Search

Accurate indoor localization remains a significant challenge due to the complex nature of indoor environments. This paper proposes a novel method for constructing a radio map (RM) based on Kernel density estimation (KDE) and human trajectories (HT) to enhance indoor localization accuracy. The proposed method utilizes historical HT data in RM construction to capture the spatial variability and complexity of indoor environments, which is crucial for accurate localization. By employing KDE, kernel density maps are generated, identifying high-density regions where additional interpolated fingerprints are strategically placed to improve localization accuracy. In contrast to the conventional method of uniformly placing interpolated points (IPs), the proposed approach better models natural walking patterns and trajectories, thereby enhancing the uniqueness and accuracy of user position identification. Through extensive experiments with various HT patterns, the proposed KDE-RM optimization method consistently outperforms the conventional approach of evenly distributed IPs using Kriging and inverse distance weighting interpolation by up to 36.4%. This demonstrates the effectiveness and potential of the proposed method as a valuable tool for enhancing indoor localization. [ABSTRACT FROM AUTHOR]

Abstract Cellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles within the aerial domain pose significant challenges to ensuring the safe operation of UAVs. This paper introduces a novel trajectory planning scheme, namely the double-map assisted UAV approach, which leverages deep reinforcement learning to address these challenges. The mission execution time, wireless connectivity, and obstacle avoidance are comprehensively modeled and analyzed in this approach, leading to the derivation of a novel joint optimization function. By utilizing an advanced technique known as dueling double deep Q network (D3QN), the objective function is optimized, while employing a mechanism of prioritized experience replay strengthens the training of effective samples. Furthermore, the connectivity and obstacle information collected by the UAV during flight are utilized to generate a map of radio and environmental data for simulating the flying process, thereby significantly reducing operational costs. The numerical results demonstrate that the proposed method effectively circumvents obstacles and areas with weak connections during flight, while also considering mission completion time.

Cellular-connected unmanned aerial vehicles (UAVs), which have the potential to extend cellular services from the ground into the airspace, represent a promising technological advancement. However, the presence of communication coverage black holes among base stations and various obstacles within the aerial domain pose significant challenges to ensuring the safe operation of UAVs. This paper introduces a novel trajectory planning scheme, namely the double-map assisted UAV approach, which leverages deep reinforcement learning to address these challenges. The mission execution time, wireless connectivity, and obstacle avoidance are comprehensively modeled and analyzed in this approach, leading to the derivation of a novel joint optimization function. By utilizing an advanced technique known as dueling double deep Q network (D3QN), the objective function is optimized, while employing a mechanism of prioritized experience replay strengthens the training of effective samples. Furthermore, the connectivity and obstacle information collected by the UAV during flight are utilized to generate a map of radio and environmental data for simulating the flying process, thereby significantly reducing operational costs. The numerical results demonstrate that the proposed method effectively circumvents obstacles and areas with weak connections during flight, while also considering mission completion time. [ABSTRACT FROM AUTHOR]

Digital Twin has emerged as a promising paradigm for accurately representing wireless communication electromagnetic environments. The resulting virtual representation of reality facilitates comprehensive insights into the propagation environment, empowering multi-layer decision-making processes at the physical communication level. This paper investigates the impact of ray-based model simulation within real-time Digital Twins. A benchmark for ray-based propagation simulations is presented to evaluate computational time, considering two urban scenarios characterized by different mesh complexity, single and multiple wireless link configurations, and simulations with/without diffuse scattering. Exhaustive empirical analyses are performed showing the behavior of different ray-based solutions. By offering standardized simulations and scenarios, this work provides a technical benchmark for practitioners involved in the implementation of real-time Digital Twins and optimization of ray-based propagation models.

Pathloss quantifies the reduction in power density of a signal radiated from a transmitter. The attenuation is due to large-scale effects such as free-space propagation loss and interactions (e.g., penetration, reflection, and diffraction) of the signal with objects such as buildings, vehicles, trees, and pedestrians in the propagation environment. Many current or planned wireless communications applications require the knowledge (or a reliable approximation) of the pathloss on a dense grid (radio map) of the environment of interest. Deterministic simulation methods such as ray tracing are known to provide very good estimates of pathloss values. However, their high computational complexity makes them unsuitable for most of the applications envisaged. To promote research and facilitate a fair comparison among the recently proposed fast and accurate deep learning-based pathloss radio map prediction methods, we have organized the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this overview paper, we describe the pathloss radio map prediction problem, provide a literature survey of the current state of the art, describe the challenge datasets, the challenge task, and the challenge evaluation methodology. Finally, we provide a brief overview of the submitted methods and present the results of the challenge.

The sixth-generation (6G) network is expected to provide both communication and sensing (C&S) services. However, spectrum scarcity poses a major challenge to the harmonious coexistence of C&S systems. Without effective cooperation, the interference resulting from spectrum sharing impairs the performance of both systems. This paper addresses C&S interference within a distributed network. Different from traditional schemes that require pilot-based high-frequency interactions between C&S systems, we introduce a third party named the radio map to provide the large-scale channel state information (CSI). With large-scale CSI, we optimize the transmit power of C&S systems to maximize the signal-to-interference-plus-noise ratio (SINR) for the radar detection, while meeting the ergodic rate requirement of the interfered user. Given the non-convexity of both the objective and constraint, we employ the techniques of auxiliary-function-based scaling and fractional programming for simplification. Subsequently, we propose an iterative algorithm to solve this problem. Simulation results corroborate our idea that the extrinsic information, i.e., positions and surroundings, is effective to decouple C&S interference.

