This paper presents a novel framework for object-oriented integrated sensing and communications (ISAC) channel modeling in urban environments over low-altitude 3D space, which jointly considers unmanned aerial vehicle (UAV)-based and ground-level measurements in conjunction with ray-tracing (RT) technology. Two types of object-oriented channels are considered, namely the static background channel, e.g., buildings, and the dynamic channel, e.g., cars and UAVs. Firstly, a systematic measurement campaign was conducted in the 4 GHz band at 80 m height using a UAV and at ground level using a vehicle. Additionally, photographs of the measurement area are taken by the UAV for an object-oriented 3D model. Based upon channel measurement data and employing RT techniques, the EM parameters of the considered coverage area are accurately calibrated and validated at both heights. This has allowed us to obtain accurate simulations across the vertical airspace of both sensing- and communication-background channels, followed by a comprehensive channel characteristics analysis. By subtracting the background channel from the dynamic channel, experimental results have demonstrated that the angle and distance information of objects can be accurately estimated. Through a case study, it has been shown that the acquired prior scene information can be utilized as a reference to improve object-oriented coverage.

Integrating dense channel fingerprints into deep learning (DL) becomes a promising way to realize precise three-dimensional (3D) indoor localization. However, most existing methods are frequency-dependent, which limits the localization precision when operating in different frequency bands. To address this challenge, this paper proposes a masked Transformer encoder (MTE) model capable of using the channel state information (CSI) data of an arbitrary number of sub-channels (frequency bands) as input. The proposed MTE model can locate a UE using frequency-scalable CSI data, to realize resilient localization. We first introduce how to transform CSI data into sequential data suitable for Transformer-based models, with length of the sequence determined by the number of sub-channels. Based on this, an MTE model is designed to achieve resilient FP localization with frequency-scalability, i.e., capable of processing the CSI data of an arbitrary number of sub-channels. Next, we construct a 3D CSI FP dataset using ray-tracing (RT) simulations based on real-world indoor scenarios and versatile electromagnetic (EM) coefficients. The reliability of the dataset is verified by measurement data. Extensive experiments demonstrate that the MTE model outperforms many state-of-the-art baselines, classical time-series models, and alternative Transformer-based methods, especially under arbitrary sub-channel CSI data. Moreover, we demonstrate that the MTE model also offers many advantages in terms of training and storage costs through comparisons with conventional models.

Accurate prediction of rain attenuation in short-range millimeter-wave (mmWave) wireless links is critical for ensuring reliable performance under rainy conditions. This study presents a measurement-based model to precisely characterize the impact of rainfall on wireless channels. A dedicated measurement system is developed to capture channel responses under controlled rainfall conditions. Based on the measurement data and the framework of the International Telecommunication Union Radiocommunication Sector (ITU-R) Recommendation P.838-3 model, the proposed model optimizes model parameters and incorporates wet antenna effects. Comparison with the ITU model demonstrates that the proposed model improves attenuation prediction accuracy by over 70% for short-range mmWave links, while cross-band validation confirms its applicability. The proposed model provides reliable technical support for wireless system design in complex meteorological environments.

Accurate modeling of wireless channels is crucial for the design and optimization of railway wireless communication systems. Ray-tracing (RT) is a widely used technique for generating multipath channel characteristics, but the high computational resource requirements make it difficult to meet real-time demands. To address this issue, a super-resolution (SR) model based on the Graph Neural Network (GNN) for generating multipath channel data is proposed. The model leverages the ability of GNNs to process graph-structured data, mapping the multipath propagation process to the graph representation. RT generates low-resolution multipath data, which is then improved by the GNN-based SR model to produce high-resolution channel characteristics. Experimental results in the railway scenario demonstrate that the proposed method significantly reduces computation time while maintaining high accuracy. The GNNbased SR model performs excellently in power, spatial, and angular domains, providing a more efficient and accurate solution for railway wireless communication systems.

