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The ever-growing demands from emerging industrial applications and services require revolutionary technology beyond fifth-generation (5G). The sixth-generation (6G) networks are the promising technology to satisfy the high data rate demands for future industrial applications. This paper examined wideband radio propagation characteristics at 28 GHz obtained through measurements in an industrial environment and compared them with those obtained at 108 GHz. The characteristics were expressed in terms of path loss, K-factor, power delay profile, root mean square delay spread, and channel capacity. The measured path-loss was used to obtain empirical propagation models in the line of sight using the floating intercept and close-in free space reference distance and they fitted very well. The obtained propagation models were then used to obtain an empirical channel capacity prediction model in terms of antenna gain, transmission frequency, distance, and propagation exponent. For the non-LoS scenario, significant degradation of channel capacity was observed, and hence the usage of intelligent reflection surface technique to compensate for the lost capacity was investigated. This work is part of the foundation needed to establish the feasibility of using IRS technique in 6G networks operating at mmWave and sub-THz bands for industrial applications. [ABSTRACT FROM AUTHOR]

In this paper, we provide a tutorial on the structured design of effective Physical Random Access CHannel (PRACH) preamble detection based on correlation power. Specifically, the main contribution of this paper is to provide the design methodology leveraging multi-stage thresholds by which Preamble ID and PRACH-related time information (Timing Advance) for identifying the transmitted PRACH preamble signal are obtained. For enhancing the effectiveness of the designed PRACH preamble detector, we also consider joint optimization of multi-stage thresholds beyond the individual optimization of each threshold based on the Power Delay Profile. Consequently, the designed PRACH preamble detector is shown to have a reasonable performance in the sense of detection and false alarm probabilities, which is validated through numerical simulations considering the 3rd Generation Partnership Project conformance test.

Abstract Radio wave propagation in an intra-vehicular (IV) environment is markedly different from other well-studied indoor scenarios, such as an office or a factory floor. While millimetre wave (mmWave)-based intra-vehicular communications promise large bandwidth and can achieve ultra-high data rates with lower latency, exploiting the advantages of mmWave communications largely relies on adequately characterising the propagation channel. Channel characterisation is most accurately done through an extensive channel sounding, but due to hardware and environmental constraints, it is impractical to test channel conditions for all possible transmitter and receiver locations. Artificial neural network (ANN)-based channel sounding can overcome this impediment by learning and estimating the channel parameters from the channel environment. We estimate the power delay profile in intra-vehicular public and private vehicle scenarios with a high accuracy using a simple feedforward multi-layer perception-based ANN model. Such artificially generated models can help extrapolate other relevant scenarios for which measurement data are unavailable. The proposed model efficiently matches the taped delay line samples obtained from real-world data, as shown by goodness-of-fit parameters and confusion matrices.

Radio wave propagation in an intra-vehicular (IV) environment is markedly different from other well-studied indoor scenarios, such as an office or a factory floor. While millimetre wave (mmWave)-based intra-vehicular communications promise large bandwidth and can achieve ultra-high data rates with lower latency, exploiting the advantages of mmWave communications largely relies on adequately characterising the propagation channel. Channel characterisation is most accurately done through an extensive channel sounding, but due to hardware and environmental constraints, it is impractical to test channel conditions for all possible transmitter and receiver locations. Artificial neural network (ANN)-based channel sounding can overcome this impediment by learning and estimating the channel parameters from the channel environment. We estimate the power delay profile in intra-vehicular public and private vehicle scenarios with a high accuracy using a simple feedforward multi-layer perception-based ANN model. Such artificially generated models can help extrapolate other relevant scenarios for which measurement data are unavailable. The proposed model efficiently matches the taped delay line samples obtained from real-world data, as shown by goodness-of-fit parameters and confusion matrices. [ABSTRACT FROM AUTHOR]

