Homeostatic mechanisms play a crucial role in maintaining optimal functionality within the neural circuits of the brain. By regulating physiological and biochemical processes, these mechanisms ensure the stability of an organism’s internal environment, enabling it to better adapt to external changes. Among these mechanisms, the Bienenstock, Cooper, and Munro (BCM) theory has been extensively studied as a key principle for maintaining the balance of synaptic strengths in biological systems. Despite the extensive development of spiking neural networks (SNNs) as a model for bionic neural networks, no prior work in the machine learning community has integrated biologically plausible BCM formulations into SNNs to provide homeostasis. In this study, we propose a Dynamic Weight Adaptation Mechanism (DWAM) for SNNs, inspired by the BCM theory. DWAM can be integrated into the host SNN, dynamically adjusting network weights in real time to regulate neuronal activity, providing homeostasis to the host SNN without any fine-tuning. We validated our method through dynamic obstacle avoidance and continuous control tasks under both normal and specifically designed degraded conditions. Experimental results demonstrate that DWAM not only enhances the performance of SNNs without existing homeostatic mechanisms under various degraded conditions but also further improves the performance of SNNs that already incorporate homeostatic mechanisms.

Integrated sensing and communication (ISAC) technology has attracted extensive attention in the future communication systems. To achieve more precise and detailed sensing capabilities, we propose and derive an imaging algorithm named adaptive successive-interference-cancellation (SIC)-aided spatial transformation imaging (ASSTI). This algorithm leverages channel responses in conjunction with the SIC method to facilitate multi-target imaging, employs a spherical wavefront model to enable accurate imaging in both far-field and near-field scenarios, and utilizes likelihood-guided refinement to optimize imaging performance. We assess the performance of the proposed algorithm using both simulation and measurement data, demonstrating image quality through the modulation transfer function (MTF) metric. The results indicate that the ASSTI algorithm achieves enhanced image contrast and quality compared to several imaging algorithms, including back-projection (BP), matched filtering (MF) and successive-interference-cancellation-aided spatial transformation imaging (SSTI). The proposed algorithm outperforms these methods in imaging capabilities and can be effectively applied in ISAC within next-generation communication systems.

With the rapid development of emerging wireless communication technologies, there is a concurrent surge in data volume alongside urging demands for real-time data processing in time-varying channels. Existing environment sensing methods can hardly realize real-time dynamic sensing due to the large amount of post-processing requirements on the static channel components. Therefore, the decomposition of static and dynamic channels has become a great challenge, contributing to efficient dynamic sensing. In this paper, a subspace-based method is proposed to separate the common and non-common components of the channel matrix, which is further used for static and dynamic channel sensing, respectively. The spatial and frequency subspaces of two channel state informations (CSIs) are extracted within the coherence time by eigenbasis projection, respectively, from which the common and non-common subspaces are separated to perform compressed sensing (CS) based CSI feedback. Spectrum-based analysis of the separated results by simulation and measurement is elaborated and the performance is numerically evaluated by correlation coefficients.

In current Intelligent Transportation Systems, 4-dimensional (4D) millimeter-wave radar is gradually conquering more important roles. However, the rich multipath phenomena of radar waves can cause virtual images to be mistaken for actual targets. Most existing solutions aim to identify distinguishable features within a single signal frame to differentiate virtual targets from actual ones, but they fail to fully leverage temporal evolution information. Moreover, they are often limited to simple scenarios with low-order bounces and cannot handle extreme high-order bounce cases. To address this gap, in this paper we establish a research methodology based on topology and propagation graph theory, and propose a time-varying 4D trajectory-based physical target discrimination (TPTD) method. This method can handle multipath virtual images formed by arbitrary-order bounces. Its effectiveness and superiority over advanced methods have been qualitatively and quantitatively validated through traceable simulated and measured data. We also discuss in detail the scalability of the algorithm and its potential bottlenecks. The proposed method can be applied to fields such as precise radar sensing and channel modeling, laying the foundation for integrated sensing and communication and autonomous driving.

Clustering characterizes the grouping of multipath components (MPCs) in radio channels. Accurate clustering is a prerequisite for cluster-based channel characterization and sensing in the beyond fifth-generation (B5G) and sixth-generation (6G) communication. However, existing clustering algorithms commonly depend on thresholds and initializations, and are not fully consistent with the characteristics of MPC distributions in the radio channel. Additionally, clustering based on power spectrum has not been thoroughly researched. In this paper, we propose a unified clustering method named power-weighted nearest-neighbor robust mean-shift (MP-NN-RMS) algorithm, which is a kernel density estimation (KDE)-based method. The K-nearest neighbor (KNN) kernel is utilized to adapt to the changes in local density. Two variants of this clustering method for the power spectrum and MPCs are provided. Both simulation and measurement-based verifications demonstrate the effectiveness of the proposed algorithms. Compared with traditional clustering methods, the proposed algorithm can achieve more accurate and robust clustering results without requiring prior information of predefined parameters or models. Moreover, mathematical proof on the convergence guarantees the rationality of the proposed algorithm. This advancement is beneficial for the development of future wireless communication systems.

Integrated Sensing and Communications (ISAC) has been identified as a key technology in the Sixth-Generation (6G) as an enabler for environment-aware communications. Although many communications-centric channel models have been developed for dynamic scenarios, they can hardly be used for sensing applications since they rely on propagation characteristics not directly related with the geometry and trajectories of the objects in the environment. On the other hand, the available sensing models are mostly deterministic, and hence not providing a stochastic description of the sensing characteristics. In this paper, we propose a sensing channel model based on geometric and kinematic parameters, such as the size, shape, position and velocity of the objects. By distinguishing between single- and multi-bounce components, the proposed channel model is able to accurately characterize propagation environments even in dense-scattered scenarios. Moreover, a novel definition is provided for the birth and death processes (BDPs), able to accurately capture the space-time correlation properties of the channels. An example of the model parametrization is provided based on actual measured data. With the channel realization approach, we exhibit the consistence between simulated and measured data and validate the efficacy of the proposed model. The work in this paper is of great significance to construct the bidirectional bridge between channel and the environment, a key point of interest for the future 6G systems.

In this paper, the correlation matrix distance (CMD) metric is employed to characterize the non-stationarity of the Vehicle-to-Vehicle (V2V) channel under three different driving states, namely following, overtaking and meeting. The analysis is based on measurements carried out at 5.9 GHz using a single-input multiple-output (SIMO) system. Firstly, the spatial structure of V2V channel under three different driving states is analyzed through power angle spectrum (PAS). Subsequently, based on the analysis results of CMD, we further calculate the quasi-stationary distance for the three different driving states. The analysis results indicate that the quasi-stationary distance can effectively reflect the spatial structural changes in the V2V channel. Moreover, the statistical characteristics of the quasi-stationary distance for the three driving states are also characterized. The analysis reveals that among the three driving states, the V2V channel in the following driving state exhibits the highest stationarity, followed by overtaking driving states. Compared to the meeting state, the V2V channel in overtaking exhibits greater stationarity.

In order to realize the future Sixth-Generation (6G), the research on Integrated Sensing and Communication (ISAC) channel modeling has attracted considerable attention. In classical cluster-based models, it is difficult to effectively build high density point clouds to outline object features. As an alternative, slightly-distributed-scatterer-based signal models are utilized in this paper, and a first-order generalized array manifold is used to approximate the spread features of channel. By combining this with the space-alternating generalized expectation-maximization (SAGE) algorithm, we achieve a framework for the efficient estimation of the dispersive-path components including the spread on the angle and the Doppler frequency domains. Based on measured data in vehicular environments, the effectiveness of the algorithm is verified, and a time-varying statistical channel model is formulated. The results obtained can help to accurately model vehicular channels and provide guidance for other applications of autonomous driving.

