RMS Delay Spread Research Articles

Channel Knowledge Map Construction Based on a UAV-Assisted Channel Measurement System

With the fast development of unmanned aerial vehicles (UAVs), reliable UAV communication is becoming increasingly vital. The channel knowledge map (CKM) is a crucial bridge connecting the environment and the propagation channel that may visually depict channel characteristics. This paper presents a comprehensive scheme based on a UAV-assisted channel measurement system for constructing the CKM in real-world scenarios. Firstly, a three-dimensional (3D) CKM construction scheme for real-world scenarios is provided, which involves channel knowledge extraction, mapping, and completion. Secondly, an algorithm of channel knowledge extraction and completion is proposed. The sparse channel knowledge is extracted based on the sliding correlation and constant false alarm rate (CFAR) approaches. The 3D Kriging interpolation is used to complete the sparse channel knowledge. Finally, a UAV-assisted channel measurement system is developed and CKM measurement campaigns are conducted in campus and farmland scenarios. The path loss (PL) and root mean square delay spread (RMS-DS) are measured at different heights to determine CKMs. The measured and analyzed results show that the proposed construction scheme can effectively and accurately construct the CKMs in real-world scenarios.

Optimum Base Station Antenna Tilt Angle for Inter-Cell Interference Limited Mobile Cellular System

This paper investigates the effect of base station (BS) antenna tilt angle on the performance of LTE-Advanced physical layer downlink channel in Inter-Cell Interference (ICI) limited scenario for five cell ranges, two carrier frequencies, and two BS antenna heights. The cellular system is deployed following the 3GPP hexagonal grid with a frequency reuse factor of one and three sectors per site. An urban based 3D ray-tracing tool is used to model the wireless channel for many BS and user equipment (UE) links. System performance is evaluated in terms of Signal power to Interference plus Noise power Ratio (SINR), received signal power, received interference power, achievable throughput (THR), Average Spectrum Efficiency (ASE) and UEs outage probability. The objective is to find the BS antenna tilt angle that provides the maximum ASE, minimum UEs outage probability, or optimum ASE and UE outage probability. The obtained simulation results unveil that the maximum ASE occurs at a tilt angle where the mean of the SINR is maximum and the mean of rms delay spread is minimum, while the minimum UEs outage occurs at a tilt angle where the received signal power is maximum. This is an important finding to facilitate the planning and the deployment of cellular system in the presence of ICI. Moreover it is found that the optimum tilt angles decrease as the cell range increase and BS antenna height decrease. The results also show that these optimum tilt angles are independent of the carrier frequency.

Efficient RMS Delay Spread-Based Physical Layer Abstraction Modelling for Enhanced 5G New Radio Performance Evaluation

Abstract— Nowadays, Fifth Generation-New Radio (5G-NR) is an important research area in telecommunication field. System level studies are essential to evaluate the performance of wireless systems and signaling techniques prior to practical deployment as well as to research purposes. System level studies of wireless systems involve many links between the base station and the user equipment, many modulations and coding schemes and many operation scenarios. Performing system level studies using detailed physical layer simulation using Link Level Simulator (LLS) is impractical and time consuming. Instead, physical layer abstraction models of wireless systems are used in system level studies. Therefore, this paper proposes a physical layer abstraction model for the 5G-NR cellular system following the Effective Signal to Interference plus Noise Ratio (ESINR) approach. The proposed abstraction model uses a novel Exponential ESINR Mapping (EESM) that involves two tunable parameters to model the Physical Downlink Shared Channel (PDSCH) of the 5G-NR system. The proposed model is validated against detailed LLS of the PDSCH. Novel equations are proposed to model the optimum values of the two tunable parameters of the EESM as a function of the RMS delay spread of the wireless channel for different modulation and coding schemes. The obtained results show that the performance - obtained using the proposed EESM abstraction model - is very close to the performance obtained from the detailed LLS. The run time of the abstraction model is independent of the size of the transport block data size. The abstraction model is 10 times faster than the detailed LLS for a transport block size of 3.1 kbits. This factor increases to 153 when the transport block size is increased to 57.376 kbits. The proposed equations for the EESM and its optimum two tunable parameters can be used as an accurate tool to evaluate the Block Error Rate (BER) and the throughput performance of PDSCH in NR wireless system for different environments.

