Indoor Wireless Local Area Network Research Articles

Radio map construction based on BERT for fingerprint-based indoor positioning system

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%.

Multiobjective planning of indoor Wireless Local Area Networks using subpermutation-based hybrid algorithms

Wireless Local Area Network (WLAN) has become the most popular technology for mobile Internet access in recent decades. This manuscript presents a novel approach, based on hybrid optimization algorithms, for planning WLANs. Two objective functions are optimized: to maximize network load balance and signal-to-noise ratio. In addition, constraints related to coverage, customer, and equipment demand are considered. A key aspect of the proposed algorithm is its new representation/decoding scheme, based on subpermutations, which considerably reduces the search space dimension. This structure guarantees feasibility of the obtained solutions and increases the computational efficiency of the method. Several tests were performed in two scenarios, one of them using real data from a large-scale WLAN. When compared to other three approaches, such results show that the proposed method provides solutions that reduce costs and improve the WLAN throughput.

A WLAN indoor location method based on quadratic wavelet analysis

The drift phenomenon of received signal strength may occur due to the influence of environmental change, which will affect the results of wireless local area network indoor positioning. This paper proposes an indoor location method based on quadratic wavelet analysis to solve this problem. The first wavelet analysis can obtain wavelet coefficients. The threshold interval of wavelet coefficients can be calculated by Tukey's test method, in which the mutation signal value can be filtered according to the interval. The drift signal value cannot be detected due to the limitation of primary wavelet analysis. The filtered received signal strength values can be analysed with quadratic wavelet analysis. The drift signal value can be found and filtered out by the mutation signal to obtain the new dataset of the received signal strength. The experimental results show that the influence of signal drift can be greatly reduced by the method of quadratic wavelet analysis proposed in this paper; the positioning accuracy error is lower than the traditional filtering algorithm; and the positioning accuracy can be improved.

Semi-circular compact CPW-fed antenna for ultra-wideband applications

This paper presents a simple structure and small size antenna design with dimensions of 43×47 mm 2 to perform an ultra-wideband (UWB) frequency range using a semicircular co-planar waveguide (CPW). This antenna has been designed and simulated by the computer simulation technology (CST) microwave studio suit. In this work, we design an ultra-wideband antenna (about 2 GHz to 10 GHz) by feeding a semi-circular compact antenna via a co-planar waveguide for input impedance of 50 Ω. The CST simulation results show that our designed antenna has a very good impedance and radiation characteristic within the intended ultra-wideband. Because of the small size and the suitable shape, this antenna can be used in many wireless communication applications, such as a radio frequency identifier (RFID), indoor wireless local area network or wireless fidelity (WiFi), internet of things (IoT), millimeter waves communications (mmWave), global positioning system (GPS), and many applications of 6G systems.

RSS-Based Wireless LAN Indoor Localization and Tracking Using Deep Architectures

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of 1.73m, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: 10.35m) and Nonparametric Information (NI) filter with variable acceleration (RMSE: 5.2m) under the same experiment environment.

Optimal deployment of indoor wireless local area networks

AbstractWe present a two‐phase methodology to address the problem of optimally deploying indoor wireless local area networks. In the first phase, we use Helmholtz's equation to simulate electromagnetic fields in a typical environment such as an office floor. The linear system which results from the discretization of this partial differential equation is solved with a state‐of‐the‐art library for sparse linear algebra. In the second phase, we formulate the network deployment problem in the setting of binary linear programming. This formulation employs the simulator output as input parameters, and jointly optimizes the number of access points, their locations, and their emission channels. We prove that this optimization problem is NP‐Hard, and use mathematical programming based techniques and heuristics to solve it. We present numerical experiments on medium‐sized buildings.