Automotive radar sensors are widely used in vehicles so as to allow the “advanced driver assistance systems (ADAS)” to work. As far as we are concerned, no mandatory regulations were established so far for interference control in the cases where multiple vehicles equipped with automotive radars exist in close proximity. Consequently, those inter-vehicle cross-interference easily result in the radar performance degradation, e.g. incapability of target detection and identification. In this paper, an interference suppression algorithm is proposed which utilizes two Recursive-Least-Square (RLS) adaptive filters to estimate, the interference signals caused by the “aggressor” radar and the echo signals from the “ego” radar. The algorithm operates in the iterative mode with its initial input being the originally received beat signals involving interference. In each iteration, the estimates of the interference signals and of the clean signals are calculated sequentially by the two RLS filters, and the beat signal is updated with parts of the fragments substituted with their interference-mitigated counterparts. Such iterative operations stop when the convergence criterion, e.g. none of the fragments in the beat signals are considered being interfered, is achieved or the iteration number reaches the limit predefined due to the practical constraints. Simulation and measurement results demonstrate that the proposed dual-RLS-based algorithm outperforms the existing methods in terms of lower interference-to-signal ratio resultant and superior capability of retrieving the original range-Doppler profile.

In this paper, the novel extended space-alternating generalized expectation-maximization (SAGE) algorithm, providing joint propagation channel multi-path component (MPC) estimation and scatterer localization underlying spherical-wavefront multipath model, is proposed. Two geometry-based models are aided for estimating the first and last hop scatterers under different bouncing orders. The performance of the proposed algorithm, as called geometry-aided SAGE (GA-SAGE), is illustrated by means of the Cramér-Rao lower bound derived for parameter estimates and the root-mean-square-estimation-errors (RMSEEs) obtained through and Monte-Carlo simulations, which shows the applicability both near- and far-field estimation. Finally, the GA-SAGE is applied to processing the experimental data obtained from measurements in an indoor office environment by using a single-input multiple-output (SIMO) configuration. The obtained results show that the method proposed outperforms the traditional SAGE algorithm in terms of MPCs estimation accuracy, convergence rate, and the extra capability of localizing scatterers involved in different bouncing order propagation paths. It is considered useful in environment sensing alike applications and makes the GA-SAGE an efficient and effective tool for the development of geometry-based stochastic channel models capable of reproducing channel realizations of the so-called spatial consistency or spatial non-stationarity, which are the basis for the design of transmission technologies using extremely-large antenna array (ELAA) for 5G and beyond.

With the rapid development of the sixth generation (6G) communication systems, the extremely large antenna arrays (ELAAs) have attracted substantial attention in both academia and industry. As the array size increases, the so-called “near-field” effect, i.e. channel parameters varying with respect to the antenna’s position becomes evident. Conventional channel models fail to describe such effects as the parameters of multi-path components (MPCs) specified in these models are fixed regardless of array configuration. Due to the mismatch between the constant MPC assumption and the actual variant behavior observed, the parameterization of the models aiming to describe the spatial non-stationary channels cannot be conducted effectively. In order to mitigate the model mismatch and reproduce the channels with spatial non-stationarity specifically for ELAA applications, in this work, a novel measurement-based stochastic channel modeling method is proposed and applied to establishing Scatterer-based Spatial Channel Model (SSCM) that can be used to unify the far- and near-field models using spherical wave and generate the spatial non-stationary channels for ELAAs by integrating the traditional spatial channel model (SCM) with “clusters-of-scatterers (CoS)”. To establish the models based on measurements, the locations of first- and last-hop scatterers existing along propagation paths in a channel are estimated based on the spherical-wave assumption. A novel clustering algorithm is used to group the MPCs taking into account those scatterers’ locations. With a channel measurement campaign conducted using a$32\!\!\times \!\!32$Rx planar antenna array at carrier frequency of 10 GHz, exemplary SSCMs are established. The SSCM is capable of reproducing the parts of the environment influencing wave propagation, and therefore can be used for the research of integrated sensing and communication (ISAC) applications.

Channel hardening in massive Multiple-Input Multiple-Output (MIMO) systems is a hot research field that needs investigation. As the number of antennas increases, channel starts to behave deterministically and small-scale fading decreases. This is called hardening phenomenon. Since there is a lack of real world study of the hardening effect in massive MIMO channels, this paper aims at investigating channel hardening in an outdoor real-world scenario by using a linear double polarized transmitting antenna array and a cylindrical dual polarized receiving array. Measurements are performed in two different routes and hardening is analyzed in terms of different polarization combinations and different antenna subsets by fixing the transmitter or receiver and using coefficient of variation (CV2) as a metric. The results show that in terms of getting more pronounced hardening, it is more beneficial to fix the position of the transmitting antenna along the array. Selecting a certain subset of antennas, it can be observed that channel does not soften. This gives rise to the possibility of using antenna selection algorithms. For achieving the same hardening level, more antennas are needed in the Line of Sight (LoS) scenario since the channel is shown to experience more hardening in Non-Line of Sight (NLoS) scenario. Furthermore, it is shown that polarization has little impact in the channel hardening of a NLoS scenario. These results are very important from the point of view of network operators.

This paper proposes a novel general sixth-generation (6G) irregular-shaped geometry-based stochastic model (IS-GBSM) for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed IS-GBSM can model spherical wavefront propagation, high delay resolution, and three-dimensional (3D) space-time-frequency non-stationarity of channels with time-space consistency, and thus can be applied to integrated communication and sensing systems. To capture the 3D non-stationarity with channel consistency, a new hybrid method combining geometric and parametric methods is developed. Based on the geometric method, i.e., visibility region (VR), the smooth and consistent appearance and disappearance of clusters during array-time evolution are modeled. With the help of the parametric method, a frequency-dependent path gain is introduced, and a visible factor based on uniform continuity theorem and bounded variation function is further proposed to capture the soft cluster power handover during cluster array-time evolution. Key channel statistics of the proposed IS-GBSM are derived and investigated. Simulation results demonstrate that the space-time-frequency non-stationarity is modeled and time-space consistency is captured. Finally, the utility of the proposed IS-GBSM is verified by the excellent agreement between simulation results and measurement.

In recent years, with the rapid development of Advanced Driver Assistance Systems (ADAS), vehicular millimeter-wave (mmWave) radar plays an important role in urban intelligent transportation system. Self-interference caused by multipath in radar channel is a widely concerned problem. However, the research on radar channel propagation characteristics is very limited and there are few models for radar channel available. In this paper, we propose a pioneering research for radar channel model. Based on measurement data, channel characteristics are analysed using the space-alternating generalized expectation-maximization (SAGE) algorithm. An empirical statistical model is established for the 77GHz mmWave radar channel in the underground parking lot scenario, which is one of the most challenging environments because of time-variation and abundant multipaths, filling the gap in the field. A path-association algorithm is used to obtain multipath component (MPC) trajectories in delay, Doppler frequency and power domains. Additionally, some trajectory (in range domain) features are analysed. Moreover, based on geometric analysis of trajectories in range domain, the deterministic component of trajectories is modeled, and then a novel method for discrimination between actual targets and ghost images is proposed and verified by simulation and measurement data.

