Indoor Wireless Local Area Network Research Articles

Path loss prediction models for Corridor propagation at 24GHz

Mm-wave bands have recently become major options for short-range, high speed communication systems especially in the indoor wireless local area networks (WLANs). The pathloss prediction model is one of the metric parameters for determining the system effectiveness and performance in wireless indoor propagation. The channel characterization of 24GHz band in corridor propagation through extensive field strength measurements in real time application was conducted in this work. The results were used to derive the path loss equation for corridor propagation at this spectrum band based on log-distance path loss model and log-normal shadowing model. The pathloss realized falls within the estimated values in such scenario, it is therefore concluded that the predicted mathematical model for the described environment is accurate. Also the predicted pathloss which is lower than the free space propagation path loss results in aggregate high data rate, hence improved system performance is achieved.

Adaptive Indoor Positioning Algorithm Based on Hybird Clustering and Feature Selection

Improving the positioning accuracy is a vital goal of indoor location estimation systems. As the static flngerprint database in traditional WLAN (Wireless Local Area Network) indoor algorithm does not adapt to environmental changes, which cause the signals to change over time even at the same location, present a challenging task for indoor location estimation. To this end, we propose a new method, using regression analysis to learn the relationship between sparse located reference points and that received by the mobile device at predeflned locations. We can adaptive update part of the static flngerprint database through real-time signal strength received from reference points without the need to rebuild the database repeatedly. The algorithm is also able to select strong discriminate subset of the access points in the actual indoor environment to locate a client’s location with high accuracy. And it combines information entropy theory with clustering and multiple regression analysis. Through collecting data and testing our algorithm in a realistic WLAN environment, we show that our algorithm can be much higher in accuracy as compared to the traditional positioning methods, while reducing the online stage computational cost.

Adaptive multi‐task compressive sensing for localisation in wireless local area networks

The spatially distributed sparsity of the mobile devices (MDs) in indoor wireless local area networks (WLANs) makes compressive sensing (CS) based localisation algorithms feasible and desirable. In this Letter, the authors exploit the most recent developments in CS to efficiently perform localisation in WLANs and design an accurate indoor localisation scheme by taking advantage of the theory of multi-task Bayesian CS (MBCS). The proposed scheme assembles the strength measurements of signals from the MDs to distinct access points (APs) and jointly utilises them at a central unit or a specific AP to achieve localisation, thus being able to alleviate the burden of MDs while simultaneously giving a precise estimation of the locations. Afterwards, they give a deeper insight into the localisation problem in more practical scenarios with varying number of MDs and investigate two different adaptive algorithms to meet the satisfactory localisation error requirement. Compared with the conventional MBCS algorithms, simulation results validate that both adaptive algorithms could provide superior localisation accuracy and exhibit stronger resilience to the changes in the number of MDs.

Testing and Design of Indoor WLAN Using Artificial Intelligence Techniques

In this study, the performance of an indoor Wireless Local Area Network (WLAN) which is installed by using traditional methods and cellular design of a WLAN that is to be installed are tested. To implement the test and design, artificial intelligence methods are used. For the WLAN test, Artificial Neural Network (ANN) is used. Access Points (APs) and receiver coordinates are determined as ANN input parameters and optimum Receive Signal Strength (RSS) have been sought. For WLAN design, the Genetic Algorithm (GA) method is used. Wall structures and AP properties are taken into account to obtain optimum Receive Signal Strengths (RSSs). For the analytic solution of the optimum RSS, the indoor setting is divided into cellular areas. Thus, the most suitable locations for APs and the number of APs are determined. As a result, we have tested the performance of a WLAN, which is installed by means of traditional methods. According the experimental results, the indoor WLAN designed with the GA method has provided better performance. DOI: http://dx.doi.org/10.5755/j01.eee.20.6.7290

