Signal Quality Measures Research Articles

Design of a Frequency Controller System for UHF Radio Wave Receiver Device

Frequency is fundamental in data communication for transmitter and receiver stations. The most common problem found in the data communication is the signal quality measurement. A LoRa module, as one of the wireless devices with UHF (Ultra High Radio) frequencies, has extended range frequencies and signal strength parameters (RSSI, Received Signal Strength Indicator). RSSI parameter on signal bandwidth and the distance between stations. However, there is no supporting research in terms of the RSSI quality and short-distance variations. In line with this, this study aims to design a frequency control system of a LoRa module and to investigate the effect of short-distance and frequency differences on RSSI. The system consists of a transmitter, a LoRa SX1278 module, and push buttons as frequency control buttons. The system's performance was tested by receiving data over various distances from 0 to 1.25 m. The results show that the system works efficiently in controlling the frequency (ranging from 433 – 525 MHz) with good flexibility and accuracy. The system maintained an RSSI level > -120 dBm using a constant power supply and varied distances. The highest RSSI level is found at the shortest distance (0.2 m) with a higher frequency (525 MHz). There is a significant correlation between distance, frequency variations, and RSSI levels (R2 > 0.75). It can be concluded that the LoRa SX1278 module can be used as a short-distance-based data communication with moderate quality (average RSSI levels are about 80 - 120 dBm). Higher frequencies provide better RSSI levels due to increased transmission energy.

Analisis Quality of Service (QoS) Menggunakan Standar Parameter Tiphon pada Jaringan Internet Berbasis Wi-Fi Kampus 1 Unjaya

Internet is a very important need in the life of people in the world. Universities can exchange information and share data and communicate with the internet network. General Achmad Yani University Yogyakarta (Unjaya) uses the internet network for activities that occur on campus. QoS testing or internet network performance aims to maintain the stability of network access on the Unjaya campus and reduce the risk of problems such as delays in data transmission. In addition, this research also aims to determine the results of signal quality measurements in the Unjaya real-time area. In this research, QoS measurement is done by using wireshark application. The QoS parameters measured are throughput, delay, jitter, and packet loss. Based on the measurements taken, the results obtained for QOS measurements show that WiFi network performance based on throughput, delay, jitter, and packet loss parameters is in the good category. The final result obtained after measuring QoS is that the Internet network at Campus 1 Unjaya is in the medium category based on TIPHON standardization with an index value of 3.

Machine Learning for Wireless Network Throughput Prediction

This paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Power), which measures the power level of reference signals; RSRQ (Reference Signal Received Quality), an indicator of signal quality that provides insight into the number of users sharing the same resources; RSSI (Received Signal Strength Indicator), which gauges the total received power in a bandwidth; SINR (Signal to Interference plus Noise Ratio), a measure of signal quality considering both interference and noise; and other KPIs, all derived from three evolved node base stations (eNodeBs). After meticulous data cleaning, a subset of measurements from one serving eNB, spanning a 20-minute duration, was selected for deeper analysis. The PDCP DL Throughput, as a vital KPI metric, plays a paramount role in evaluating network quality and resource allocation strategies. Leveraging the high granularity of the data, the primary aim was to predict throughput. For this purpose, I compared the predictive capabilities of two machine learning models: Linear Regression and Random Forest. Metrics such as Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used to examine the models as they offer a comprehensive insight into the models’ accuracies. The comparative analysis highlighted the superior performance of the Random Forest model in predicting the PDCP DL Throughput. The insights derived from this research can potentially guide network engineers and data scientists in optimizing network performance, ensuring a seamless user experience. Furthermore, as the telecommunication industry advances towards the integration of 5G and beyond, the methodologies explored in this paper will be invaluable in addressing the increasingly complex challenges of future wireless networks.