In this paper, we combined linguistic complexity and (dis)fluency features with pretrained language models for the task of Alzheimer’s disease detection of the 2021 ADReSSo (Alzheimer’s Dementia Recognition through Spontaneous Speech) challenge. An accuracy of 83.1% was achieved on the test set, which amounts to an improvement of 4.23% over the baseline model. Our best-performing model that integrated component models using a stacking ensemble technique performed equally well on cross-validation and test data, indicating that it is robust against overfitting.

During the last few years, with the decrease of their sizes and costs, Unmanned Aerial Vehicles (UAVs) became more feasible for their use in general-purpose applications. As an important basis for UAV applications, the Air-to-Ground (A2G) radio propagation channel has gained attention in the channel modeling literature. However, whereas the A2G propagation channel is inherently dynamic (time-varying), the majority of the available models do not consider such a time-variability. This paper proposes a channel multi-path component (MPC) tracking algorithm and shows its ability to analyze the data collected by a real A2G measurement campaign in a suburban environment. Based on the obtained results, a time-varying statistical channel model for A2G communications in realistic suburban scenarios is proposed. The model is able to stochastically characterize parameters related to the birth and time-of-life of the multi-path components (MPCs), as well as their evolution in terms of delay, Doppler frequency or magnitude. Correlation coefficients to relate different channel characteristics are also obtained. Our work shows that the MPCs evolution over time for the UAV A2G channel can be described by simple regular patterns.

In parallel with the decrease in cost, size and weight of Unmanned Aerial Vehicles (UAVs) and the increase of their flight autonomy, many commercial applications are rapidly arising. Most of those applications rely on a communications system between a terrestrial base station and the UAV. Due to the UAV movement, time-variant channel models are required. In this article, we propose a geometrical model for the channel Multipath Components (MPCs) evolution with the UAV flight that supports MPCs that are born and die in several occasions due to blockages. Based on this model, the novel Geometry-Based Spatial-Consistent MPC Tracking Method (GSTM) is proposed and its performance on channel MPCs tracking was shown both by simulations and by an Air-to-Ground (A2G) low-height UAV measurement campaign. The GSTM also provides the parameters of a geometrical model of the evolution of the main MPCs of the channel, which allows to identify the scatterers that lead to the MPCs and greatly contributes to the understanding of the propagation mechanisms in A2G environments. The correctness of the MPC tracking is proven to be higher than 90% and the results show that the model obtained by the GSTM includes more than 95% of the received power.

Under the impetus of global communications organizations, 5G mobile communications becomes a hot technology and moves towards commercial use. In this paper, we propose an efficient downlink channel characterization scheme based on passive sounding in sub-6 GHz 5G commercial network. We first introduce the framework of passive sounding system containing a space-time-division multiplexing (STDM) sounder. Then we elaborate the extraction procedure for 5G downlink signals including synchronization signal block (SSB) and channel state information reference signal (CSI-RS) according to New Radio (NR) technical specifications proposed by 3GPP. Through a pilot test in the anechoic chamber, we also demonstrate the practice of signal extraction. Besides, we derive a dual-polarimetric space-alternating generalized expectation-maximization (SAGE) algorithm which matches with the passive sounding system for multi-dimensional channel parameter estimation. The estimator performance is analyzed by synthetic channel simulation, and results reveal its high accuracy and robustness. Finally, we verify the feasibility of the proposed scheme through analyzing channel characteristics derived from passive sounding in stationary field measurements respectively conducted in industrial park and parking lot with the 5G commercial network built by China Unicom Communications Corporation (CUCC).

In this paper, a recent measurement campaign for high-speed-train (HST) channel characterization is conducted at the speed of about 250 km/h in a suburban environment with residential areas. A passive channel sounder is applied to collect the down-link signals with the sampling rate of 100 MHz based on the in-service fifth-generation new radio (5G-NR) networks at 3.45 GHz. The channel impulse responses (CIRs) are extracted from the received Channel-State Information Reference Signals (CSI-RSs) and delay dispersion can be observed clearly in the concatenated power delay profiles (CPDPs). Both the large-scale and small-scale parameters including path loss, shadow fading, delay spread and K-factor are analyzed and modeled. The velocity and distances between the base station (BS) and the UE can be estimated by the delay trajectory and the empirical path loss model and shadow fading can be derived based on the estimated results. The cumulative distribution functions (CDFs) of delay spread and K-factor are both well fitted with the log-Normal distribution. This statistical model for HST propagation channel constructed based on measurements in in-service 5G network, as the first of its kind, is useful for generating channel realizations for simulating of 5G communication systems.

The use of millimeter-wave bands is undoubtedly one of the key technologies considered by the Fifth Generation Communication Systems (5G). However, the performance of millimeter-wave communication systems can be severely affected by the propagation environment. In this paper, the propagation channel characteristics in scatterer-rich environments for the 73GHz millimeter-wave band is studied based on measurements. The channel characteristics under study include the propagation loss and its variation depending on the elements around the receiver, the channel dispersion in delay represented by the Root-Mean-Square (RMS) channel delay spread and the multipath behavior in the form of multi-path component (MPC) clusters. The proposed models for the mentioned channel characteristics are of great value as reference for millimeter-wave 5G deployments.

In this article, we propose a novel 3-D geometry-based stochastic model (GBSM), i.e., a two-cylinder model, for unmanned aerial vehicle (UAV) multiple-input-multiple-output (MIMO) Ricean fading channels. The received signal is a sum of the Line-of-Sight (LoS) component, single-bounced (SB) rays at the UAV side, SB rays at the ground station side, SB rays on the ground, and double-bounced rays. This makes our model adaptable to a wide variety of UAV communication scenarios. More importantly, the proposed UAV model is the first two-cylinder model that considers ground reflections. Moreover, our model has the ability to investigate the impact of some unique UAV-related parameters (e.g., the UAV's moving direction, UAV's altitude, and antenna orientation) on channel characteristics in a 3-D nonisotropic propagation environment. From the proposed model, we derive and study some significant statistical properties, including the space-time correlation function (CF), Doppler power spectral density (PSD), envelope level crossing rate (LCR), and average fade duration (AFD). Some numerical results and interesting observations are provided, which can be considered useful guidance for the design of UAV-MIMO communication systems. Finally, the utility of our model is verified by the close agreement between the theoretical results and some example measurement data.

Abstract Point cloud semantic segmentation, a crucial research area in the 3D computer vision, lies at the core of many vision and robotics applications. Due to the irregular and disordered of the point cloud, however, the application of convolution on point clouds is challenging. In this article, we propose the “coordinate convolution,” which can effectively extract local structural information of the point cloud, to solve the inapplicability of conventional convolution neural network (CNN) structures on the 3D point cloud. The “coordinate convolution” is a projection operation of three planes based on the local coordinate system of each point. Specifically, we project the point cloud on three planes in the local coordinate system with a joint 2D convolution operation to extract its features. Additionally, we leverage a self‐encoding network based on image semantic segmentation U‐Net structure as the overall architecture of the point cloud semantic segmentation algorithm. The results demonstrate that the proposed method exhibited excellent performances for point cloud data sets corresponding to various scenes.