Indoor Location Fingerprinting Using FM Radio Signals

Indoor positioning has become highly important because of the difficulties global positioning system experiences operating in such areas. Many wireless local area network indoor localization studies use the fingerprinting technique. In this study, a new positioning system is proposed based on frequency modulation (FM) broadcast as a signal of opportunity, with significant benefits for indoor positioning. This localization system uses FM signal strength fingerprinting. The deterministic and probabilistic approaches of fingerprinting are considered. A new method which is the combination of deterministic and probabilistic techniques is also proposed to improve the accuracy. Several algorithms are compared from four perspectives: mean distance error (MDE), data acquisition time, sensed channel number, and number of reference points. The results demonstrate that the combined method is the best, with a minimum MDE of less than 2.5 m. The comparison between using fingerprinting for FM and Wi-Fi is also discussed.

Single‐feed cross‐slot loaded compact circularly polarized microstrip antenna for indoor WLAN applications

ABSTRACTA novel single‐feed circularly polarized microstrip antenna with compact size is proposed for indoor wireless local area network (WLAN) applications. The antenna structure consists of eight slits which are introduced at the boundary and the corners in the radiating square patch with a cross‐slot at the center. The proposed antenna shows a compactness of 42% compared with the conventional circularly polarized antenna design at indoor WLAN frequency band. It is found that the resonance frequency of the proposed structure is highly depending on length and width of cross‐slot and corner slit for entire band of circular polarization. The 3 dB axial ratio bandwidth of the proposed antenna is 1.9%. Proposed structure is fabricated on the FR‐4 epoxy substrate and fed by a single coaxial probe. Measured results show a good agreement with the simulated results. Antenna shows stable radiation characteristics for the entire operating band. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:1313–1317, 2014

PhyCon: Discovering Physical Connectivity for Indoor WLAN Using Mobility

The concept of connectivity in wireless networks is a well-established term referring to the ability of nodes to communicate with other nodes directly or through other nodes working as relays. In this paper, a different aspect of connectivity is presented named physical connectivity, which we defined as the ability of nodes to physically reach other nodes, not only through open spaces, but also through corridors, doors, rooms, etc. For indoor wireless local area networks (WLANs), we believe that awareness of physical connectivity is a key factor for developing emerging applications such as guiding, localization, tracking, physical routing, etc. Related studies only consider the problem of direct connectivity or line-of-sight (LOS), however, we consider physical connectivity should span beyond LOS conditions enabling nodes to reach other nodes through any path available. In this paper a novel method to discover physical connectivity named PhyCon is presented. PhyCon combines node mobility and the inverse-square relationship between received signal strength and distance to discover physical connectivity paths among wireless users. Results from simulations and testbed experiments show PhyCon can discover a high percentage of physical connectivity paths using normal mobility behavior of users found in indoor WLAN.

Multidimensional optimization of signal space distance parameters in WLAN positioning.

Accurate indoor localization of mobile users is one of the challenging problems of the last decade. Besides delivering high speed Internet, Wireless Local Area Network (WLAN) can be used as an effective indoor positioning system, being competitive both in terms of accuracy and cost. Among the localization algorithms, nearest neighbor fingerprinting algorithms based on Received Signal Strength (RSS) parameter have been extensively studied as an inexpensive solution for delivering indoor Location Based Services (LBS). In this paper, we propose the optimization of the signal space distance parameters in order to improve precision of WLAN indoor positioning, based on nearest neighbor fingerprinting algorithms. Experiments in a real WLAN environment indicate that proposed optimization leads to substantial improvements of the localization accuracy. Our approach is conceptually simple, is easy to implement, and does not require any additional hardware.

