Indoor Wireless Networks Research Articles

High-Capacity Dynamic Indoor Network Employing Optical-Wireless and 60-GHz Radio Techniques

We demonstrate a hybrid radio–optical wireless communication system using a photonic integrated all-optical-cross-connect (OXC) chip to realize a highly reconfigurable and reliable bidirectional indoor wireless network that provides the ultimate capacity per user. By remote wavelength-tuning, narrow optical beams are directed to the users employing a passive two-dimensional beam steering module fixed at the ceiling in each room. Robustness regarding line-of-sight issues is achieved by implementing 60-GHz radio protection channels generated optically using a shared amplitude modulator via the OXC chip. The use of a 35-GHz reflective electroabsorption modulator (REAM) monolithically integrated with a semiconductor optical amplifier (SOA) chip is proposed for upstream wideband analog applications. The REAM-SOA provides similar performances as an REAM, but over a wide range of input optical powers and wavelengths. This mitigates the need for accurate control of the input signal, especially at remote sites where simplicity is strictly required. System experiments showed downstream optical-wireless transmission capacities of up to 35 Gb/s per user and 60-GHz protection channels with a capacity of 20 Gb/s. Upstream radio-over-fiber transmission rates of more than 35 Gb/s have also been achieved.

Optimal Target Tracking Based on Dynamic Fingerprint in Indoor Wireless Network

Indoor target tracking is a crucial application of indoor localization systems. Wi-Fi fingerprint-based target tracking/positioning has been extensively studied due to its deployability under pervasive indoor wireless local area networks. However, the majority of fingerprint-based schemes adopted the static radio map and did not make full use of continuous motion information of the target. Therefore, we build a ubiquitous indoor tracking/localization system that can maintain a dynamic fingerprint database at low cost in this paper. First, we propose an optimal target tracking algorithm exploiting the historical data of the target location information to help improve the accuracy, which adopts a modified particle filter algorithm to reduce the number of samples and computing overhead. Second, we present a database self-update method according to trajectory continuity, which is employed to update fingerprint database dynamically for keeping the system robust. Finally, we tested the optimal target tracking based on dynamic fingerprint algorithm (OTTDF) in a complex laboratory area with diverse target motion conditions and various obstacles, our experimental results indicate that the OTTDF scheme successfully handles complex indoor structure, including different target motion state, signal fingerprint changes caused by obstacles, simultaneously provides better performance in localization cost, and localization/tracking accuracy in indoor wireless network.

Reliable and Cooperative Target Tracking Based on WSN and WiFi in Indoor Wireless Networks

Indoor localization and tracking have attracted growing attention because of its widely application for indoor location-based services (LBSs). However, for indoor localization, only with single positioning technology, the positioning accuracy, and localizability are difficult to satisfy the requirement due to the complex and crowed indoor environment. An indoor cooperative localization and tracking algorithm (CLTA) based on grid is developed to solve above problems. The CLTA is divided into offline phase and online phase. In the offline phase, a cooperative localization fingerprint database is established based on reliable nodes. In the online phase, a region overlapping mechanism is used to narrow location area in multi-network surroundings at first. Then, we use a prediction mechanism to predict the mobile target position in order to further narrow the location area. At last, a cross grid strategy is used to update the data in fingerprint database if possible, aiming at improving the accuracy of localization. Simulation and experiment results show that the proposed algorithm is better than single network localization algorithm on localization and tracking performance.

Application of rogue algorithms in the WLAN planning task

The aim of this article is to examine and compare swarm optimization methods in the task of planning indoor wireless networks (WLAN). For this purpose, in the process of searching for the extremum of the criterion function, which is an optimization indicator, six swarm algorithms were used: artificial bees colony, bat, bee, cuckoo, firefly, particle swarm (bird).

A Self-Learning Based Antenna System for Indoor Wireless Network

This paper provides an intelligent antenna system for indoor coverage wireless network. With the proposed antenna system, it can estimate user equipment (UE) distribution by a long-term self-learning mechanism. Based on such estimated UE distribution, it can reallocate radio power on each antenna. As a result, it can increase frequency spectrum efficiency and improve system capacity compared with the traditional system. In addition, this solution can also save energy and control interference to neighbor system.

A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

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.