Massive multiple-input multiple-output (MIMO) transmission is one of the key technologies utilized in the fifth generation (5G) wireless communication system. For the algorithm design and the performance evaluation of massive-MIMO, channel characterization based on real measurements is necessary. In this paper, we introduced a measurement campaign conducted in a Line-of-Sight (LoS) scenario of a suburban outdoor environment. The channel sounder is equipped with a 64-element transmitter array and a 64-element receiver array. Multiple aspects of massive-MIMO channel characteristics are considered, including the power delay profile, the K-factor and the delay-spread. We found that for the outdoor LoS scenario considered, the polarization plays an important role generating orthogonal channels observed through large-scale antenna arrays.

This paper presents a measurement-based analysis of 39 GHz millimeter wave (mm-wave) radio propagation through vegetation using an ultra-wideband (UWB) channel sounder. The measurement campaign was conducted in a suburban area on campus of Tongji University, Shanghai, China. Two different kinds of measurements were performed to investigate the foliage influence on the characteristics of radio propagation in the mm-wave bands. A direction-scan sounding (DSS) approach was used in the measurements aiming to explore the vegetation attenuation as well as dispersive characteristics of the propagation channel. Approximately 6.7 to 8.7 dB single-tree attenuation and 1.86 to 2.23 dB/m vegetation attenuation were observed at the so-called boresight aligned direction (BAD) of a transmitter (Tx) and a receiver (Rx). The presence of vegetation leads to dispersion in delay and angular domains. Both delay spread and azimuth of arrival spread were proportional to the number of trees existing between the Tx and the Rx. Measurements with the purpose of investigating the relationship between the foliage loss and Tx/Rx distances away from trees were also conducted.

This paper provides new insights into the performance of Uplink and Downlink Splitting (UDS) in highly loaded wireless communication systems, in terms of both serving nodes and the number of users with high traffic activity. The study puts special focus on the gains that UDS could bring in terms of SINR and throughput when compared with systems with cell range expansion (CRE) in the classic downlink based cell association. CRE not only helps to offload users from macro‐ to pico‐eNBs, but also improves UL service. Instead of an aggregated throughput analysis, a detailed classification of users is performed to figure out the causes of users’ gain or loss after applying each strategy at the system level. Results show marginal gains of a pure path loss based UDS when compared with the intrinsic UL gains of CRE. Given the extra flexibility in radio resource management that splitting both links could bring, using an individual UL adjustable cell offset appears to be an interesting strategy to allow for a finer control of UL interference. The dependency of UDS performance with small cell density has also been a matter of study. Results show that the gains of UDS do decrease after a certain density of pico‐cells is surpassed.

Recently, the use of unmanned aerial vehicle (UAV) is going to receive great interest in various areas. As a newly emerging area, UAV-aided communications encounter unique communication scenarios and thus need corresponding UAV channel models for better design of such UAV communication systems. In this paper, we propose a three-dimensional (3D) sphere UAV air-to-ground (A2G) multiple-input-multiple-output (MIMO) channel model. Based on the proposed channel model, the space-time (ST) correlation function of this model is derived and analytically studied in terms of various parameters. Some interesting and useful observations are obtained. Finally, we verify the usefulness of the proposed channel model by comparing it with measured data. This model provides a new and practical approach to investigate A2G MIMO channels and gives guidelines for UAV communication system design.

Both the conventional time-division-multiplexing channel sounder and the horn-antenna-rotating millimeter-wave channel sounder suffer from a serious problem that significant time consumptions are inevitable when sounding a spatial channel in 3- dimensions. In this paper, a novel channel sounder utilizing Hybrid BeamForming (HBF) techniques is presented which can complete a 3D channel measurement in microseconds, facilitating wideband MIMO channel sounding in time-variant cases, such as vehicular scenarios. The sounder is equipped with 64 transmitter (Tx) antennas and 64 receiver (Rx) antennas. The Tx makes use of digital beamforming (DBF) to compose 8 directive radiation patterns by superimposing multiple weighted patterns each generated with 8 antennas through analog beamforming. The Rx receives signals through 8 parallel front-end chains each connected to an 8- element antenna array. A 26-GHz channel measurement campaign was conducted by using the sounder in a line-of-sight indoor environment. The results demonstrate the applicability of the sounder in measuring millimeter-wave channels. Moreover, special notes are given on the appropriate usage of parameter estimation algorithms to processing the HBF channel sounding data.

A three-dimensional (3D) theoretical model for unmanned aerial vehicle (UAV) communications is proposed in this paper. From the theoretical model, the envelope level crossing rate (LCR) and average fade duration (AFD) are derived under a 3D propagation environment. Based on the derived expressions, we for the first time investigate the LCR and AFD for UAV channels with different UAV- related parameters. The close agreement between the theoretical results and measured data demonstrates the utility of the proposed model.

Unmanned Aerial Vehicles (UAVs) have been a promising platform in realizing high-speed wireless networks. As an emerging scenario, the UAV communication is distinct from widely used cellular systems or vehicular networks, requiring the development of practical yet easy-to-use channel models. In this paper, for the first time we introduce the geometry-based stochastic model (GBSM) to UAV channel modeling, and propose a new three- dimensional (3D) GBSM for UAV Multi-Input Multi- Output (UAV-MIMO) channels. Based on the proposed model, we derive and investigate the space-time correlation function (STCF) under a 3D moving and scattering environment. The usefulness of this model is verified by the comparison between the theoretical results and some measurement data.

In this contribution, an algorithm based on the space-alternating generalized expectation-maximization principle is proposed for estimating the locations of scatterers involved in the last-hops of propagation paths when a large-scale antenna array is used in a receiver for channel measurement. The underlying generic parametric model is constructed under the spherical wave-front assumption, which allows characterizing a path with a new parameter, i.e., the distance between the scatterer at the last-hop of the path and a specific receiving antenna, additional to the conventional parameters characterizing a specular path under the plane wave-front assumption. Cramér-Rao lower bounds of mean squared errors are derived for the parameter estimators in a single-path scenario, and their accuracy is evaluated through Monte Carlo simulations. The performance of the algorithm when being applied in reality is also evaluated through experiments conducted in an office with a carrier frequency of 9.5 GHz, a bandwidth of 500 MHz, and the receiver equipped with a 121-element virtual array. The proposed signal model and algorithm can be extended to the case of localizing the scatterers in the first- and last-hops of paths when large-scale antenna arrays are used in both the transmitter and the receiver.

Railway, subway, airplane, and other transportation systems have drawn an increasing interest on the use of wireless communications for critical and noncritical services to improve performance, reliability, and passengers experience. Smart transportation systems require the use of critical communications for operation and control, and wideband services can be provided using noncritical communications. High speed train (HST) is one of the best test cases for the analysis of communication links and specification of the general requirements for train control and supervision, passenger communications, and onboard and infrastructure wireless sensors. In this paper, we analyze in detail critical and noncritical networks mainly using the HST as a test case. First, the different types of links for smart rail transportation are described, specifying the main requirements of the transportation systems, communications, and their applications for different services. Then, we propose a network architecture and requirements of the communication technologies for critical and noncritical data. Finally, an analysis is made for the future technologies, including the fifth-generation (5G) communications, millimeter wave (mmWave), terahertz (THz), and satellites for critical and high-capacity communications in transportation.

