NLOS Conditions Research Articles

ANALYSIS OF 5G CELLULAR TECHNOLOGY PATHLOSS PREDICTION ON MICRO URBAN CELLS USING ABG PREDICTION MODEL IN PONTIANAK CITY

The implementation of 5G cellular technology in Indonesia needs to be studied in several aspects. So that several cellular operators in Indonesia have experienced difficulties in implementing 5G cellular technology in Indonesia, especially in Pontianak City. Therefore, the analysis of pathloss values in cellular systems is an approach to large-scale fading aspects to calculate service coverage. This research calculates the pathloss value with a case study in Pontianak City with micro urban cell characteristics. In this research, ABG (Alpha-Beta-Gamma) prediction model is used with working frequency of 3.5 GHz and 28 GHz with bandwidth of 100 MHz. The calculation results of the ABG prediction model at a frequency of 3.5 GHz has a value of 91.78 dB for LOS conditions and in NLOS conditions has a value of 123.26 dB. While at a frequency of 28 GHz the pathloss value generated is 110.71 dB in LOS conditions, while in NLOS conditions the value is 140.42 dB. The 28 GHz working frequency has a slightly higher pathloss value than the 3.5 GHz frequency and has a difference in value of 18.99 dB for LOS conditions while in NLOS conditions it has a difference in value of 17.16 dB.

Hardware Realization of Long Range (LoRa) Based Telemetry System for Aquaculture Monitoring

Freshwater fish farming must pay attention to the condition of the water content in the pond and the water quality in the pond. Fish will feel comfortable if the efficiency of oxygen levels and pond water content is maintained. Freshwater fish farming needs to optimize crop yields so as not to get losses. The problem that is often encountered is that many fish die in the pond due to lack of water. Irrigation channels leading to fishponds experience blockages. The smoothness of the irrigation channel affects the volume of water in the pond. Blockage of irrigation channels is usually due to garbage trapped in irrigation, causing dirty water to settle and water sequestration to occur in the pond. This tool system aims to improve water quality and increase the efficiency of oxygen levels by using TDS and ultrasonic sensors. The parameter value on the sensor affects the work of the tool actuator. The tool actuator is a water pump that is used as a substitute for irrigation channels and an aerator motor to increase dissolved oxygen in water by moving the propeller on the surface of the pond water. Wireless technology is used for data communication. Because the land of freshwater fish farming ponds is a large area and is far from settlements, wireless communication is suitable for enabling easy and fast access to information and services. In this research, the testing methods used are functional testing and system performance testing. Functional testing is used to prove whether the system that has been implemented can meet the requirements of operational functions as planned. System performance testing is intended to monitor several parameters that can show the ability and reliability of the system in carrying out its operational functions. The result of the overall test is the LoRa communication distance that can communicate up to about 1000 meters, proving that LoRa technology has a strong enough ability in terms of range wireless communication. The PLE (Path Loss Exponent) of the LoRa module with 100 meters in LoS (indoor) conditions is 7.77, while in nLoS conditions in obstructed in-building, it is 10.13. The average error of the ultrasonic sensor type JSN SR-04T is 0.16% and has a difference of ± 1 cm. The TDS sensor with dissolved pool water content has an average value of 142.6 ppm with an error value of 1.05%. The PDAM water content has an average value of 112.1 ppm with a sensor value of 1.38%. The water content of the lime and detergent mixture has the highest observed ppm value, which is an average of 737.33 ppm, with a sensor error of 0.14%. The water pump activates when the pool water is low (when the ultrasonic distance reaches 60 cm), and the aerator activates when the water content is contaminated (when the TDS sensor value exceeds 500 ppm).

Enabling Ray Tracing for 5G

RT (Ray Tracing) models are widely used in RAN for channel modelling. Another possible application in processing chain of base station with multiple purposes: positioning, channel estimation/prediction, radio resources scheduling and others. In this paper RT positioning technique is addressed for Urban Outdoor scenario. Proposed robust approach achieves several meters accuracy even in NLOS and multipath conditions. Developed RT tracking was used for multiuser (MU) precoder prediction and demonstrated significant capacity gain. Also, this paper discloses practical aspects for achieving high accuracy.

Analysis of MIMO Channel Capacity at 28/73 GHz with NYUSIM Channel Simulator

Analyzing the channel models in the mm-wave bandwidth is critical for 5G system performance. This study investigated the effects of 28 GHz and 73 GHz frequencies, the number of transmitting and receiving antennas, and LOS/NLOS parameters on 5G channel capacity using the NYUSIM channel simulator. As a result of the analysis, changing from a 2x2 to a 64x64 antenna structure for 28 GHz increased capacity by 29.78 times for LOS and 26.91 times for NLOS. When changing the MIMO configuration from 2x2 to 64x64 at 73 GHz, the channel capacity rises 36.88 times for LOS and 29.00 times for NLOS. With a 64x64 antenna structure, the channel capacity for 28 GHz and LOS is 8.81 times higher than for 73 GHz, and it is 12.56 times higher for NLOS. For the 28 GHz 64x64 structure and LOS condition, the channel capacity is 215.69 times higher than the NLOS condition, while this value is 307.7 times for 73 GHz.