In this paper, we consider a wireless energy-carrying communication network of a UAV. In this communication network, the internet of things (IoT) devices maintain their work via the power supply of batteries. The energy of batteries is slowly consumed over time. The UAV adopts the full-duplex working mode and the flight hover protocol, that is, they can power the target device in the hover position and collect base station data at the same time. Unlike traditional methods, which seek to achieve wireless energy transmission, this paper adopts deep reinforcement learning. On the one hand, the deep reinforcement learning algorithm seeks to solve the dynamic programming problem without a model. The traditional method often requires a prior channel model, to form a formula about variables for convex optimization, while reinforcement learning only requires the interaction between agents and the environment. Then, the strategy is optimized according to the reward function feedback. On the other hand, traditional optimization methods generally solve static programming problems. Since IoT devices constantly collect information from the surrounding physical environment, and their requirements for power supply change dynamically, traditional methods are relatively complex and require huge computational overhead, while deep reinforcement learning performs well in complex problems. The purpose of our work is that with the assistance of the radio map, a UAV can find the best hover position, maximize the energy supplied by the UAV, maximize the data throughput collected, and minimize the energy consumption. The simulation results show that the proposed algorithm can well find the best hovering position of the UAVs and significantly improve the performance of the UAV-assisted wireless energy transmission communication network. [ABSTRACT FROM AUTHOR]

Abstract Due to the heavy workload of RSS collection, the instability of WLAN signal strength and the disappearance of signals caused by complex indoor environments, the construction of radio map for wireless local area network (WLAN) fingerprint-based indoor positioning system is time-consuming and laborious. In order to rapidly deploy indoor WLAN positioning system, the bidirectional encoder representation from transformers (BERT) model is used to fill the missing signal in radio map and quickly build radio map. The radio map is imported into the BERT model in the form of natural language text, and the missing signal is filled by the BERT model. Since the number of input data in BERT model cannot exceed 512 words, the structure of BERT model is not suitable for WLAN signals with large data volume. Therefore, we redefine the model structure based on the original BERT model and fill in the missing signals in the radio map in parallel. In addition, the loss function is redefined. Except that each segment has a loss function, the weighted average value of all segment loss functions is defined as the total loss function. The experimental results show that the BERT model is better than the traditional linear interpolation method, compressed sensing algorithm and matrix completion algorithm in filling the missing signals in the fingerprint database, and the probability of error within 2 m reaches about 94%.

Radio maps, which can provide metrics for signal strength at any location in a geographic space, are useful for many applications of 6G technologies, including UAV-assisted communication, network planning, and resource allocation. However, current crowd-sourced reconstruction methods necessitate large amounts of privacy-sensitive user data and entail the training of all data with large models, especially in deep learning. This poses a threat to user privacy, reducing the willingness to provide data, and consuming significant server resources, rendering the reconstruction of radio maps on resource-constrained UAVs challenging. To address these limitations, a self-supervised federated learning model called RadioSRCNet is proposed. The model utilizes a super-resolution (SR)-based network and feedback training strategy to predict the pathloss for continuous positioning. In our proposition, users retain the original data locally for training, acting as clients, while the UAV functions as a server to aggregate non-sensitive data for radio map reconstruction in a federated learning (FL) manner. We have employed a feedback training strategy to accelerate convergence and alleviate training difficulty. In addition, we have introduced an arbitrary position prediction (APP) module to decrease resource consumption in clients. This innovative module struck a balance between spatial resolution and computational complexity. Our experimental results highlight the superiority of our proposed framework, as our model achieves higher accuracy while incurring less communication overheads in a computationally and storage-efficient manner as compared to other deep learning methods. [ABSTRACT FROM AUTHOR]

Due to the heavy workload of RSS collection, the instability of WLAN signal strength and the disappearance of signals caused by complex indoor environments, the construction of radio map for wireless local area network (WLAN) fingerprint-based indoor positioning system is time-consuming and laborious. In order to rapidly deploy indoor WLAN positioning system, the bidirectional encoder representation from transformers (BERT) model is used to fill the missing signal in radio map and quickly build radio map. The radio map is imported into the BERT model in the form of natural language text, and the missing signal is filled by the BERT model. Since the number of input data in BERT model cannot exceed 512 words, the structure of BERT model is not suitable for WLAN signals with large data volume. Therefore, we redefine the model structure based on the original BERT model and fill in the missing signals in the radio map in parallel. In addition, the loss function is redefined. Except that each segment has a loss function, the weighted average value of all segment loss functions is defined as the total loss function. The experimental results show that the BERT model is better than the traditional linear interpolation method, compressed sensing algorithm and matrix completion algorithm in filling the missing signals in the fingerprint database, and the probability of error within 2 m reaches about 94%. [ABSTRACT FROM AUTHOR]

A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (MPLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations. The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths. By consideringmultipath signal propagations from different APs, the inherent noise from these signal paths can be alleviated. Firstly, locations of the signal data with standard deviations higher than the threshold are identified. The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints. The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance. Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44% compared to the conventional interpolation techniques such as the Inverse DistanceWeighting, Kriging, and single Path LossModel. As a result, we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance. [ABSTRACT FROM AUTHOR]