Small- and medium-sized unmanned aerial vehicles (UAVs) can fly for a short distance (<2 km) from a control station in a nonsegregated air space (altitudes < 100 m). It is of great interest to model the propagation channel under such condition, where there is an important influence from the environment. This paper presents multiple measurements carried out in low altitudes with a medium-sized UAV flying over a semiurban environment. Path loss exponent is given based on the measurements done at different altitudes and a height-dependent Rician K factor model is proposed. The results clearly reveal the existence of two propagation zones with very distinct channel characteristics. The breakpoint indicates the height where the condition of the channel changes rapidly. At low altitudes, the obstacles generate a large amount of multipath and the propagation is greatly affected, while at higher altitudes the influence mitigates. Our results are useful for the modeling of low altitude air-to-ground (AG) propagation channels and the performance analysis of UAV-enabling AG communication systems, such as the channel capacity and the throughput.

In this contribution, a recently conducted measurement campaign is introduced for investigating the characteristics of propagation channels for massive multiple-input multiple-output (MIMO) scenarios in a lecture hall environment. The channel responses for waves of higher frequency band ranging from 13 GHz to 17 GHz was measured with a vertically standing virtual two-dimensional (2-D) 20 × 20 = 400-element planar antenna array at the receiver (Rx) side, and an omni-directional antenna at the transmitter (Tx) side. Measurements were performed for four different locations of the Tx antenna in line-of-sight (LoS) scenarios. The variation of channel characteristics such as the narrowband channel gain, the K-factor and the composite delay spread, across the 2-D array aperture is investigated. The results are important for generating realistic channel realizations for the designing and performance evaluation of the algorithms for massive MIMO communication in the context of the fifth generation (5G) wireless networks.

In this contribution, a channel measurement campaign was carried out which employed directional scanning approach to capture the temporal and spatial characteristics of higher-frequency-band (HFB) wave propagation channel ranging from 13 to 17 GHz in lecture hall environments. The omni-directional antenna was equipped in the transmitter (Tx), and a horn antenna of certain half-power-beamwidth (HPBW) installed in the receiver (Rx) side was rotated during the channel measurements. The Space-Alternating Generalized Expectation-maximization (SAGE) algorithm was utilized to extract the multipath components (MPCs) based on the received signals. Furthermore, a threshold-based clustering approach was adopted to group the MPCs in delay, and angular domains. Moreover, composite and cluster-level channel behaviors and statistics were investigated based on the outputs of the SAGE algorithm and compared among the results for measurements conducted with various configurations. It is noted that the ratio of the antenna HPBW to the rotation step size, denoted as γ exerts significant effects on the antenna angular resolution, and thus influences the estimation performance. Additionally, it is observed that with the growth of γ, delay spread, AoA spread, cluster delay spread, cluster EoA spread and cluster path-loss exponent versus transmission distance have a tendency to decline.

In this paper, the Space-Alternating Generalized Expectation-maximization (SAGE) approach is applied to estimating the parameters of multipath components (MPCs) in propagation channels with the observations being the signal eigenvectors (SEVs) calculated from channel impulse responses (CIRs) collected via direction-scan sounding (DSS), i.e. rotating a horn antenna in directions. The performance of such an SEV-based-SAGE (SEV-SAGE) algorithm is compared with the conventional Raw-Data-based SAGE (RD-SAGE) algorithm in terms of either root mean square estimation errors (RMSEEs) in single-path scenarios or the composite multipath component distances (MCDs) in multi-path scenarios. Furthermore, both algorithms are applied to the measured data for 13-17 GHz indoor propagation channels obtained via DSS. These results demonstrate the superiority of applying the SEV-SAGE algorithm over the RD-SAGE algorithm for the capability of estimating the overall channels with a fewer number of MPCs.

In this contribution, a measurement campaign for high-speed-train (HST) channels is introduced, which collects the down-link signals of an in-service Long Time Evolution-Advanced (LTE-A) network deployed along the HST railway from Beijing to Shanghai, China. The channel impulse responses (CIRs) are extracted from the received signals, and the concatenated power delay profiles (CPDPs) of the CIRs are illustrated. Measurement scenarios of interest are separated into tunnel and non-tunnel propagation categories according to the delay trajectories of dominant components in channels. The statistics of the delay spreads and K-factor of the channels are investigated for both scenarios. The results illustrate clear distinctions of these characteristics for the two scenarios.

In this paper, by using a multiple-input multiple-output (MIMO) antenna array, a channel measurement campaign in outdoor environments is introduced. The measurement campaign captures the spatial characteristics of centimeter wave (cm-wave) propagation at the center frequency of 26 GHz . By using the parametric approach of the Space-Alternating Generalized Expectation-maximization (SAGE) algorithm, the channel characteristics in the delay and spatial domains are extracted, and compared with the channel model below 6 GHz. Our analysis shows that the channel dispersions in the delay and directional domains are smaller at the 26 GHz. Furthermore, single-user (SU)-MIMO and multi-user (MU)-MIMO channel matrix properties are investigated, and show that the MU-MIMO channel has good orthogonal property. It implies that MU-MIMO will be one promising MIMO technique in cm-wave band.

Recently, a measurement campaign for characterizing the channels in underground subway environments was conducted in Shanghai, China. Downlink signals transmitted by 46 universal mobile telecommunication system cells deployed along a 34-km-long subway were collected. Channel impulse responses are extracted from the data received in the common pilot channels, based on which parameters of multipath components are estimated by using a high-resolution parameter algorithm derived using the space-alternating generalized expectation-maximization principle. Multiple time-evolving clusters are obtained, each representing the channel from a remote-radio-unit of a base station to the receiver. Based on a total of 98 time-evolving clusters, channels observed in the station scenario and the tunnel scenario are modeled separately for their distinctive behaviors in many aspects, particularly in the variations of clusters’ trajectories. Intracluster characteristics parameterized by cluster delay and Doppler frequency spreads,$K$-factor, and dependences among these parameters are investigated. Intercluster parameters, including coexisting cluster number, delay offset, power offset, and cross correlations, are investigated for the station scenario. A path loss model is established for the tunnel scenario.

In this paper, we propose a blind estimation method for the antenna mutual coupling matrix and the direction of arrival. First of all, the higher order cumulants based blind estimation method is used to calculate the steering vector of the array. The estimated steering vector can differ from the true steering vector by a factor of -1 or by exchanging of the real and imaginary parts. These differences as well as the direction of arrival and the mutual coupling matrix are estimated afterwards using the least square error criteria. Simulations show the proposed algorithm can converge to the exact solution very quickly and is suitable for the closely aligned antenna arrays for the massive multiple input and multiple output (MIMO) systems.

Channel measurements and modelling have been long considered as the foundation for effective and efficient wireless communication system designs. Recently, there has been an explosive growth of research work dedicated to the so‐called device‐to‐device (D2D) communications. In the mean time, however, measurements and modelling of D2D channels seem to somewhat fall behind. To promote research on these aspects, in this study, the authors provide a critical overview of the current state of research on D2D channels, and comprehensively discuss future trends and research directions.

In this paper, a recently conducted measurement campaign for high-speed-train (HST) channels is introduced, where the downlink signals of an in-service Universal Mobile Terrestrial System (UMTS) deployed along an HST railway between Beijing and Shanghai were acquired. The channel impulse responses (CIRs) are extracted from the data received in the common pilot channels (CPICHs). Within 1318 km, 144 base stations (BSs) were detected. Multipath components (MPCs) estimated from the CIRs are clustered and associated across the time slots. The results show that, limited by the sounding bandwidth of 3.84 MHz, most of the channels contain a single line-of-sight (LoS) cluster, and the rest consists of several LoS clusters due to distributed antennas, leaking cable, or neighboring BSs sharing the same CPICH. A new geometry-based random-cluster model is established for the clusters' behavior in delay and Doppler domains. Different from conventional models, the time-evolving behaviors of clusters are characterized by random geometrical parameters, i.e., the relative position of BS to railway, and the train speed. The distributions of these parameters, and the per-cluster path loss, shadowing, delay, and Doppler spreads, are extracted from the measurement data.