A performance comparison of in-band full duplex and dynamic TDD for 5G indoor wireless networks

In-band full duplex has emerged as a solution for high data rate and low access delay for 5G wireless networks after its feasibility has been demonstrated. However, the impact of the in-band full duplex on the system-level performance of multi-cell wireless networks has not been investigated thoroughly. In this paper, we conduct an extensive simulation study to investigate the performance of in-band full duplex for indoor 5G small cell wireless networks. Particularly, we compare the in-band full duplex with static and dynamic time division duplexing schemes which require much less hardware complexity. We examine the effects of beamforming and interference cancellation under various traffic demands and asymmetry situations in the performance comparison. Our objective is to identify under which condition and with which technology support the in-band full duplex becomes advantageous over the simpler duplexing schemes. Numerical results indicate that for highly utilized wireless networks, in-band full duplex should be combined with interference cancellation and beamforming in order to achieve a performance gain over traditional duplexing schemes. Only then in-band full duplex is considered to be advantageous at any number of active mobile stations in the network and any downlink to uplink traffic proportion. Our results also suggest that in order to achieve a performance gain with the in-band full duplex in both links, the transmit power of the access points and the mobile stations should be comparable.

Low-Crosstalk Full-Duplex All-Optical Indoor Wireless Transmission With Carrier Recovery

We propose and demonstrate a novel bi-directional free-space (FS) optical wireless communication system for indoor wireless networks. A 2-D infrared beam-steered system supporting full-duplex communication of at least 10 Gb/s capacity per wireless terminal with simple NRZ-OOK modulation format is experimentally demonstrated. The uplink (UL) is implemented using the optical carrier recovery technique, in which the downlink (DL) OOK modulation is erased by means of two cascaded SOAs operating in the saturation region. We experimentally demonstrate the system with asymmetric speeds (10 Gb/s DL/2.5 Gb/s UL) and symmetric speeds (10 Gb/s) duplex communication over an FS transmission distance of 3 m. We also report the crosstalk in such a system.

Easily Optimizable Dual-Band Frequency-Selective Surface Design

Mutual interference between indoor adjacent wireless networks is becoming an important issue as it reduces the communication speed significantly. Isolating the wireless networks by using frequency-selective surfaces (FSSs) can be an efficient solution for such interference problems. As a solution proposal, a new periodic element geometry design, which stops incoming 2.4 and 5.8 GHz industrial, scientific, and medical radio (ISM) signals, is presented in this work. Mutual effect between each resonator (2.4 and 5.8 GHz) mostly leads to the optimization stage's being inadequate or time consuming at multiband FSS designs. Therefore, a new simple design technique is presented to be able to optimize each resonance frequency independent from the other. Besides, as an important feature, this technique also allows adjusting the frequency interval between the stopbands. Analysis of the proposed surfaces is executed with Ansoft HFSS v.15 software.

Antenna Selection for Reliable MIMO-OFDM Interference Alignment Systems: Measurement-Based Evaluation

Interference alignment (IA) is a promising transmission technology enabling, essentially, the maximum achievable degrees of freedom (DOF) in $K$ -user multiple-input–multiple-output (MIMO) interference channels. The ideal DOF of IA systems have been obtained using independent MIMO channels, which is, usually rarely observed in reality, particularly in indoor environments. Therefore, the data sum rate and symbol error-rate of IA are dramatically degraded in real-world scenarios since the correlation between MIMO channels decreases the signal-to-noise ratio (SNR) of the received signal after alignment. For this reason, an acceptable sum rate of IA in real MIMO-orthogonal frequency-division multiplexing (MIMO-OFDM) interference channels was obtained in the literature by modifying the distance between network nodes and the separation between the antennas within each node to minimize the spatial correlation. In this paper, we propose to apply transmit antenna selection to MIMO-OFDM IA systems either through bulk or per-subcarrier selection, aiming at improving the sum-rate and/or error-rate performance under real-world channel circumstances, while keeping the minimum spatial antenna separation of 0.5 wavelengths within each node. Two selection criteria are considered: maximum sum rate (Max-SR) and minimum error rate (Min-ER). To avoid subcarrier imbalance across the antennas of each user, which is caused by per-subcarrier selection, a constrained per-subcarrier antenna selection is operated. Furthermore, a suboptimal antenna selection algorithm is proposed to reduce the computational complexity of the optimal algorithm. An experimental validation of MIMO-OFDM IA with antenna selection in an indoor wireless network scenario is presented. The experimental results are compared with deterministic channels that are synthesized using hybrid electromagnetic (EM) ray-tracing models. Our performance evaluation shows that the practical feasibility of MIMO-OFDM IA systems is significantly increased by antenna selection in real-world scenarios.