UWB-VIO Fusion for Accurate and Robust Relative Localization of Round Robotic Teams

The relative pose estimation is one of the most fundamental components for multi-robot systems, while it still remains an open and challenging research topic in infrastructure-free environment. In this letter, we target improving the accuracy and robustness of relative pose estimation for ground robotic teams, and propose to fuse range and odometry measurements to estimate the relative pose using sliding window optimization. In the system, multiple UWB tags for ranging are equipped on each robot, and visual inertial odometry is applied for estimating the ego-motion pose for each robot. Aiming for simple and effective relative pose initialization, the triangulation uncertainty for multi-tag robots is analyzed, and an initialization method is designed. To cope with the complex environments such as continuous NLOS condition, a NLOS detection and range measurements filtering method is presented. We have conducted series of experiments to demonstrate the performance of the proposed approach.

Simulation and Measurement of on-body and off-body Communication Propagation Channels by using dual-band Magnetic Antenna Textile

Evolution of Wireless Body Area Network (WBAN) in telemedicine field keep increased along with needs and enhancement of services, nevertheless an efficient and effective form of communication becomes essential for any type of wireless communication network. One of the most important parameter for designing communication network is path loss exponent. Based on that, the purpose of this research is to know the propagation characteristics for on-body and off-body communication by comparing the simulation and measurement using magnetic textile antenna at 2.45 GHz and 924 MHz with distance variation in LOS and NLOS conditions. In this research, friis formula is used to get the path loss exponent value that shows the characteristics of propagation. The calculation of path loss exponent value for simulation and measurement for on-body communication with LOS and NLOS conditions are between 1.7-1.9 while off-body with LOS and NLOS conditions are between 2.1-5.2. These results show that the body affects path loss exponents value and have own characteristics.

Experimental test and performance of RSSI-based indoor localization in LoRa Networks

LoRa (Long Range) is a wireless communication technology, which has established itself in recent years as one of the most widely used LPWAN (Low-Power Wide-Area Network) technologies in various IoT (Internet of things) applications. It is based on Chirp spread spectrum modulation (CSS) in which the number of bits per symbol depends on the spreading factor used, that varies from 7 to 12. In this paper, we have considered the RSSI (Received Signal Strength Indicator) parameter in the indoor environment. For LoRa, we use the LoRa transceiver based on two HTCC AB01 modules for their ability to modify the spreading factor (SF). In our work, we transmit in the 868MHz frequency band, and we use a bandwidth 125 KHz and three spreading factors SF7, 9 and 12. Subsequently, we tried to measure RSSI in an indoor environment with a short distance under LOS and NLOS conditions for three spreading factors. We sent the temperature measured by the DHT11 sensor from the transmitter to the receiver to measure the RSSI for each distance and for each spreading factor. The experimental results show that LoRa can be used in an indoor context, and that the spreading factor improve the performance of this technology in terms of RSSI. Moreover, the power losses of the received signal are more important in the NLOS conditions compared to those observed in the LOS conditions.

An optimized deep learning-based trust mechanism In VANET for selfish node detection

PurposeThis study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection.Design/methodology/approachThe authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance.FindingsA deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead.Practical implicationsThe experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks.Originality/valueThis paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

Evaluation Method of Mesh Protocol over ESP32 and ESP8266

Internet of Things (IoT) is one of the newest matters in both industry and academia of the communication engineering world. On the other hand, wireless mesh networks, a network topology that has been debate for decades that haven’t been put into use in great scale, can make a transformation when it arises to the network in the IoT world nowadays. A Mesh IoT network is a local network architecture in which linked devices cooperate and route data using a specified protocol. Typically, IoT devices exchange sensor data by connecting to an IoT gateway. However, there are certain limitations if it involves to large number of sensors and the data that should be received is difficult to analyze. The aim of the work here is to implement a self-configuring mesh network in IoT sensor devices for better independent data collection quality. The research conducted in this paper is to build a mesh network using NodeMCU ESP 8266 and NodeMCU ESP 32 with two types of sensor, DHT 11 and DHT 22. Hence, the work here has evaluated on the delay performance metric in Line-of-Sight (LoS) and Non-Line-of-Sight (nLos) situation based on different network connectivity. The results give shorter delay time in LoS condition for all connected nodes as well as when any node fail to function in the mesh network compared to nLoS condition. The paper demonstrates that the IoT sensor devices composing the mesh network is a must to leverage the link communication performance for data collection in order to be used in IoT-based application such as fertigation system. It will certainly make a difference in the industry once being deployed on large scale in the IoT world and make the IoT more accessible to a wider audience.