Graph theory-based channel modeling is an efficient approach to simulate multipath propagation taking into account the reverberation of electromagnetic waves. In this contribution, without modifying the modeling framework, we proposed a semi-deterministic graph modeling approach by associating the scatterers with realistic environment objects, and by calculating the coefficients of the propagation paths on the base of a proven diffuse scattering theory. An isolated office building scenario is adopted to illustrate the procedure of the proposed method. The performance is thus evaluated by comparing the simulated power-delay-profiles (PDPs) with ray-tracing and real channel measurement data. The results illustrate that the proposed method can accurately predict both the decaying slopes and the diffuse tails of the PDPs.

In this paper, a novel interference cancellation (IC) algorithm is introduced which is applicable to improving the signal to interference power ratio (SIR) for the signals of a specific user by estimating the channels accurately. In this algorithm, a so-called generalized array manifold (GAM) generic channel model is adopted for parameter estimation which makes use of the Firstorder Taylor-series expansion to approximate the contribution of individual dispersive components in the channel to the received signal. Since the GAM model takes into account the influence of unresolvable distributed components, the IC algorithm is superior in terms of higher SIRs to conventional methods that reconstruct the signals of a desired user for dominant specular-path components. Monte-Carlo simulation results show that the SIR improvements vary with the delay and Doppler frequency spreads of a propagation channel. Besides, experimental results obtained by processing the measurement data collected in an in-service Universal Mobile Telecommunication System (UMTS) network demonstrate that by using the proposed IC algorithm and under the assumption that a channel consists of single dispersive component, which is usually the case where rich scattering occurs in the vicinity of a user, the SIR can be improved significantly when the delay and Doppler frequency spreads of channel are within certain ranges.

For cluster-based stochastic channel modeling, selection of clustering method is crucial for the final modeling results. Since a clustering algorithm can generate as many partitions as required, identification of the optimal number of clusters is a vital consideration in clustering, which is called cluster validity. In this contribution, five widely used indices for cluster validity are employed to jointly determine the optimal number of clusters. By putting forward a novel likelihood aggregation approach for combining the decisions of multi-indices, the clustering results are more stable and reasonable. Four kinds of synthetic data are used to illustrate the feasibility of the proposed method in the case where the given data set is either easily clustered or not. Moreover, the performance of the proposed approach is evaluated by using real channel measurement data with convincing results.

Dual-polarized antenna systems have received more and more attention due to its potential to double the channel capacity compared with omnidirectional single-input single-output (SISO) systems. However, the implementation of dual-polarized systems into real three-dimensional (3-D) environments may result in the power loss from the third polarization direction. Therefore, the channel capacity of dual-polarized systems in real 3-D scenarios will be significantly affected by the power loss. Based on the newly proposed 3-D two-spheric double-bounced geometry-based stochastic channel model, this paper for the first time analyzes this impact of the power loss on the channel capacity and demonstrates the critical maximum elevation spread, where the channel capacity of dual-polarized systems is equal to that of omnidirectional SISO system. The work in this paper is significant for the guidance of the proper application of dual-polarized systems in the future.

In this contribution, two measurement campaigns are reported which were conducted in urban areas of Shanghai for characterizing the radio channels in an operating Universal Mobile Telecommunications System (UMTS). The signals on the common pilot channels transmitted by multiple NodeBs (NBs) are used to calculate the channel impulse responses (CIRs). A Space-Alternating Generalized Expectation-maximization (SAGE) algorithm is applied to extracting the delays, Doppler frequencies and complex attenuations of multiple specular path components from the CIRs. Based on the delay spreads calculated, the channels are categorized into three types, i.e. the single-path, two-path and multi-path. Based on the measurements at more than 1400 locations for 74 NBs, the statistics of system-level channel characteristics, such as the percentage of channels per NB belonging to different categories and the length of the distance within which the same channel category is observed are extracted.

In this contribution, the 3-dimensional (3D) channel characteristics, particularly in the elevation domains, is extracted from measurement data collected in typical urban macro- and micro-environments in the city of Xi'an, China. Stochastic channel models are established based on high-resolution multi-path parameter estimates. Additionally, a modified spatial channel model (SCM) for the line-of-sight (LoS) scenario is proposed where the LoS polarization matrix is defined more reasonably than in the conventional models. Measurement results demonstrate the consistency of the revised model with the reality.

In this contribution, both theoretical studies and measurement results are reported for investigating the anisotropic property of propagation channels with respect to (w.r.t.) the (linear) polarizations of the transmitter (Tx) and receiver (Rx) antennas. Under the assumption that the channel impulse response (CIR) is polarization isotropic, analytical expressions are derived for the K-factor and delay spread as functions of Tx-Rx polarizations, and furthermore, our deduction also shows that the rank of the signal subspace spanned by a multi-polarized narrowband channel is upper-bounded. Based on the measurements conducted in indoor scenarios, empirical CIRs measured with 12 Tx- and 2 Rx-polarization directions are applied to investigating the channel polarization anisotropy. It can be observed that the measured channels exhibit properties inconsistent with the theoretical results deduced under the polarization-isotropy assumption, implying that the realistic indoor propagation channels can be anisotropic w.r.t. the Tx-Rx polarizations.

In this paper, we use complex network theory to analyze the evolving topology of wireless sensor networks (WSNs). Based on the BA model, we propose a novel complex network model, i.e., Neighborhood Log-on and Log-off model with energy awareness (NLL-E). NLL-E assumes locally preferential attachment, which exists in many complex networks. We further consider node addition and invalidation in topology evolvement, and add compensation mechanism for network robustness. The statistical properties and dynamics of NLL-E are analytically studied. Numerical simulations indicate that, comparing with BA, NLL-E has decreased average path length and increased network connectivity.

In this paper, we investigate characteristics of multi-link channel and propose the cross-correlation models of it based on measurements in cooperative multiple input multiple output (MIMO) scenario at 3.7 GHz. The statistics of the cross-correlation for the large-scale parameters (LSPs) of two channels are investigated with respect to a geometrical variable, i.e. the average distance defined to be the arithmetic mean of the distances from a base station (BS) to a mobile station (MS), and from a relay station (RS) to the MS. The modeling results, in terms of analytical curves fitted to the empirical scatter plots of the cross-correlation coefficients, are presented. We also explain qualitatively the general trends of the cross-correlation coefficients of the LSPs with respect to the average distance.

In this paper, stochastic modeling of the cross-correlation of small-scale fading (SSF) is performed for co-existing propagation channels based on measurement data collected using the relay-Band-Exploration-and-Channel-Sounder (rBECS) systems in urban areas, Korea. We define six geometrical parameters which are considered as variables in the cross-correlation models proposed. These parameters characterize the geographic properties of a three-node co-operative relay system that consists of a base station, a relay station and a mobile station. The sensitivity of the distribution of SSF cross-correlation coefficients with respect to the proposed variables is assessed. Three of them are identified as sensible variables for modeling.

