Indoor Propagation Environment Research Articles

Design of Acoustic Signal for Positioning of Smart Devices.

This paper addresses the limitations of using smartphones in innovative localization systems based on audio signal processing, particularly in the frequency range of 18-22 kHz, due to the lack of technical specifications and noise characterization. We present a comprehensive study on signal design and performance analysis for acoustic communication in air ducts, focusing on signal propagation in indoor environments considering room acoustics and signal behavior. The research aims to determine optimal parameters, including the frequency band, signal types, signal length, pause duration, and sampling frequency, for the efficient transmission and reception of acoustic signals for commercial off-the-shelf (COST) devices. Factors like inter-symbol interference (ISI) and multiple access interference (MAI) that affect signal detection accuracy are considered. The measurements help define the frequency spectrum for common devices like smartphones, speakers, and sound cards. We propose a custom signal with specific properties and reasons for their selection, setting the signal length at 50 ms and a pause time of 5 ms to minimize overlap and interference between consecutive signals. The sampling rate is fixed at 48 kHz to maintain the required resolution for distinguishing individual signals in correlation-based signal processing.

Sub-Terahertz MIMO Spatial Multiplexing in Indoor Propagation Environments

Sub-Terahertz MIMO Spatial Multiplexing in Indoor Propagation Environments

A Multipath-Aided Localization Method for MIMO-OFDM Systems via Tensor Decomposition

Wi-Fi indoor localization has witnessed an increasing interest, benefiting from its low cost, easy accessibility and wide deployment. However, its applications are often restricted due to harsh multipath propagation in indoor environments. In this letter, we propose a tensor-based localization algorithm that fully exploits the multipath components (MPCs) within the channel state information. We first formulate the location parameter estimation problem as a tensor decomposition problem and ensure the uniqueness by a preprocessing step. Then, a multipath-aided localization method is proposed to achieve high-accuracy localization via fusing time-difference-of-arrival and angle-of-arrival information from all MPCs. Experimental results show that the proposed method can provide a stable sub-meter-level localization performance in multipath environments.

A Survey of Blind Modulation Classification Techniques for OFDM Signals.

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed.

Indoor Localization with a Single Access Point Based on TDoA and AoA

Indoor localization in 5G and the Internet of Things has been paid increasing attentions. Recent advances have demonstrated reasonable localization accuracy that can be provided by using a single access point (AP). Yet, existing single AP localization solutions require frequency hopping or suffer from degraded accuracy performance due to complicated indoor propagation environment. This paper proposes a new single AP indoor localization method based on time difference of arrival (TDoA) and angle of arrival (AoA). We divide multiple antennas into two groups, one group with close distance for AoA estimation and the other group with the farther distance for TDoA estimation. Then, a joint estimation of AoA and ToA is carried by a spatial smoothing-based multiple signal classification (MUSIC) algorithm. Finally, we propose a weighted least squares method based on TDoA and AoA estimations to achieve indoor localization. Simulation results verify the effectiveness of the proposed single AP localization method.

Design and Testbed Implementation of Blind Parameter Estimated OFDM Receiver

Designing an intelligent or adaptive transceiver system is becoming a promising technology for upcoming generations of wireless communication systems due to its adaptivity, spectrum efficiency, and low-latency characteristics. However, there is no work available until now that characterizes and demonstrates complete adaptation in the physical layer for orthogonal frequency-division multiplexing (OFDM) systems. In this article, we propose and implement sequential blind parameter estimation methods for OFDM signals using radio frequency (RF) testbed setup in a realistic scenario. The estimations include the number of subcarriers, symbol duration, cyclic prefix, oversampling factor, symbol timing offset (STO), and carrier frequency offset (CFO). The proposed algorithms also include blind modulation classification for linearly modulated signals over a frequency-selective fading channel. The parameter estimation has been carried out through a cyclic cumulant process. The modulation formats are classified by using normalized fourth-order cumulant in the frequency domain. The STO and CFO are estimated by a proposed modified maximum likelihood algorithm. The performances of parameter estimations, modulation classification, and synchronization are measured through analytical, simulation, and measurement studies. The overall performance of the OFDM system is provided in terms of the received constellation diagram and bit error rate (BER) over an indoor propagation environment.