Fine-Scale Phase-Based Ranging Through Walls and Obstructions Using Tunneling RFID Tags

Tunneling tags have already shown their long-range capability for communications and localization in line-of-sight (LoS) with a reader operating in the 5.8 GHz band. In this paper, a received signal phase-based positioning method is investigated using semi-passive and tunneling radio frequency identification (RFID) tags in various non-line-of-sight (NLoS) environments. Experimental results show that a tunneling tag achieves gains above 10.2 dB and 19.6 dB when communicating through a cinder block and a plaster wall, respectively. When using the tunneling tag, with proper calibration, an average distance estimation accuracy of 1.33% and 2.37% is achieved in a hallway and classroom environment with clear LoS. An average estimation error of 1.03% and 2.12% is observed in through-obstruction and through-wall NLOS conditions, respectively.

High Precision Indoor Positioning System Based on UWB/MINS Integration in NLOS Condition

High Precision Indoor Positioning System Based on UWB/MINS Integration in NLOS Condition

The Attenuation Characteristics of the Body Tissue on Frequency Function in WBAN Channel

The Wireless Body Area Network (WBAN) refers to a communication network between sensors placed on the inside, on the surface, or around the body wirelessly. WBAN system cannot be separated from body tissues. Body tissues also have electrical properties depending on frequency. Therefore, body tissue can affect the phenomena occurring in radio wave propagation in the WBAN channel. One of the phenomena is attenuation. This study investigates the impacts of body tissue on the WBAN channel and the effects of frequency on the attenuation of body tissue in the WBAN channel. The measurement of magnitude response was carried out with the human body as the measurement object by utilizing the S21 parameter measurement with a vector network analyzer. In NLOS conditions, a human body was located between two coplanar Vivaldi antenna. Measurements were conducted on the head, chest, and abdomen. The frequency used was in the range of 2 GHz to 6 GHz. The body tissue attenuation was obtained by finding the difference between the magnitude measurement response on the LOS and NLOS conditions. The attenuation data were analyzed using statistical and numerical analysis to determine the effect of frequency on the attenuation of the human body tissues. Based on the analysis results, it was identified that the frequency affected the human body tissue attenuation. The enhancement attenuation of the human body tissues occurred when the frequency was higher. Moreover, there was a significant difference in the body tissue attenuation in different parts of the body.Keywords: attenuation, body tissues, s-parameters, wireless body area network.

Zero Knowledge Authentication Modification for Drone and Server Communication Security

Drones are now being used in various fields. One of the uses of drones is for delivery service. When delivering goods, the drone needs to report its condition while traveling to the server so that the server can monitor the drone and the server can give some commands to the drone. However, when the drone receives data from the server, there are many vulnerabilities it has against the attacker that can take control of the drone device. Taking control of the drone can occur if the attacker manages to send a script that can take over control of the drone. If the attacker can take control over the drone device, the attacker can steal the drone both physically or data of the drone. For this reason, in this study, we propose an authentication algorithm that can filter out who is allowed to send data to the system. The algorithm used is Zero-Knowledge Authentication. To improve the performance of the Zero-Knowledge Authentication algorithm in terms of authentication speed, we try to combine it with the secret key generated from the Secret Key Generation process. From the results of authentication testing, the effectiveness of the proposed algorithm after a Brute Force attack experiment is 100% for single attacker scenarios and for multiple attackers the effectiveness is 100% in LOS and NLOS conditions. Because drones have limited resources, the authentication time experiment is also performed and the result is that in all scenarios the time required to authenticate is in microseconds so that the proposed algorithm can be implemented on drones with limited resources.

Data-Selective Least Squares Methods for Elliptic Localization With NLOS Mitigation

In this letter, we consider the problem of 2-D elliptic localization, where multiple spatially separated sensors, including the transmitters and receivers, are exploited to locate the signal reflecting/relaying target in the mixed line-of-sight/nonline-of-sight (NLOS) environments. We begin by revisiting a plain closed-form linear least squares (LS) solution. As it is vulnerable to the existence of erroneous time-sum-of-arrival (TSOA) measurements under the NLOS conditions, we then devise two new data-selective LS methods, by which the outliers can be identified and mitigated and a higher level of resistance to the NLOS bias errors can be provided. To conduct data selection, the first algorithm combines the use of the traditional linear LS estimator and an additional cost function, whereas the second relies on the parameterization of the TSOA-defined ellipses and follows a nonlinear LS estimation criterion. Based on the simulations, we demonstrate the effectiveness of the proposed methods in NLOS error mitigation at acceptable computational costs.