In this paper, a channel modeling method based on random-propagation-graph is elaborated, validated, and applied to characterizing time-variant channels observed in typical environments for high-speed railway wireless communications. The advantage of the proposed method is that the frequency-tempo-spatial channel coefficients, as well as the multi-dimensional channel impulse responses in delay, Doppler frequency, direction of arrival (i.e. azimuth and elevation of arrival) and direction of departure are calculated analytically for specific environments. The validation of the proposed method is performed by comparing the statistics of two large-scale parameters obtained with those described in the well-established standards. Finally, stochastic geometry-based models in the same format as the well-known spatial channel model enhanced (SCME) are generated by using the proposed method for the high-speed scenarios in the rural, urban, and suburban environments.

In this paper, propagation channel measurement data obtained by using the wide-band multiple-input-multiple-output (MIMO) channel sounder - PROPsound - is applied to analyzing the cross-correlation behavior of spatial-polarimetric (SP) channels in indoor environments. Four antennas are selected from the transmitter (Tx) array and from the receiver (Rx) array to form a 2×2 spatial MIMO channel, with each spatial element consisting of co-located ±45°-polarized antennas. The characteristics of the resultant 16 × 16 narrow-band SP Channel Correlation Matrix (CCM) are studied for five indoor environments of different types. Results show that the Kronecker model proposed in the 3rd Generation Partnership Project (3GPP) TS36.101, which describes the SP CCM as the Kronecker product of the separate Tx and Rx spatial CCMs, and the 4×4 polarimetric CCM, is inapplicable in indoor scenarios. As an alternative, we propose to consider the cross-correlation coefficient of SP channels as a random variable, and apply its empirical statistics for stochastic modeling. Results also show that the empirical distribution of the off-diagonal elements of SP CCM is distinctive with respect to the types of environments.

Accurate models of the cross-correlation of the large-scale-fading in different propagation channels is essential for designing wireless systems using distributed transmission topologies, such as the coordinated multi-point (CoMP) transmission system. In this contribution, a new parameter defined as the normalized power of common paths in two propagation channels is applied to modeling the cross-correlation of the large-scale-fading in different channels. The common paths here are referred to as the paths with identical parameters, i.e.\ the delays, Doppler frequencies, directions of arrival and directions of departure. Under the uncorrelated scattering (US) assumption, a new geometrical approach is proposed for modeling the large-scale-fading cross-correlation. Experimental investigations show that the proposed modeling method can be used to generate channel cross-correlation coefficients consistent with the experimental results to a certain extent. We postulate that the discrepancies observed between the empirical and theoretical results are caused by the unrealistic US assumption.

In order to extract the statistical characteristics of propagation channels, multiple impulse responses of the channels are measured and saved e.g. in terms of cycles or bursts. In the case where measurements are performed in time-variant environments, the received signals need to be divided into multiple segments. It is essential to perform the data segmentation reasonably in order to guarantee that each segment contains the observations of the same stationary process. In this contribution, we first show experimentally the impact of the number of data bursts per segment on the clustering results. Then a novel Kolmogorov-Smirnov hypothesis-testing-based approach is proposed for automatically determining the number of bursts in each segment. The applicability of this approach is evaluated using indoor channel measurement data. The results obtained show that the number of bursts in a segment follows lognormal distributions with parameters dependent on the environments and the mobilities of the transmitter, receiver and the scatterers during the measurement campaigns.

In this contribution, we derive the spread function and power spectrum of the so-called "relay channel'' along which electromagnetic waves propagate from a source station to a destination station via multiple amplify-and-forward (AF) relay stations. These results show analytically that the propagation over such a relay channel can be influenced by the propagation in individual "hops'' and the responses of the relay stations. Furthermore, we find that the uncorrelated-scattering assumption is inapplicable for the AF-relay channels in the case where the relay stations receive and retransmit signals using mulitple antennas. We propose to simulate the relay channels by three steps: i) selecting channel models for individual "hops''; ii) defining the correlations of model parameters among the hops; and iii) constructing realizations of relay channels by taking into account the responses of relay stations, which are specified by considering the response of antennas and the relay mode according to system configurations. We also show in this contribution that the spreads of a multi-relay channel in delay, Doppler frequency and directions are lower-bounded by the weighted summation of the spreads of individual source-relay-destination channels.

Due to the significantly reduced cost and effort for system calibration time-division multiplexing (TDM) is a commonly used technique to switch between the transmit and receive antennas in multiple-input multiple-output (MIMO) radio channel sounding. Nonetheless, Baum et al. have shown that phase noise of the transmitter and receiver local oscillators, when it is assumed to be a white Gaussian random process, can cause large errors of the estimated channel capacity of a low-rank MIMO channel when the standard channel matrix estimator is used. Experimental evidence shows that consecutive phase noise samples affecting measurement samples collected with real TDM-MIMO channel sounders are correlated. In this contribution a capacity estimator that accounts for the phase noise correlation is proposed. The estimator is based on a linear minimum mean square error estimate of the MIMO channel matrix. It is shown by means of Monte Carlo simulations assuming a measurement- based phase noise model, that the MIMO channel capacity can be estimated accurately for signal-to-noise ratios up to about 35 dB.

In this contribution a low-complexity particle filtering algorithm is proposed to track the parameters of time-variant propagation paths in multiple-input multiple-output (MIMO) radio channels. A state-space model is used to describe the path evolution in delay, azimuth of arrival, azimuth of departure, Doppler frequency and complex amplitude dimensions. The proposed particle filter (PF) has an additional resampling step specifically designed for wideband MIMO channel sounding, where the posterior probability density functions of the path states is usually highly concentrated in the multi-dimensional state space. Preliminary investigations using measurement data show that the proposed PF can track paths stably with a small number of particles, e.g. 5 per path, even in the case where the paths are undetected by the conventional SAGE algorithm.

The wireless hybrid enhanced mobile radio estimators (WHERE) consortium researches radio positioning techniques to improve various aspects of communications systems. In order to provide the benefits of position information available to communications systems, hybrid data fusion (HDF) techniques estimate reliable position information. Within this paper, we first present the scenarios and radio technologies evaluated by the WHERE consortium for wireless positioning. We compare conventional HDF approaches with two novel approaches developed within the framework of WHERE. Yet, HDF may still provide insufficient localization accuracy and reliability. Hence, we will research and develop new cooperative positioning algorithms, which exploit the available communications links among mobile terminals of heterogeneous wireless networks, to further enhance the positioning accuracy and reliability.

In this contribution, the Fisher-Bingham-5 (FB5) probability density function (pdf) is used to model the shape of the direction power spectral density function (psdf) of individual path components in the radio channel. The FB5 distribution is selected because, among all direction distributions, it maximizes the entropy under the constraints that the first and second distribution moments are specified. A SAGE (space-alternating generalized expectation-maximization) algorithm is derived based on this model for estimation of the parameters characterizing the direction psdf of each path component in a multi-path scenario. The performance of the SAGE algorithm is evaluated using measurement data. Preliminary results show that the estimated direction psdfs of individual path components exhibit different ovalnesses and tilt angles. These density functions are noticeably more concentrated than the corresponding footprints in the Bartlett spectrum.

Wideband channel measurements have been performed at 5.25 GHz in hotspot areas in Beijing with a multiple-input multiple-output channel sounder. The measured scenarios include indoor line-of-sight (LOS) and non-line-of-sight, and outdoor LOS and obstructed-line-of-sight. Statistical results of channel characteristics are presented which include the path loss models, root mean square delay spread, circular azimuth spread and average envelope correlation with respect to antenna separation. Comparative analysis of the results are provided. These information are of significance for the design and performance evaluation of international mobile telecommunications (IMT)-advanced systems.