Design and Testbed Implementation of Multiuser CFOs Estimation for MIMO SC-FDMA System

In this paper, we propose and implement blind multiuser carrier frequency offsets (CFOs) estimation for multi-input multi-output (MIMO) single carrier frequency division multiple access (SC-FDMA) uplink system using orthogonal propagator method (OPM) over a noisy frequency-selective channel. The performance of typical OPM is limited at low signal-to-noise ratio (SNR) due to its noise-free signal model. The additive white Gaussian noise mainly impacts the diagonal elements of the data covariance matrix (DCM) of the received signal. First, the proposed fractional CFOs estimator minimizes the noise effect by reconstructing the denoised diagonal elements of the DCM for the received signal through cubic Hermite interpolation. Therefore, we use modified OPM to estimate multiple CFOs for MIMO SC-FDMA systems. Second, we have used an iterative method based on the first-order Taylor series approximation of the CFO vector to avoid peak ambiguity and reduce the number of iterations required in the grid search. The mean square error of the proposed CFO estimator considerably improves compared with the existing methods at a low SNR regime. The bit error rate performance of the proposed method for MIMO SC-FDMA is also compared with the existing methods. Finally, the proposed estimator is validated by implementing it on radio frequency hardware testbed over an indoor propagation environment.

Impact of People’s Movement on Wi-Fi Link Throughput in Indoor Propagation Environments: An Empirical Study

There has been tremendous growth in the deployment of Wi-Fi 802.11-based networks in recent years. Many researchers have been investigating the performance of the Wi-Fi 802.11-based networks by exploring factors such as signal interference, radio propagation environments, and wireless protocols. However, exploring the effect of people’s movement on the Wi-Fi link throughout the performance is still a potential area yet to be explored. This paper investigates the impact of people’s movement on Wi-Fi link throughput. This is achieved by setting up experimental scenarios by using a pair of wireless laptops to file share where there is human movement between the two nodes. Wi-Fi link throughput is measured in an obstructed office block, laboratory, library, and suburban residential home environments. The collected data from the experimental study show that the performance difference between fixed and random human movement had an overall average of 2.21 ± 0.07 Mbps. Empirical results show that the impact of people’s movement (fixed and random people movements) on Wi-Fi link throughput is insignificant. The findings reported in this paper provide some insights into the effect of human movement on Wi-Fi throughputs that can help network planners for the deployment of next generation Wi-Fi systems.

Channel Impulse Response at 60 GHz and Impact of Electrical Parameters Properties on Ray Tracing Validations

This paper outlines a study of the effect of changing the electrical properties of materials when applied in the Wireless InSite (WI) ray-tracing software. The study was performed at 60 GHz in an indoor propagation environment and supported by Line of Sight (LoS) and Non-LoS measurements data. The study also investigates other factors that may affect the WI sensitivity, including antenna dimensions, antenna pattern, and accuracy of the environment design. In the experiment, single and double reflections from concrete walls and wooden doors are analysed. Experimental results were compared to those obtained from simulation using the WI. It was found that materials selected from the literature should be similar to those of the environment under study in order to have accurate results. WI was found to have an acceptable performance provided certain conditions are met.

Design and Implementation of Blind Modulation Classification for Asynchronous MIMO-OFDM System

In this article, we design and implement a tree-based blind modulation classification algorithm for asynchronous multiple-input–multiple-output and orthogonal frequency-division multiplexing (MIMO-OFDM) systems. It can classify many of the linearly modulated signals, such as binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), offset QPSK, minimum shift keying, and 16-quadrature amplitude modulation. The proposed classifier works in the presence of unknown frequency, timing, and phase offsets and with no prior knowledge of channel state information. Classification is performed in three steps. In the first step, preprocessing is done on the received signal to nullify the effect of timing offset. In the second step, key features are extracted by calculating higher order cumulants of the frequency-domain signal. In the third step, thresholds are determined by using the likelihood ratio test. A closed-form theoretical derivation for the probability of correct classification is obtained. The Monte Carlo simulations are conducted to compare the performance of the proposed algorithm with the existing algorithms. Finally, the proposed algorithm is validated through radio frequency testbed measurements over an indoor propagation environment.

Analysis on ceiling space radio wave propagation in indoor environment

AbstractIn this paper, received power levels in 420 M/920 M/2.4G/5.3 GHz bands for sensor networks and WLANs are obtained by ray‐trace analysis in an office environment and the detail properties of propagation paths are analyzed. As a result, 25% average power of the total power arrives through ceiling spaces in the rooms where electromagnetic waves are hard to arrive through the walls. This means that the power remains in −6 dB even though the propagation paths are blocked by several metallic furniture beside the walls. In this indoor environment, the propagation paths through ceiling spaces can be relatively good for wireless communication. The results confirm that modeling of structures in ceiling spaces is necessary to obtain practical received power levels.