Fingerprint-based localization methods are regarded as a promising solution in sixth-generation (6G) wireless communication because of their ubiquitous infrastructure and high precision in lab-level experiments. However, received signal strength (RSS) instability and fingerprint spatial ambiguity (FSA) significantly undermine the precision of localization methods under real-world applications. Signal spatial difference (SSD) is one of the approaches which can effectively address RSS instability and FSA in fingerprint localization. However, few of these approaches are effectively incorporated into localization methods. In this paper, we propose an SSD-based advantage actor-critic (A2C) method, SSD-A2C, for 3D indoor localization. This method is the first to combine SSD and reinforcement learning (RL), achieving a highly efficient method that can address FSA and RSS instability. A 3D indoor localization simulation environment is developed based on ray-tracing (RT) simulation results of two real-world indoor scenarios, and the proposed method is trained and evaluated by it. Experiment results proved the excellent performances of SSD-A2C in minor localization errors and a high success rate of predicting the desired location. SSD is found to be more appropriate than RSS for RL. The strengths, shortcomings, and future research directions of the proposed method are also discussed in this paper.

This article presents a new sensing channel modeling approach by jointly considering ray-tracing (RT) and stochastic methods, to accurate and efficient model sensing channels for vehicle-to-everything (V2X) applications. For the former, moving targets are modeled through accurate RT simulations while for the latter a statistical approach is used for generating complex environmental clutter by emphasizing for the first time individual object modeling. This approach is used to form a feature library of objects which ensures space-time consistency while significantly improving the modeling speed. The channel transfer functions generated by RT and stochastic methods are jointly considered through coherent superposition to form a more complete sensing channel which includes both clutters and targets. To verify its effectiveness and accuracy, a comprehensive experimental study has been conducted taking systematic measurements using a 77-GHz mmWave radar as it is the prevalent equipment for sensing used for intelligent driving applications. We have considered a typical V2X scenario, with the radar deployed on vehicles traveling along roads at an urban intersection. The experimental results obtained have demonstrated that the accuracy in target distance detection and velocity estimation has improved leading to errors of less than 0.5 m and less than 0.2 m/s, respectively, while for clutter modeling, the error of power is 3 to 6 dB. Moreover, compared to traditional RT methods, the proposed approach is 20 times faster. Through the proposed approach, realistic sensing channel data can be obtained in a systematic, effective, and accurate manner, facilitating research of sensing-assisted communication applications.

This paper presents systematic experimental and related analytical studies for the accurate channel modeling of vehicle-to-vehicle (V2V) radio channels in mountainous plateau environments (MPEs) at the 376MHz band which is allocated for emergency communications. The measurement campaign has been conducted in the Ailao Mountains (Yunnan Province of China) and the influence of this mountainous environment on the radio channel characteristics in the power domain has been thoroughly investigated. The time-varying characteristics of the measured V2V radio channels have identified the stationarity time based on the local scattering function as a prerequisite for accurate modeling. Furthermore, Akaike’s information criteria and Kolmogorov-Smirnov tests have revealed that the Weibull probability distribution function is the best fit for the measured data. With the Weibull shaping parameter, the line of sight (LOS) and non-LOS (NLOS) channels can be conveniently and accurately classified. As large variations of the LOS probability and the shape parameter over long communication distances and LOS/NLOS conditions have been observed, an analytical approach using a 3rd order sum of sinusoid functions has been used to model them. Through this model, the maximum distance for LOS communications has been derived directly from the measured data. The large-scale propagation properties, including path loss, shadow fading, and shadow fading correlation, have been thoroughly studied, and accurate path loss models specifically for the MPEs have been proposed. Moreover, a new shadow fading correlation model has been presented. Compared to previously known models, this model is more accurate and more suitable for long-distance emergency communication scenarios.

Millimeter-wave (mmWave) automotive radar can detect the surrounding environment using highly directional, high-frequency electromagnetic waves. With these advantages, mmWave radar has become an important component of autonomous driving and integrated sensing systems. Designing systems and sensing algorithms requires the application of realistic channel models and simulation. In this paper, the propagation channel is measured, characterized, and modeled for mmWave automotive radar with typical configurations in scenarios. Based on the channel measurements in urban street and expressway environments, ray-tracing (RT) technology is verified for modeling important objects regarding radar cross-section and echo power. A hybrid channel model is proposed by integrating RT with the stochastic modeling of targets and surrounding environments, which significantly improves simulation efficiency and provides more flexibility for virtual tests in various complex environments.

