This letter proposes an efficient tensor decomposition scheme, termed tensor flower (TF), for rapid matrix format factorization of large-scale spectrum environment data. TF leverages the divide-and-conquer methodology to break down higher-order tensors into lower-order components to complete matrixized tensor decomposition. This is achieved by representing the tensor as an ordered collection of factor matrices resembling an inflorescence structure. Then, a streamlined algorithm based on alternating least-squares (ALS) is devised to validate the feasibility, while a hierarchical algorithm with adaptive ranks (HAR) is developed to achieve faster TF decomposition. Simulation results demonstrate that TF, as a general-purpose tensor decomposition scheme, can efficiently process large-scale spectrum environment data.

To address the urgent demands of sixth-generation (6G) wireless networks for higher data rates and spectral efficiency, deploying extremely large-scale multiple-input multiple-output (XL-MIMO) systems in complex near-field environments has become an inevitable choice. While accurate channel estimation remains pivotal for enabling effective beamforming and supporting near-field propagation dynamics, existing channel estimation techniques face challenges, including channel models inconsistent with physical propagation, algorithms primarily designed for uniform linear arrays (ULAs) and narrowband systems, and a focus on estimating channel matrices rather than path parameters. This paper proposes innovative solutions for channel modeling and estimation in near-field XL-MIMO systems with uniform planar arrays (UPAs). First, we utilize a novel reflection channel model based on an end-to-end spherical wavefront (E2ESW) propagation framework. Unlike existing near-field models (which mainly characterize the curvature of spherical wavefronts from the last bouncing scatterer (LBS)), this model introduces a virtual transmitting source concept. The spherical wave propagation from virtual sources to receivers aligns with physical propagation laws, enabling non-line-of-sight (NLoS) paths to be effectively represented as line-of-sight (LoS) paths while accurately capturing phase variations of the same path across different antenna elements. Based on this model, we propose a low-complexity initialization method, termed near-field delay–angle separation estimation for UPA (NDASE-UPA). We then develop two specialized UPA channel estimation algorithms: the polar-domain simultaneous iterative gridless weighted algorithm for UPA (P-SIGW-UPA) and the polar-domain sparse Bayesian learning algorithm for UPA (P-SBL-UPA) both capable of achieving higher precision in path parameter estimation than traditional grid-based methods. We derive non-parametric and parametric Cramér-Rao lower bounds (CRLBs) for the estimated channel matrix to establish theoretical performance benchmarks. The CRLB provides a rigorous benchmark for evaluating estimation accuracy, ensuring the designed methods achieve theoretical optimality in efficiency and reliability. Simulation results demonstrate that the new algorithms significantly reduce channel estimation errors in near-field scenarios compared to conventional approaches.

Channel estimation is vital for wireless communication and channel sounding. In this paper, we propose a tensor-based high-resolution estimation method for a millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) based channel sounding system with hybrid beamforming (HBF) adopted. At the transmitter (Tx), the OFDM pilot signal is sent with the help of a precoder with random phases. At the receiver (Rx) configured with a random-phased combiner, the received signal is concatenated into a third-order tensor which follows a CANDECOMP/PARAFAC (CP) decomposition to prospect implicit channel information. The CP decomposition is solved by leveraging the Vandermonde structure of the factor matrices, from which the channel parameters are estimated by correlationbased searching. Simulation results with the synthesized channel data illustrate that the proposed tensor-based algorithm can obtain high stability and accuracy. The influence of the pilot sequence length on the accuracy of the proposed algorithm is also discussed. Furthermore, we find that the proposed algorithm is not affected by the quantization bit of the precoder and combiner.

Specific emitter identification (SEI) is the process of identifying a unique emitter based on the internal features carried by the received signal, which has been widely used in spectrum management. In this paper, we consider the influence of modulation mode and power amplifier (PA) on radio frequency fingerprint (RFF) and propose an identification scheme based on squeeze-and-excitation network (SENet) and multi-head attention mechanism (MHA). The proposed scheme can use the abundant information of signal nonlinear characteristics for identifying emitters. Different from most existing SEI schemes based on feature transformation, our scheme can learn the feature from time domain signal, which can extract the overall characteristics hidden in the received signal. We evaluate the proposed algorithm through extensive simulation experiments, discuss the impact of frequency offset, the number of emitters, and channel conditions on the identification performance, and compare the proposed scheme with several advanced deep learning network architectures to prove its effectiveness.

Atmospheric ducts provide a potential way to establish beyond-line-of-sight (B-LoS) links for maritime communications. Based on the ray tracing (RT) method, long distance propagation in the maritime evaporation duct environment is studied in this paper. The variations of horizontal distance and height are calculated according to the refractivity distribution. Eigenrays between transmitter (Tx) and receiver (Rx) are determined by tracing the ray trajectories. Furthermore, the path loss, delay spread, and angles of arrival (AoAs) are estimated. Simulation results calculated by the RT method match theoretical results generated by parabolic equation (PE) methods in the short range. In the B-LoS range, the path loss estimated by RT is a bit higher than that estimated by PE. In the RT model, the increase in the number of rays calculated in the B-LoS range may lead to this case. Simulation results show that the RT method can well evaluate the anomalous propagation in the evaporation duct and estimate channel parameters effectively.

