Mobile Communication Signal Selection Algorithm for Signal of Opportunity Navigation

With the rapid development of mobile communication technology, ranging and angling methods based on LTE and 5G signals have been widely used in location-based services (LBSs). However, mobile communication signals also undergo different degrees of occlusion and refraction during propagation because of the influence of buildings and human activities. The conventional ranging and angling methods are unable to extract high-precision navigation observations from these non-line-of-sight (NLOS) signals. Regarding the NLOS signals that seriously affect the performance of wireless positioning systems, this paper proposes a deep learning method based on the self-attention mechanism by distinguishing the difference characteristics in the autocorrelation functions of line-of-sight (LOS) and NLOS signals. It provides a basis for developing a stable LBS by accurately determining the state of received signals in complex indoor and outdoor environments. The simulation and field tests were carried out separately to evaluate the performance of the method developed in this paper under different scenarios, which achieved a discrimination accuracy better than 99.9% for NLOS signals.

In the field of telecommunication, network planning plays an important role to maximize the system performance and it is very much related to received signal strength (RSS). While transmitting signal from the transmitter to the receiver, signal has to face so many obstacles like varying environment conditions, high-rise buildings, dense forests etc. Signal strength gets affected with varying distance between the transmitter and receiver too. So for efficient network planning, information about the effect of all these parameters on received signal strength (RSS) should be known to us. So this study aimed to investigate the variation in Received Signal Strength for LTE signal (RSSLTE) with respect to the variation of four input parameters i.e. Relative humidity (25 % to 75 %), Temperature (12 C to 48 C), Air quality index for Particulate Matter 2.5 (50 to 500) and Distance from the base station (98 m to 300 m). Optimization of received signal strength was also carried out by using response surface methodology. Optimum value of received Signal Strength for LTE signal (RSSLTE) was found to be -87.0393 dBm when the input parameters i.e. Temperature, Relative Humidity, Air Quality Index and Distance from Base Station were 13.10 C, 31.30 %, 52.10 and 98 m respectively.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

In today’s era, mobile communication is the fastest-growing application in the field of wireless communication. In mobile communication received signal strength of radio waves at the receiver end plays an important role in network planning. When the radio waves travel from the transmitter to receivers end through the wireless channel, it has to face many challenges due to varying environment conditions or due to the presence of some obstacles, such as high-rise buildings, some vegetation, water body, etc. All these factors may affect the received signal strength of these radio waves. So for proper mobile network planning, it is required to have complete knowledge of how these parameters effects the received signal strength. So keeping this aspect in mind, this research work was focused on studying the variation in received signal strength for GSM signal with respect to four input parameters, that is, varying atmospheric temperature, Relative Humidity, Air quality index (particulate matter 2.5) and the distance from the base station and optimization of received signal strength was carried out by using response surface methodology.KeywordsRSMGSMReceived signal strengthOptimization

Studies has suggested that the association between Received Signal Strength Indication (RSSI) and measured distance between the transmitter and receiver can be one of the key elements for ranging and localization technologies. In this study, measurements of RSSI and the distance between 1800MHz receiver and transmitter within an indoor environment were acquired, recorded and analyzed. Using the data, correlation for a select indoor environment was derived. By comparing the recorded distance values acquired at set RSSI with values calculated based on the Free Space Path Loss equation, provisional estimation of the indoor signal fading can be established. The experiment was the initial part of a three-stage work that will later entail the comparison to that of outdoor as well as clean room environments. The final result of the study hopes to achieve a simple yet improved indoor fading model experienced by mobile communication signals.

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

Comparative metrics for computational approaches in non-uniform street-canyon flows

Location based services are gathering an even wider interest also in indoor environments and urban canyons, where satellite systems like GPS are no longer accurate. A much addressed solution for estimating the user position exploits the received signal strengths (RSS) in wireless local area networks (WLANs), which are very common nowadays. However, the performances of RSS based location systems are still unsatisfactory for many applications, due to the difficult modeling of the propagation channel, whose features are affected by severe changes. In this paper we propose a localization algorithm which takes into account the nonstationarity of the working conditions by estimating and tracking the key parameters of RSS propagation. It is based on a Sequential Monte Carlo realization of the optimal Bayesian estimation scheme, whose functioning is improved by exploiting the Rao-Blackwellization rationale. Two key statistical models for RSS characterization are deeply analyzed, by presenting effective implementations of the proposed scheme and by assessing the positioning accuracy by extensive computer experiments. Many different working conditions are analyzed by simulated data and corroborated through the validation in a real world scenario.

Differences between digital mapping data and actual propagation conditions in urban canyon environments challenge most geographical routing algorithms proposed for use in Vehicular Ad Hoc Networks (VANETs). We explore a strategy for capturing this complex, time-variant, radio-frequency (RF) propagation environment. We demonstrate that spatial loss behaviors in urban canyon propagation topographies can be reconstructed from measurements with useful fidelity using a tomographic estimation algorithm. The algorithm uses measurements of Received Signal Strength (RSS) seen by VANET nodes and the geographical positions of these nodes at the time of measurement. Simulation of tomographic reconstructions for monotonically increasing positioning errors, which are typical of Global Navigation Satellite System (GNSS) based positioning schemes, demonstrated useful fidelity and good convergence, even for positioning errors that are typical in low-cost commodity hardware.