Signal quality evaluation of single-channel respiratory sEMG recordings

This work presents a learning-based approach to respiratory surface electromyography (sEMG) quality evaluation. For this purpose, we define the signal-to-disturbance ratio (SDR), which quantifies the relative impact of disturbances on affected signal components. The SDR values for different disturbance types are estimated based on signal and disturbance characteristics and serve as a measure of signal quality. For the multivariate regression task, a fully connected neural network with three layers is trained based on standard and handcrafted signal features. The features are extracted before and after removing cardiac artifacts. This integration of domain-specific knowledge enables us to leverage shallow neural networks, which contributes to the interpretability of the method. For training and testing, artificially disturbed signals are generated based on undisturbed clinical sEMG recordings of mechanically ventilated patients and different disturbance models. This includes single and combined disturbances with different SDRs. The results show that the root-mean-square error (RMSE) of the estimated to the applied SDR regarding powerline (2.3dB) and spike-like disturbances (2.1dB) is smaller than the RMSE referring to high-frequency disturbances (3.6dB) and motion artifacts (5.8dB). Our findings also indicate that the SDR is estimated similarly well regardless of whether one or more contaminants are present simultaneously. Overall, the SDR can be determined with an RMSE of 3.8dB and a correlation coefficient of 0.97. This work contributes to automatically quantifying the impact of disturbances and objectively assessing the quality of sEMG signals of the respiratory muscles.

Using functional near-infrared spectroscopy to measure prefrontal cortex activity during dual-task walking and navigated walking: A feasibility study.

While functional near-infrared spectroscopy (fNIRS) can provide insight into motor-cognitive deficits during ecologically valid gait conditions, the feasibility of using fNIRS during complex walking remains unknown. We tested the process and scientific feasibility of using an fNIRS device to measure cortical activity during complex walking tasks consisting of straight walking and navigated walking under single and dual-task (DT) conditions. Nineteen healthy people from 18 to 64 years (mean age: 45.7 years) participated in this study which consisted of three complex walking protocols: (i) straight walking, DT walking (walking while performing an auditory Stroop task) and single-task auditory Stroop, (ii) straight and navigated walking, and (iii) navigated walking and navigated DT walking. A rest condition (standing still) was also included in each protocol. Process feasibility outcomes included evaluation of the test procedures and participant experience during and after each protocol. Scientific feasibility outcomes included signal quality measures, and the ability to measure changes in concentration of deoxygenated and oxygenated hemoglobin in the prefrontal cortex. All participants were able to complete the three protocols with most agreeing that the equipment was comfortable (57.9%) and that the testing duration was adequate (73.7%). Most participants did not feel tired (94.7%) with some experiencing pain (42.1%) during the protocols. The signal qualities were high for each protocol. Compared to the rest condition, there was an increase in oxygenated hemoglobin in the prefrontal cortex when performing dual-task walking and navigation. We showed that our experimental setup was feasible for assessing activity in the prefrontal cortex with fNIRS during complex walking. The experimental setup was deemed acceptable and practicable. Signal quality was good during complex walking conditions and findings suggest that the different tasks elicit a differential brain activity, supporting scientific feasibility.

Robustness of electrocardiogram signal quality indices.

Electrocardiogram (ECG) signal quality indices (SQIs) are essential for improving diagnostic accuracy and reliability of ECG analysis systems. In various practical applications, the ECG signals are corrupted by different types of noise. These corrupted ECG signals often provide insufficient and incorrect information regarding a patient’s health. To solve this problem, signal quality measurements should be made before an ECG signal is used for decision-making. This paper investigates the robustness of existing popular statistical signal quality indices (SSQIs): relative power of QRS complex (SQIp), skewness (SQIskew), signal-to-noise ratio (SQIsnr), higher order statistics SQI (SQIhos) and peakedness of kurtosis (SQIkur). We analysed the robustness of these SSQIs against different window sizes across diverse datasets. Results showed that the performance of SSQIs considerably fluctuates against varying datasets, whereas the impact of varying window sizes was minimal. This fluctuation occurred due to the use of a static threshold value for classifying noise-free ECG signals from the raw ECG signals. Another drawback of these SSQIs is the bias towards noise-free ECG signals, that limits their usefulness in clinical settings. In summary, the fixed threshold-based SSQIs cannot be used as a robust noise detection system. In order to solve this fixed threshold problem, other techniques can be developed using adaptive thresholds and machine-learning mechanisms.

Collective Variational Inference for Personalized and Generative Physiological Modeling: A Case Study on Hemorrhage Resuscitation.

Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models. To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation. In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population. The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data. The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.

