Wireless Local Area Network Signals Research Articles

Two‐layer modelling of IEEE 802.11x channel occupancy

AbstractLow power IoT communication signals (e.g., Bluetooth and ZigBee) may seriously suffer from the presence of high‐power co‐channel interference like wireless local area network (WLAN) signal. They may effectively avoid WLAN interference by exploiting dynamic characteristics of WLAN traffic. Representing the arrival/departure of WLAN users using an M/M/m/m queueing structure, the characterization of large‐scale dynamics of WLAN channel occupancy is considered. The characterization of small‐scale dynamics of WLAN channel occupancy is also considered by generating WLAN signal using a two‐state semi‐Markovian process. Simulation results show that the proposed model generates WLAN signal having similar statistical characteristics to those of real WLAN signal.

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%.

Activity‐related multifractal properties of Wi‐Fi signals

AbstractIn wireless local area networks (WLANs) compliant with the IEEE 802.11n standard, channel state information (CSI) is known to embed information about the surrounding environment, including the positions and movements of people. So far, this information has been inferred by exploiting certain temporal and spectral features of the related WLAN signals. In this letter, their multifractal, i.e. structural properties are examined by means of detrended fluctuation analysis. Preliminary results support the claims that (a) CSI traces exhibit multifractal properties and (b) these are influenced by human presence and activity/posture within the room.

A Hybrid Indoor Positioning Model for Critical Situations Based on Localization Technologies

The domains of positioning and tracking have undergone substantial evolution and advancements recently, especially within the concept of the Internet of Things (IoT) and in health care. Unfortunately, neither the current satellite positioning systems nor the standalone cellular systems remain useful for successfully localizing and tracking inside buildings. This paper proposes a new model that could improve the accuracy of localization in indoor environments. In addition, a broad review is conducted to discover the state-of-the-art indoor localization technologies appropriate for disasters and rescue situations. After a comprehensive study, three important technologies that need to be deeply reviewed are identified, which are wireless local area network (WLAN), dead reckoning (DR), and hybrid approaches. Based on these, a novel architecture is introduced that is more convenient to meet the operation of rescuing injured or older people in critical situations, where other technologies might be unavailable or require some extra infrastructures. The proposed model has two modes and selects one of these modes automatically. The first mode assumes the existence of both WLAN signals and smartphone sensors to be used for identifying the position of the object; otherwise, only smartphone sensors will be employed to achieve positioning. Significantly, the designated components and the flow control depicted provide a proper and suitable horizon for the next researchers who desire to develop a new indoor positioning system in this discipline with a low positioning root-mean-squared error on the centimeter scale that can later be incorporated in numerous applications relating to the IoT, health care, and evacuation plans.

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.

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 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.

Wireless Local Area Network Signal Strength Measurement for Sensor Localization without New Anchors

Wireless Local Area Network Signal Strength Measurement for Sensor Localization without New Anchors

Performance of UWB communication systems in presence of perfect/imperfect power control MAI and IEEE802.11a interference

This paper evaluates the bit error rate performance of Impulse Radio-Ultra-Wideband (IR-UWB) communication system under the impact of both multiple access and narrowband interferences operating. The Narrowband Interference (NBI) signal is modelled as the IEEE802.11a orthogonal frequency division multiplexing-based wireless local area network signal, which can be approximated by the sum of independent asynchronous tone interferers with arbitrary frequencies. Multiple Access Interference (MAI) is assumed a zero mean Gaussian process, where it has been investigated in both perfect and imperfect power control scenarios. The bit error rate performance is evaluated in three different channel models: ideal channel model (additive white Gaussian noise channel), Nakagami-m multipath fading channel model and the IEEE802.15.3a UWB channel model. It will be shown that the performance of a UWB system is severely degraded due to the presence of both types of interference, yet the NBI has more impact on the performance of UWB communication systems compared to the multiple access one.

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.