Vehicular communication and sensing technologies are key to enabling 6G Intelligent Autonomous Transportation Systems (IATS). With the introduction of massive sensors and artificial intelligence (AI) fusion applications, IATS is needed to support data transmission rates up to 10 Gb/s. Millimeter-wave (mmWave) technology has attracted extensive attention owing to abundant spectrum resources, which can support the timely transmission of massive data. However, performance degradation of mmWave due to signal blockage has become one of the critical technical challenges. Road bridges as one of the common obstacles in urban scenarios, which has severe blockage effects on communication links. Therefore, this paper comprehensively studies the impact of road bridge blockage effects on mmWave vehicle-to-infrastructure (V2I) links and proposes an empirical model that can accurately characterize the bridge blockage effect. First, we use a self-developed mmWave channel sounder to carry out channel measurements on typical urban roads. Measurement results indicate that a maximum extra propagation loss of up to 23 dB is caused by road bridges. In addition, to address the deficiencies of existing propagation prediction models, the Single Road Bridge (SRB) model is proposed in this work. This model reveals for the first time the extra propagation loss caused by the road bridge to the channel. Compared with existing models, the SRB model can make the mean absolute error (MAE) and root mean square error (RMSE) within 5 dB. The proposed SRB model is of great value for accurately simulating real-world road bridge blockage events when designing future IATS.

The terahertz (THz) band (0.1-10 THz) is widely considered to be a candidate band for the sixth-generation mobile communication technology (6G). However, due to its short wavelength (less than 1 mm), scattering becomes a particularly significant propagation mechanism. In previous studies, we proposed a scattering model to characterize the scattering in THz bands, which can only reconstruct the scattering in the incidence plane. In this paper, a three-dimensional (3D) stochastic model is proposed to characterize the THz scattering on rough surfaces. Then, we reconstruct the scattering on rough surfaces with different shapes and under different incidence angles utilizing the proposed model. Good agreements can be achieved between the proposed model and full-wave simulation results. This stochastic 3D scattering model can be integrated into the standard channel modeling framework to realize more realistic THz channel data for the evaluation of 6G.

Sensing function in integrated sensing and communication (ISAC) system concentrates on collecting and extracting information of the targets from noisy observations, which can assist positioning the users and enable a precise directional communication link. This paper deals with noise reduction via tensor ring (TR) decomposition and total variation (TV) for linear frequency modulated continuous-wave (FMCW) signals in the multiple-input multiple-output ISAC system. Specifically, TR decomposition is used to exploit the low-rankness and describe the global correlation among different dimensions of the high-order received signal. The noise suppression is addressed by the integration of a TV regularization and a Frobenius norm term to ensure sufficient signal-to-noise ratio (SNR). The corresponding optimization problem is solved using augmented Lagrange multiplier (ALM) and proximal alternating minimization. Simulation results illustrate that the proposed method improves denoising performance, leading to a higher output SNR of the target and a better detection probability.

The smart home integrates home-related facilities, which can greatly facilitate people’s life. To support applications such as breath detection and health monitoring for the smart home, it is significant to understand the effect of the human body on the characteristics of the wireless channel. In this paper, the channel characteristics for human exhalation and inhalation at 6.5 GHz are measured and analyzed. Based on the measurement results in the living room scenario, the ray tracing (RT) simulator is calibrated and used to analyze the continuous process of exhalation and inhalation. According to the measurement and simulation results, the effect of human exhalation and inhalation processes on the power and phase of the received signal is studied. The analysis of this paper could be useful in guiding the deployment of smart home devices for human body monitoring. The calibrated electromagnetic (EM) parameters of typical indoor materials will support RT-based channel simulation and modeling in indoor scenarios.

To realize intelligent connection of everything and the digital twin (DT) of the physical world in 6G and beyond, new communication and sensing solutions are demanded. The potential of multiple spectrums is maximized for various applications and scenarios. In such a context, an accurate, efficient, and pervasive multi-spectrum propagation model is needed as a critical and unified baseline for testing the performance of the solutions in various scenarios. This work presents ray-tracing (RT) oriented methods for the DT presentation of radio propagation at multiple frequency bands from microwave to visible light. The material- and field-measurement-based approaches are proposed to characterize the electromagnetic properties of materials. On that basis, the propagation mechanisms are developed and validated, and the corresponding parameters are inverted. For the real-time simulation demand, RT and artificial intelligence (AI) algorithms are fused to develop a super-resolution modeling method. The experimental results indicate that the proposed method outperforms the baseline model regarding stability and accuracy. It can significantly reduce the computation time with comparable accuracy to the RT-only approach. The proposed methodologies and the in-depth discussions in this work are expected to pave the way to realize the DT of multi-spectrum propagation for evaluating 6G and beyond technologies.