Passive Fingerprinting Reinforced by Active Radiomap for WLAN Indoor Positioning System

Passive fingerprinting collects the received signal strength (RSS) of wireless local area network (WLAN) signals emitted from a smartphone to create fingerprints and then to construct a radiomap for an indoor positioning system (IPS). However, human-impassable paths and irregular signal collection periods result in missing values in fingerprints. Moreover, missing fingerprints in shaded areas result in the degradation of the positioning accuracy. In this paper, we propose a novel passive fingerprinting method, introducing the concept of a WiFi monitor adjacency matrix and a method of transforming legacy active fingerprints to passive fingerprints by a linear regression model. Our system estimates walking paths with RSS data, removing outliers of human-impassable paths. Moreover, we can construct a high-performance radiomap by supplementing the missing signals as well as missing fingerprints in shaded areas. In an experiment conducted in a complex building environment, the proposed method achieved a positioning accuracy of 2.18 m, which is 32% better than that of a state-of-the-art system. The radiomap construction time was also greatly reduced.

Mobility Support for Millimeter Wave Communications: Opportunities and Challenges

Millimeter-wave (mmWave) communication technology offers a potential and promising solution to support 5G and B5G wireless networks in dynamic scenarios and applications. However, mobility introduces many challenges as well as opportunities to mmWave applications. To address these problems, we conduct a survey of the opportunities and technologies to support mmWave communications in mobile scenarios. Firstly, we summarize the mobile scenarios where mmWave communications are exploited, including indoor wireless local area network (WLAN) or wireless personal area network (WPAN), cellular access, vehicle-to-everything (V2X), high speed train (HST), unmanned aerial vehicle (UAV), and the new space-air-ground-sea communication scenarios. Then, to address users’ mobility impact on the system performance in different application scenarios, we introduce several representative mobility models in mmWave systems, including human mobility, vehicular mobility, high speed train mobility and ship mobility. Next we survey the key challenges and existing solutions to mmWave applications, such as channel modeling, channel estimation, anti-blockage, and capacity improvement. Lastly, we discuss the open issues concerning mobility-aware mmWave communications that deserve further investigation. In particular, we highlight future heterogeneous mobile networks, dynamic resource management, artificial intelligence (AI) for mobility and integration of geographical information, deployment of large intelligent surface and reconfigurable antenna technology, and finally, the evolution to Terahertz (THz) communications.

Indoor WLAN Personnel Intrusion Detection Using Transfer Learning-Aided Generative Adversarial Network with Light-Loaded Database

The Internet of Everything (IoE) provides a platform that allows devices to be remotely connected, sensed, and controlled across the network infrastructure. The smart home in the era of the IoE is born on the basis of the high integration of emerging communication technologies such as big data, sensors, and machine learning. In this paper, we focus on wireless detection technologies using smartphones and computers in smart homes. Among them, the indoor Wireless Local Area Network (WLAN) personnel intrusion detection technology based on the database construction has become one of the comprehensive detection technologies by advantages of the convenient accessibility of the WLAN signal and minimal hardware requirement. However, the considerable labor and time cost involved in the database construction affects the popularity and application of database-based intrusion detection systems. To cope with this problem, we propose a new indoor WLAN personnel intrusion detection approach with the reduced overhead of the database construction. Specifically, first of all, the offline database is extended by fake Received Signal Strength (RSS) data, which are generated by the Generative Adversarial Network (GAN) based supervised learning from actual labeled RSS data. Second, the difference between the extended database and online RSS data caused by the time-variant environment noise is reduced by minimizing the Maximum Mean Discrepancy (MMD) between marginal distributions of RSS data through the transfer learning. Finally, the intrusion detection is achieved by classifying online RSS data with classifiers trained from the extended database. Furthermore, experimental results show that the proposed approach can not only perform well in reducing the database overhead and the difference of data in source and target domains, which are corresponding to the same environment state but also detect environment states with satisfactory accuracy.