Due to recent technological developments such as online navigation, augmented reality (AR), virtual reality (VR), and digital twins, and the high demand from users for various location-based services (LBS), research on location estimation techniques is being actively conducted. As a result, there is an increasing demand for effective localization technologies that can be used in places where the use of Global Positioning System (GPS) is limited, especially in indoor spaces with very large areas. In this paper, a new structure for an indoor localization system in which wireless fingerprinting and visual-based positioning are hierarchically combined—the so-called Fi-Vi system—is proposed. This scheme consists of two steps: fingerprint-based localization (FBL) followed by visual-based localization (VBL). In the first positioning step (i.e., the FBL stage), the entire area of a significantly broad range for localization is divided into multiple regions, the size and the number of which depend on the target accuracy of this step. Moreover, a machine-learning (ML) or deep-learning (DL) model trained on a Wi-Fi fingerprint radio map selects suitable candidate regions among these multiple regions. In the second positioning step (i.e., the VBL stage), the final location is precisely estimated through visual-based positioning based on the received information regarding the candidate regions. The FBL stage uses a sparse radio map (SRM) for fingerprinting, which can be constructed with relatively little effort and cost compared to radio maps used in conventional fingerprinting methods. As a result, it can be easily combined with existing visual-based positioning methods with almost negligible implementation complexity. Because of the hierarchical structure and SRM, the proposed scheme shows a significant performance improvement in terms of computational load and time required for indoor localization compared to the use of the existing visual-based indoor positioning method alone. In addition, it provides high accuracy and robustness even in a dynamically changing indoor wireless environment where conventional wireless fingerprinting methods show significant performance degradation. Finally, the performance analysis of the proposed scheme was performed using the UJIIndoorLoc dataset. Experiments and theoretical analysis have shown that when the estimation accuracy of the candidate region for the test dataset was achieved at about 99% through the FBL stage, the average computational amount of the VBL stage for the final position estimation was only about 16% of that in cases where the visual-based positioning method was used alone.

The radio map has attracted attention as a promising tool for accurately estimating radio propagation and determining communication parameters. Conventional radio maps are mainly used in wireless systems with a single target transmitter. However, in dense transmitter environments, such as cellular systems, interference-limited observations may become dominant, and thus, the received signal power from a target transmitter may be calculated to be higher than the actual value because of survivorship bias. As a result, the coverage area may be overestimated and communication parameters inappropriately determined. In this paper, we propose a radio map extrapolation method for multiple-transmitter environments. The proposed method compensates for the empirical cumulative distribution function (CDF) of the target power by taking into account the number of missing data in each mesh. The median target power was extrapolated based on the compensated empirical CDF. The emulation results using datasets over the 3.5 GHz band show that the proposed method can improve the mean error of the target power from 7 [dB] to 8 [dB], compared with the multiple imputation (MI) method, a non-compensated radio map, and kriging-based spatial interpolation.

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell. [ABSTRACT FROM AUTHOR]

A communication enabled indoor intelligent robots (IRs) service framework is proposed, where non-orthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state in order to construct the radio map. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent, which can save training time and hardware costs. Build on the proposed communication model, motions of IRs who need to reach designated mission destinations and their corresponding down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to solve this optimization problem, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation results demonstrate in the following: 1) with the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability. [ABSTRACT FROM AUTHOR]

Although Bluetooth Low Energy (BLE) fingerprinting localization has become a hot research topic with encouraging results, it is difficult to predict the location depending on a short duration received signal strength indication (RSSI) sequence in realistic scenarios due to the severe fluctuation of RSSI. We introduce a new perspective to view the indoor positioning problem by radio map fingerprint. We argue that even though beacons may be independently deployed, the RSSI series bear certain spatial relation because of their copresence in the same physical space. The latent relation implicitly conveyed by the coexistence of their signals at various indoor locations. Unlike existing approaches that try to find a direct mapping between sensed signals and the corresponding location, we explore the spatial relation of beacons from the input data to estimate location. We propose a deep learning localization system, termed DRVAT, which is based on the distributed representation vector (DRV) and self‐attention (AT) among the pairs of MAC‐RSSI. First, we obtain DRVs which represent dense features in low dimensionality through pre‐training on all MAC‐RSSIs. Then we exploit self‐attention mechanism to learn the latent spatial relation of beacons. Finally, MAC‐RSSIs labeled with locations are used to fine‐tune the model for estimating location. Localization accuracy results demonstrated the superior performance as compared with other positioning methods, and the visualization of DRV and attention mechanism are consistent with the spatial deployment of BLE. [ABSTRACT FROM AUTHOR]