Analysis of High-Frequency Communication Channel Characteristics in a Typical Deep-Sea Incomplete Sound Channel

Deep-sea acoustic communication has attracted widespread attention in recent years. Research on deep-sea acoustic communication channel characteristics is essential for the development and system design of deep-sea acoustic communication technologies. However, the structural and spatiotemporal characteristics of deep-sea high-frequency acoustic communication channels (unlike those of shallow-water acoustic communication channels) are poorly understood. Based on a channel measurement experiment in a typical deep-sea incomplete sound channel environment in the South China Sea, this paper analyzes the structural and spatiotemporal characteristics of high-frequency underwater acoustic channels in the direct-arrival and shadow zones. The channel multipath vertical structure, amplitudes of different path clusters, root mean square (RMS) delay spread, and channel temporal coherence are investigated at different depths. The randomness of different path clusters is quantified as the coefficient of variation. The channels in the shadow zone are characterized by a complex multi-path structure, high RMS delay spread, and low temporal coherence. On the contrary, the channels of the direct-arrival zone show multi-path convergence, low RMS delay spread, and high temporal coherence. Understanding the dynamic changes in these parameters can guide the statistical modeling of channels and the design of communication algorithms in different zones in deep-sea high-frequency acoustic communication scenarios.

Statistical RMS delay spread representation in 5G mm-Wave analysis using real-time measurements

Statistical RMS delay spread representation in 5G mm-Wave analysis using real-time measurements

Statistical LOS/NLOS Classification for UWB Channels

Ultrawideband (UWB) technology has attracted a lot of attention for indoor and outdoor positioning systems due to its high accuracy and robustness in non-line-of-sight (NLOS) environments. However, UWB signals are affected by multipath propagation which causes errors in localization. To overcome this problem, researchers have proposed various techniques for NLOS identification and mitigation. One of the approaches is statistical LOS/NLOS classification, which uses statistical parameters of the received signal to distinguish between LOS and NLOS channels. In this paper, we formulated several techniques which can be used for effectively classifying Line of Sight (LOS) channel from a Non-Line of Sight (NLOS) channel. Various parameters obtained from Channel Impulse Response (CIR) like Skewness, Kurtosis, Root Mean Squared Delay Spread (RDS), Mean Excess Delay (MED), Energy, Energy Ratio and Mean of Covariance Matrix are used for channel classification. In addition to this, the Joint Probability Density Functions (PDFs) of various parameters are used to improve the accuracy of UWB LOS/NLOS channel classification. Two different criteria-Likelihood Ratio and Hypothesis Test are used for the identification of channel.

Channel Characteristics and Link Adaption for Visible Light Communication in an Industrial Scenario.

Visible light communication (VLC) is one of the key technologies for the sixth generation (6G) to support the connection and throughput of the Industrial Internet of Things (IIoT). Furthermore, VLC channel modeling is the foundation for designing efficient and robust VLC systems. In this paper, the ray-tracing simulation method is adopted to investigate the VLC channel in IIoT scenarios. The main contributions of this paper are divided into three aspects. Firstly, based on the simulated data, large-scale fading and multipath-related characteristics, including the channel impulse response (CIR), optical path loss (OPL), delay spread (DS), and angular spread (AS), are analyzed and modeled through the distance-dependent and statistical distribution models. The modeling results indicate that the channel characteristics under the single transmitter (TX) are proportional to the propagation distance. It is also found that the degree of time domain and spatial domain dispersion is higher than that in the typical rooms (conference room and corridor). Secondly, the density of surrounding objects and the effects of user heights on these channel characteristics are also investigated. Through the analysis, it can be observed that the denser objects can contribute to the smaller OPL and the larger RMS DS under the single TX case. Furthermore, due to the blocking effect of surrounding objects, the larger OPL and the smaller RMS DS can be observed at the receiver with a low height. Thirdly, due to the distance dependence of the channel characteristics and large time-domain dispersion, the link adaption method is further proposed to optimize the multipath interference problem. This method combines a luminary adaptive selection and delay adaption technique. Then, the performance of the link adaption method is verified from four aspects through simulation, including the signal-to-noise (SNR), the RMS DS, the CIRs, and the bit-error rate (BER) of a direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) system. The verification results indicate that our proposed method has a significant optimization for multipath interference.