골든 부호 기반의 공간 변조 다중 안테나 시스템

본 논문에서는 실내 근거리 무선 통신에 적합하고 대역폭 효율을 향상시키는 공간 변조(spatial modulation) 다중 안테나 시스템을 제안한다. 기존의 공간 변조 다중 안테나 시스템은 다중 송신 안테나 중 하나의 안테나를 사용하여 채널 간에 간섭을 피할 수 있어서 다중 경로가 많은 실내 환경에서 적합하다. 그러나 심벌 전송 구간마다 한 개의 안테나만을 선택하여 전송하므로 다중 안테나의 최대 부호 이득을 얻을 수 없다. 이를 극복하기 위하여 시공간블록부호화 방식을 적용한 공간 변조 다중안테나 시스템은 두 개의 안테나를 사용하여 수신 성능을 향상시키지만 대역폭 효율이 향상되지 않는 단점이 있다. 본 논문에서는 골든 부호(Golden code)를 적용한 공간 변조를 제안하여 공간 변조 다중 안테나 시스템의 수신 성능과 대역폭 효율을 개선한다. 그리고 컴퓨터 모의실험을 통하여 기존 시스템과 제안하는 시스템의 성능을 비교 평가한다.

Neural network-based multiuser detection for SDMA–OFDM system over IEEE 802.11n indoor wireless local area network channel models

Space division multiple access – orthogonal frequency division multiplexing-based wireless communication has the potential to offer high-spectral efficiency, system performance and capacity. This article proposes an efficient blind multiuser detection (MUD) scheme using artificial neural network models such as the radial basis function. The proposed MUD technique is consistently outperforming the existing minimum mean square error and minimum bit error rate (MBER) MUDs with the performance close to the optimal maximum likelihood (ML) detector. Besides that, the computational complexity of the proposed one is comparatively lower than both the MBER and ML detectors. Further, it can also outperform MBER MUD in the overload scenario, where the number of users is more than that of the number of receiving antennas simulation-based study showing BER performance and complexity are carried out to prove the efficiency of the proposed techniques. This analysis is carried through the IEEE 802.11n standard channel models, which are designed for indoor wireless local area network applications of bandwidth up to 100 MHz at frequencies 2 and 5 GHz.

A Novel Algorithm Based on Clustering and Access Points Selection for Indoor Fingerprint Localization

With the growing popularity of location-based service (LBS), wireless local area networks (WLAN) indoor positioning has gained widespread attention. Unlike the traditional algorithm concentrating on positioning accuracy, we discuss how to improve the real-time property in WLAN indoor fingerprinting localization systems. In this paper, we present a novel algorithm which first divides the positioning area into sub-areas utilizing k-means clustering, and then selects appropriate access points (APs) for positioning to make the calculated amount as less as possible. By collecting data and performing in the real WLAN environment, our proposed algorithm shows high positioning accuracy while the computational burden has been decreased almost 93.7%.

Deflecting-Obstacle Effects on Signal Propagation in the 60 GHz Band

This paper reports the non-line-of-sight (NLOS) results of broadband measurements and modeling of signal propagation characteristics in the 60 GHz band. The purpose of this work was to evaluate the importance of the presence of deflecting obstacles (DOs) for indoor wireless local area network (WLAN) applications in the unlicensed 60 GHz band. Based on sweeping-frequency sounding technique (59.6-60.6 GHz), an indoor propagation measurement campaign was carried out in a typical T-shaped intersection of two long corridors under non-line-of-sight (NLOS) at a distance of 20 meters between transmit and receive antennas, with and without the presence of DOs. Based on Ray-Tracing (RT) technique, deterministic models are derived for the considered environment. An interesting agreement is achieved between the predicted and the experimental results in both frequency and time domains. Thus, this work demonstrates that mm-waves communications are very sensitive to the propagation environment as compared with lower frequency bands. In NLOS corridor environments, the signal transmission relies mainly on the unavoidable diffraction phenomenon from the intersection corners. However, the presence of a DO might overcome significantly the hostile propagation characteristics that are normally encountered under NLOS propagation conditions.

