Traditional Received Signal Strength Research Articles

Securing the Future: A Resourceful Jamming Detection Method Utilizing the EVM Metric for Next-Generation Communication Systems

This paper addresses the escalating threat of malicious jamming in next-generation communication systems, propelled by their continuous advancement in speed, latency, and connectivity. Recognizing the imperative for communication security, we propose an efficient jamming detection method with distinct innovations and contributions. Motivated by the growing sophistication of jamming techniques, we advocate the adoption of the error vector magnitude (EVM) metric, measured in IQ symbols, deviating from traditional received signal strength and bit error rate-based measurements. Our method achieves enhanced jamming detection sensitivity, surpassing existing approaches. Furthermore, it introduces low complexity, ensuring resource-effective detection. Crucially, our approach provides vital jammer frequency information, enhancing counteraction capabilities against jamming attacks. It demonstrates stable results against varying system parameters, such as modulation type and code rate, thereby contributing to adaptability. Emphasizing practicality, the method seamlessly integrates into 5G and LTE systems without imposing additional overhead. Versatility is demonstrated through successful operations in diverse scenarios that are run by extended simulation conditions. Theoretical analysis substantiates these advantages, reinforcing the validity of our methodology. The study’s success is further validated through laboratory experiments, providing empirical evidence of its effectiveness. The proposed method represents a significant step toward fortifying next-generation communication systems against evolving jamming threats.

WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments

ABSTRACT Indoor positioning systems based on WiFi round-trip time (RTT) measurement were reported to deliver sub-metre-level accuracy using trilateration, under ideal indoor conditions. However, the performance of WiFi RTT positioning in complex, non-line-of-sight environment remains an open research question. To this end, this article investigates the properties of WiFi RTT in several real-world indoor environments on heterogeneous smartphones. We present three datasets collected on a large-scale building floor, an office room and an apartment. The datasets contain both RTT and received signal strength (RSS) signal measures with correct ground-truth labels for further research. Our results indicated that in a complex indoor environment, RTT fingerprinting system delivered an accuracy of 0.6 m which was 107% better than traditional RSS fingerprinting and 6 m better than RTT trilateration which failed to deliver sub-metre accuracy as claimed.

CSI-based sliding window fingerprinting method tailored for a signal blocking environment in VLP systems.

In visible light indoor positioning systems, the localization performance of the received signal strength (RSS)-based fingerprinting algorithm would drop dramatically due to the occlusion of the line-of-sight (LOS) signal caused by randomly moving people or objects. A sliding window fingerprinting (SWF) algorithm based on channel state information (CSI) is put forward to enhance the accuracy and robustness of indoor positioning in this work. The core idea behind SWF is to combine CSI with sliding matching. The sliding window is used to match the received CSI and the fingerprints in the database twice to obtain the optimal matching value and reduce the interference caused by the lack of the LOS signal. On this premise, in order to reflect the different contributions of various paths in CSI to the calculation of match values, a weighted sliding window fingerprinting (W-SWF) is also proposed for the purpose of further improving the accuracy of fingerprint matching. A 4 m × 4 m × 3 m indoor multipath scene with four LEDs is established to evaluate the positioning performance. The simulation results reveal that the mean errors of the proposed method are 0.20 cm and 1.43 cm respectively when the LOS signal of 1 or 2 LEDs is blocked. Compared with the traditional RSS algorithm, the weighted k-nearest neighbor (WKNN) algorithm, and the adaptive residual weighted k-nearest neighbor (ARWKNN) algorithm, the SWF algorithm achieves over 90% improvement in terms of mean error and root mean square error (RMSE).