The profound knowledge of radio wave propagation is essential for the design and test of wireless communication systems especially in demanding environments and for mobile transmitters and receivers. In this article we provide an accurate description of the relevant propagation parameters for train-to-train scenarios considering typical environments such as railway stations, open field and hilly terrain with cutting. At the beginning of this contribution we shortly describe the comprehensive train-to-train measurement campaign which is the basis for all further evaluations. We treat the stationarity of the channel in time and frequency and derive power delay profiles, and the Doppler spectral densities. Furthermore, distance-variant statistics about the $k$ -factor, delay spread and Doppler frequency spread are presented. We show that these parameters change very much for different environments and distances. For all stochastic channel parameters we propose distance dependent model parameters.

ultra-wideband (UWB) technology enables centimeter-level localization systems based on the accurate estimation of the actual distance between transmitter and receiver, by means of the precise estimation of the signal time-of-flight. However, this is only possible when correctly detecting the first path of the incoming signal instead of a bounce or a reflection, which becomes challenging in non line-of-sight (NLOS) situations. There are many different approaches in the literature to alleviate the wrong detection of the first incoming UWB signal path. One of them considers machine learning techniques to design classifiers capable of distinguishing between line-of-sight (LOS) and NLOS propagation from available signal features. However, the performance and complexity of the obtained classifiers depend largely on the size of the input data associated to such features. Thus, features such as the channel impulse response (CIR) produce large amounts of data, yielding very complex classifiers. In this paper, we propose using a downsampled power delay profile (PDP) as an alternative feature consisting of input data much smaller than the CIR, although sufficiently representative, hence resulting in a lower computational cost while exhibiting a similar classification performance. Furthermore, another of the tasks addressed in this work is the study of the impact on the classification results of using a dataset for training where the samples of each class are not balanced from the point of view of energy. Finally, this work also studies how the classifiers based on the CIR or the PDP improve their performance when considering additional signal features such as the estimated range value or its energy level.

Channel estimation normally provides information about indoor and outdoor fading channel statistics. The adaptive channel estimation models play an important role to generate the required channel state information (CSI) using the estimated channel coefficient vector. The CSI can be utilized to generate an angle vector that controls the steering mechanism of a beamformer. The beamformer provides better directive gain for linear antenna array and helps to improve the signal to noise ratio of the wireless receiver. The proposed estimation model process the transmitted quadrature amplitude modulation (QAM) data samples in the frequency domain. The adaptive design incorporates norm-based sparsity through block recursive least square (BRLS) algorithm to develop a computationally efficient model. The proposed sparse-FBRLS (Fast BRLS) model has simultaneously addressed the problems of channel estimation and beamforming in case of indoor and outdoor communication. The performance of the model is tested by different performance measures under practical mobility conditions. [ABSTRACT FROM AUTHOR]

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This paper introduces a measurement campaign to investigate the characteristics of massive multiple-input multiple-output (MIMO) channels in both line-of-sight (LoS) and obstructed LoS suburban outdoor environments. The measurements are conducted at the center frequency of 3.5 GHz with a bandwidth of 160 MHz. The transmitter is equipped with a 4 × 8 planar patch antenna array and the receiver is equipped with a 4 × 8 cylindrical patch antenna array. Each patch consists of two antenna ports which have main polarizations of +45° and -45°, respectively, resulting in a total of 64 × 64 sub-channels. We examined the influence of different polarization combinations on channel characteristics such as power delay profile (PDP), K-factor and delay spread for both LoS and obstructed LoS scenarios. Results show that the characteristics are more dependent on the Rx antennas rather than the Tx antennas. When the Rx antennas are facing the Tx antenna array, the received power is higher, the K-factor is lower and the delay spread is lower in cross-polarization cases with respect to the co-polarization ones in general. When the Rx antennas are back-facing the Tx antenna array, lower powers, higher K-factors and higher delay spreads can be observed in cross-polarization cases with respect to the co-polarization cases. In conclusion, the polarimetric influence on the channel characteristics depends on the antenna structure as well as the environment.