利用最大散度差于室内无线网络定位分析

在本研究中将最大散度差应用在无线网络室内定位上，最大散度差主要是可以解决线性鉴别分析在计算类内散度矩阵时所遭遇的奇异性矩阵问题，它是利用类间散度矩阵与类内散度矩阵之差作为目标函数，由于不需要构造类内散度矩阵的反矩阵，故可以避免掉奇异性矩阵的问题。本研究中其定位的方式是透过指纹特征比对法的概念来进行定位，共分为两个阶段：在离线阶段，利用最大散度差将原始讯号数据库作前置处理；在在线阶段，将接收到的实时讯号数据利用最大似然法计算出目前接收讯号的所在位置。将最大散度差应用于室内无线网络进行定位，不仅可以避免奇异性矩阵的问题并且还可以降低定位时的计算量，从实验结果得知此应用是可以实行的。 In this paper, we utilize Maximum Scatter Difference (MSD) for indoor localization over Wireless Local Area Network (WLAN) received signal data. Maximum Scatter Difference can solve the singular matrix problems by linear discriminant analysis calculates the inverse matrix of within-class scatter matrix. The objective functions of Maximum Scatter Difference are to maximize the inter-class scatter matrix and minimize the within-class scatter matrix. Therefore, Maximum Scatter Difference cannot need to construct the inverse matrix of within-class scatter matrix. It can avoid the singular matrix problems. We utilize location fingerprinting approaches for indoor localization. The approaches are divided into two parts including off-line and on-line stages. In the off-line stage, we collected received signal data are projected onto the feature space of Maximum Scatter Difference. In the on-line stage, we receive real-time signal data compared with the off-line stage that was collected data using Maximum Likelihood (ML) to estimate current location. Maximum Scatter Difference applied to indoor WLAN localization that not only avoids the singular matrix but also reduces the localization computation. From Simulation results show that this algorithm can be implemented.

Access Point Height Based Location Accuracy Characterization in LOS and OLOS Scenarios

Location estimation in a wireless local area network (WLAN) using received signal strength indication (RSSI) has gained considerable attention in recent years. In a conventional RSSI based indoor WLAN localization, mobile node position is estimated through access point (AP) placed at ceiling height. Researchers have proposed solutions for location estimation in line of sight (LOS) scenarios, by installing the AP at a fixed position. This paper demonstrates the improved location accuracy in LOS and obstructed line of sight (OLOS) scenarios by placing the AP at lower heights. The RSSI variations caused by shadow fading for changing AP heights are used to estimate the location accuracy. The localization performance is computed in terms of Cramer-Rao lower bound (CRLB) of range estimate under dynamic environments which is relatively less complex computation technique and is calibration free. Simulation results reveal that the proposed method has better performance than the multilateration with linearization for access point localization algorithm. The minimum mean localization errors are obtained by deploying the access point at 2 m height. The results also demonstrate that the indoor localization accuracy improves for higher order path loss exponent.

Wireless Node Localization for V-shaped and Rectangular-shaped Node Deployment Schemes

positioning of wireless nodes in indoor Wireless Local Area Network (WLAN) using Received Signal Strength Indication (RSSI) has become an attractive low cost localization solution. The location accuracy of RSSI based algorithms is dependent on node placement topology. The localization performance analysis reported for nodes arranged in V-shaped linear configuration. The uniqueness of this work ensures comparative study of localization performance in V- boundary array and its counterpart regular rectangular boundary node layout. The localization performance is computed in terms of Cramer - Rao Lower Bound (CRLB) of range estimate under dynamic indoor environments. The test bed experiment and simulation results demonstrate that the localization improves for lower included angle of the V- shaped array geometry and for higher aspect ratio of rectangular geometry. Interesting result of our analysis is that for V-shaped linear array with included angle of 22.5 o has better localization performance compared to its corresponding rectangular shaped node configuration.

