Projection-Based Location System via Multiple Discriminant Analysis in Wireless Local Area Networks

The Finite-Difference Time-Domain (FDTD) technique is presented in this paper as an estimation method for radio propagation prediction in large and complex wireless local area network (WLAN) environments. Its validity is shown by comparing measurements and Ray-trace method with FDTD data. The 2 GHz (802.11 b/g) and 5 GHz (802.11 a) frequency bands are used in both the calculations and experiments. The electric field (E-field) strength distribution has been illustrated in the form of histograms and cumulative ratio graphs. By using the FDTD method to vary the number of human bodies in the environment, the effects on E-field distribution due to human body absorption are also observed for 5 GHz WLAN design.

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

The ability to estimate the location of wireless device users has led to many applications that use location information to provide users and service providers with commercially valuable real time information. In this paper, we propose a low-complexity method for location estimation in an indoor wireless local area network (WLAN) deployment. The proposed method relies on measurements of signal strength (SS) of multiple access-points (APs) in a WLAN and is based on a piecewise-linear model that represents distances from APs in terms of SS measurements. The proposed scheme has significantly lower computational and storage requirements as compared to conventional location estimation methods.

In wireless local area networks (WLANs), link-layer multicast is a promising technology for many multimedia applications, e.g., video conference, as multicast frames can reach multiple clients simultaneously. However, the efficiency of multicast in WLANs is unsatisfactory since multicast frames are transmitted at low data rates to reach clients with poor channel quality. Moreover, the reliability of multicast cannot be guaranteed either, as multicast transmissions are not acknowledged. Some recent works have utilized smart antennas to improve multicast performance. But most of them require customized hardware and are not designed for the latest IEEE 802.11 standard, 802.11n WLANs. In this paper, we consider link-layer multicast in 802.11n WLANs with smart antennas. We partition clients into several groups, then select an antenna pattern from smart antennas and a multicast rate for each group, and transmit the same frame to each group. We first examine the gain of smart antennas and reliability of various 802.11n data rates for multicast in indoor WLANs via experiments. We then present the system model for multicast over smart antennas and formulate the problem into a mixed integer program. After that, we propose an optimal algorithm for the mixed integer program, under the condition that the packet reception ratio (PRR) of all antenna patterns and data rates is known for every client. As clients join and leave the network frequently and the wireless channel is time varying, we also propose an on-line algorithm that is able to adapt the partition of clients, antenna pattern and multicast rate for each group dynamically, based on PRR reports from clients. We have implemented the on-line algorithm on off-the-shelf WLAN products and conducted extensive experiments to evaluate the performance. The results show that the proposed algorithm can significantly improve multicast throughput compared to other strategies, and at the same time guarantee high PRR for all clients.

Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization.

Wireless Local Area Network is one of the important technologies of computer networking. An emerging Wireless Local Area Network standard given by IEEE is 802.11ac. 802.11 ac is exclusively a 5GHz technology. It is faster compared to 802.11n. Huge number of Personal Computers like 2-in-1s, desktops, and laptops are equipped with Wireless-AC adapters. Empirical study is conducted to form a path-loss model in the Faraday Chamber, the path-loss exponent determined from this empirical study improves the accuracy in relationship between distance and Received Signal Strength Indicator of the Wireless Local Area Network.

Accurate localization of people and mobile devices in building plays important roles in order to deliver helpful location-based services such as direction and object finding, and content delivery. This paper presents a hybrid feed-forward neural networks (FNNs)-based indoor localization system trained by means of recently developed metaheuristic algorithm. The received signal strength (RSS) from the access points (APs) is employed as input to the proposed algorithm to estimate the user location in the wireless local area network (LAN) environment. The performance of developed hybrid <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> NN-FNNs algorithms were compared with the basic <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN algorithm in term of error distance achieved. The comparative study has shown that the developed hybrid <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN-FNNs algorithm is more efficient when estimating the user location with accuracy of 86.39 percent in comparison to its predecessor algorithm (with 69.67 percent) as the error distance was minimized using metaheuristic algorithm.

In this paper, we address the received signal strength (RSS)-based indoor localization problem in a wireless local area network (WLAN) environment and formulate it as a multi-class classification problem using survey locations as classes. We present a discriminatively regularized least square classifier (DRLSC)-based localization algorithm that is aimed at making use of the class label information to better distinguish the RSS samples taken from different locations after proper transformation. Besides DRLSC, two other regularized least square classifiers (RLSCs) are also presented for comparison. We show that these RLSCs can be expressed in a unified problem formulation with a closed-form solution and convenient assessment of the convexity of the problem. We then extend the linear RLSCs to their nonlinear counterparts via the kernel trick. Moreover, we address the missing value problem, utilize clustering to reduce the training and online complexity, and introduce kernel alignment for fast kernel parameter tuning. Experimental results show that, compared with other methods, the kernel DRLSC-based algorithm achieves superior performance for indoor localization when only a small fraction of the data samples are used.

Wireless local area network (WLAN) fingerprinting has been extensively studied for indoor localization due to the formidable deployability of WLAN in indoor environment, but one major bottleneck of its practical implementation is the extensive calibration effort required to construct the radio map through the fingerprinting which is time consuming and labor intensive. In response to this compelling problem, we newly design the graph-based semi-supervised manifold alignment approach which relies on the concept of graph construction to construct a cost-efficient radio map with a small number of labeled fingerprints. In addition, the unlabeled user traces are also considered to compensate for the sparsity of the raw radio map as well as enhance its robustness to the environmental change. The extensive experiments conducted in an actual indoor WLAN environment demonstrate the performance improvement (by 27.48% at most with respect to the confidence probability of errors within 3 m) by our system compared with the existing ones using the fingerprints solely.