This paper presents broadband simulations and measurements of Millimeter-Waves (mm-wave) propagation in a rugged underground mine environment. Mathematical formulation was carried out in the framework of Uniform Theory of Diffraction (UTD) to develop a deterministic Ray-Tracing (RT) model under Line-Of-Sight (LOS) condition. The developed theoretical model was then validated experimentally in Frequency Domain (FD) and Time Domain (TD). A significant agreement between simulations and measurements is achieved in both domains. The rough surfaces of the mine are modeled deterministically as groups of diffracting wedges having random dimensions (heights, angles) being transversely oriented throughout the gallery. Acute (inferior to 30°) and obtuse (superior to 120°) wedges angles are found to have significant effects on the overall propagation performance. In the mm-wave band, the UTD diffraction phenomenon is evident and must be considered in the design of underground mine channels. In fact, the presented model is found to be capable of predicting the complex multipath of underground mine channels, due to the ray-optical behavior at mm-wave bands.

Millimeter-wave (mmWave) transmission over the unlicensed 60-GHz spectrum is a potential solution to realize high-speed internet access, even inside mass transit vehicles. The solution involves communication between users and a mmWave-band on-board unit that aggregates/disseminates data streams from/to commuters and maintains the connection with the nearest terrestrial network infrastructure node. In this paper, we provide a measurement-based channel model for the 60-GHz mmWave propagation inside a typical inter-city bus. The model characterizes power delay profile (PDP) of the wireless intra-vehicular channel, and it is derived from about 1000 data sets measured within the bus. The proposed analytical model is further translated into a simple simulation algorithm that generates in-vehicle channel PDPs. Different goodness-of-fit tests confirm that the simulated PDPs are in good agreement with the measured data. Finally, a tapped-delay-line (TDL) channel model is formulated from the proposed PDP model, and the TDL model is used to study the bit error rate (BER) performance of the mmWave link inside bus under varying data rates and link lengths.

In this study, radio channel measurements were conducted in an urban inland river environment at 5.9GHz. The measurements consisted of both the line of sight (LOS) and non-line of sight (NLOS) cases. We estimate and model channel characteristics based on the measured data. For small-scale fading properties, best fit small-scale fading distribution is determined. Statistical models of the Ricean K-factors and Weibull shape parameters are provided. In addition, a distance-dependent Ricean K-factor model for the urban inland river environment is proposed. For the power delay profile (PDP), we perform cluster identification and obtain the cumulative distribution function (CDF) of the inter-cluster interval. A statistical model of the intracluster decay time constant is also presented, and an exponential decay model is applied for modeling the inter-cluster decay of the PDP. For delay spread and Doppler spread, the root mean square (RMS) delay spreads in the LOS case are compared with those in the NLOS case. Moreover, we estimate and analyze RMS Doppler spreads in both the LOS and NLOS cases. Finally, a bimodal Gaussian mixture distribution (BGMD) is employed to characterize the RMS delay spreads and RMS Doppler spreads. The BGMDs exhibit good matching levels. [ABSTRACT FROM AUTHOR]

The 3.5 GHz and 28 GHz bands are both very important for the fifth-generation (5 G) wireless communication system. To model their propagation characteristics and study their channel properties, we conduct measurements at these two bands in indoor and outdoor scenarios. The measured bandwidths at 3.5 and 28 GHz are 100 and 400 MHz, respectively. To make a fair comparison, the measurement results at 28 GHz are divided into four groups with bandwidths from 100 to 400 MHz. This is also helpful to study the effect of bandwidth on the channel properties, especially numbers of clusters and their growth rate. We choose the Saleh-Valenzuela (S-V) model to analyze the wireless channel. Its cluster-based property is very suitable to describe the power delay profiles (PDPs) in our measurement. To maintain the time continuity of multipath components (MPCs) during the clustering, we propose a new heuristic cluster algorithm. Based on the clustering results, we obtain the channel parameters, i.e., the number of clusters, inter-cluster interval, intra-cluster root mean square (RMS) delay spread, cluster decay factor, and ray decay factor. From the comparative study, we can find that the rays suffer a larger attenuation at 28 GHz than at 3.5 GHz. Increasing bandwidth can increase the ray decay factor. But the effects of frequency on the cluster decay rate are different in indoor and outdoor scenarios. The cluster decay rate is smaller at 28 GHz in the indoor scenario. [ABSTRACT FROM AUTHOR]