Adaptive Autocorrelation Approach for Fingerprint-based Distance Dependent Positioning Algorithms in WLAN Indoor Areas

This paper addresses the adaptive autocorrelation approach for the fingerprinting-based distance dependent positioning algorithms (DDPAs) in wireless local area network (WLAN) indoor environment. As far as we know, although the DDPAs, like nearest neighbor (NN), K nearest neighbors (KNN) and weighted KNN (WKNN) algorithms, have been widely utilized for the indoor and outdoor location based services (LBS), the guarantee of location accuracy and precision has always been one of the significant compelling problems. Therefore, in response to this challenging task, the expected errors and associated confidence probabilities are mathematically deduced by the assumptions of logarithmic attenuation model and Gaussian distributions of received radio signal strength (RSS) at the receiver. However, because of the non line of sight (NLOS) property, time-varying interference and multipath effect, the measured radio strength varies a lot in the real-world indoor environment. Therefore, in order to fill this gap, a novel adaptive autocorrelation preprocessing approach is utilized to eliminate the singular strength from the original prestored radio map and improve the matching accuracy of DDPAs. Finally, compared with traditional DDPAs without adaptive autocorrelation preprocessing, the feasibility and effectiveness of the adaptive autocorrelation-based DDPAs are verified by approximately decreasing the average errors from 1.13m to 0.75m.

Channel capacity study of polarization reconfigurable slot antenna for indoor MIMO system

AbstractThis article presents a polarization reconfigurable slot antenna with compact feed.Fed by dual modes of coplanar waveguide, a dual orthogonal linear polarization is excited in the slot and controlled by two PIN diodes. The proposed antenna has been built and its reflection coefficient, radiation pattern, gain were tested. The capacity performance in an indoor wireless local area network scenario was measured by using two proposed antennas as access point in a 2 × 2 multi‐input multi‐output system. The results show the advantage of polarization reconfigurable antennas in channel capacity, especially at the condition of non‐line‐of‐sight. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:1209–1213, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26014

An Effective Adaptive Autocorrelation-Based Neighboring Matching Location-Aware Computing in WLANs

The recent advances of ubiquitous wireless infrastructures and requirements for high speed context-aware computing have created the opportunities to supply the high efficient location based service (LBS) in indoor wireless local area network (WLAN) environment. Because of the serious multi-path effect, unpredictable co-channel interference and inherent equipment noise, the measured signal strengths vary a lot in the real-world indoor environment. And this strength variation will also result in the performance deterioration of radio map-based neighboring matching algorithm. In response to this compelling problem, we propose the adoption of adaptive autocorrelation-based signal preprocessing method as a specific solution by effectively eliminating the singular strength from the original fingerprint set. Finally, the feasibility and effectiveness of autocorrelation-based preprocessing are also verified by decreasing about 33.4% and 32.9% of errors in k nearest neighbor (KNN) and weighted KNN (WKNN).

On Localization Using Location Fingerprints and Energy Efficient Operation of WLANs in Indoor Areas

This study focuses on the power efficient localization by fingerprinted KNN algorithm in the indoor wireless local area network (WLAN) environment. To the best of our knowledge, although the fingerprint algorithm has been utilized to supply some special location based service (LBS) with high precision and accuracy, these associated location systems have resulted in significant energy consumption. Therefore, serious attention should be paid on the energy consumption because of the hundreds to thousands of access points (APs) with high-density deployed in our university campuses and corporate offices. In response to this compelling problem, we discuss the relationship between energy costs and radio map-based location performance for the Gaussian radio signal strength (RSS) distribution with different neighboring points in KNN. Furthermore, using our results, the guidelines on the power efficient fingerprint location algorithm is also provided.

Indoor Positioning in WLAN Environment Based on Support Vector Regression and Space Partitioning

Indoor positioning system in wireless local area network (WLAN) has been a subject of intensive research due to its cost effectiveness and reasonable positioning accuracy. A new WLAN indoor positioning algorithm based on support vector regression (SVR) and space partitioning is proposed. The whole positioning environment is partitioned into several subspaces by combining k-means clustering method and binary support vector classifiers (SVC). Then the mapping function between received signal strength (RSS) and the physical space is established by SVR machine for each subspace. Subspace with much smaller physical range means more compact input feature space and leads to the enhancement of generalization capability for each SVR machine. The proposed algorithm and other well-known positioning algorithms are carried and compared in a real WLAN environment. Experimental results show that the proposed algorithm achieves 14.6 percent (0.31m) improvement than the single SVR algorithm in the sense of mean positioning error.