VLC localization: deep learning models by Kalman filter algorithm combined with RSS

In this paper, a new framework is presented for indoor visible light communication (VLC) system, based on Yolo v3, EfficientNetB3, and DenseNet121 deep learning (DL) models, as well as an optimization strategy. The proposed framework consists of two steps: data collecting and DL model training. To start, data is acquired using MATLAB and Kalman Filtering (KF) with averaging approaches. Second, the received signal strength (RSS) is employed as the DL models input, with the Cartesian coordinates as the DL models output. The averaging RSS approach combined with KF algorithm are used in the suggested framework. This work introduces the impacts of Non-Line-of-Sight (NLoS) for initial reflection and Line-of-Sight (LoS) based on the three mentioned DL models. Furthermore, we used Bayesian optimization and automatic hyper-parameter (HP) optimization to increase system efficiency and to reduce positioning error in DL models. The obtained results show that the models outperform existing the HP-RSS-KF-LoS-DL models in terms of localization error when compared to traditional RSS signal-based localization techniques. Many performance indicators are considered to evaluate the proposed framework resiliency, including accuracy (ACC), area under the curve (AUC), sensitivity (Se), and precision (Pr), as well as F1-score, root mean square error (RMSE), training, and testing time. The DL models are generated and trained using Python software on a Kaggle Notebook GPU cloud (2 CPU cores and 13 GB RAM). The achieved results are: 99.99% ACC, 99.98% AUC, 98.88% Se, 98.98% Pr, 99.97% F1-score, 0.112 cm RMSE, and 0.29 s testing time. The proposed system could be easily deployed for autonomous applications, based on the analysis of the experimental data. Several applications can be used depending on enhancing the localization of VLC system in military systems, underwater systems, and indoor systems like hospitals, hotels, libraries and malls.

The Usage of ANN for Regression Analysis in Visible Light Positioning Systems.

In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error () in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that is low even for lower values of SNR, i.e., values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.

Social Prediction-Based Handover in Collaborative-Edge-Computing-Enabled Vehicular Networks

Collaborative edge computing (CEC) can realize the cooperation and integration of heterogeneous resources distributed in adjacent areas, increasing the overall resource utilization efficiency. In a CEC-supported heterogeneous vehicular network composed of different access solutions, including cellular vehicle-to-everything (C-V2X) and dedicated short-range communications (DSRC), good network connections can guarantee timely access to edge resources. How to maintain stable and high-quality network connections for vehicles is a crucial issue. With traditional received signal strength (RSS)-based handover schemes, vehicles may encounter severe ping-pong effects and even direct handover failures leading to data packet loss. In this article, to overcome the frequent handover problem caused by vehicles' high-speed motion and the ever-changing network environment, we propose a trajectory prediction-based handover scheme. In this scheme, the sojourn time of a vehicle staying in each candidate network's coverage can be obtained through a social long short-term memory (social-LSTM)-based prediction model. Together with the signal strength, available bandwidth, and cost, the sojourn time is also taken as a handover decision attribute parameter. Simulation results show that our proposed scheme can reduce the number of handovers effectively.

Improving the visible light communication localization system using Kalman filtering with averaging

Two techniques are proposed for improving the accuracy of localization estimation in indoor visible light communication systems, namely, averaging and Kalman filtering with averaging schemes. In the averaging technique, the receiver position is estimated using the received signal strength (RSS) indication method multiple times (e.g., N samples), and the acquired estimations are averaged over all samples. To further improve the localization, the Kalman filtering algorithm is adopted to estimate the received power over N samples, followed by applying the RSS technique on the average received power. The proposed techniques are analyzed mathematically, considering the effects of both line-of-sight (LOS) and first-reflection from non-LOS propagations. The performance of the proposed techniques is determined by evaluating the positioning errors in a typical room. The results are compared to that of the traditional RSS system. Simulation results reveal that an improvement of about 33.3% in the average positioning error is achievable when using the averaging scheme as compared to that of the traditional RSS scheme. This improvement increases to 72.2% when adopting the proposed Kalman filtering scheme.