Millimeter-wave automotive radar is essential for realizing autonomous driving. Realistic channel model and simulation are important for system and sensing algorithm design. This work introduces channel measurements and ray-tracing (RT) channel simulations for frequency modulated continuous wave (FMCW) automotive mmWave radar. The 77 GHz channel measurements in an urban crossroads environment are presented. The dominant echoes (multi-path components) of the measurement are detected and matched with corresponding objects for each frame. A measurement-based electromagnetic (EM) parameter estimation method is proposed to find the optimal parameter set that minimizes the error of simulated radar cross section (RCS). As a result, the issue of lacking reliable EM material parameters for RT simulation at 77 GHz frequency band is tackled. Simulation results are validated in power, range, velocity, and angle domains with the provided EM parameter set. Extending validated RT simulations in similar environments with various configurations allows more reliable channels for rigorous testing without the limitation of channel measurement.

Channel modeling has always been the core part in communication system design and development, especially in 5G and 6G era. Traditional approaches like stochastic channel modeling and ray-tracing (RT) based channel modeling depend heavily on measurement data or simulation, which are usually expensive and time consuming. In this paper, we propose a novel super resolution (SR) model for generating channel character-istics data. The model is based on multi-task learning (MTL) convolutional neural networks (CNN) with residual connection. Experiments demonstrate that the proposed SR model could achieve excellent performances in mean absolute error and standard deviation of error. Advantages of the proposed model are demonstrated in comparisons with other state-of-the-art deep learning models. Ablation study also proved the necessity of multi-task learning and techniques in model design. The contribution in this paper could be helpful in channel modeling, network optimization, positioning and other wireless channel characteristics related work by largely reducing workload of simulation or measurement.

The vision of smart maritime requires a seamless high-data rate wireless connectivity, which can be realized by the fifth-generation communication system (5G). As one of the most widely used 5G frequency spectrum, 3.5 GHz is a promising candidate band to provide high-quality wireless coverage inside ships. Hence, in this paper, the wireless channel in the intra-ship scenario at 3.5 GHz is characterized through extensive ray-tracing (RT) simulations. Channel parameters are extracted and analysed in terms of root-mean-square delay spread (RMS DS), Rician$K$-factor (KF), azimuth/elevation angular spread of arrival/departure (ASA, ASD, ESA, ESD), cross-polarization ratio (XPR), and their cross-correlations. Based on these results, we provide valuable insights into the system design and evaluation for the 5G in the intra-ship scenario.

Abstract This paper investigates an advanced effective signal processing technique to suppress noise, addressing a modern high‐performance detection in the field of radar sensing. To achieve a higher accuracy, the frequency modulated continuous wave radar is taken as a case study to derive the algorithm based on Karhunen ‐ Loève transform (KLT) before detection. KLT defines a linear projection of the signal statistics on the eigenfunctions domain, which makes the input‐dependent signals orthogonal to each other under new eigen‐basis and eigenvalues. The highest energy along slow time dimension of each range bin is concentrated in the transformed domain corresponding to the largest N eigenvalues. The performance of the algorithm is evaluated by different eigenvalue selection strategies. Numerical experiments are employed to obtain the relationship between signal‐to‐noise ratio and different eigenvalue selection strategies. Pertaining to the detection performance, constant false alarm ratio detector is applied to demonstrate the detection ability as a result of the processor by use of probability of detection ( P d ).

The millimeter wave (mmWave) communication with large bandwidth is a key enabler for both the fifth-generation mobile communication system (5G) and smart rail mobility. Thus, in order to provide realistic channel fundamental, the wireless channel at 22.6 GHz is characterized for a typical high-speed railway (HSR) environment in this paper. After importing the three-dimensional environment model of a typical HSR scenario into a self-developed high-performance cloud-computing Ray-Tracing platform - CloudRT, extensive raytracing simulations are realized. Based on the results, the HSR channel characteristics are extracted and analyzed, considering the extra loss of various weather conditions. The results of this paper can help for the design and evaluation for the HSR communication systems enabling smart rail mobility.