This study proposes a new method to estimate room shape using radio waves. Unlike existing heuristic methods, the proposed method uses accumulated radio channel data and the neural network to predict room shapes. The radio channel data is generated by three dimensional (3D) ray tracing simulation and actual measurements. The neural network used the channel data for the training process. After training the neural network, we evaluated the performance variation resulting from changes in the channel environment parameters used in the neural network training, the size of the room, hyperparameters of the neural network, and the location of the transceivers. The results showed that better performance was observed when more sets of channel parameters were used as inputs to the neural network. In addition, the results obtained from measurements show that the estimation of the room shape through the neural network works well not only in the simulation but also in the real world. [ABSTRACT FROM AUTHOR]

Starting from outdoor transmission, crossing vegetation, the wideband and narrowband characterization of the indoor mobile radio channel in the 700 MHz band is obtained from experiments carried out in the indoor environment of the Engineering building at Fluminense Federal University, and from the processed data, respectively, the power-delay profiles obtained permitted to calculate the time dispersion parameters and the envelope of the signal permitted to adjust probability density functions to the signal variability, concluding about the influence of vegetation on those characteristics. [ABSTRACT FROM AUTHOR]

Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers.

WINNER and COST2100 channel models are geometry based statistical models，they are the most important channel models for MIMO（multi-input multi-output）system simulations in the fourth generation（4G）radio systems. Due to different physical mechanism advantages for WINNER and COST2100 models，there is almost no work available for the comparative studies between the two channel models and effectiveness and agreement in a specific scenario. The channel simulation and measurement studies for the two channel models in indoor environment was focused on. In the line-of-sight（LOS）and non-line-of-sight（NLOS）cases，the power-delay-profile （PDP），Ricean factor，channel capacity，rms delay and rms angular spread were simulated using the two channel models and measurement data. The comparative studies between the simulation and measurement results are performed to test the effectiveness and agreement of the two models in a practical application environment.

Industrial wireless sensor networks (IWSNs) promote innovations in the industry such as structural status mapping, instrument fault diagnosing, and oriented automation system associating. However, due to the shared nature of the wireless propagation environment, the emerging sensor nodes (SNs) with wireless properties are vulnerable to external malicious attacks. The security threats, especially Sybil attacks, impose great difficulties in fulfilling quality requirements of industrial applications. What is more complicated is that the harsh industrial environment brings about new challenges which can degrade the accuracy of detecting Sybil threats. In this paper, we focus on how to detect the malicious packets transmitted from Sybil attackers without adding extra authentication overhead into the transmission frame. We develop a multi-Kernel-based expectation maximization (MKEM) scheme to detect Sybil attacks in IWSNs. Instead of directly investigating the radio resource of SNs, we produce channel-vectors which are extracted from the power gain and delay spread of the channel impulse response obtained from the received packets to represent each SN. Specifically, a kernel-oriented method is designed to discriminate the malicious packets from benign ones without establishing a pre-defined database of channel features of all SNs. Meanwhile, we allocate different kernel weights to the proposed kernels and combine them to improve the discrimination ability of the scheme. Moreover, a kernel parameter optimization method is developed to regulate each kernel weight and parameter to reduce the effects of transmission impairments in IWSNs. To avoid poor detection accuracy when the number of Sybil attackers increases, we use the gap statistical analysis method to verify and EM method to summarize the detection results. The simulation results show that the proposed MKEM scheme can achieve high accuracy on detecting malicious packets transmitted from Sybil attackers from benign ones, and tolerate the effects of transmission impairments in the industrial environment. Moreover, the MKEM scheme can guarantee the detection accuracy even if the number of Sybil attackers increases. [ABSTRACT FROM AUTHOR]

The millimeter wave (mmWave) frequency band spanning from 30 to 300 GHz constitutes a substantial portion of the unused frequency spectrum, which is an important resource for future wireless communication systems in order to fulfill the escalating capacity demand. Given the improvements in integrated components and enhanced power efficiency at high frequencies, wireless systems can operate in the mmWave frequency band. In this paper, we present a survey of the mmWave propagation characteristics, channel modeling, and design guidelines, such as system and antenna design considerations for mmWave, including the link budget of the network, which are essential for mmWave communication systems. We commence by introducing the main channel propagation characteristics of mmWaves followed by channel modeling and design guidelines. Then, we report on the main measurement and modeling campaigns conducted in order to understand the mmWave band’s properties and present the associated channel models. We survey the different channel models focusing on the channel models available for the 28, 38, 60, and 73 GHz frequency bands. Finally, we present the mmWave channel model and its challenges in the context of mmWave communication systems design. [ABSTRACT FROM AUTHOR]