In urban canyons, most of Global Navigation Satellite System (GNSS) signals are blocked or reflected by high-rising buildings. The lack of redundant fault-free measurements poses a great challenge for detecting and isolating faulty measurements, degrading the GNSS positioning accuracy, particularly in the cross-street direction. The current GNSS positioning accuracy in urban canyons is not sufficient for identifying the sides of a street, which is highly demanded by numerous pedestrian applications. To address this issue, this study proposes a sidewalk matching technique to improve the redundancy of fault-free measurements and achieve positioning accuracy at sidewalk level in urban canyons. The technique relies on a simple pedestrian map and smartphone sensors, without Three-Dimensional (3D) building models. A new algorithm is proposed to identify the sides of the street, and a searching algorithm is developed to find GNSS position under constraints of the sidewalk. Extensive evaluation of the proposed technique based on a real-time platform demonstrated that the accuracy of GNSS positioning was less than 5 m, enabling seamless determination of the street sides in urban canyons. The new sidewalk matching technique can be used in various real-life applications, including pedestrian collision avoidance systems, online ride-hailing service, and jaywalking monitoring.

Passive source localization is an important research area with numerous applications in mobile communications and signal processing. This paper presents an analysis of energy-based localization performance with unknown transmit power using received signal strength (RSS) and RSS difference (RSSD) models. These models are widely used because of their low cost and simple implementation. Previous studies were based on the assumption that the source node transmit power is known, which is not practical in many situations. The Cramer–Rao lower bound for the RSS and RSSD models with correlated noise is derived as a performance benchmark for the mean squared error (MSE) of the location and transmit power estimation. It is shown that the MSE can be factored into two independent terms corresponding to the geometric distribution of the sensors and the channel parameters including the noise variance. The effect of the sensor and source node positions on the location accuracy and the MSE is derived via the geometric dilution of precision (GDOP). The GDOP is then used to evaluate the effect of the joint estimation of unknown power and source location on the performance. Lower bounds on the GDOP are derived to obtain the minimum MSE. The RSSD solution is obtained by formulating the nonlinear and nonconvex objective functions into a convex optimization problem through relaxation and semidefinite programming. Simulation results are presented with the source inside or outside the sensor network.

WLAN is now widely used for emergency purposes because of its fast deployment capability. Efficient and intelligent arrangement and selection of access points (AP) are very crucial in emergency situations. Coverage and capacity are two central problems faced when APs are placed in a disaster scene. The paper proposes a power efficient algorithm for AP selection in emergency situations. The current AP selection algorithm only considers received signal strength, and this algorithm arise problems like overloading in APs, congestion and power wastage in networks. But the proposed Dynamic Load Balancing Fairness Algorithm (DLBFA) overcomes all these problems by using a new selection score, obtained by optimizing channel throughput and voice quality. Apart of this score the number of nodes connected to an AP and RSSI are also considered. The main advantage of DLBA is that it can work on both data and voice applications.

Next generation wireless networks must be able to coordinate services between heterogeneous networks using a mobile terminal with multiple access interfaces. One of the challenges for continuous seamless mobility is vertical handoff which is the decision for a mobile node to switch for one network to another different type of networks. Moreover, how to select a desired target network is an important issue to balance against the network condition and user preferences. In this paper, the policies of multiple metrics to trigger a handoff while a mobile node is located in UMTS and WLAN/WiMAX are designed. The received signal strength (RSS) of neighbor networks predicted by the back propagation neural network is beneficial to perform handoff early. We propose a dwell time calculation depending to the user speed and moving pattern. If the RSS conditions are consistently true during dwell time, then unnecessary handoffs are avoidable. To select an optimal target network, policies in the merit function is presented. The weights of each handoff metric are dynamic to several networks. The results indicate that the proposed policy-enabled vertical handoff decision algorithm and network selection outperforms the other two approaches in reducing the number of vertical handoffs, Grade of Service (GoS) while increasing the average utilization per call of WLAN/WiMAX networks.

We are investigating how to use urban design approaches to conduct the layout and basic design of newly-built high-rise buildings to reduce wind obstruction and create effective urban ventilation. Few studies have addressed this issue. This study analyzes the effects of high-rise building on pedestrian-level wind in downstream street canyons based on wind tunnel test results, and examines the suitability of an urban design specification. The height (H) of high-rise buildings and the airflow passage width (S) between adjacent high-rise buildings are key control factors for this issue; H/D = 3 and S/D = 1.125 are critical parameters and recommended values (D is the height of the downstream street blocks).

In this article, we present a new architecture for the Internet of Vehicles (IoVs) that feature reduced system complexity and enhanced radio‐frequency (RF) performance. The proposed system requires no complex coding or selection algorithms, yet it significantly improves the received signal strength. We implement dual‐port antennas comprising a linearly polarized (LP) antenna and a circularly polarized (CP) antenna, which minimize the polarization loss between multipath signals and the receiving antenna. Using Wi‐Fi communications, we designed, fabricated, and examined a prototype system comprising seven circuit modules that employ spatial diversity and revised polarization diversity simultaneously. We test its performance using two antenna combinations with distinct peak gain characteristics. Our results show these antennas have highly desirable impedance matching and radiation properties and enhanced received signal strength compared to conventional techniques. As such, the proposed system can greatly improve signal quality and reliability in non‐line‐of‐sight (NLOS) IoV scenarios.