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

With the rapid increase in the number of sensors in Power Internet of Things (PIOT), the data volume surges and efficient communications are needed. Precise estimation of delay spread is critical to improve the communication performance. In this paper, we propose a statistical learning (SL) based root mean square delay spread (RMSDS) estimation method, which consists of offline training and online estimation. During the offline training process, the instantaneous delay spread (IDS) for each data frame is firstly computed based on the known pilots. The acquired IDS is a random variable and its statistical parameter is related to the actual RMSDS, which is preset and known during offline training. Then, the functional relationship between the statistical parameter and the preset RMSDS is acquired through the SL algorithm. The acquired functional relationship can be used in the online estimation process to accurately estimate the RMSDS. Simulation results indicate that the proposed method can accurately estimate the RMSDS and greatly improve the bit error rate performance.

Multiple Delay-Moments Based Estimators for True Coherence Bandwidth

The traditional technique for coherence bandwidth estimation exploits only the second-order information of a power delay profile (PDP), thus often failing to act on coherence functions of varying shape, albeit a wide-range multiplier is adopted in the reciprocal formula of rms-delay spread. In this letter, we exploit constituent delay moments inherent in a general coherence function to construct a new PDP based estimator, resulting in a much better coherence bandwidth estimation.

V2V channel modeling at 5.2 GHz for highway environment

To design and evaluate vehicle-to-vehicle (V2V) communication systems in intelligent transportation system (ITS), it is important to understand the propagation mechanisms and channel models of V2V channels. This paper aims to analyze the channel models at 5.2 GHz for the highway environment in obstructed line-of-sight (OLoS) and line-of-sight (LoS) scenarios, particularly the vehicle connectivity probability derivation based on the propagation model obtained from measurement. First, the path loss (PL), shadow fading (SF), narrowband K-factor, and small-scale amplitude fading are analyzed. Results showed that the received signal magnitude follows Rice and Weibull distribution in LoS and OLoS scenarios, respectively. Second, we develop simple and low-complexity tapped delay line (TDL) models with a 10 MHz bandwidth for LoS and OLoS scenarios; in addition, we investigate the wideband K-factor, the root mean square delay spread (RMS-DS), and delay-Doppler spectrum. Third, we derive the closed form connectivity probability between any two vehicles in the presence of Weibull fading channel, and analyze the effects of Weibull fading channel and traffic parameters on connectivity. It is found that Weibull fading parameter, transmit power and vehicle density have positive impact on connectivity probability, PL exponent has negative impact on connectivity probability.

Wearable UWB Technology for Daily Physical Activity Tracking, Detection, and Classification

A methodology for daily physical activity tracking, detection, and classification is proposed using wearable ultrawideband (UWB) technology. The compact UWB antennas act as sensors to provide useful information and metrics regarding the activity, derived from channel information (path loss and rms delay spread) and received signal features. The activities consist of daily activities, such as walking, sitting, running, opening/closing door, and ascending/descending stairs, which are considered as main activities in everyday life. Experimental validation is carried out showing effective tracking capabilities based on the interdistance variation, giving an estimate of the range of movement during various phases of the activity cycle. The computation of patterns of different daily activities cycle provides unique recognition/classification characteristics, which have been validated through template-based and machine learning techniques. The proposed method provides high accuracy, is cost-effective, and has low computation complexity, making it suitable for various healthcare applications, such as elderly/remote monitoring, rehabilitation, patient activity tracking, and general fitness.

Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks

The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.