This paper proposes a new geometry-based shallow underwater acoustic (UWA) channel model allowing for nonisovelocity ocean conditions. The fact that the isovelocity assumption does not hold in many real-world scenarios motivates the need for developing channel models for nonisovelocity UWA propagation environments. Starting from a geometrical model, we develop a stochastic channel model for a single-input single-output (SISO) vehicle-to-vehicle UWA channel assuming that the ocean surface and bottom are rough and that the speed of sound varies with depth. The effect of the nonisovelocity condition has been assessed regarding its influence on the temporal autocorrelation function, the frequency correlation function (FCF), and the power delay profile of the UWA channel model. The UWA channel model has also been validated by matching its FCF as well as the therefrom derived main characteristic quantities, such as the average delay, delay spread, and coherence bandwidth against measurement data. The proposed UWA channel model is very useful for the design and performance analysis of UWA communication systems under realistic propagation conditions. [ABSTRACT FROM PUBLISHER]

Based on signal measurements carried out in the 5.8 GHz band, which is reserved for unlicensed WiMAX in Brazil, this paper evaluates narrowband and wideband characteristics on the suburban area of Tanguá city in Rio de Janeiro State. Fading statistics, coverage and adjusted prediction model such as Hata-COST 231, SUI and UFPA were used in order to find the model that best fits the measurement data before ultimately determining that the UFPA model proved to be the best one. Temporal dispersion is studied through the delay spread and coherence band calculated from the power delay profiles obtained in the sounded channel.

Pilot contamination in the uplink (UL) can severely hurt the channel acquisition performance at the base station (BS) in a massive multiple-input multiple-output (MIMO) system. To mitigate the interference, it is critical to explore all the allowed avenues to provide more orthogonal resources for the users to transmit non-interfering UL pilots. In this paper, with a realistic massive MIMO orthogonal frequency-division multiplexing (OFDM) system model, allowing the channels to be both frequency-selective and time-selective, we propose to mitigate the UL pilot contamination through aligning the channel power in multiple domains, i.e., the time, angular, and Doppler domains. First, we show how to align the power-delay profiles of different users so that the pilots sent within one common reference OFDM symbol are orthogonal. Second, we demonstrate our proposed time-varying pilots can asymptotically mitigate the pilot contamination by aligning the Doppler power spectra (DPSs) judiciously even when the UL channels vary with time. Furthermore, in the case of massive MIMO, as the angles of arrival of the UL paths are acquired by the BS, we show the pilot contamination can be reduced or eliminated by jointly aligning the DPSs and the angular power spectra. Extensive computer simulations convince us the proposed Power Aligning principle can serve as the baseline design philosophy for the UL pilots in massive MIMO. [ABSTRACT FROM PUBLISHER]

This paper proposes a new geometry-based channel model for shallow-water ocean environments, in which the ocean bottom can slope gently down/up. The need for developing such an underwater acoustic (UWA) channel model is driven by the fact that the standard assumption of a flat ocean bottom does not hold in many realistic scenarios. Starting from a geometrical model, we develop a stochastic channel model for wideband single-input single-output vehicle-to-vehicle UWA channels using the ray theory assuming smooth ocean surface and bottom. We investigate the effect of the ocean-bottom slope angle on the distribution of the channel envelope, instantaneous channel capacity, temporal autocorrelation function, frequency correlation function, Doppler power spectral density, and the power delay profile. Theoretical and simulation results show that even a relatively small slope angle influences considerably the statistical properties of UWA channels. The validation of the proposed UWA channel model has been performed by fitting its main characteristic quantities (average delay, delay spread, and coherence bandwidth) to measurement data. In comparison with the conventional UWA channel model, which has been developed on the assumption of a flat ocean bottom, it is shown that the proposed UWA channel model enables the modeling of measured channels with higher precision. [ABSTRACT FROM PUBLISHER]