Design of printed monopole antenna with band notch characteristics for ultra‐wideband applications

A novel design of printed monopole antenna for the ultra-wideband (UWB) applications with band notch characteristics at wireless local area network (WLAN) and X-band satellite communication system has been proposed in this manuscript. Two different types of feed lines, that is, simple and tapered are used to excite the proposed antenna. Initially, designed antenna is excited by using simple 50 Ω transmission line after that tapered transmission line is introduced to enhance the impedance matching throughout the UWB frequency range (3.1-10.6 GHz). A comparison of designed antenna with simple and tapered feed line on the basis of impedance matching has also been made in this article, and found that later case reports better impedance matching in terms of Voltage Standing Wave Ratio (VSWR) bandwidth (2.45-12.0 GHz). Further, the notch bands are embodied into the designed antenna by using two split ring slots and one horizontal slot. The antenna with outer ring slot rejects the WLAN frequency band and antenna with inner ring junks the X-band satellite communication signal. The notch frequency has been adjusted by optimizing the dimensions of split ring slots as well as horizontal slot, which are incised on the structure of proposed antenna. By adjusting all the parameters of the etched slots of proposed antenna, dual band notched characteristics centered at frequencies 5.4 and 7.4 GHz has been achieved. Designed antenna is also compact (30 × 30 mm2) and capable to suppress the interference of WLAN and X-band satellite communication signal from the entire range of UWB passband. The proposed antenna is designed on low cost FR4 glass epoxy substrate with 1.6 mm thickness and dielectric constant 4.4. Antenna is designed and simulated by using HFSS V13 simulator and further, it is fabricated and tested for the validation of results. Simulated and measured results are also juxtaposed and found in good agreement with each other.

Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network.

This paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput of the SU and the probability of collisions depend on the pattern of PU activities. The pattern of PU activity was obtained and modelled from the experimental data that measure the wireless local area network (WLAN) environment. The WLAN signal has detected the transmission opportunity length (TOL) which was analyzed and clustered into large and small durations in the CTOL model. The performance of the SU is then analyzed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs.

Wireless LAN-Based CSI Monitoring System for Object Detection

Sensing services for the detection of humans and animals by analyzing the environmental changes of wireless local area network (WLAN) signals have attracted attention in recent years. In object detection using WLAN signals, a widely known technique is the use of time changes in received signal strength indicators that are easily measured between WLAN devices. Utilizing channel response, including power and phase values per subcarrier on multiple input multiple output (MIMO), the orthogonal frequency division multiplexing transmission was researched as channel state information (CSI) to further improve detection accuracy. This paper describes a WLAN-based CSI monitoring system that efficiently acquires the CSI of multiple links in a target area where multiple CSI measuring stations are distributed. In the system, a novel CSI monitoring station captures wireless packets sent within the area and extracts CSI by analyzing the packets on the sounding protocol, specified by IEEE 802.11ac. The paper also describes the system configuration and shows that indoor experimental measurements confirmed the system’s feasibility.

Compact UWB bandpass filter with a reconfigurable notched band

This article presents a design of compact ultra-wideband (UWB) bandpass filter with a narrow notched band. The UWB frequency responses are achieved by using a microstrip–CPW broadside coupling structure. And a pair of side-coupled short-circuited microstrip lines is introduced to generate a narrow notched band. Based on this design, by loading a pair of varactor diodes at the end of the side-coupled short-circuited microstrip lines, a reconfigurable notched-band UWB filter can also be obtained, which can avoid the existing interferences such as 5.8 GHz wireless local-area network signals and 6.8 GHz RF identification communication systems signals and in the UWB frequency range. Commercial software ANSYS HFSS is used to analyze and design this filter. Simulated results show that it has good filtering performances, compact size, and notched-band reconfigurability.

A 21-dBm $I/Q$ Digital Transmitter Using Stacked Output Stage in 28-nm Bulk CMOS Technology

This paper proposes the use of a high-power stacked output stage for a current-based in-phase/quadrature (I/Q) direct digital to RF modulator (DDRM) in bulk CMOS. The main nonlinearities associated with implementing the stacked transistor on top of the I/Q DDRM are easily compensated by a simple 2-D digital predistortion. A prototype implemented in 28-nm bulk CMOS achieves a saturated output power (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAT</sub> ) of 25 dBm and a peak output power (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> ) of 21 dBm at 1-GHz carrier frequency ( f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> ). Their corresponding efficiencies are 45% power added efficiency and 33% system efficiency (η <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sys</sub> ), respectively. In addition, it achieves 11.5% η <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sys</sub> with a -30.5-dB error vector magnitude when transmitting a 40-MHz 64 quadrature amplitude modulation wireless local area network (WLAN) signal. The WLAN signal is transmitted at 12-dBm average P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> , and at 1-GHz f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> with 8.73-dB peak to average power ratio (peak Pout of 20.73 dBm).