High-Resolution Delay Spread of Wide-Band Wireless Link in Long Tunnels—Theory and Experimental Verification

The need for wireless communication appears everywhere, and with as few interruptions as possible. An extensive and in-depth study has been conducted on the limitations of wireless communication in pedestrian tunnels. Using an analytical mathematical ray tracing model, and performing tunnel measurements, with an autonomous measuring setup, the model was validated. An extraordinary wide band was used for the experiments, which also featured optical systems to ensure accurate results. Since the tunnel is narrow, the differences between the different paths are small, and in order to distinguish them a resolution of 0.2 ns was used. The RMS delay spread, στ, decreases throughout the tunnel, and at no point exceeds 1 ns.

Person Density Dependency on Path Loss and Root Mean Square Delay Spread for Smart Office Scenarios

Novel empirical path-loss and root mean square delay spread (RDS) models for smart office scenarios are proposed. The effects of person density on the path loss and RDS are investigated based on the extensive measurements at 2.3–2.5 GHz. First, both of the measured path loss and RDS data are modeled as the dual log-distance functions. It is caused by the regular structure and furniture in the office environment. Second, in the proposed path-loss model, the path-loss exponents and the additional attenuation factor are modeled as quadratic functions of the person density. Meanwhile, the RDS is found to be uncorrelated with the person density. These phenomena reveal that the persons in the environments can be regarded as absorbers rather than scatters. Then, the accuracy of the proposed models is validated by the measured data and compared with two traditional models. Finally, the effect of the persons’ movements on the path loss and RDS is investigated, and the proposed models are extended to millimeter wave bands by a ray tracing technology. The proposed models and results can provide necessary information for link budget and algorithm design for the Internet of Things smart office scenarios.

An Improved SVM-Based Air-to-Ground Communication Scenario Identification Method Using Channel Characteristics

Scenario identification plays an important role in assisting unmanned aerial vehicle (UAV) cognitive communications. Based on the scenario-dependent channel characteristics, a support vector machine (SVM)-based air-to-ground (A2G) scenario identification model is proposed. In the proposed model, the height of the UAV is also used as a feature to improve the identification accuracy. On the basis, an improved scenario identification method is developed including dataset acquisition, identification model training, and height-integrated model feedback. The shooting and bouncing ray/image (SBR/IM) method is used to obtain the datasets of channel characteristics, i.e., root-mean-square delay spread (RMS-DS), K factor, and angle spread (AS) under five typical scenarios: over-sea, suburban, urban, dense urban and high-rise urban. SBR/IM is a symmetry-based ray tracing (RT) simulation method. After the identification model is trained, a height-integrated feedback scheme is used to increase the identification performance. The simulation results show that the identification accuracy of improved method is about 14% higher than the method without height feature, which reaches nearly 100% under the over-sea and suburban and over 80% in urban, dense urban, and high-rise urban.

Propagation Characteristics Comparisons between mmWave and Visible Light Bands in the Conference Scenario

Millimeter-wave (mmWave) communications and visible light communications (VLC) are proposed to form hybrid mmWave/VLC systems. Furthermore, channel modeling is the foundation of system design and optimization. In this paper, we compare the propagation characteristics, including path loss, root mean square (RMS) delay spread (DS), K-factor, and cluster characteristics, between mmWave and VLC bands based on a measurement campaign and ray tracing simulation in a conference room. We find that the optical path loss (OPL) of VLC channels is highly dependent on the physical size of the photodetectors (PDs). Therefore, an OPL model is further proposed as a function of the distance and size of PDs. We also find that VLC channels suffer faster decay than mmWave channels. Moreover, the smaller RMS DS in VLC bands shows a weaker delay dispersion than mmWave channels. The results of K-factor indicate that line-of-sight (LOS) components mainly account for more power for mmWave in LOS scenarios. However, non-LOS (NLOS) components can be stronger for VLC at a large distance. Furthermore, the K-Power-Means algorithm is used to perform clustering. The fitting cluster number is 5 and 6 for mmWave and VLC channels, respectively. The clustering results reveal the temporal sparsity in mmWave bands and show that VLC channels have a large angular spread.