Essential parameters of physical, propagation-based MIMO channel models are the fading statistics and the directional spread of multipath clusters. In this paper we determine these parameters in the azimuth-of-arrival/azimuth-of-departure (AoA/AoD) domain based on comprehensive indoor MIMO measurements at 5.2 GHz in a cluttered office environment using the SAGE algorithm for parameter estimation. Due to cluster identification in AoA/AoD-domain we found a greater number of clusters than those reported in previous publications. Regarding the fading statistics of clusters, so far not studied, strong (obstructed-)line-of-sight clusters show Rician fading, corresponding to few dominant propagation paths, whereas most clusters exhibit Rayleigh fading, corresponding to many paths with approximately equal powers and uncorrelated phases. Root-mean-square cluster azimuth spreads (CASs) were estimated with a novel method by appropriately restricting the support of the cluster azimuth distribution. We found that the estimated CASs are different when seen from transmitter or receiver, i.e. their ranges are from 2deg to 9deg and from 2deg to 7deg at the transmitter side and the receiver side, respectively

An important parameter of MIMO channel models is the cluster root-mean-square (rms) directional spread. In this paper we determine this parameter in the azimuth-of-arrival/azimuth-of-departure (AoA/AoD) domain based on comprehensive indoor MIMO measurements at 5.2 GHz in a cluttered office environment. This is done in a four-step procedure: (i) the SAGE algorithm is used to extract propagation paths, (ii) clusters of estimated paths in the double-azimuth domain are defined, (iii) the estimated propagation paths are allocated to the clusters, (iv) the cluster directional spreads are estimated based solely on propagation paths within the clusters. We found that the spreads are different when seen from transmitter or receiver due to different propagation conditions resulting in AoD rms cluster spreads lying in the range from 2 to 9 degrees and AoA rms cluster spreads in the range from 2 to 7 degrees.

In this paper, an azimuth spread estimator (ASE) for slightly distributed scatters (SDSs) is derived based on the generalized-array-manifold (GAM) approximation model [1]. To improve the performance of this estimator we propose an array size adaptation (ASA) technique that adjusts the array aperture selectively for each SDS by modifying the number of antennas. This technique is applied to uniform linear arrays but can be extended to two or three dimensional arrays. Simulation results demonstrate the improvement achieved with the combined ASA-ASE scheme. In particular, it is shown that this scheme outperforms the conventional Spread-ESPRIT technique.

To save hardware and reduce the effort to calibrate the system, channel sounding with Tx and Rx antenna arrays is commonly performed in TDM mode where the array elements are successively switched. We refer to this technique as TDM-MIMO channel sounding. The ISI-SAGE algorithm (Fleury, B.H. et al., 2003; Yin, X. et al., 2003), applied in combination with TDM-MIMO channel sounding, makes it possible to extend the Doppler frequency estimation range (DFER) (Yin, X. et al., 2003). The extension is significant when arrays with large element numbers are employed. We derive the signal model for TDM-MIMO channel sounding and report analytical investigations showing that this DFER extension requires selection of switching modes (SMs) tailored to the array characteristics. The SM of a switched array is the temporal order in which the array elements are switched. In fact, the traditionally used SMs of uniform linear and planar arrays, where the elements are switched according to their natural spatial ordering, prove to be inappropriate as they lead to an ambiguity in the joint estimation of DF and directions. We also introduce the concept of normalized sidelobe level (NSL) associated to the SM of a switched array. We show that minimizing the NSL is a sensible criterion for the identification of SM, leading to DF and direction estimates with nearly optimum performance in terms of root mean square estimation error. Finally experimental investigations illustrate the impact of the SM of a uniform planar array on the behaviour of the DF and direction of arrival estimates computed with the ISI-SAGE algorithm.

We proposed (Fleury, B.H. et al., Proc. XXVII Gen. Assy of Int. Union of Radio Scientists, p.2127, 2002; Proc. IEEE Veh. Tech. Conf., VTC 2002, p.522-6, 2002) the ISI-SAGE (improved-search-and-initialization space-alternating generalized expectation-maximization) algorithm for joint estimation of the relative delay, the directions of departure and of incidence, the Doppler frequency (DF) and the complex weight (or attenuation) of waves propagating from the transmitter (Tx) site to the receiver (Rx) site in mobile radio environments. We now present theoretical and simulation results describing the performance of the scheme for DF estimation applied in combination with channel sounding using switched multiple-element Tx and Rx antennas. The maximum absolute DF that can be estimated is shown to be half the switching rate rather than half the measurement cycle rate as commonly believed. The switching rate is the rate with which the pairs of Tx and Rx elements are switched, while the measurement cycle rate is the rate with which any fixed pair of Tx and Rx elements is switched. The ratio of the former rate by the latter is larger than or equal to the product of the numbers of Tx and Rx elements. Hence, the scheme allows for DF estimation in a range much larger than commonly believed. Monte-Carlo simulations also show that above a certain signal-to-noise-ratio (SNR) threshold, depending on the characteristics of the TDM mode, the estimator performs close to optimum. The results make it possible to link the parameter setting of the TDM mode to the operational SNR range for individual waves to be estimated.

This contribution describes an extension of the ISI-SACR (initialization-and-search-improved space-alternating generalized expectation maximization) algorithm originally published in [B.H. Fleury, et al., 2001] and [2002] to include polarization estimation. The proposed scheme allows for joint estimation of the relative delay, the direction (i.e. azimuth and co-elevation) of departure, the direction of incidence, the Doppler frequency and the complex polarization matrix of propagation paths between the transmitter (Tx) site and the receiver (Rx) site in mobile radio environments. This contribution presents the initialization procedure of the extended ISI-SAGR algorithm and discusses necessary and sufficient conditions regarding the responses of multiple-element Tx and Rx arrays for the system consisting of these two arrays to be able to estimate all coefficients of the polarization matrix. Experimental investigations in a line-of-sight microcellular environment illustrate the application potential of the ISI-SACR algorithm for gaining insight into the polarization states of individual paths. This information is of paramount importance for the design of stochastic and prediction models ut the radio channel for communication systems equipped with multiple Tx and Rx dual-polarized antennae.

This contribution presents results from an application of the SAGE (space-alternating generalized expectation-maximization) algorithm published in Fleury et al. (2002) to measurement data collected in a selected propagation environment. The scheme jointly estimates the delay, the direction (i.e. azimuth and coelevation) of departure, the direction of incidence, as well as the Doppler frequency of waves propagating from the transmitter to the receiver. An improved initialization and search procedure is proposed which enhances the probability of identifying weak paths and therefore allows for a more comprehensive and detailed characterization of the propagation mechanisms. In order to relate the estimated waves to the propagation environment, a simple method based on ray tracing attempts to reconstruct the propagation paths based on the estimated wave parameters while assuming oneand two-bounce scattering. Significant objects can be identified in the environment that coincide with the interaction points of the calculated paths. The same conclusions can be drawn from similar analyses of other environments. The presented results demonstrate the high potential of high-resolution estimation techniques performing true joint estimation of delay, direction of departure, and direction of incidence for detailed investigations of dispersion in the radio channel jointly in delay and in direction at both transmitter and receiver sites. These studies are of paramount importance for an accurate characterization of MIMO (multiple-input multiple-output) systems operating in these channels.