The spectrum cartography (SC) technique constructs multi-domain (e.g., frequency, space, and time) radio frequency (RF) maps from limited measurements, which can be viewed as an ill-posed tensor completion problem. Model-based cartography techniques often rely on handcrafted priors (e.g., sparsity, smoothness and low-rank structures) for the completion task. Such priors may be inadequate to capture the essence of complex wireless environments—especially when severe shadowing happens. To circumvent such challenges, offline-trained deep neural models of radio maps were considered for SC, as deep neural networks (DNNs) are able to “learn” intricate underlying structures from data. However, such deep learning (DL)-based SC approaches encounter serious challenges in both off-line model learning (training) and completion (generalization), possibly because the latent state space for generating the radio maps is prohibitively large. In this work, an emitter radio map disaggregation-based approach is proposed, under which only individual emitters’ radio maps are modeled by DNNs. This way, the learning and generalization challenges can both be substantially alleviated. Using the learned DNNs, a fast nonnegative matrix factorization-based two-stage SC method and a performance-enhanced iterative optimization algorithm are proposed. Theoretical aspects—such as recoverability of the radio tensor, sample complexity, and noise robustness—under the proposed framework are characterized, and such theoretical properties have been elusive in the context of DL-based radio tensor completion. Experiments using synthetic and real-data from indoor and heavily shadowed environments are employed to showcase the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The deployment of an Indoor Position System (IPS) in the real-world raised many challenges, such as installation of infrastructure, the calibration process or modelling of the building’s floor plan. For Wi-Fi-based IPSs, deployments often require a laborious and time-consuming site survey to build a Radio Map (RM), which tends to become outdated over time due to several factors. In this paper, we evaluate different deployment methods of a Wi-Fi-based IPS in an industrial environment. The proposed solution works in scenarios with different space restrictions and automatically builds a RM using industrial vehicles in operation. Localization and tracking of industrial vehicles, equipped with low-cost sensors, is achieved with a particle filter, which combines Wi-Fi measurements with heading and displacement data. This allows to automatically annotate and add new samples to a RM, named vehicle Radio Map (vRM), without human intervention. In industrial environments, vRMs can be used with Wi-Fi fingerprinting to locate human operators, industrial vehicles, or other assets, allowing to improve logistics, monitoring of operations, and safety of operators. Experiments in an industrial building show that the proposed solution is capable of automatically building a high-quality vRM in different scenarios, i.e., considering a complete floor plan, a partial floor plan or without a floor plan. Obtained results revealed that vRMs can be used in Wi-Fi fingerprinting with better accuracy than a traditional RM. Sub-meter accuracies were obtained for an industrial vehicle prototype after deployment in a real building. [ABSTRACT FROM AUTHOR]

The indoor localization based on WiFi fingerprints has attracted great attention, but the most significant and challenging work is the construction of the radio map without the need of site survey. With the rapid popularization of mobile and wearable devices, the method of collecting massive trajectory data by crowd-sourcing is becoming increasingly convenient and cost-effective, making unsupervised radio-map learning from unlabeled trajectories possible and promising. However, these crowd-sourced trajectories have low qualities and tend to present an imbalanced distribution due to the presence of hot spots, restricted areas in the building, as well as the random movement of participants. To address above challenges, MapICT scheme is proposed in this paper, using low-quality trajectory data to build radio map without site survey. First, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm is suggested to extract target fingerprint vertexes, so as to balance the distribution of trajectory data. Second, target edges that represent relationship between vertexes are extracted by inertial data. Finally, a two-dimensional radio map can be constructed by vertexes and edges. Through simulation experiments and experiments in actual environment, the MapICT scheme proves to be feasible and effective, with the locating accuracy in teaching building and in mall being 11.98% and 7.38% respectively higher than that achieved by existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the three-dimensional (3D) path planning problem for cellular-connected unmanned aerial vehicle (UAV) taking into account the impact of 3D antenna radiation patterns. The cellular-connected UAV has a mission to travel from an initial location to a destination. In this process, there is a trade-off between the flight time and the expected communication outages duration, which requires planning a suitable route to avoid the weak coverage region. However, as the existing cellular networks are primarily designed for ground coverage, the communication coverage in the sky tends to be intermittent and irregular due to the potential for severe interference and obstructions (such by buildings), which brings great challenges to 3D path planning. To address this challenge, we first construct a 3D coverage map that stores the expected outage probability over each locations. We then propose a multi-step dueling DDQN (multi-step D3QN) based algorithm to design the local optimal UAV path by leveraging the constructed coverage map. In this algorithm, the UAV acts as the agent to learn the appropriate action to complete the flight mission. Numerical results show the effectiveness of the proposed algorithm for connectivity-aware UAV path planning and the superiority of 3D path design over its 2D counterparts. [ABSTRACT FROM AUTHOR]

Optimal unmanned aerial vehicle (UAV) placement in a 3-dimensional (3D) space to build a connection between a base station (BS) and a ground user is studied herein. A key challenge is to avoid signal propagation blockage due to obstacles. Much prior work uses probabilistic terrain models with model parameters learned from the statistics over a large area, and therefore, the optimization for a specific user in a small local area is poor. In contrast, this paper seeks the optimal UAV position over actual and fine-grained terrain, and develops efficient UAV positioning strategy adaptive to the degree of location-dependent line-of-sight (LOS) condition measured on the fly. It is proven that the globally optimal UAV position in 3D can be determined from the proposed search trajectory which has merely linear length in the diameter of the target area. Therefore, the proposed strategy can be practically implemented. Numerical experiments are performed over a real-world urban topology and demonstrate superior performance gain over existing strategies based on probabilistic models. [ABSTRACT FROM AUTHOR]