Connected Objects Geo-Localization Based on SS-RSRP of 5G Networks

The Global Positioning System (GPS) is not the only way to solve connected objects’ geo-localization problems; it is also possible to use the mobile network infrastructure to geo-locate objects connected to the network, using antennas and signals designed for voice and data transfer, such as the 5th generation network. 5G is considered as a least expensive solution because there is no specific equipment to set up. As long as the object is in an area covered by the network, it connects to the nearest 5G Micro-Cell (MC). Through exchange of signals with the MC node we can locate the object. Currently, this location is very fast with less than 5 s but not very precise because it depends on the number of MC antennas of the operator in question and their distance. This paper presents a novel technique to geo-locate connected object in a covered 5G area. We exploit the 5G SS-RSRP used for signal quality measurement, to estimate the distance between two Connected Objects (COs) in move and in a dense urban area. The overall goal is to present a new concept laying on the 5G SS-RSRP signalling. The proposed solution takes into consideration the Deterministic and the Stochastic effect of the received signals which is not treated by the previous works. The accuracy is optimum even after approaching to the distance of one meter which is not reached in previous works too. Our method can also be deployed in the upcoming 5G network since it relies on 5G signals itself. This work and that of Wang are both based on RSRP and give comparable theoretical complexities therefore comparable theoretical execution times as well. However, to obtain a reliable learning Wang requires a huge amount of data which makes it difficult to get a real time aspect of this algorithm. The use of RSRP and the elimination of the learning phase will give more chance to our work to achieve desired performances. Numerical results show the appropriateness of the proposed algorithms and good location accuracy of around one meter. The Cramer Rao Lower Bound derivations shows the robustness of the proposed estimator and consolidate the work.

On Signal Quality Measures in Dimmable Visible Light Communications: Definitions and Conversions

This letter suggests the use of signal quality measures that accommodate various signaling configurations in dimmable visible light communications. Although a popular measure of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$E_{b} /N_{0}$ </tex-math></inline-formula> has still been used in the performance analysis of optical transmission techniques, it fails not only to capture essential physical characteristics in optical signals but also to reflect the dimming adjustment feature properly. This letter assesses various alternatives of the quality measure that have been used for optical communication signals and suggests to use a new measure of the ratio of bit peak signal level to noise standard deviation denoted by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A_{b} /\sigma $ </tex-math></inline-formula> . The proposed measure allows for the joint consideration of code rates and optical channel properties, such as dimming support and signal transmission based on intensity modulation and direct detection.

The Effect of (3‐Mercaptopropyl)trimethoxysilane (MPS) Coating on the Genetic Detection Performance of Quartz Crystal Microbalance‐Dissipation (QCM‐D) Biosensor: Novel Intact Double‐Layered Surface Modification on QCM‐D

AbstractBiosensor's ability depends on both platform performance and surface modification. In mass sensitive biosensors, a stable coating with molecular structural dynamism increases sensitivity. Also, its orientation and smoothness directly affect the performance of cross‐linker molecules and receptors. In this study, the performance of intact double‐layered (ID) coating used on QCM−D for the first time in DNA detection of MPS was investigated. The sensitivity of the QCM−D with ID‐MPS was compared to the uncoated. At the concentrations of 1010 and 1013 copies/mL, the method with ID‐MPS was approximately 6.4 times more sensitive. A shift of 1.22(±0.42) Hz was observed at the detection limit at 108 copies/mL, while it was 0.19(±0.05) Hz in the negative control. Thus, a signal‐to‐noise‐ratio greater than 3, which is a measure of signal quality in biosensors, was exceeded with a ratio of 3.33, and it was shown that the detection signal at the sensitivity limit was sufficient. In addition, the two methods were compared visually with atomic force microscopy in terms of the amount and distribution of captured molecules, and it was determined that the ID‐MPS was more efficient.

Installation of Handphone Signal Booster Antennas at 900 MHz Frequency in Onggari Village Merauke Regency

The importance of cellular communication demands to improve the quality of good and quality services in all regions including rural areas. The limitations of BTS signal propagation in Kumbe village have disrupted cellular communication services in Onggari village. By using the Yagi antenna 13 elements as a cellular signal amplifier that is modified by using a repeater so that the handphone signal can be accessed at a greater distance from the BTS location. The results obtained after mounting the signal amplifier antenna is very good where the signal increase is at 4 Barr, the signal strength is -75 dBm at a distance of 1 meter with “excellent” sound quality, while the results of the measurement of signal strength and signal quality for sunny weather are better than when it rains.