A glass based dual band frequency selective surface for protecting systems against WLAN signals

A polarization independent, double layer frequency selective surface on glass substrate is proposed that can transmit 900MHz and 1.8GHz GSM bands but inhibit transmission of wireless local area network (WLAN) bands, 2.4–2.5GHz and 4.98–5.825GHz. A parametric analysis is conducted to tailor the resonance frequencies at WLAN bands and the effect of each geometrical parameter on band stop response is investigated. Band stop responses of each FSS layer and the double layer final design are presented both numerically and experimentally. The simulation results showed a good agreement with the experimental results and supported the polarization independent behavior. In particular, very wide rejection bands are obtained (−10dB bandwidths more than 1.75GHz) for the 5GHz band, both numerically and experimentally, for vertical and horizontal polarizations, under normal incidence. The design can be used in double layer window glasses for protecting systems against interference with WLAN signals in indoor environments and buildings.

Cluster-Based RF Fingerprint Positioning Using LTE and WLAN Signal Strengths

Wireless Local Area Network (WLAN) positioning has become a popular localization system due to its low-cost installation and widespread availability of WLAN access points. Traditional grid-based radio frequency (RF) fingerprinting (GRFF) suffers from two drawbacks. First it requires costly and non-efficient data collection and updating procedure; secondly the method goes through time-consuming data pre-processing before it outputs user position. This paper proposes Cluster-based RF Fingerprinting (CRFF) to overcome these limitations by using modified Minimization of Drive Tests data which can be autonomously collected by cellular operators from their subscribers. The effect of environmental changes and device variation on positioning accuracy has been carried out. Experimental results show that even under these variations CRFF can improve positioning accuracy by 15.46 and 22.30% in 95 percentile of positioning error as compared to that of GRFF and K-nearest neighbour methods respectively.

Reduction of wireless signals in indoor environments by using an active frequency selective wall based on spectrum sensing

SummaryThis paper presents a wireless local area network (WLAN) spectrum control system that uses active frequency selective shielding walls to selectively reduce or pass WLAN signals in the 5 GHz band indoors. The proposed system utilizes a well‐known active frequency selective structure based on a rectangular loop with PIN diodes and is thus capable of either reducing or passing the WLAN frequency by using the on/off switching of the PIN diode based on the measured electric field strength in indoor. We designed and simulated the proposed active frequency selective shielding wallpaper by using commercial electromagnetic simulation software and confirmed that the proposed structure can reduce the WLAN signal by switching the PIN diode by applying the manufactured prototype on the exterior of an experimental apparatus containing a WLAN receiver antenna that is capable of measuring the received electric field strength. Extension of the results presented herein can be applied to reduce the wireless signal to enhance the spectrum efficiency of an indoor space.

Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges

Wireless local area networks (WLANs) have become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLANs have been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently garnered great attention due to its promising performance. Notwithstanding, WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and corresponding state-of-the-art solutions. This paper consists of three main parts: 1) conventional localization schemes; 2) state-of-the-art approaches; and 3) practical deployment challenges. Since all proposed methods in the WLAN literature have been conducted and tested in different settings, the reported results are not readily comparable. So, we compare some of the representative localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of the approaches in the literature and guide to future improvement opportunities.

Semi-Supervised Learning for Indoor Hybrid Fingerprint Database Calibration With Low Effort

The interest of indoor localization based on the IEEE 802.11 wireless local area network signal increases remarkably to support pervasive computing applications, but the process of fingerprints calibration, which is point-by-point conducted manually, is time consuming and labor intensive. To address this problem, we propose to use a novel improved semi-supervised manifold alignment approach by integrating the execution characteristic function to reduce both the number of reference points (RPs) and sampling time involved in the radio map construction. Specifically, the radio map is constructed from a small number of calibrated fingerprints and a batch of user traces, which are sporadically collected in the target environment. The user traces enable to compensate for the effort of reducing the calibration cost as well as improving the effectiveness of radio map. In addition, the cubic spline interpolation approach is applied to enrich the radio map with the limited number of RPs. Extensive experiments show that the proposed approach is capable of not only reducing the effort of fingerprints calibration remarkably, but also guaranteeing the high localization accuracy.