In this paper, we study the three-dimensional (3D) path planning for a cellular-connected unmanned aerial vehicle (UAV) to minimize its flying distance from given initial to final locations, while ensuring a target link quality in terms of the expected signal-to-interference-plus-noise ratio (SINR) at the UAV receiver with each of its associated ground base stations (GBSs) during the flight. To exploit the location-dependent and spatially varying channel as well as interference over the 3D space, we propose a new radio map based path planning framework for the UAV. Specifically, we consider the channel gain map of each GBS that provides its large-scale channel gains with uniformly sampled locations on a 3D grid, which are due to static and large-size obstacles (e.g., buildings) and thus assumed to be time-invariant. Based on the channel gain maps of GBSs as well as their loading factors, we then construct an SINR map that depicts the expected SINR levels over the sampled 3D locations. By leveraging the obtained SINR map, we proceed to derive the optimal UAV path by solving an equivalent shortest path problem (SPP) in graph theory. We further propose a grid quantization approach where the grid points in the SINR map are more coarsely sampled by exploiting the spatial channel/interference correlation over neighboring grids. Then, we solve an approximate SPP over the reduced-size SINR map (graph) with reduced complexity. Numerical results show that the proposed solution can effectively minimize the flying distance/time of the UAV subject to its communication quality constraint, and a flexible trade-off between performance and complexity can be achieved by adjusting the grid quantization ratio in the SINR map. Moreover, the proposed solution significantly outperforms various benchmark schemes without fully exploiting the channel/interference spatial distribution in the network. [ABSTRACT FROM AUTHOR]

This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for potential blockage of a direct propagation link, we propose an approach capable of leveraging local terrain information to offer performance guarantees. The proposed method allows to strike the best trade-off between minimizing propagation distances to ground terminals and discovering good propagation conditions. The algorithm only requires several propagation parameters, but it is capable to avoid deep propagation shadows and is proven to find the globally optimal UAV position. Only a local exploration over the target area is required, and the maximum length of search trajectory is linear to the geographical scale. Hence, it lends itself to online search. Significant throughput gains are found when compared to other positioning approaches based on statistical propagation models. [ABSTRACT FROM AUTHOR]

The Wi-Fi fingerprint, which can be used on existing wireless networks, is one of the main indoor positioning techniques that utilizes the received signal strength (RSS). In smartphones, the positioning performance of the fingerprint has been significantly improved through fusion algorithms along with terrestrial magnetism and acceleration sensors. However, the positioning accuracy and speed of the fingerprint is based on radio maps. Although these maps are separate databases obtained without using these sensors, they are important reference elements for initial position estimation and sensor error compensation. In order to minimize the DB of fingerprint and to improve the speed of system construction according to the area of positioning is expanded, this paper proposes a Wi-Fi fingerprint using a radio map construction model based on the minimum description length principle, which can automatically optimize radio maps and the Euclidean distance algorithm based on the Chi squared test. Unlike the existing RSS-classification-based radio map construction method, the proposed access point (AP) classification-based radio construction model not only automatically distinguishes the continuity of all the RSSs acquired from the APs but also optimizes the radio map by eliminating unnecessary APs, based on the information gain. In the positioning phase, based on the proposed radio map, the accuracy of the signals is distinguished using a Chi squared test for the AP RSSs measured in real-time. Therefore, the Euclidean distance, based on the Chi squared test, improves the positioning performance by determining the position accuracy using weighted values of the RSSs, with high reliability. [ABSTRACT FROM AUTHOR]

We have proposed to supplement the model of decision making about abnormal states of a wireless network under conditions of uncertainty by another attribute ‒ the location of wireless mobile and stationary devices in a controlled network. The method of trilateration, based on the measurement of signal strength at three points, is considered. This method has a high accuracy of determining the location of a wireless device, provided that the most accurate model of radio waves propagation is constructed. However, given the specificity of radio waves propagation inside the premise, it is rather difficult to build such a model for them. Therefore, it is proposed to use the method of radio fingerprints. This method is based on the construction of radio maps for each of the three access points, which indicates the signal level from a typical wireless device located at a certain number of reference points. We have also considered the possibility of the combined application of two methods, which will make it possible to determine the location of a wireless device even when it is outside the radio map. Experimental studies were carried out, including the creation of radio maps for a room of area 70 m2 with 26 reference points. We employed three identical routers and a smartphone. During the experiment, it turned out that, depending on the orientation of the mobile device (in fact, its antenna), the measured power changes, so the radio maps were constructed based on average power for six different positions of the mobile device. It is shown that the level of the signal is almost independent of the door and window position in the room. This analysis of the principles of organizing various types of attacks on wireless networks has revealed that accounting for the position makes it possible to detect attacks of the types “man in the middle” and “false access point” that were not identified by the base model. In addition, the improved model allows determining the source of interference at the “muting” attack. [ABSTRACT FROM AUTHOR]

Multiple location-based device-free activity recognition systems indicate that some activities related to specific locations can be inferred from the location system and adding location information can improve the accuracy of activity recognition. Therefore, localization technology is the basis for activity recognition and other applications. Radio-Map is an effective measure in Device-free localization (DFL). Traditional fingerprint systems that can provide such accuracy are suffering from human cost in Radio-Map construction and update. Although the human cost in update phase has been paid attention, the higher costs caused by the initially created are ignored. In addition, existing systems assume that RSS change measurements caused by different targets are fixed distribution in any region. The two drawbacks will greatly affect the practicability and robustness of Radio-Map. In this paper, we propose, DTransfer, an extremely low-cost DFL approach that localize different kinds of targets in different regions. We design an optimized low-rank matrix completion model based on singular value decomposition (SVD) to construct the sensing matrix (i.e., radio-map) of the original region, which greatly reduces the overhead. Next, we employ a rigorously designed quadratic transfer scheme to accurately locate different categories of targets in different regions. Finally, we apply the location information to the activity recognition algorithm; experiments have shown that the accuracy of the algorithm for adding location information is increased by approximately 6%. Extensive experimental results illustrate that DTransfer achieves delightful performance. [ABSTRACT FROM AUTHOR]