Powered by the Internet of Things, the vehicular ad-hoc networks are expected to evolve into the Internet of Intelligent Vehicles in which each vehicle can be much more efficient in various vehicular and transportation applications. In order to realize this vision, a seamless low-latency and ultrareliable vehicle-to-vehicle (V2V) communication network is required. Thus, it is of importance to characterize the V2V channels in various realistic environments, especially when the line-of-sight between transmitter (Tx) and receiver (Rx) is obstructed. In this paper, we characterize obstructed V2V channels in the 5-GHz band through measurement-calibrated ray-tracing (RT) simulations. To begin, the main objects in the real world are divided into two groups: 1) the small-scale structures (e.g., lampposts, traffic signs, etc.) and 2) the large-scale structures (such as buildings and ground). Then, we integrate the radar cross sections of the small-scale structures into our RT simulator through a framework based on high frequency prediction techniques. For the large-scale structures, we calibrate the electromagnetic and scattering parameters of the large-scale structures through V2V channel measurements. After such integration and calibration, extensive RT simulations for V2V channels with Tx and Rx located on vehicles traveling in the opposite or same direction are realized with various antenna deployments in urban and open space environments, with and without sloped terrain. Based on the RT results, we characterize the path loss, shadow fading, and delay spread of the channel for each case, and show agreement with measured results in the literature for all these channel characteristics.

More people prefer to using rail traffic for travel or for commuting due to its convenience and flexibility. As the record of the maximum speed of rail has been continuously broken and new applications are foreseen, the high-speed railway (HSR) communication system requires higher data rate with seamless connectivity, and therefore, the system design faces new challenges to support high mobility. Millimeter-wave (mmWave) technologies are considered as candidates to provide wideband communication. However, mmWave is rarely explored in HSR scenarios. In this paper, channel characteristics are studied in the 5G mmWave band for typical HSR scenarios, including urban, rural, and tunnel, with straight and curved route shapes. Based on the wideband measurements conducted in the tunnel scenario by using the “mobile hotspot network” system, a 3-D ray tracer (RT) is calibrated and validated to explore more channel characteristics in different HSR scenarios. Through extensive RT simulations with 500-MHz bandwidth centered at 25.25 GHz, the power contributions of the multipath components are studied, and the dominant reflection orders are determined for each scenario. Path loss is analyzed, and the breakpoint is observed. Other key parameters, such as Doppler shifts, coherence time, polarization ratios, and so on, are studied. Suggestions on symbol rate, sub-frame bandwidth, and polarization configuration are provided to guide the 5G mmWave communication system design in typical HSR scenarios.

Passenger trains and especially metro trains have been identified as one of the key scenarios for 5G deployments. The wireless channel inside a train car is reported in the frequency range between 26.5 GHz and 40 GHz. These bands have received a lot of interest for high‐density scenarios with a high‐traffic demand, two of the most relevant aspects of a 5G network. In this paper we provide a full description of the wideband channel estimating Power‐Delay Profiles (PDP), Saleh‐Valenzuela model parameters, time‐of‐arrival (TOA) ranging, and path‐loss results. Moreover, the performance of an automatic clustering algorithm is evaluated. The results show a remarkable degree of coherence and general conclusions are obtained.

The tunnel scenario is a major rail communication scenario. In this paper, the radio channel characteristics of tunnel scenarios with different carrier frequencies, different distances between the transmitter (Tx) and receiver (Rx), and cross sections are simulated with a ray‐tracing tool. Key parameters such as path loss, Rician K‐factor, root mean square (RMS) delay spread, and angular spread are studied. According to the results, higher frequencies introduce larger path loss and the presence of the vehicle body increases the path loss by about 35 dB in the scenario; at the same time it will also cause the fluctuation and instability of the path loss. Besides, the influence of reflections from the side walls is significant on radio propagation. The channel experiences more severe fading in a narrow tunnel compared with others.

The millimeter wave (mmWave) communications and massive multiple-input multiple-output (MIMO) are both widely considered to be the candidate technologies for the fifth generation mobile communication system. It is thus a good idea to combine these two technologies to achieve a better performance for large capacity and high data-rate transmission. However, one of the fundamental challenges is the characterization of mmWave massive MIMO channel. Most of the previous investigations in mmWave channel only focus on single-input single-output links or MIMO links, whereas the research of massive MIMO channels mainly focus on a frequency band below 6 GHz. This paper investigates the channel behaviors of massive MIMO at a mmWave frequency band around 26 GHz. An indoor mmWave massive MIMO channel measurement campaign with 64 and 128 array elements is conducted, based on which, path loss, shadow fading, root-mean-square (RMS) delay spread, and coherence bandwidth are extracted. Then, by using our developed ray-tracing simulator calibrated by the measurement data, we make the extensive ray-tracing simulations with 1024 antenna elements in the same indoor scenario, and get insights into the variation tendency of mean delay and the RMS delay with different array elements. It is observed that the measurement and the ray-tracing-based simulation results have reached a good agreement.