Doppler Shift Effect at The Communication Systems with 10 GHz around Building

This research described the Doppler shift effect for the communication systems. The mobile station moves with various velocities around the building’s environment. Doppler’s shift influences the communication systems. The frequency communication was used 10 GHz and its influenced by atmospheric attenuation. This research consisted of propagation with LOS and NLOS conditions, mobile station velocity variation, height buildings variation, and transmitter power variation. This research described frequency maximum at Doppler shift, coherence time, and signal to noise ratio. More increase Doppler shift of coherence time caused signal noise ratio to decrease.

Propagation Measurements and Modeling for Low Altitude UAVs From 1 to 24 GHz

In most countries, small (<2 kg) and medium (<25 kg) size unmanned aerial vehicles (UAVs) must fly at low altitude, below 120 m, and with permanent radio communications with ground for control and telemetry. These communications links can be provided using 4G/5G networks or dedicated links, but in either this case the communications can be significantly degraded by frequent Non Line of Sight (NLoS) propagation. In this case, reflection and diffraction from ground objects are critical to maintain links, and hence accurate propagation models for this must be considered. In this letter we present a model for path loss when the UAV is flying in NLOS conditions. The study is based on measurements made at frequencies of 1, 4, 12, and 24 GHz with a UAV flying in a suburban environment. Measurements have been used to model NLOS propagation below 4 GHz, where the dominant mechanism is diffraction, and above 4 GHz where multipath is the dominant propagation mechanism. The model can be of use in predicting excess losses when UAVs fly in suburban NLOS conditions.

Improving the Accuracy and Robustness of Ultra-Wideband Localization Through Sensor Fusion and Outlier Detection

This article presents sensor fusion techniques for ultra-wideband-based localization to achieve sufficient accuracy and robustness for the control of vehicles in an industrial environment. We propose two outlier detection methods in combination with an extended Kalman Filter, and present experimental validation where 10 cm accuracy is achieved even in difficult NLOS conditions.

Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks.

In this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on the basis of the measurement data for dynamic scenarios in an indoor environment. The obtained results clearly prove the validity of the proposed DL approach in the UWB WBANs and high (over 98.6% for most cases) efficiency for LOS and NLOS conditions classification.

Adaptive Modulation and Coding (AMC) around Building Environment for MS Communication at The Train

This paper focused at communication systems when train moved. The communication propagation was influenced by building environment. The communication condition that used uplink direction. Mobile station was placed inside the train where moved with 500 km/hour velocity. The analysis was used consists of Doppler effect, atmospheric, and building environment. The variation communication frequency was used consists of 2.6 GHz, 5 GHz, and 10 GHz. Diffraction mechanism caused building was used single knife edge method. The result was showed SNR value from the communication frequency variation, distance comparison between LOS and NLOS, alteration adaptive modulation and coding (AMC), and coverage area percentage. Modulation and Coding Scheme (MCS) was used for AMC consists of QPSK, 16 QAM, and 64 QAM. Decreases of SNR value can be occured when communication distance for NLOS condition farther then LOS condition. That distance became increases because was obstructed with high building. Changeable of AMC value was caused propagation condition. The coverage area percentage when communication frequency that was used consists of 2.6 GHz, 5 GHz, and 10 GHz was obtained 88.4%, 88.4%, and 81.7%.

Intelligent Context-Aware Communication Paradigm Design for IoVs Based on Data Analytics

IoVs have been envisioned to improve road safety and efficiency, and provide Internet access on the move, by providing a myriad of safety and infotainment applications to drivers and passengers. However, with limited spectrum resource, harsh wireless channel, and variable vehicle density, IoV communication faces severe challenges to achieve scalability, efficiency, and reliability. In this article, we propose a context-aware IoV paradigm design to enhance the communication performance, where the high-level contextual information is utilized to bring intelligence in the design. Specifically, through big data analytics on large-scale IoV communication traces collected from an extensive experiment conducted in Shanghai, we investigate the impacts of different contextual information on V2V communication performance. We reveal that among many types of contextual information, the NLoS link condition is a major one that significantly affects V2V link performance. Based on that observation, we discuss three critical but challenging communication paradigm designs with context awareness of V2V link conditions: smart medium resource allocation, efficient routing establishment, and reliable safety message broadcasting. Furthermore, we present a case study of a cooperative beaconing scheme, where machine learning methods are utilized to learn the real-time link contextual information, and vehicles in deep NLoS condition choose helpers to enhance the overall beaconing reliability.