Trajectory estimation is a problem derived from a common indoor positioning scenario: the user is perceiving the environment while in motion. This paper focuses on an instance: the dead reckoning data of the user are provided by foot-mounted inertial positioning and the observations in the environment are position estimated from global positioning system (GPS) and Wi-Fi received signal strengths. An approach is proposed to merge the three types of data from multiple users using the graph-based SLAM framework. In constructing the graph, Wi-Fi-based edges are established adaptively and GPS-based edges are established as prior information for the corresponding positions from the dead reckoning generated trajectories. The users’ trajectories can be aligned and calibrated using the proposed graph construction strategies. Afterwards, a crowsourced Wi-Fi-based radio map (RM) can be established for other users who need the fingerprinting-based Wi-Fi indoor positioning service. The accuracy of the users’ trajectories and the crowdsourced RM are tested to validate the proposed approach. [ABSTRACT FROM AUTHOR]

Differently from most existing studies either directly eliminating redundant WiFi APs with trivial importance or adopting unsupervised dimension reduction methods, e.g. principal component analysis (PCA), this paper employs a supervised approach to take the full advantage of the information available for building radio maps, i.e. location labels attached to fingerprints, to compress original radio maps. Specifically, in the offline phase, the supervised kernel PCA (SKPCA) method is employed to derive a nonlinear and optimal embedding in a low-dimensional subspace; in the online phase, any sample vector containing received signal strengths can be projected onto the optimal subspace in real-time for further localization processing. Experiments are carried out not only in a real environment but also using an open dataset. It is shown that the compressed radio maps based on SKPCA have much smaller sizes than their original radio maps, but achieve similar localization performance and significantly outperform the other two popular PCA- based unsupervised dimension reduction methods, i.e. PCA and PCA-MLE. [ABSTRACT FROM AUTHOR]

Indoor WLAN fingerprint localization systems have been widely applied due to the simplicity of implementation on various mobile devices, including smartphones. However, collecting received signal strength indication (RSSI) samples for the fingerprint database, named a radio map, is significantly labor-intensive and time-consuming. To solve the problem, this paper proposes a semi-supervised self-adaptive local linear embedding algorithm to build the radio map. First, this method uses the self-adaptive local linear embedding (SLLE) algorithm based on manifold learning to reduce the dimension of the high-dimensional RSSI samples and to extract a neighbor weight matrix. Secondly, a graph-based label propagation (GLP) algorithm is employed to build the radio map by semi-supervised learning from a large number of unlabeled RSSI samples to a few labeled RSSI samples. Finally, we propose a k self-adaptive neighbor weight (kSNW) algorithm, used for radio map construction in this paper, to realize online localization. The results of the experiments conducted in a real indoor environment show that the proposed method reduces the demand for large quantities of labeled samples and achieves good positioning accuracy. With only 25% labeled RSSI samples, our system can obtain positioning accuracy of more than 88%, within 3 m of localization errors.

Fingerprinting-based Wi-Fi indoor positioning has great potential for positioning in GPS-denied areas. However, establishing a fingerprinting map (also called a radio map) prior to positioning (site survey) is normally a labor-intensive task. This paper proposes a method for easy site survey without need for any extra hardware. The user can conduct the site survey adopting only a smart phone. The collected inertial-based readings are processed using the pedestrian dead-reckoning algorithms to generate a raw trajectory. Then a factor graph optimization method is proposed to re-estimate the trajectory by adding constraints originated from collected Wi-Fi fingerprints and landmark positions. The proposed method is verified through an experiment in a mall. The mean positioning error is 1.10 m and the maximum error is 2.25 m. This level of positioning accuracy is considered sufficient for radio map generation purposes. A classical baseline algorithm, the k-Nearest Neighbor (kNN) algorithm, is adopted to test the positioning performance of the radio map (RM), which also validates the quality of the constructed RM from the proposed method. [ABSTRACT FROM AUTHOR]

Received signal strength fingerprints based on Wi-Fi spectrum have been widely adopted in the recent years for indoor localization purposes due to cost-effectiveness and availability. However, until the peer hand-shake (PHS), existing work had not constrained the schematic dimension of the target area, which could dramatically reduce the localization error. As the demand for sensors everywhere schemes for 5G networks keeps on booming, effective signal propagation characterization with in indoors is very essential for Internet of things (IoT) indoor localization and navigation applications. We review, extend the validation of the PHS technique that leverages the schematic dimensions of the target area within the total indoor environment to construct, auto-dynamically transform and update fingerprint in complex indoor environments. Extensive experimental validation has been carried out in two scenarios; Scenario 1 categorizes lobby area while Scenario 2 categorizes corridor areas. We analyze the accuracy performance using Nearest Neighbor (NN) and the KNN algorithms. Experimental results show robustness of the PHS, achieving lower average localization error in diverse indoor dimensionalities than comparisons. [ABSTRACT FROM AUTHOR]

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m2, resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data.