Identifying Indoor Plant Propagation Research and Education Needs of Specialty Crop Growers

There are economic and knowledge-based challenges that must be addressed for indoor farms to be viable in the United States despite their potential benefits. A mixed-methods approach was used to identify the needs of specialty crop growers and stakeholders interested in or currently using indoor propagation environments to grow seedlings, cuttings, and tissue-cultured plants. An online survey evaluated specialty crop growers’ current use of indoor plant propagation environments and their needs related to indoor plant propagation. A focus group was then conducted to further understand the needs for indoor plant propagation by stakeholders. Industry participants were largely motivated to adopt indoor propagation environments to reduce crop losses (“shrinkage”), increase productivity per unit of land area, ensure faster germination or rooting, improve plant quality, and profit from anticipated economic benefits. Research and education priority areas identified by stakeholders included economic costs and benefits (including capital investment and energy costs), improved crop quality, production time, uniformity, reduced shrinkage, and strategies to improve light management indoors. Based on the results, research efforts must determine and prioritize the most important economic considerations and production advantages to fill important gaps in knowledge about indoor plant propagation.

Analysis on Ceiling Space Radio Wave Propagation in Indoor Environment

Analysis on Ceiling Space Radio Wave Propagation in Indoor Environment

Multiple CFOs Estimation and Implementation of SC-FDMA Uplink System Using Oversampling and Iterative Method

Multiple carrier frequency offsets (CFOs) estimation in high speed single carrier frequency division multiple access (SC-FDMA) uplink system is a challenging issue. Most of the existing blind CFO estimation methods work for a certain type of carrier mapping scheme (CMS) or address a single CFO estimation by utilizing long data over frequency selective fading channel. These estimation methods become irrelevant at high speed and multi-user scenarios where CFO changes with symbols and users. In this paper, we propose a blind multiple CFOs estimation method for all CMS of SC-FDMA and implement over testbed. The proposed method requires only one SC-FDMA symbol to attain reliable estimation even in a high mobility environment, i.e., in a doubly selective channel. It exploits the phase difference present between even and odd samples of the received oversampled signal to estimate CFOs. Subsequently, an iterative joint CFO estimation and compensation (IJCEC) method is introduced to remove residual interference and increase CFOs estimation accuracy. Cramer-Rao lower bound is derived to characterize the CFO estimation accuracy of the proposed method. The work is compared with the conventional methods such as MUSIC, ESPRIT, CAZAC, and some existing methods and validated through Monte Carlo simulations. Finally, we authenticate the proposed IJCEC method by performing implementation and measurement on National Instrument testbed setup in an indoor propagation environment.

Blind Modulation Classification for Asynchronous OFDM Systems Over Unknown Signal Parameters and Channel Statistics

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. However, till date, only a few works have been reported in the literature for blind MC of orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading environment. In this paper, a blind MC algorithm has been proposed and implemented over National Instruments (NI) testbed setup for linearly modulated signals of OFDM system by using discrete Fourier transform (DFT) and normalized fourth-order cumulant. The proposed MC algorithm works in the presence of synchronization errors, i.e., frequency, timing, and phase offsets and without the prior information about the signal parameters and channel statistics. To nullify the effect of timing offset in the feature extraction process, a statistical average has been taken over OFDM symbols after introducing uniformly distributed random timing offsets in each of the OFDM symbols. In this work, we have classified a more extensive pool of modulation formats for OFDM signal, i.e., binary phase-shift keying (BPSK), quadrature PSK (QPSK), offset QPSK (OQPSK), minimum shift keying (MSK), and 16 quadrature amplitude modulation (16-QAM). Classification is performed in two stages. At the first stage, a normalized fourth-order cumulant is used on the DFT of the received OFDM signal to classify OQPSK, MSK, and 16-QAM modulation formats. At the second stage, first we compute the DFT of the square of the received OFDM signal and then a normalized fourth-order cumulant is used to classify BPSK and QPSK modulation formats. The success rate of the proposed MC algorithm is evaluated through analytical and Monte Carlo simulations and compared with existing methods. Finally, the work is validated by providing an experimental setup on NI hardware over an indoor propagation environment.