The power of fuzzy systems was approved in overcoming uncertainties and identifying ambiguities. Localization in wireless environments is found to have many uncertainties. In this paper a multivariable fuzzy inference system is combined with multi-nearest neighbor algorithm to estimate location of an object in an indoor wireless local area network. The proposed combination utilizes from the robustness of fuzzy systems to overcome some of the uncertainties associated with radio wave propagation and small scale signal variation. This work is focusing on the available localization techniques and how fuzzy systems are merged with other techniques to enhance the location estimation then it utilizes from the estimated position parameters and its correlation with the received signal strength, where the augmentation of one more input variable has grabbed non negligible attention in overcoming some of the uncertainties in location estimation especially the ones associated with the small scale variations due to changes in the physical environment.

In dense wireless local area network (WLAN) environments, the haphazard deployment of WLAN devices causes them to overlap. In such situations, a coverage adaptation through transmission power control (TPC) is required. At the same time, coverage holes should be taken into consideration for connectivity maintenance among devices. In this paper, a TPC scheme is proposed that reduces overlaps in an autonomous manner, without generating target coverage holes. The problem is formulated as a target coverage problem, in which wireless stations (STAs) are treated as target points, and the transmission power is appropriately controlled by the proposed scheme. In addition, we prove that the target coverage game is a potential game, and the proposed method is guaranteed to converge to a Nash equilibrium after a finite number of iterations. In order to prevent the occurrence of coverage holes that cannot be detected by the available STA information, we propose the exclusion of coverage boundary access points (APs) from reducing transmission power, with the idea that each boundary AP always contains the region covered by it alone. The coverage boundary APs are determined through Delaunay triangulation of all APs. The proposed scheme is evaluated by performing some simulations. The simulation results indicate that the proposed TPC scheme is effective in reducing coverage overlaps without generating coverage holes.

The 6 GHz band (5925-7125 MHz) has recently been proposed by the FCC to be shared with unlicensed devices This band is allocated to fixed, mobile (temporary fixed) and fixed satellite service links. The 13 GHz band (12700-13250 MHz) could also be subject to more flexible use, considering that it is allocated to the same type of incumbents. The purpose of this paper is to analyze coexistence between Wireless Local Area Networks (WLANs) and current terrestrial fixed links in these bands based on a case study approach, which can be replicated to other parts of the country. The coexistence analysis uses information from real links and accounts for the terrain morphology and actual land use to calculate the potential interference. We have conducted interference simulations from low-power indoor (LPI) WLANs to terrestrial fixed links and vice versa, from terrestrial fixed links to LPI WLANs, in the Denver metro area. The simulations have been performed in the upper part of the 6 GHz band, from 6875 to 7125 MHz, designated as the U-NII-8 band by the FCC, and in the 13 GHz band. To validate the simulations, we measured the incumbent signal power detected on the ground in a few locations in this area. We found that terrestrial fixed links operating in these bands can coexist with LPI WLANs without significant interference between each other, which will benefit consumers and encourage technology and innovation.

Recently, indoor wireless local area network (WLAN) and broadband communication systems operating at millimeter-wave frequencies have attracted much interests because they offer the advantage of high data rates. Two distinct technologies employing non radiative dielectric (NRD) guides and rectangular waveguides have been rigorously developed to efficiently achieve stringent radio-frequency (RF) system specifications [1,2]. Yoneyama et al. reported high-performance, low-loss millimeter-wave building blocks such as filters, voltage-controlled oscillators, mixers, modulators and antennas; these components can be integrated by the NRD technology. On the other hand, a single-substrate transceiver module for 76–77 GHz autonomous cruise control (ACC) radar applications was reported, where (monolithic microwave integrated circuits) MMICs and discrete devices were mounted in a multi chip module (MCM) fashion by wire-bonding and flipchip techniques. Both millimeter-wave module integration technologies are aimed for low-cost, high-volume and high-yield designs. The guiding technologies mentioned above perform well at millimeter-wave frequencies, however, some modules would require complicated analyses and mechanical fabrication processes, hence increasing the cost and complexity of the system integration efforts. For example, the well-known horn antennas are three-dimensional; thereby they need transitions to handle mode conversion and impedance matching from waveguides to planar circuits and vice versa [3]

In this paper, we have proposed a Kalman Filter (KF) - Recurrent Neural Network (RNN) based channel estimator block for Multiple-Input Multiple-Output (MIMO) multipath fading channels based on the IEEE 802.11n channel models for indoor wireless local area networks (WLAN). Two transmit antennas and two receive antennas are used here. The estimator block has been simulated in STBC coded MIMO multipath fading channel scenario with and without CSI, and figure of merit in terms of Bit Error Rate (BER) has been obtained. The design criteria and performance curve shown in the paper clearly proves the effectiveness of the proposed hybrid estimator, both in terms of performance and hardware realizability.

The 3D statistical propagation model for WLAN (Wireless Local Area Network) indoor radio coverage in 2.4GHz frequency band is proposed. Environment that was the subject of analyses is the new type of construction building characteristic for our region. The measurement campaign carried out in Traffic Faculty in Belgrade is used for the prediction model development, optimization and verification.