On Distance Statistics of First Arriving Multi-Paths in Indoor mm-Wave Communications

This paper presents a mathematical framework to evaluate distance statistics of first arriving multi-path components at millimeter wave (mm-Wave) frequencies in a typical indoor environment. The proposed modeling approach is based on a numerically tractable channel model that accounts for the line-of-sight (LoS) component and first-order reflection components from surrounding walls. Based on the derived statistics, an approximation for coverage probability of mm-Wave indoor wireless local area networks (WLAN)s is obtained. Further, the coverage probability for an access point (AP) to user node link is determined after accounting for blockage of LoS component and optimal antenna beamforming in the modeling framework. The analysis of coverage probability provides useful insights which can be applied to plan optimum AP location layout in a typical mm-Wave indoor WLAN.

Indoor intrusion detection based on deep signal feature fusion and minimized-MKMMD transfer learning

Indoor intrusion detection based on Wireless Local Area Network (WLAN) has been widely used for security monitoring, smart homes, entertainment games, and many other fields in the Beyond 5G (B5G) wireless communication network environment. Most existing indoor intrusion detection methods directly exploit the Received Signal Strength (RSS) data collected by Monitor Points (MPs) and pay little attention to the instability of WLAN signals in complex indoor environments. In response to this problem, this paper proposes a novel WLAN indoor intrusion detection method based on deep signal feature fusion and Minimized Multiple Kernel Maximum Mean Discrepancy (Minimized-MKMMD). Firstly, the multi-branch deep convolutional neural network is used to conduct the dimensionality reduction and feature fusion of the RSS data, and the tags are obtained according to the features of the offline and online RSS fusion features that are corresponding to the silence and intrusion states, and then based on this, the source domain and target domain are constructed respectively. Secondly, the optimal transfer matrix is constructed by minimizing MKMMD. Thirdly, the transferred RSS data in the source domain is utilized for training the classifiers that are applying in getting the classification of the RSS fusion features in the target domain in the same shared subspace. Finally, the intrusion detection of the target environment is realized by iteratively updating the process above until the algorithm converges. The experimental results show that the proposed method can effectively improve the accuracy and robustness of the intrusion detection system.

Indoor localisation algorithm based on MSIA and FCSO

The wireless local area network indoor localisation method based on fingerprints has been widely researched and applied due to its higher positioning accuracy and lower cost. However, its engineering is limited because of the large offline workload and time-varying signal. To address these problems, an indoor localisation algorithm based on Markov state iterative analysis (MSIA) and fingerprint clustering structural optimisation (FCSO) is proposed in this study. First, the received signal strength time variation can be solved using the MSIA algorithm, which is based on the correlation of the memory source. Then, the offline workload can be considerably reduced and positioning accuracy can be improved with the FCSO algorithm, which contains fingerprint structural and clustering optimisation stages. In the structural optimisation stage, about half of the fingerprints can be omitted. In the clustering optimisation stage, high errors can be avoided. Finally, the locations of the positioning point can be obtained through the combination of MSIA and FCSO. Experimental results show the proposed algorithm can reduce the offline workload by about 50%, and the positioning accuracy can be increased when using MSIA and FCSO algorithms compared with other algorithms.

Indoor WLAN Intelligent Target Intrusion Sensing Using Ray-Aided Generative Adversarial Network

An indoor target intrusion sensing technique has been used in many fields, such as smart home management, security monitoring, counter-terrorism, and disaster relief. At the same time, with the wide deployment of wireless local area network (WLAN) and general support of the IEEE 802.11 protocol by various mobile devices, the target intrusion sensing can be realized based on the existing WLAN infrastructure without requiring the target to carry any special device. However, the existing indoor WLAN target intrusion sensing approaches usually depend on the radio map construction with huge labor and time cost, which is the major barrier of current systems. In response to this compelling problem, we propose the new ray-aided generative adversarial model (RaGAM) to automatically construct the radio map, which is used for the indoor WLAN intelligent target intrusion sensing and localization. To achieve the low labor and time cost, the RaGAM uses the adaptive-depth ray tree based the quasi three-dimensional ray-tracing model to depict the difference of WLAN signals between the silence and intrusion environments with the purpose of constructing the synthetic radio map. Considering the gap between the synthetic and actual radio maps, we modify the conventional generative adversarial network by the joint synthetic and unsupervised learning (or called S+U learning) from the actual unlabeled received signal strength (RSS) data to improve the precision of the proposed ray-tracing model, and consequently obtain the refined radio map. After that, the statistical characteristics of the refined radio map are utilized to construct the training set for the probabilistic neural network (PNN), and then by using the well-trained PNN to classify the newly collected RSS data, the target intrusion sensing, and localization are achieved. The experimental results show that the proposed approach cannot only perform well in terms of computation cost and the ray-tracing accuracy, but also sense the target intrusion states accurately.