A Comparison of Empirical and Ray-Tracing Models for Indoor Radio-Wave Propagation [Wireless Corner

Both empirical and ray-tracing models are powerful tools for studying propagation in indoor environments. Although the empirical models perform well in many cases, their physical significance is not clearly understood. Ray tracing is based on physics fundamentals, and its application as well as the interpretation of its results is more general. We compare the performance of both models for indoor environments. We found that the path-loss (PL) prediction error of the ray-tracing model is comparable to the error of the linear regression fit of the PL in a site-specific empirical model. Those empirical models provide some advantages when making estimations in the same environment in which they were trained, but their performance degrades in other environments. Ray-tracing models obtain better results when a wider range of application environments is sought. Ray tracing also provides a good insight into the specific propagation phenomena that take place in the wideband radio channel, so the relevant contributions can be identified.

Dictionary-Based Statistical Fingerprinting for Indoor Localization

Indoor localization is a challenging task as the signal propagation in indoor environments does not adhere to the classical path loss or other simple models. Modern high-accuracy indoor localization algorithms rely on fingerprinting methods that entail an offline phase of data collection and mapping phase. This work puts forth a dictionary learning framework for fingerprinting indoor locations using GSM, WiFi, and other sensor measurements. Different from the existing works, the proposed methods are model-free, can handle missing measurements, and fingerprints can be flexibly updated in an online fashion. The dictionary learning algorithm itself is appropriately modified to ensure incoherence between fingerprints of nearby locations and appropriate weighing of the sparse coefficients. A hidden markov model based tracking algorithm is also provided that considers the recent history while estimating the user location. Detailed experiments are carried out over four different maps to demonstrate the efficacy and usefulness of the proposed algorithm, in comparison to the state-of-the-art classification-based and model-based indoor localization algorithms.

Design and Implementation of a Tree-Based Blind Modulation Classification Algorithm for Multiple-Antenna Systems

This paper presents the design and testbed implementation of a tree-based algorithm, which classifies many of the linearly modulated signals, such as binary phase-shift keying (PSK), quadrature PSK (QPSK), offset QPSK, $\pi $ /4-QPSK, 8-PSK, minimum shift keying, and 16-quadrature amplitude modulation. The proposed modulation classification algorithm is applicable to spatially multiplexed multiple antenna systems over frequency-selective fading channels. It works in the presence of timing, phase, and frequency offsets, without having prior knowledge of the channel state information. Classification is performed by utilizing the combined properties of the correlation functions, cyclic cumulant (CC), and cumulant. The correlation function of the received baseband signal exhibits peaks at a particular set of time lag, which represents distinctive features for the modulation formats. The CC uses the position of nonzero cycle frequency of the received baseband signals to classify modulation formats, while the cumulant employs threshold values. To authenticate the proposed algorithm, implementation and measurement are performed in an indoor propagation environment by using a National Instrument testbed setup. The performance of the proposed algorithm is compared with existing methods via Monte Carlo simulations.

RSS-Based Indoor Localization Using Belief Function Theory

Received signal strength (RSS) is a simple and low-cost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor WSN environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster–Shafer theory to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness, and computation time and its superiority compared with state-of-the-art methods. Note to Practitioners —This paper was motivated by the problem of indoor localization in the context of ambient intelligence applications. The localization technique proposed in this paper exploits RSS measurements to estimate the target node position. This technology is very attractive to system designers, due to its simplicity and low cost. This paper also suggests a new approach using, on the one hand, the belief function theory to represent and manage the imperfection of RSS measurements and the reliability of the RSS sources, and, on the other hand, a more realistic modeling of the variability of RSS measurements due to interference and attenuation phenomena that strongly affect signal propagation in indoor environments. Experimental results obtained in two different indoor environments (a residential apartment and a laboratory) are provided to demonstrate the effectiveness of the proposed approach and its superiority compared to state-of-the-art localization techniques.

A time difference of arrival/angle of arrival fusion algorithm with steepest descent algorithm for indoor non-line-of-sight locationing

In complex indoor propagation environment, the non-line-of-sight error caused by various obstacles brings great error to node positioning. Choosing the appropriate signal transmission methods is important to improve node indoor positioning accuracy. In this research, ultra-wideband technology, as baseband with high theoretical positioning accuracy and real-time performance, is implemented to transmit indoor signals. The proposed fusion algorithm with ultra-wideband baseband takes advantages from both time difference of arrival and angle of arrival algorithms, combined through the steepest descent algorithm. The non-line-of-sight signal estimation error is iteratively eliminated to achieve effective positioning accuracy. The experimental results indicate that the novel time difference of arrival/angle of arrival fusion algorithm with steepest descent algorithm can largely improve node positioning accuracy and stability.