Signal quality as Achilles\u2019 heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

Graph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

Transfer learning from ECG to PPG for improved sleep staging from wrist-worn wearables

Objective. To develop a sleep staging method from wrist-worn accelerometry and the photoplethysmogram (PPG) by leveraging transfer learning from a large electrocardiogram (ECG) database. Approach. In previous work, we developed a deep convolutional neural network for sleep staging from ECG using the cross-spectrogram of ECG-derived respiration and instantaneous beat intervals, heart rate variability metrics, spectral characteristics, and signal quality measures derived from 5793 subjects in Sleep Heart Health Study (SHHS). We updated the weights of this model by transfer learning using PPG data derived from the Empatica E4 wristwatch worn by 105 subjects in the ‘Emory Twin Study Follow-up’ (ETSF) database, for whom overnight polysomnographic (PSG) scoring was available. The relative performance of PPG, and actigraphy (Act), plus combinations of these two signals, with and without transfer learning was assessed. Main results. The performance of our model with transfer learning showed higher accuracy (1–9 percentage points) and Cohen’s Kappa (0.01–0.13) than those without transfer learning for every classification category. Statistically significant, though relatively small, incremental differences in accuracy occurred for every classification category as tested with the McNemar test. The out-of-sample classification performance using features from PPG and actigraphy for four-class classification was Accuracy (Acc) = 68.62% and Kappa = 0.44. For two-class classification, the performance was Acc = 81.49% and Kappa = 0.58. Significance. We proposed a combined PPG and actigraphy-based sleep stage classification approach using transfer learning from a large ECG sleep database. Results demonstrate that the transfer learning approach improves estimates of sleep state. The use of automated beat detectors and quality metrics means human over-reading is not required, and the approach can be scaled for large cross-sectional or longitudinal studies using wrist-worn devices for sleep staging.

The effects of ocular surface disease on optical coherence tomography test results in patients with glaucoma.

To evaluate the effects of current ocular surface disease (OSD) on OCT signal quality and peripapillary retinal nerve fiber layer (RNFL) thickness measurements in patients treated with antiglaucomatous medications and the changes in these test results by dry eye treatment. Fifty-five patients, diagnosed with primary open-angle glaucoma (POAG) and OSD, who were treated for glaucoma with topical medications for at least 6 months were included in this study. Patients were treated with topical preservative free polyvinyl alcohol + povidone artificial tear drops four times a day for at least 20 days and topical loteprednol etabonate drops four times a day for 1 week. Patients were divided into groups according to the number of active substances in their glaucoma drops, daily drop numbers, and duration of drug utilizations. OCT signal quality and mean RNFL thickness measurements were evaluated within these groups before and after OSD treatment. Pre-treatment mean OCT signal quality was 19.15 ± 3.739 and mean RNFL thickness was 93.07 ± 13.931µ; post-treatment mean OCT signal quality was 23.93 ± 3.839 and mean RNFL thickness was 98.27 ± 14.863 µ (p < 0.05). Post-treatment measurements were significantly improved compared to pre-treatment measurements in our patients, but the differences among subgroups were not statistically significant. There was a strong positive correlation between pre-treatment signal quality measurements and Schirmer II test values (p < 0.0001). Treatment of OSD in glaucoma patients being treated with long-term anti-glaucoma medications, seem to improve the quality and reliability of OCT test results.

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population.

In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low- and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

An objective measure of signal quality for pediatric lung auscultations.

A stethoscope is a ubiquitous tool used to 'listen' to sounds from the chest in order to assess lung and heart conditions. With advances in health technologies including digital devices and new wearable sensors, access to these sounds is becoming easier and abundant; yet proper measures of signal quality do not exist. In this work, we develop an objective quality metric of lung sounds based on low-level and high-level features in order to independently assess the integrity of the signal in presence of interference from ambient sounds and other distortions. The proposed metric outlines a mapping of auscultation signals onto rich low-level features extracted directly from the signal which capture spectral and temporal characteristics of the signal. Complementing these signal-derived attributes, we propose high-level learnt embedding features extracted from a generative auto-encoder trained to map auscultation signals onto a representative space that best captures the inherent statistics of lung sounds. Integrating both low-level (signal-derived) and high-level (embedding) features yields a robust correlation of 0.85 to infer the signal-to-noise ratio of recordings with varying quality levels. The method is validated on a large dataset of lung auscultation recorded in various clinical settings with controlled varying degrees of noise interference. The proposed metric is also validated against opinions of expert physicians in a blind listening test to further corroborate the efficacy of this method for quality assessment.