Calculation and Simulation of Electromagnetic Wave Propagation Path Loss Based on Matlab

Abstract In order to reliable data transmission of wireless sensor network (WSN) in indoor environment, the indoor field intensity distribution and transmission characteristics of electromagnetic wave were researched. First of all, the 3D model in specific indoor environment was built by the finite difference time domain method (FDTD).Then, layout of room, different furniture, position of field source and field source frequency had an influence on indoor field intensity distribution that were studied, and the field intensity distribution was simulated by MATALAB. According to simulation of three dimensional field intensity distribution, and it had directly shown that various factors had an influence on the indoor field intensity distribution, thus indoor wireless sensor network nodes can be reasonable deployed by it, the packet loss rate of WSN transmission was reduced from information source, and it provided theoretical basis for further improving WSN information transmission reliability.

A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization

In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi access points (APs) and long term evolution (LTE) femtocells. We propose a design framework based on multi-objective biogeography-based optimization (MOBBO). We apply the MOBBO algorithm to network planning design cases in a real office environment. To validate this approach we compare results with other multi-objective algorithms like the nondominated sorting genetic algorithm-II (NSGA-II) and the generalized differential evolution (GDE3) algorithm. The results of the proposed method indicate the advantages and applicability of the multi-objective approach.

Wireless indoor network planning for advanced exposure and installation cost minimization

AbstractThe possibility of having information access anytime and anywhere has caused a huge increase of the popularity of wireless networks. Requirements of users and owners have been ever-increasing. However, concerns about the potential health impact of exposure to radio frequency (RF) sources have arisen and are getting accounted for in wireless network planning. In addition to adequate coverage and reduced human exposure, the installation cost of the wireless network is also an important criterion in the planning process. In this paper, a hybrid algorithm is used to optimize indoor wireless network planning while satisfying three demands: maximum coverage, minimal full installation cost (cabling, cable gutters, drilling holes, labor, etc.), and minimal human exposure. For the first time, wireless indoor networks are being optimized based on these advanced and realistic conditions. The algorithm is investigated for three scenarios and for different configurations. The impact of different exposure requirements and cost scenarios is assessed.

Joint minimization of uplink and downlink whole-body exposure dose in indoor wireless networks.

The total whole-body exposure dose in indoor wireless networks is minimized. For the first time, indoor wireless networks are designed and simulated for a minimal exposure dose, where both uplink and downlink are considered. The impact of the minimization is numerically assessed for four scenarios: two WiFi configurations with different throughputs, a Universal Mobile Telecommunications System (UMTS) configuration for phone call traffic, and a Long-Term Evolution (LTE) configuration with a high data rate. Also, the influence of the uplink usage on the total absorbed dose is characterized. Downlink dose reductions of at least 75% are observed when adding more base stations with a lower transmit power. Total dose reductions decrease with increasing uplink usage for WiFi due to the lack of uplink power control but are maintained for LTE and UMTS. Uplink doses become dominant over downlink doses for usages of only a few seconds for WiFi. For UMTS and LTE, an almost continuous uplink usage is required to have a significant effect on the total dose, thanks to the power control mechanism.

Study on the Propagation Characteristics of Radio Wave for Indoor Non-Line-of-Sight

The propagation characteristics of radio wave have been an important topic in wireless communications. Based on the ray tracing method, the propagation of radio wave for indoor NLOS environment is simulated and analyzed. In this paper, the received power is obtained by simulation, and we will analyze the impact of various factors on the received power. Later, the study can be used to simplify indoor radio wave propagation model and provide a theoretical basis, which is in favor of indoor wireless communication network planning and optimization and can lay the foundation of the development of future technology.

Indoor Office Wideband Millimeter-Wave Propagation Measurements and Channel Models at 28 and 73 GHz for Ultra-Dense 5G Wireless Networks

Ultra-wideband millimeter-wave (mmWave) propagation measurements were conducted in the 28- and 73-GHz frequency bands in a typical indoor office environment in downtown Brooklyn, New York, on the campus of New York University. The measurements provide large-scale path loss and temporal statistics that will be useful for ultra-dense indoor wireless networks for future mmWave bands. This paper presents the details of measurements that employed a 400 Megachips-per-second broadband sliding correlator channel sounder, using rotatable highly directional horn antennas for both co-polarized and crosspolarized antenna configurations. The measurement environment was a closed-plan in-building scenario that included a line-of-sight and non-line-of-sight corridor, a hallway, a cubicle farm, and adjacent-room communication links. Well-known and new single-frequency and multi-frequency directional and omnidirectional large-scale path loss models are presented and evaluated based on more than 14 000 directional power delay profiles acquired from unique transmitter and receiver antenna pointing angle combinations. Omnidirectional path loss models, synthesized from the directional measurements, are provided for the case of arbitrary polarization coupling, aswell as for the specific cases of co-polarized and cross-polarized antenna orientations. The results show that novel large-scale path loss models provided here are simpler and more physically based compared to previous 3GPP and ITU indoor propagation models that require more model parameters and offer very little additional accuracy and lack a physical basis. Multipath time dispersion statistics formmWave systems using directional antennas are presented for co-polarization, crosspolarization, and combined-polarization scenarios, and show that the multipath root mean square delay spread can be reduced when using transmitter and receiver antenna pointing angles that result in the strongest received power. Raw omnidirectional path loss data and closed-form optimization formulas for all path loss models are given in the Appendices.