We present a measurement campaign to characterize an indoor massive multiple-input multiple-output (MIMO) channel system, using a 64-element virtual linear array, a 64-element virtual planar array, and a 128-element virtual planar array. The array topologies are generated using a 3D mechanical turntable. The measurements are conducted at 2, 4, 6, 11, 15, and 22 GHz, with a large bandwidth of 200 MHz. Both line-of-sight (LOS) and non-LOS (NLOS) propagation scenarios are considered. The typical channel parameters are extracted, including path loss, shadow fading, power delay profile, and root mean square (RMS) delay spread. The frequency dependence of these channel parameters is analyzed. The correlation between shadow fading and RMS delay spread is discussed. In addition, the performance of the standard linear precoder—the matched filter, which can be used for intersymbol interference (ISI) mitigation by shortening the RMS delay spread, is investigated. Other performance measures, such as entropy capacity, Demmel condition number, and channel ellipticity, are analyzed. The measured channels, which are in a rich-scattering indoor environment, are found to achieve a performance close to that in independent and identically distributed Rayleigh channels even in an LOS scenario.

Rail traffic is widely acknowledged as an efficient and green transportation pattern and its evolution attracts a lot of attention. However, the key point of the evolution is how to develop the railway services from traditional handling of the critical signaling applications only to high data rate applications, such as real-time videos for surveillance and entertainments. The promising method is trying to use millimeter wave which includes dozens of GHz bandwidths to bridge the high rate demand and frequency shortage. In this paper, the channel characteristics in an arched railway tunnel are investigated owing to their significance of designing reliable communication systems. Meantime, as millimeter wave suffers from higher propagation loss, directional antenna is widely accepted for designing the communication system. The specific changes that directional antenna brings to the radio channel are studied and compared to the performances of omnidirectional antenna. Note that the study is based on enhanced wide-band ray tracing tool where the electromagnetic and scattering parameters of the main materials of the tunnel are measured and fitted with predicting models.

High-speed train (HST) communications at millimeter-wave (mmWave) band have received a lot of attention due to their numerous high-data-rate applications enabling smart rail mobility. Accurate and effective channel models are always critical to the HST system design, assessment, and optimization. A distinctive feature of the mmWave HST channel is that it is rapidly time-varying. To depict this feature, a geometry-based multipath model is established for the dominant multipath behavior in delay and Doppler domains. Because of insufficient mmWave HST channel measurement with high mobility, the model is developed by a measurement-validated ray tracing (RT) simulator. Different from conventional models, the temporal evolution of dominant multipath behavior is characterized by its geometry factor that represents the geometrical relationship of the dominant multipath component (MPC) to HST environment. Actually, during each dominant multipath lifetime, its geometry factor is fixed. To statistically model the geometry factor and its lifetime, the dominant MPCs are extracted within each local wide-sense stationary (WSS) region and are tracked over different WSS regions to identify its “birth” and “death” regions. Then, complex attenuation of dominant MPC is jointly modeled by its delay and Doppler shift both which are derived from its geometry factor. Finally, the model implementation is verified by comparison between RT simulated and modeled delay and Doppler spreads.

The rapidly time-varying channel in high-speed railway poses tough design challenges, which necessitates the research of accurate channel models. Existing researches focus on the isolated high-speed railway scenarios, and mainly deal with path loss, shadowing fading, Ricean K-factor and delay spread. However, few studies have been done in Doppler domain. In this paper, a deterministic channel model that employs ray- tracing algorithm is presented. The proposed deterministic modeling approach is applied in composite high-speed railway scenario rather than isolated one. The scenario is flexibly reconstructed through SketchUp. The simulation results are validated by the data measured in the same scenario. The channel characteristics in Doppler domain and the effect of Doppler shift are statistically analyzed based on the deterministic channel model. The transition regions in the composite scenario are emphatically investigated, and the results are compared with those of prior studies.