A Novel CT-Mode Through-the-Wall Imaging Method Based on Time Delay Estimation

Through-the-wall (TTW) imaging is an important class of applications with the goal of sensing the environment inside the unknown area. In the computerized tomography (CT)-mode TTW imaging method, the layout of the area is reconstructed with the received signal strength (RSS). However, the multipath included in the RSS is difficult to mitigate, resulting in blurring, wall missing, or ghosts. In this letter, a novel imaging method is proposed. First, the received signal is processed by pulse compression. The multipath signals are separated from the directly transmitted wave (DTW), and then, the time delay of the latter one is easily estimated. Second, an improved total variation regularization algorithm is used to reconstruct the layout by assuming that there are only horizontal and vertical walls in the area. The validity of the proposed method is verified by the experimental results, where a clearer image could be obtained compared with the one generated by the traditional RSS method.

Compressed sensing grid-based target stepwise location method in underground tunnel

PurposeTo solve the problem that the traditional received signal strength indicator real-time location method does not test the attenuation characteristics of the electromagnetic wave transmission in the location area, which cannot guarantee the accuracy of the location, resulting in a large location error.Design/methodology/approachAt present, the compressed sensing (CS) reconstruction algorithm can be roughly divided into the following two categories (Zhouzhou and Fubao, 2014; Lagunas et al., 2016): one is the greedy iterative algorithm proposed for combinatorial optimization problems, which includes matching pursuit algorithm (MP), positive cross matching tracking algorithm (OMP), greedy matching tracking algorithm, segmented orthogonal matching tracking algorithm (StOMP) and so on. The second kind is the convex optimization algorithm, which also called the optimization approximation method. The common method is the basic tracking algorithm, which uses the norm instead of the norm to solve the optimization problem. In this paper, based on the piecewise orthogonal MP algorithm, the improved StOMP reconstruction algorithm is obtained.FindingsIn this paper, the MP algorithm (OMP), the StOMP and the improved StOMP algorithm are used as simulation reconstruction algorithms to achieve the comparison of location performance. It can be seen that the estimated position of the target is very close to the original position of the target. It is concluded that the CS grid-based target stepwise location method in underground tunnel can accurately locate the target in such specific region.Originality/valueIn this paper, the offline fingerprint database in offline phase of location method is established and the measurement of the electromagnetic noise distribution in different localization areas is considered. Furthermore, the offline phase shares the work of the location process, which greatly reduces the algorithm complexity of the online phase location process and the power consumption of the reference node, meanwhile is easy to implement under the same conditions, as well as conforms to the location environment.

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point.

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

Localization Using Blind RSS Measurements

Localization using received signal strength (RSS) measurements becomes popular due to the simplicity of practical implementation. Traditional RSS measurements are obtained after successful demodulation such that the impact of the background noise (BGN) is ignored. However, critical information for demodulation might be expensive or difficult to obtain in hostile or harsh environments. In this case, the RSS measurements need to be blindly collected without demodulation and hence characterized by a recent model with the BGN power (already validated by real-life data). This kind of measurement is referred to as “blind RSS measurement”. In this letter, we introduce four models for the localization using the blind RSS measurements, respectively considering the BGN power and the transmit power to be known or unknown. A general semi-definite programming solution that applies to all these models is proposed. The corresponding Cramer–Rao lower bounds are presented, indicating a significant impact of the BGN power on the estimation accuracy. Numerical results show the proposed method yields a good and reliable performance with different models.

Kalman Filtering Framework-Based Real Time Target Tracking in Wireless Sensor Networks Using Generalized Regression Neural Networks