ON THE PERFORMANCE OF OUTDOOR AND INDOOR WI-FI NETWORKS

Today, Wi-Fi networks can be found almost everywhere: in homes, businesses, hotels, restaurants, transportation, streets, etc. Research subject. In this study, we analyze the performance of outdoor and indoor wireless local area networks. Method. To achieve this goal, the following empirical research methods were used: measurement, experiment, and comparison. Core results. In the first part of the study, experimental data on radio frequency spectrum usage around and inside St. Petersburg State University of Telecommunications are provided. The second part of the study is devoted to the peculiarities of outdoor and indoor Wi-Fi. The effective data rate, retry rate, and noise floor are compared for the 2.4 and 5 GHz frequency bands. Practical relevance. The obtained results can be used for design and optimization of outdoor Wi-Fi networks.

Maximum Mean Discrepancy Minimization Based Transfer Learning for Indoor WLAN Personnel Intrusion Detection

Indoor personnel intrusion detection has been recognized as an active research topic over the last decade due to the remarkably growing demand for indoor security management, elderly monitoring, and smart home. In this circumstance, indoor wireless local area network (WLAN) personnel intrusion detection is one of the most promising approaches by considering its advantages of the handy accessibility of WLAN signal and convenient use of WLAN devices. Many existing studies rely on only the offline WLAN received signal strength (RSS) data to train a heuristic model, which is, then, used for online intrusion detection without considering the time-variant RSS property in the actual indoor environment. To address this problem, we propose a new maximum mean discrepancy (MMD) minimization based transfer learning approach for indoor WLAN personnel intrusion detection. Specifically, first of all, the source and target domains are constructed from offline labeled and online unlabeled RSS data, respectively. Second, the MMD of marginal distributions of the RSS data in source and target domains are calculated as the difference of these two domains. Third, the optimal transfer matrix corresponding to the minimum difference of source and target domains (or called minimum MMD) is constructed to transfer the RSS data in these two domains into the data in a same subspace. Finally, the classifiers used for intrusion detection are trained from these data with the purpose of enhancing the robustness of the proposed approach.

WLAN indoor target intrusion detection algorithm based on adaptive-depth ray tree

With the wide deployment of wireless local area network (WLAN) and general support of WLAN protocol by various intelligent terminals, the intrusion detection with respect to the indoor target can be realized by using the existing WLAN infrastructure. To achieve this goal, the adaptive-depth ray tree based quasi 3D ray-tracing model is constructed to model the received signal strength (RSS) propagation property under the indoor silence and intrusion scenarios. Then, the RSS mean, variance, maximum, minimum, range, and median are allied to construct the training database for the probabilistic neural network (PNN). Finally, after the training, the PNN is utilized to perform the multi-classification decision with respect to the newly-collected RSS data, and consequently achieve the indoor target intrusion detection and area localization. The experimental results indicate that the proposed algorithm is featured with high intrusion detection rate as well as low database construction cost.

CC-DTW: An Accurate Indoor Fingerprinting Localization Using Calibrated Channel State Information and Modified Dynamic Time Warping.