Unlike solid obstacles, the excess loss of semiclosed obstacles (SCOs) can be considerably overestimated by directly applying existing diffraction models, i.e., multiedge diffraction models. By regarding the propagation situation as a superposition of the cases of the “Open Field” and the “Closed Obstacle,” this paper presents a simple way to model the excess loss of SCOs that widely exists in intelligent transportation systems. By estimating two weight coefficients according to the specific situation, this model structure can be applied to different SCOs. To illustrate our modeling concepts, two typical cut-and-cover tunnels in high-speed railway are studied in detail. Combining this case with our previous implementations for train stations and crossing bridges, a complete set of coefficients for the excess loss of the main SCOs in railway settings is presented. This case study shows that the proposed approach provides an effective and fairly simple way to include various SCOs in the network planning, simulation, and design of communication systems. As our approach has determined the coefficients empirically, the proposed model structure can provide the foundation for future work that aims to streamline the excess loss prediction via estimation of coefficients either analytically or via a reduced set of measurements.

The complexity of planning a wireless sensor network is dependent on the aspects of optimization and on the application requirements. Even though Murphy's Law is applied everywhere in reality, a good planning algorithm will assist the designers to be aware of the short plates of their design and to improve them before the problems being exposed at the real deployment. A 3D multi-objective planning algorithm is proposed in this paper to provide solutions on the locations of nodes and their properties. It employs a developed ray-tracing scheme for sensing signal and radio propagation modelling. Therefore it is sensitive to the obstacles and makes the models of sensing coverage and link quality more practical compared with other heuristics that use ideal unit-disk models. The proposed algorithm aims at reaching an overall optimization on hardware cost, coverage, link quality and lifetime. Thus each of those metrics are modelled and normalized to compose a desirability function. Evolutionary algorithm is designed to efficiently tackle this NP-hard multi-objective optimization problem. The proposed algorithm is applicable for both indoor and outdoor 3D scenarios. Different parameters that affect the performance are analyzed through extensive experiments; two state-of-the-art algorithms are rebuilt and tested with the same configuration as that of the proposed algorithm. The results indicate that the proposed algorithm converges efficiently within 600 iterations and performs better than the compared heuristics.

HELLO protocol or neighborhood discovery is essential in wireless ad hoc networks. It makes the rules for nodes to claim their existence/aliveness. In the presence of node mobility, no fix optimal HELLO frequency and optimal transmission range exist to maintain accurate neighborhood tables while reducing the energy consumption and bandwidth occupation. Thus a Turnover based Frequency and transmission Power Adaptation algorithm (TFPA) is presented in this paper. The method enables nodes in mobile networks to dynamically adjust both their HELLO frequency and transmission range depending on the relative speed. In TFPA, each node monitors its neighborhood table to count new neighbors and calculate the turnover ratio. The relationship between relative speed and turnover ratio is formulated and optimal transmission range is derived according to battery consumption model to minimize the overall transmission energy. By taking advantage of the theoretical analysis, the HELLO frequency is adapted dynamically in conjunction with the transmission range to maintain accurate neighborhood table and to allow important energy savings. The algorithm is simulated and compared to other state-of-the-art algorithms. The experimental results demonstrate that the TFPA algorithm obtains high neighborhood accuracy with low HELLO frequency (at least 11% average reduction) and with the lowest energy consumption. Besides, the TFPA algorithm does not require any additional GPS-like device to estimate the relative speed for each node, hence the hardware cost is reduced.

In this paper, a simulation tool for assisting the deployment of wireless sensor network is introduced and simulation results are verified under a specific indoor environment. The simulation tool supports two modes: deterministic mode and stochastic mode. The deterministic mode is environment dependent in which the information of environment should be provided beforehand. Ray tracing method and deterministic propagation model are employed in order to increase the accuracy of the estimated coverage, connectivity and routing; the stochastic mode is useful for large scale random deployment without previous knowledge on geographic information. Dynamic Source Routing protocol (DSR) and Ad hoc On-Demand Distance Vector Routing protocol (AODV) are implemented in order to calculate the topology of WSN. Hence this tool gives direct view on the performance of WSN and assists users in finding the potential problems of wireless sensor network before real deployment. At the end, a case study is realized in Centro de Electronica Industrial (CEI), the simulation results on coverage, connectivity and routing are verified by the measurement.