Traditional received signal strength indicators (RSSI’s)-based moving target localization and tracking using wireless sensor networks (WSN’s) generally employs lateration/angulation techniques. Although this method is a very simple technique but it creates significant errors in localization estimations due to nonlinear relationship between RSSI and distance. The generalized regression neural network (GRNN) being a one-pass learning algorithm is well known for its ability to train quickly on sparse data sets. This paper proposes an implementation of GRNN as an alternative to this traditional RSSI-based approach, to obtain first location estimates of single target moving in 2-D in WSN, which are then further refined using Kalman filtering (KF) framework. Two algorithms namely, GRNN + KF and GRNN + unscented KF (UKF) are proposed in this paper. The GRNN is trained with the simulated RSSI values received at moving target from beacon nodes and the corresponding actual target 2-D locations. The precision of the proposed algorithms are compared against traditional RSSI-based, GRNN-based approach as well as other models in the literature such as traditional RSSI + KF and traditional RSSI + UKF algorithms. The proposed algorithms demonstrate superior tracking performance (tracking accuracy in the scale of few centimeters) irrespective of nonlinear system dynamics as well as environmental dynamicity.

WiFi‐based passive sensing system for human presence and activity event classification

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

Subcarrier selection for efficient CSI-based indoor localization

Indoor positioning systems have received increasing attention for supporting location-based services. In recent Wi-Fi networks, the rich information in the physical layer, known as channel state information (CSI), has been recognized an effective positioning characteristic rather than traditional received signal strength. However, the positioning performance depends on a very high-dimensional CSI due to all pairs of transceiver antenna, which may incur over-fitting problems. This paper proposes a subcarrier-selection approach based on information theoretic learning to compensate for over-fitting problems in CSI-based localization systems. After equalizing the histogram of CSIs, the proposed algorithm computes the information gain of each subcarrier and forms a new low-dimensional subset of CSIs to reduce the complexity and to decrease possible over-fitting caused by redundant CSIs. We demonstrate the effectiveness of the proposed algorithm through experiments. On-site experimental results demonstrate that the proposed approach outperforms traditional feature selection schemes.

Metameric Indoor Localization Schemes Using Visible Lights

In indoor environments, visible light communications paradigm is emerging as a viable promising solution complementary to well-known radio frequency technology. At the same time, the information about user's location is useful for accessing the medium via space-division multiplexing, handling over or providing access to location-based contents. In this paper, we present two localization mechanisms based on the wavelength domain by assuming that each anchor point uses a spectrally dedicated signature for the user to readily identify it. The first approach, i.e., wavelength-based localization, assumes a simultaneous transmission of three different pulse streams emitted by the red–green–blue (RGB) light emitting diodes (LEDs). The second method, i.e., color-based localization, considers the subsequent transmission of RGB pulses. Localization is then computed through traditional received signal strength and time difference of arrival approaches. Moreover, we resort to the properties of metamerism so that the red, green, and blue components used by LEDs provide the white light sensation to the human eye. The performances of the two proposed schemes are close to theoretical bounds. Even in the worst cases, the estimation error variance is in the order of $10^{-4}$ m $^2$ . Finally, the signaling request for estimating user position is less than others in the literature and is independent from the number of anchor points.

ConFi: Convolutional Neural Networks Based Indoor Wi-Fi Localization Using Channel State Information

As the technique that determines the position of a target device based on wireless measurements, Wi-Fi localization is attracting increasing attention due to its numerous applications and the widespread deployment of Wi-Fi infrastructure. In this paper, we propose ConFi, the first convolutional neural network (CNN)-based Wi-Fi localization algorithm. Channel state information (CSI), which contains more position related information than traditional received signal strength, is organized into a time-frequency matrix that resembles image and utilized as the feature for localization. The ConFi models localization as a classification problem and addresses it with a five layer CNN that consists of three convolutional layers and two fully connected layers. The ConFi has a training stage and a localization stage. In the training stage, the CSI is collected at a number of reference points (RPs) and used to train the CNN via stochastic gradient descent algorithm. In the localization stage, the CSI of the target device is fed to the CNN and the localization result is calculated as the weighted centroid of the RPs with high output value. Extensive experiments are conducted to select appropriate parameters for the CNN and demonstrate the superior performance of the ConFi over existing methods.