Extraction of respiratory rate from PPG using ensemble empirical mode decomposition with Kalman filter

This Letter suggests a simple but effective approach for accurate estimation of respiratory rate (RR) from the photoplethysmogram (PPG). In the suggested technique, the PPG signal is first decomposed into a number of intrinsic mode functions (IMFs) using the ensemble empirical mode decomposition (EEMD). The IMFs comprising respiratory information are selected and used to reconstruct the signal. A Kalman filter coupled with the signal quality measure of PPG is utilised to process the reconstructed signal to derive the respiratory signal for RR estimation. Experiments are conducted on two independent datasets, namely CapnoBase and MIMIC. Based on experimental results, the technique is found to be outperforming the existing methods by achieving lower error in the estimated RRs on both datasets. This work demonstrates the potential of the EEMD method with Kalman filter for precise estimation of RR from PPG to contribute to the advancement of portable healthcare systems with a fewer number of sensors.

Signal-to-Noise Ratio Estimation in Electromyography Signals Contaminated with Electrocardiography Signals

When electromyography (EMG) signals are collected from muscles in the torso, they can be perturbed by the electrocardiography (ECG) signals from heart activity. In this paper, we present a novel signal-to-noise ratio (SNR) estimate for an EMG signal contaminated by an ECG signal. We use six features that are popular in assessing EMG signals, namely skewness, kurtosis, mean average value, waveform length, zero crossing and mean frequency. The features were calculated from the raw EMG signals and the detail coefficients of the discrete stationary wavelet transform. Then, these features are used as inputs to a neural network that outputs the estimate of SNR. While we used simulated EMG signals artificially contaminated with simulated ECG signals as the training data, the testing was done with simulated EMG signals artificially contaminated with real ECG signals. The results showed that the waveform length determined with raw EMG signals was the best feature for estimating SNR. It gave the highest average correlation coefficient of 0.9663. These results suggest that the waveform length could be deployed not only in EMG recognition systems but also in EMG signal quality measurements when the EMG signals are contaminated by ECG interference.

Assessing the Quality of Wearable EEG Systems Using Functional Connectivity

Assessing the data quality of wearable electroencephalogram (EEG) systems is critical to collecting reliable neurophysiological data in non-laboratory environments. To date, measures of signal quality and spectral characteristics have been used to characterize wearable EEG systems. We demonstrate that these traditional measures do not provide fine-grained differentiation between the performance of four popular wearable EEG systems (the Epoc+, OpenBCI, DSI-24 and Quick-30 Dry EEG). Using two computationally inexpensive metrics of undirected functional connectivity (phase lag index) and directed functional connectivity (directed phase lag index), we compare the integrity of the phase relationships captured by wearable systems to those recorded from a high-density research-grade EEG system (Electrical Geodesics Inc). Our results demonstrate that functional connectivity analyses provide additional discriminatory information about wearable EEG systems, with clear differentiation of the cosine similarity between research-grade functional connectivity patterns and those generated by each wearable system. We provide a freely available Matlab toolbox containing all metrics described in this paper such that researchers and non-experts interested in wearable EEG systems can easily assess the quality of systems not characterized in this study, thus advancing the translation of EEG research into non-laboratory settings.

Peningkatan Kualitas Sinyal 4G Berdasarkan Nilai KPI Dengan Metode Drivetest Cluster Padang

The real signal quality measurement on the 4G network is a reference to improve signal quality. Measurements were made for two different operators on the Padang cluster. The measurement parameters taken are the RSRP value, SINR and throughput as the basic parameters of the KPI (Key Performance Indicator) standard. Measurement of these basic parameters is done by the drive test method using the Tems Pocket device while for data analysis using Tems discovery and Mapinfo Pro. Planning the data collection path is an important part to find out the bad spot areas of the two operators for planning signal quality improvement. The measurement results in the process of collecting data for bad categories and not being detected at all are used to determine the position of the new site by taking into account the three signal parameters, the number of users in the area, the minimum limit of the bad signal radius and the site positions that already exist for both operators. Using the cross method, three new site coordinate points are obtained, namely first at longitude 100.403428 ° and latitude -0.870309 °, second point at longitude 100.376331 ° and latitude -0.858726 °, and third point at longitude 100.404072 ° and latitude -0.966503 ° and 47 bad spot area.