Particle-Filter-Based Radio Localization for Mobile Robots in the Environments With Low-Density WLAN APs

This paper proposes a new localization method for mobile robots based on received signal strength (RSS) in indoor wireless local area networks (WLANs). In indoor wireless networks, propagation conditions are very difficult to predict due to interference, reflection, and fading effects. As a result, an explicit measurement equation is not available. In this paper, an observation likelihood model is accomplished using kernel density estimation to characterize the dependence of location and RSS. Based on the measured RSS, the robot's location is dynamically estimated using the proposed adaptive local search particle filter (ALSPF), which adopts the covariance adaptation for correcting the system states and updating the motion uncertainty. To deal with low sensor density in large-space environments, we present a strategy based on the strongest signal with minimum variance to choose a subset of detectable access points (APs) for enhancing robot localization and reducing the computational burden. The proposed approaches are verified by realistic low-density WLAN APs to demonstrate the feasibility and suitability. Experimental results indicate that the proposed ALSPF provides approximately 1-m error and significant improvements over particle filtering.

Wireless Indoor Positioning Method with Evaluation of Channel Propagation Model by TR-FMM

The positioning method based on time of flight (TOF) measurement, link the time delay to the position of the small mobile communications terminal to be located. Its accuracy depends on the suitability of the channel propagating model used for the actual wireless positioning system. In an indoor wireless network, the propagating condition is very difficult to predict due to indoor environment is variable, which comes from variant room layout or staff activities. In this paper, we present a novel method which dynamically estimates the propagating model based on an inverse evaluation procedure, using only received measurement TOF obtained by the fixed access points (APs) in real time. This method is a combination of simultaneous algebraic reconstruction technique and fast matching method (FMM). Once the optimized propagating model is estimated, it is possible to accurately determine the position of MS using our existing time reversal (TR) method, a positioning method employ transceivers interoperability principles for the MS and each AP. The proposed TR-FMM method is validated by simulations. These results show that it has great advantages in accuracy and complexity for indoor positioning system. The positioning deviation is about 3.2 cm based on the simulative analysis, and the accuracy of the positioning is improved 23.66 times with taking about 2 min comparing to the time reversal without the channel information.

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these requirements are contradictory, it is not straightforward to design optimal wireless networks. Those contradicting demands have to satisfy certain requirements in practice. In this paper, a combination of two algorithms, a genetic algorithm and a quasi-particle swarm optimization, is developed, yielding a novel hybrid algorithm that generates further optimizations of indoor wireless network planning solutions, which is named hybrid indoor genetic optimization algorithm. The algorithm is compared with a heuristic network planner and composite differential evolution algorithm for three scenarios and two different environments. Results show that our hybrid-algorithm is effective for optimization of wireless networks which satisfy four demands: maximum coverage for a user-defined capacity, minimum power consumption, minimal cost, and minimal human exposure.

Exploring Mobility Indoors: an Application of Sensor‐based and GIS Systems

AbstractThe popularization of tracking devices, such as GPS, accelerometers and smartphones, have made it possible to detect, record, and analyze new patterns of human movement and behavior. However, employing GPS alone for indoor localization is not always possible due to the system's inability to determine location inside buildings or in places of signal occlusion. In this context, the application of local wireless networks for determining position is a promising alternative solution, although they still suffer from a number of limitations due to energy and IT‐resources. Our research outlines the potential for employing indoor wireless network positioning and sensor‐based systems to improve the collection of tracking data indoors. By applying various methods of GIScience we developed a methodology that can be applicable for diverse human indoor mobility analysis. To show the advantage of the proposed method, we present the result of an experiment that included mobility analysis of 37 participants. We tracked their movements on a university campus over the course of 41 days and demonstrated that their movement behavior can be successfully studied with our proposed method.