rapidly converges to a favorable local solution. The proposed method is validated using real RSS measurements from 5G NR networks in Chengdu and Shenzhen, China. The experimental results demonstrate that the proposed approach significantly outperforms state-of-the-art methods, even with limited RSS data.

Action segmentation serves as a pivotal component in comprehending videos, encompassing the learning of a sequence of semantically consistent action units known as actoms. Conventional methodologies tend to require a significant consumption of time for both training and learning phases. This paper introduces an innovative unsupervised framework for action segmentation in video, characterized by its fast learning capability and absence of mandatory training. The core idea involves splitting the video into distinct actoms, which are then merging together based on shared actions. The key challenge here is to prevent the inadvertent creation of singular actoms that attempt to represent multiple actions during the splitting phase. Additionally, it is crucial to avoid situations where actoms associated with the same action are incorrectly grouped into multiple clusters during the merging phase. In this paper, we present a method for calculating the similarity between adjacent frames under a subspace assumption. Then, we employ a local minimum searching procedure, which effectively splits the video into coherent actoms aligned with their semantic meaning and provides us an action segmentation proposal. Subsequently, we calculate a spatio-temporal similarity between actoms, followed by developing a merging process to merge actoms representing identical actions within the action segmentation proposals. Our approach is evaluated on four benchmark datasets, and the results demonstrate that our method achieves state-of-the-art performance. Besides, our method also achieves the optimal balance between accuracy and learning time when compared to existing unsupervised techniques. Code is available at https://github.com/y66y/SaM.

Constructing channel state information (CSI) maps may help wireless communications and localization. However, CSI map construction requires up-to-date CSI measurement data with location labels, which induces a huge challenge in practice. Conventional CSI embedding methods project the CSI to a low dimensional latent space which may not have a clear physical meaning for localization purpose. This paper attempts to extract the user locations from CSI measurements and recover the trajectory of the user in an outdoor vehicular communication scenario. A graph-based hidden Markov model (HMM) is constructed, and an alternating algorithm is developed to learn the model parameters and recover the trajectory of the user. A proof-of-concept experiment is conducted using real measurement data from 5G network and demonstrates a localization accuracy of 23 meters only based on reference signal received power (RSRP) measurements from a few nearby base stations, which is a promising result for CSI map construction.

Subspace-based models have been extensively employed in unsupervised segmentation and completion of human motion sequence (HMS). However, existing approaches often neglect the incorporation of temporal priors embedded in HMS, resulting in suboptimal results. This paper presents a subspace variety model for HMS, along with an innovative Temporal Learning of Subspace Variety Model (TL-SVM) method for enhanced segmentation and completion in HMS. The key idea is to segment incomplete HMS into motion clusters and extracting the subspace features of each motion through the temporal learning of the subspace variety model. Subsequently, the HMS is completed based on the extracted subspace features. Thus, the main challenge is to learn the subspace variety model with temporal priors when confronted with missing entries. To tackle this, the paper develops a spatio-temporal assignment consistency (STAC) constraint for the subspace variety model, leveraging temporal priors embedded in HMS. In addition, a subspace clustering approach under the STAC constraint is proposed to learn the subspace variety model by extracting subspace features from HMS and segmenting HMS into motion clusters alternatively. The proposed subspace clustering model can also handle missing entries with theoretical guarantees. Furthermore, the missing entries of HMS are completed by minimizing the distance between each human motion frame and its corresponding subspace. Extensive experimental results, along with comparisons to state-of-the-art methods on four benchmark datasets, underscore the advantages of the proposed method.

Cluster analysis constitutes a pivotal component of database mining, with DBSCAN being one of the most extensively employed algorithms in this domain. Nevertheless, DBSCAN is encumbered by several limitations, including challenges in processing high-dimensional datasets, a pronounced sensitivity to input parameters, and inconsistencies in generating reliable clustering outcomes. This paper presents a refined version of DBSCAN that utilizes the block-diagonal property of similarity graphs to enhance the clustering process. The core concept involves the construction of a graph that assesses the similarity among high-dimensional data points, capable of transformation into a block-diagonal form via an unknown permutation. This is followed by a cluster-ordering procedure that establishes the requisite permutation, thereby facilitating the straightforward identification of clustering structures through the recognition of diagonal blocks in the permuted graph. The principal obstacle addressed in this study is the construction of a graph that inherently possesses a block-diagonal structure, the permutation of this graph to actualize such a structure, and the autonomous identification of diagonal blocks within the permuted graph. To surmount these challenges, we initially devise a block-diagonal constrained self-representation model to create a similarity graph that exhibits a block-diagonal form post-permutation. A gradient descent-based methodology is proposed to resolve this problem effectively. Concurrently, we engineer a traversal algorithm, inspired by DBSCAN, that discerns clusters of high density within the graph and generates an enhanced cluster ordering. The attainment of a block-diagonal structure is then realized through permutation aligned with the traversal sequence, laying a robust foundation for both automated and interactive cluster analysis. Moreover, a novel split-and-refine algorithm is introduced to autonomously identify all diagonal blocks within the permuted graph, offering theoretical optimality under specific conditions. Extensive evaluations of our method across twelve rigorous real-world benchmark datasets affirm its superiority over contemporary state-of-the-art clustering techniques.

Indoor localization is important for many location- based services. The fundamental challenge is the high deployment cost for device, infrastructure, and calibration. This paper develops a blind calibration approach for received signal strength (RSS)-based localization. The essential idea is to employ a device that visits each region exactly once in an indoor area to complete a blind data collection process without recording the route, locations, and timestamps. Thus, the key challenge is to cluster the blind training data into groups and extract the key features to identify the location regions. Classical clustering algorithms fail to work as the data naturally appears as non-clustered due to mutipaths and noise. In this paper, an integrated segmentation and subspace clustering method is developed to exploit both the sequential data structure from the blind data collection process and the signal subspace structure due to the segmented propagation environments. Based on real measurements from an office space, the proposed scheme reduces the region localization error by roughly 50% from a weighted centroid localization (WCL) baseline. In addition, the performance is also comparable to k-nearest neighbor (KNN) and support vector machine (SVM) that require labeled data for calibration.

This paper studies the problem of predicting the spectrum efficiency (SE) for massive multiple-input multiple-output (MIMO) empowered 5G networks based on the reference signal received power (RSRP) collected from the drive test (DT). This problem is challenging because there is no precise model between the RSRP and the SE. The SE not only depends on the RSRP, which only captures the statistic of the channel, but also the beamforming strategy of the serving base station (BS) and the interference from the neighboring cells, which are not measured at the 5G client. This paper adopts a model-assisted data-driven approach to develop a machine learning model for the SE prediction. Specifically, a joint interference and SE prediction network is built, demonstrating prediction improvement over pure data-driven neural networks. In addition, a classification-assisted SE prediction network is constructed, which substantially reduces the prediction error at the low SE regime with marginally compromising the total prediction error. It is found that the model-assisted approach generally enhances the SE prediction accuracy by 2% approximately over a purely data-driven approach.