Indoor wireless local area network (WLAN) based positioning technologies have boomed recently because of the huge demands of indoor location-based services (ILBS) and the wide deployment of commercial Wi-Fi devices. Channel state information (CSI) extracted from Wi-Fi signals could be calibrated and utilized as a fine-grained positioning feature for indoor fingerprinting localization. One of the main factors that would restrict the positioning accuracy of fingerprinting systems is the spatial resolution of fingerprints (SRF). This paper mainly focuses on the improvement of SRF for indoor CSI-based positioning and a calibrated CSI feature (CCF) with high SRF is established based on the preprocess of both measured amplitude and phase. In addition, a similarity calculation metric for the proposed CCF is designed based on modified dynamic time warping (MDTW). An indoor fingerprinting method based on CCF and MDTW, named CC-DTW, is then proposed to improve the positioning accuracy in indoors. Experiments are conducted in two indoor office testbeds, and the performances of the proposed CC-DTW, one time-reversal (TR) based approach and one Euclidean distance (ED) based approach are evaluated and discussed. The results show that the SRF of CC-DTW outperforms the TR-based one and the ED-based one in both two testbeds in terms of the receiver operating characteristic (ROC) curve metric, and the area under curve (AUC) metric.

Dynamic spatial modulation for next generation networks

A major challenge for fifth generation (5G) wireless communication, which is expected to be deployed around 2020 is to provide 10 times higher spectral efficiency and energy efficiency than fourth generation (4G). Spatial modulation (SM) is a novel multiple input and multiple output (MIMO) technique, which provides an exceptional trade-off between spectral efficiency and energy efficiency. All the conventional SM variants do not perform well under spatially correlated channel conditions. Hence, they cannot be employed for indoor wireless local area network (WLAN) and compact wireless mobile applications, where the effect of spatial correlation is imminent. In this paper, a new SM variant, dynamic spatial modulation (DSM) is introduced for 4×4 and 8×8 MIMO configurations, which activates either one or two transmit antennas based on input bit pattern. DSM employs dynamic mapping, which maximizes the Euclidean distance between active spatial bits by choosing antenna subsets with maximum spatial separation to minimize the effect of spatial correlation. The average bit error rate (ABER) performance of DSM under uncorrelated and spatially correlated channel conditions is quantified in comparison with conventional redesigned spatial modulation (ReSM). Later, a low complexity, suboptimal, ordered weight minimum mean square error based conditional maximum likelihood search (OWMMSE-CML) detection scheme is proposed for the DSM system. Through extensive simulations under various channel conditions and complexity analysis, it is shown that proposed OWMMSE-CML detection scheme offers performance nearer to optimal ML and also outperforms suboptimal signal vector based minimum mean square error (SVMMSE) detection scheme with a significant reduction in computational complexity.

WLAN indoor positioning method based on gradient boosting and particle filtering

Indoor positioning technology has shown its great application prospects in smart cities. The main purpose of this paper is to study a low-cost, low-error indoor positioning method that can get a accurate indoor position when communicating with wireless local area networks (WLAN). The paper optimises the traditional WLAN indoor positioning method based on location fingerprint database, and algorithms about in indoor signal simulation, similarity matching of vector and continuously-positioning are tested in work of this paper, and a WLAN indoor positioning method based on gradient boosting and particle filtering is proposed. The paper finally shows the indoor positioning result with an average error of 1.7 metres. These research results verify the feasibility of WLAN indoor positioning and show that the positioning accuracy will be improved with the further optimisation of the positioning method. The potential application values of WLAN technology make it more convenient for internet of things (IoT) technology.

WLAN indoor positioning method based on gradient boosting and particle filtering

Indoor positioning technology has shown its great application prospects in smart cities. The main purpose of this paper is to study a low-cost, low-error indoor positioning method that can get a accurate indoor position when communicating with wireless local area networks (WLAN). The paper optimises the traditional WLAN indoor positioning method based on location fingerprint database, and algorithms about in indoor signal simulation, similarity matching of vector and continuously-positioning are tested in work of this paper, and a WLAN indoor positioning method based on gradient boosting and particle filtering is proposed. The paper finally shows the indoor positioning result with an average error of 1.7 metres. These research results verify the feasibility of WLAN indoor positioning and show that the positioning accuracy will be improved with the further optimisation of the positioning method. The potential application values of WLAN technology make it more convenient for internet of things (IoT) technology.