The radio map construction is usually time-consuming and labor-sensitive in indoor fingerprinting localization. We propose a fast construction method by using an adaptive path loss model interpolation. Received signal strength (RSS) fingerprints are collected at sparse reference points by using multiple smartphones based on crowdsourcing. Then, the path loss model of an access point (AP) can be built with several reference points by the least squares method in a small area. Afterwards, the RSS value can be calculated based on the constructed model and corresponding AP’s location. In the small area, all models of detectable APs can be built. The corresponding RSS values can be estimated at each interpolated point for forming the interpolated fingerprints considering RSS loss, RSS noise and RSS threshold. Through combining all interpolated and sparse reference fingerprints, the radio map of the whole area can be obtained. Experiments are conducted in corridors with a length of 211 m. To evaluate the performance of RSS estimation and positioning accuracy, inverse distance weighted and Kriging interpolation methods are introduced for comparing with the proposed method. Experimental results show that our proposed method can achieve the same positioning accuracy as complete manual radio map even with the interval of 9.6 m, reducing 85% efforts and time of construction.

Localization of users in indoor spaces is a common issue in many applications. Among various technologies, a Wi-Fi fingerprinting based localization solution has attracted much attention, since it can be easily deployed using the existing off-the-shelf mobile devices and wireless networks. However, the collection of the Wi-Fi radio map is quite labor-intensive, which limits its potential for large-scale application. In this paper, a visual-based approach is proposed for the construction of a radio map in anonymous indoor environments. This approach collects multi-sensor data, e.g.,Wi-Fi signals, video frames, inertial readings, when people are walking in indoor environments with smartphones in their hands. Then, it spatially recovers the trajectories of people by using both visual and inertial information. Finally, it estimates the location of fingerprints from the trajectories and constructs a Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53 m. A weighted k-nearest neighbor method is also used to evaluate the constructed radio map. The average localization error is about 3.2 m, indicating that the quality of the constructed radio map is at the same level as those constructed by site surveying. However, this approach can greatly reduce the human labor cost, which increases the potential for applying it to large indoor environments. [ABSTRACT FROM AUTHOR]

We present a novel method of fast and reliable data gathering for the purpose of location services based on radio signal strength services such as WiFi location/ navigation. Our method combines the acquisition of location and mapping based on computer vision methods with WiFi signal strength stochastic data gathering. The output of the method is threefold: 3D metric space model, 2D floor plan map and metric map of stochastic radio signal strength. The binding of location data with radio data is done completely automatically, without any human intervention. The advantage of our solution lies also in a significant speed-up and density increase of Radio Map generation which opens new markets for WiFi navigation services. We have proved that presented solution produces a map allowing location in office space of accuracy 1.06 m. [ABSTRACT FROM AUTHOR]

Wi-Fi-based fingerprint indoor localization is widely used owing to its low cost and the rapid increase of its network, even though this method requires radio map creation and maintenance which accompany time-consuming and continuous monitoring. Considering these advantages and limitations and the fact that conditions in a ship are more likely to suffer from scattered and distorted wireless signals caused by a special structure of ship, this paper presents an efficient shipboard positioning technique using existing shipboard Wi-Fi network and additional ultra-wide band (UWB) nodes which are the IEEE 802.15.4a modules capable of precise positioning. The proposed method can reduce computational complexity that can occur during signal acquisition and automatically create and update more rapid and precise modified radio maps, by fusing the distance information obtained from UWB and Wi-Fi fingerprint. In addition, the advantages of proposed system are that selective computation technique which can remove weak signals is applied to reduce the amount of computation needed to create modified radio map and the positioning errors can be improved by assigning a positioning weight to each preceding position value because the same signal intensities can occur in other areas within the Wi-Fi network. The efficiency of the proposed method and its comparison with other methods are demonstrated. [ABSTRACT FROM AUTHOR]

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS. [ABSTRACT FROM AUTHOR]

Fingerprint is a typical indoor-positioning algorithm, which measures the strength of wireless signals and creates a radio map. Using this radio map, the position is estimated through comparisons with the received signal strength measured in real-time. The radio map has a direct effect on the positioning performance; therefore, it should be designed accurately and managed efficiently, according to the type of wireless signal, amount of space, and wireless-signal density. This paper proposes a real-time recursive radio map creation algorithm that combines Wi-Fi and geomagnetism. The proposed method automatically recreates the radio map using geomagnetic radio-map dual processing (GRDP), which reduces the time required to create it. It also reduces the size of the radio map by actively optimizing its dimensions using an entropy-based minimum description length principle (MDLP) method. Experimental results in an actual building show that the proposed system exhibits similar map creation time as a system using a Wi-Fi–based radio map. Geomagnetic radio maps exhibiting over 80% positioning accuracy were created, and the dimensions of the radio map that combined the two signals were found to be reduced by 23.81%, compared to the initially prepared radio map. The dimensions vary according to the wireless signal state, and are automatically reduced in different environments.