Experimental Investigation of Body-Centric Indoor Localization Using Compact Wearable Antennas and Machine Learning Algorithms

A simple and effective body-centric localization algorithm has been proposed in this article using ultra-wideband (UWB) wearable technology. The algorithm has been validated through measurement campaigns with a human subject volunteer in an indoor environment. The algorithm takes into account statistical channel parameter analysis, machine learning (ML) algorithms, and time-of-arrival (TOA)-based range estimation/data fusion techniques. Two channel parameters namely path loss magnitude and rms delay spread are proposed as classification features to be applied to the ML algorithm to accurately classify the off-body channel links into LOS, PNLOS, and NLOS scenarios. Multiclass support vector machine (MC-SVM) classifier along with SMOTE algorithm to take into account class imbalance is applied with the channel classification accuracy of 98.63%. Threshold-based range estimation algorithms are applied in order to mitigate NLOS scenarios caused mainly due to the presence of the human subject. Results report human localization accuracy in 0.5–3 cm range using TDOA data fusion technique for target estimation. Further validation is presented considering wide range of Tx–Rx distance, presence of another obstruction between the Tx and Rx links, and performance in different environment which shows the suitability of the proposed methodology.

Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels

The use of vehicular communications is anticipated to improve safety in road traffic. The traditional radio channel models that describe the effects of radio wave propagation in dynamic vehicular environments have their own limitations. In this paper, machine learning techniques are applied for radio channel modeling in urban vehicular environments. A large data set of path loss and RMS delay spread is computed using ray-tracing for a Line-of-Sight (LOS) straight road and a Non Line-of-Sight (NLOS) intersection road scenario. Fourteen input features are used to train three machine learning models for vehicular channel prediction. The models considered in this work include Multi-layer perceptron (MLP), Convolutional neural network (CNN) and Random forest (RF). The results show that RF gives better performance than MLP and CNN models in prediction of path loss and RMS delay spread in urban vehicular channels.

Sub-6 GHz Channel Modeling and Evaluation in Indoor Industrial Environments

This paper presents sub-6 GHz channel measurements using a directional antenna at the transmitter and a directional or omnidirectional antenna at the receiver at 4.145 GHz in sparse and dense industrial environments for a line-of-sight scenario. Furthermore, the first measured over-the-air error vector magnitude (EVM) results depending on different 5G new radio modulation and coding schemes (MCSs of16 QAM, 64 QAM and 256 QAM) are provided. From the measurement campaigns, the path loss exponents (PLE) using a directional and an omnidirectional antenna at the receiver in the sparse and the dense environment are 1.24/1.39 and 1.35/1.5, respectively. PLE results are lower than the theoretical free space PLE of 2, indicating that indoor industrial environments have rich multipaths. The measured power delay profiles show the maximum root mean square (RMS) delay spreads of 11 ns with a directional antenna and 34 ns with an omnidirectional antenna at the receiver in a sparse industrial environment. However, in a dense industrial environment the maximum RMS delay spreads are significantly increased: maximum RMS delay spreads range from 226 to 282 ns for the omnidirectional and the directional antenna configuration. EVM measurements show that to increase coverage and enable higher MCS modes to be used for reliable data transmission, in both industrial environments using a directional antenna at the transmitter and the receiver is required. The large-scale path loss models, multipath time dispersion characteristics and EVM results provide insight into the deployments of 5G networks operating at sub-6 GHz frequency bands in different industrial environments.

A Novel Denoising Method Based on Machine Learning in Channel Measurements

Machine learning (ML) is playing an increasingly important role in processing large amounts of data generated by communication networks, since it can efficiently solve the problems of non-linearity and unstructured data. Recently, ML has been widely used in the processing of wireless channel data, as the noisy channel in real propagation environment is usually non-linear and unstructured. In this paper, a denoising method based on ML is presented. Two ML algorithms are used to classify and remove noise in channel impulse responses. Then, the results of the traditional noise threshold denoising are compared with ML denoising, and it is found that the denoising classifier using the bidirectional recurrent neural network has the better denoising performance. Finally, some channel parameters such as RMS delay spread are estimated based on measured channel data using different denoising methods. The results are evaluated and compared to explore the impact of denoising method on the extracted channel parameters.