Similarity Analysis-Based Indoor Localization Algorithm With Backscatter Information of Passive UHF RFID Tags

Passive radio frequency identification (RFID) is a low cost and low complexity localization technology. With introduction of reference tags, RFID technology can provide on-line reference information for pattern matching localization algorithms. One of the most popular algorithms is $k$ -nearest neighbor algorithm. While how to decide the $K$ value in estimation is still a problem. In addition, traditional received signal strength indicator (RSSI)-based algorithms suffer from multipath effects, it is difficult to further improve the localization accuracy. In this paper, we analyze the similarity of backscatter signals and present a novel range based indoor localization method, SAIL. According to the propagation distances of signals, reference tags that have similar distances to a reader antenna can be considered as a group. Inspired by the idea of grouping, clustering algorithm is utilized to get candidate tags for ranging. This method does not need to fix the number of reference tags in estimation. Besides RSSI, the RFID reader can also extract phase of signals coming from backscattered signals of responding tags. This guides us to combine RSSI and phase in similarity measure for clustering. In the simulation and experimental tests, SAIL is superior to other RFID schemes considering cost, location estimation error, and flexibility.

Channel State Reconstruction Using Multilevel Discrete Wavelet Transform for Improved Fingerprinting-Based Indoor Localization

Recently, channel state information (CSI) has been adopted as an enhanced wireless channel measurement instead of received signal strength (RSS) for indoor WiFi positioning systems. However, although CSI contains richer location information, a challenging problem is the severe dynamic range and fluctuation among the high-dimensional channels, which may degrade accuracy and cause overfitting problems. This paper proposes a novel algorithm for improved fingerprinting-based indoor localization. The proposed algorithm decomposes the CSI sequence using the multilevel discrete wavelet transform (MDWT) and normalizes the wavelet coefficients by employing histogram equalization. The robust features were then extracted by reconstructing CSI through the inverse MDWT of the normalized coefficients. We demonstrate the effectiveness of the proposed algorithm through experiments. The results show that the proposed algorithm outperforms traditional RSS, CSI, and two CSI-based algorithms, FIFS and MIMO.

Time and Energy Efficient TOF-Based Device-Free Wireless Localization

Device-free wireless localization (DFL) is a promising technique. It can localize and track a target without carrying any electronic device. Compared with traditional received signal strength (RSS)-based DFL, the recently proposed time-of-flight (TOF)-based DFL technique could achieve better performance. However, TOF-based DFL requires that each pair of nodes should perform a pairwise TOF measurement sequentially, which makes it impractical for being utilized in time and energy sensitive applications. Inspired by the fact that a target shadows only a small subset of wireless links, which travel through its spatial impact area, we incorporate link state estimation function into particle filter (PF) framework to predict shadowed links and design a parallel scanning scheme to scan multiple shadowed links simultaneously. The aforementioned methods guarantee that the effective TOF measurements can be acquired timely and energy efficiently. Meanwhile, to achieve reasonable localization performance with these reduced-under-sampled link measurements, we utilize compressive sensing (CS) algorithm to reconstruct the shadowing effect map (SEM). With the map as observation likelihood function, PF algorithm could estimate targets location accurately. Experimental results with an 802.15.4a chirp spread spectrum ranging hardware testbed show that the average running time and energy consumption reduce remarkably.

Multipath cancellation by frequency diversity: a training‐free and analytical approach to accurate RSS ranging in ground‐deployed wireless sensor networks

Received signal strength (RSS) ranging in multipath environments suffers from two major drawbacks: laborious training and poor accuracy. Based on the observation that frequency-squared weighted RSS is periodic with respect to carrier frequency, multipath cancellation by frequency diversity (MCFD) is proposed, which is a novel training-free approach to RSS ranging that makes RSS measurements at multiple frequencies and combines them together to cancel out the multipath effect. Simulations and experimental results in two common scenarios for ground-deployed wireless sensor networks show that MCFD outperforms traditional RSS ranging in accuracy.