This paper introduces radio map framework for creation of GSM (Global System for Mobile communications) network radio maps. These radio maps are structured in the way that they can be utilized later on as a data source for fingerprinting-based localization methods. Attributes of fingerprinting localization method as well as particular radio signal parameters are explained. The framework handles data measurement, data transfer and data visualization scenarios. Data are basically various radio signal parameters coupled with position coordinates. These data make up a database, which allows centralized storage and opens a path to service-oriented localization architecture in the future. Visualization provides means to verify radio map quality, distribution and density in perceptible and user friendly way as well as means to present and publish achieved results. Entire system is aimed to provide usability by multiple users and further optimization. Centralization allows easy implementation of changes and system maintenance at one place.

Currently there is almost ubiquitous availability of wireless signaling for data communications within commercial building complexes resulting in receiver signal strength (RSS) observables that are typically sufficient for generating viable location estimates of mobile wireless devices. However, while RSS observables are generally plentiful, achieving an accurate estimation of location is difficult due to several factors affecting the electromagnetic coupling between the mobile antenna and the building access points that are not modeled and hence contribute to the overall estimation uncertainty. Such uncertainty is typically mitigated with a moderate redundancy of RSS sensor observations in combination with other constraints imposed on the mobile trajectory. In this paper, the Fisher Information (FI) of a set of RSS sensor observations in the context of variables related to the mobile location is developed. This provides a practical method of determining the potential location accuracy for the given set of wireless signals available. Furthermore, the information value of individual RSS measurements can be quantified and the RSS observables weighted accordingly in estimation combining algorithms. The practical utility of using FI in this context was demonstrated experimentally with an extensive set of RSS measurements recorded in an office complex. The resulting deviation of the mobile location estimation based on application of weighted likelihood processing to the experimental RSS data was shown to agree closely with the Cramer Rao bound determined from the FI analysis. [ABSTRACT FROM AUTHOR]

This paper presents three key techniques to realize a global indoor positioning system (GIPS), and a global in- and-outdoor integrated navigation system (GINS). A crowdsourcing radio map construction method, a positioning algorithm for crowdsourced radio maps, and an indoor and outdoor environment detection method are developed as the three key techniques. The developed techniques have been integrated into a crowdsourcing-based indoor positioning system, named KAILOS, aiming to realize the GIPS and GINS. The system was deployed at KAIST, Daejeon campus, and now an in- and-outdoor integrated navigation service is available at KAIST campus area. The successful launch of KAILOS foretells that the GIPS and GINS are becoming a reality. [ABSTRACT FROM AUTHOR]

Channel-based radio-frequency fingerprinting such as a channel impulse response (CIR), channel transfer function (CTF), and frequency coherence function (FCF) have been recently proposed to improve the accuracy at the physical layer; however, their empirical performance, advantages, and limitations have not been well reported. This paper provides a comprehensive empirical performance evaluation of RF location fingerprinting, focusing on a comparison of received-signal strength, CIR-, CTF-, and FCF-based fingerprinting using the weighted k-nearest neighbor pattern recognition technique. Frequency domain channel measurements in the IEEE 802.11 band taken on a university campus were used to evaluate the accuracy of the fingerprinting types and their robustness to human-induced motion perturbations of the channel. The localization performance was analyzed, and the results are described using the spatial and temporal radio propagation characteristics. In particular, we introduce the coherence region to explain the spatial properties and investigate the impact of the Doppler spread in time-varying channels on the time coherence of RF fingerprint structures. [ABSTRACT FROM AUTHOR]

Indoor positioning could provide interesting services and applications. As one of the most popular indoor positioning methods, location fingerprinting determines the location of mobile users by matching the received signal strength (RSS) which is location dependent. However, fingerprinting-based indoor positioning requires calibration and updating of the fingerprints which is labor-intensive and time-consuming. In this paper, we propose a visual-based approach for the construction of radio map for anonymous indoor environments without any prior knowledge. This approach collects multi-sensors data, e.g. video, accelerometer, gyroscope, Wi-Fi signals, etc., when people (with smartphones) walks freely in indoor environments. Then, it uses the multi-sensor data to restore the trajectories of people based on an integrated structure from motion (SFM) and image matching method, and finally estimates location of sampling points on the trajectories and construct Wi-Fi radio map. Experiment results show that the average location error of the fingerprints is about 0.53m. [ABSTRACT FROM AUTHOR]

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area. [ABSTRACT FROM AUTHOR]

Fingerprinting localization using received signal strength (RSS) has been considered as a promising solution for indoor localization systems, due to its ease of use in deployed wireless networks. A major bottleneck is the difficult trade-off between localization accuracy and site survey costs. Crowdsourcing-based fingerprint collection is cost efficient because of user participation, but its accuracy is questioned due to the noisy sample locations. In this paper, we present a probabilistic radio map construction scheme with crowdsourcing collection, considering both accuracy and survey costs. In particular: 1) based on further studies of RSS properties, we introduce a concept of unfixed data collection—in which sample locations are estimated by an additional localization mechanism—and verify its effectiveness, although sample locations are noisy; 2) we present a modified parametric fitting method to better describe location signatures by transforming RSS into signal envelope; and 3) we propose a clustering-based space partitioning algorithm to improve the fitting effect by reducing the number of multimodal distributions. Extensive simulations and experiments show the proposed method can construct an accurate radio map at a low cost, and achieve a localization accuracy of about 2.5 m, 20% improvement compared to using the classical radio map in our experimental scenario. [ABSTRACT FROM PUBLISHER]