In order to improve spectrum efficiency in emergency communications, a dynamic spectrum sharing (DSS) scheme based on federated learning (FL) and deep reinforcement learning (DRL) is proposed. The operation model follows the paradigm of cognitive radio networks (CRNs), in which multiple secondary users (SUs) with different bandwidth requirements, spectrum sensing and access capabilities randomly access idle frequency bands that primary users (PUs) do not occupy. Different users in emergency communications are considered as SUs or PUs according to their communication priorities. A maximum entropy based multi-agent actor-critic (ME-MAAC) algorithm is used to realize an optimal spectrum sharing strategy by updating varying rewards to SUs. During the learning process, the FL algorithm is used to assign appropriate weights to SUs. Simulation results show that the performance of proposed scheme is better in terms of reward value, access rate, and convergence speed.

To solve the communication system throughput demand due to the rapid growth of communication devices and the blocking problem of millimeter-wave (mmWave) communication, reconfigurable intelligent surface (RIS) technology is used to improve communication quality. In the RIS-assisted multiple-users wireless communication system, we investigate the joint optimization problem of the base station (BS) beamforming and the RIS phase control to maximize the sum rate of the system. To be more practical, we consider the RIS cell’s gain and the antenna’s radiation pattern in the RIS-assisted wireless channel model. We apply a deep deterministic policy gradient (DDPG) algorithm based on deep reinforcement learning (DRL) to solve the nonconvex joint optimization problem. The parameters that affect the convergence effect of the proposed algorithm are discussed. Simulation results show that the phase discretization of the RIS cell decreases the sum rate. Furthermore, we find the DDPG algorithm obtains a higher system sum rate than the benchmark algorithm.

In this paper, we first propose a 3-dimensional (3-D) non-stationary geometry-based stochastic model (GBSM) for maritime massive multiple-input multiple-output (MIMO) communication systems with the uniform circular array (UCA) configuration. To reduce the model complexity and improve the mathematical tractability, a novel beam domain channel model (BDCM) is then proposed based on the transformation of the corresponding GBSM from the array domain to the beam domain for maritime communications. In the proposed BDCM, the beamforming matrices suitable for UCA structures are constructed and their invertibility is demonstrated to ensure the practicability of the BDCM. Two methods are used to characterize the array non-stationarity in maritime massive MIMO channels. First, the evolution of clusters over the large UCA is modeled by the visibility regions (VRs) attached to individual multipath components (MPCs). Second, the sphere wavefront (SWF) effect is captured by dividing the UCAs into several sub-arrays. Based on the proposed GBSM and BDCM, some important channel statistical properties are studied and compared, including channel power, power leakage, space-time-frequency correlation function (STF-CF), and root-mean-square (RMS) Doppler/beam spreads. Also, the importance of considering the array non-stationarity in maritime communication channels is revealed.

A novel spectrum sharing and aggregation scheme based on the maximum entropy actor-critic (MEAC) algorithm is proposed to solve the spectrum shortage problem in dynamic spectrum access (DSA). The spectrum sharing and aggregation problem is modeled as a three-state Markov model, where secondary users (SUs) try to access multiple idle channels. In a time slot, the SUs with the spectrum sensing, sharing, and aggregation capabilities select available spectrum slots and channels through spectrum sharing and aggregation. The actor-critic algorithm is used to construct the spectrum framework, and a novel maximum entropy (ME) scheme is proposed to achieve an optimal spectrum sharing and aggregation policy. The ME scheme can include more exploration and ensure that the output action is more stochastic than other schemes. Simulation results indicate that the proposed scheme can achieve better performance than Deep Q-Network (DQN) algorithm.

In this paper, a novel 3-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed to mimic the ship-to-ship multiple-input multiple-output (MIMO) communication channels. To reflect the realistic maritime propagation environment, the effects of rough sea surface scattering and evaporation duct propagation are investigated in the proposed channel model. The model considers the movements of transmitter (Tx), receiver (Rx), and scatterers, and is capable of capturing the channel characteristics, including non-stationary characteristics, spatial consistency, location-dependent property, etc. Through taking the array cluster evolution into account, the proposed channel model can describe massive MIMO channels and can easily switch to conventional MIMO channel model by adjusting corresponding parameters. Based on the proposed model, key statistical properties like delay/angular/Doppler power spectrum density (PSD), space-time correlation function (STCF), stationary interval, and root mean square (RMS) Doppler/delay spreads in multi-scenarios are derived. The usefulness and accuracy of the proposed model are demonstrated by comparing theoretical results, simulation results, and corresponding measurement results.

A point cloud is a set of points representing a three-dimensional (3D) object or scene. To compress a point cloud, the Motion Picture Experts Group (MPEG) geometry-based point cloud compression (G-PCC) scheme may use three attribute coding methods: region adaptive hierarchical transform (RAHT), predicting transform (PT), and lifting transform (LT). To improve the coding efficiency of PT, we propose to use a Kalman filter to refine the predicted attribute values. We also apply a Kalman filter to improve the quality of the reconstructed attribute values at the decoder side. Experimental results show that the combination of the two proposed methods can achieve an average Bjøntegaard delta bitrate of −0.48%, −5.18%, and −6.27% for the Luma, Chroma Cb, and Chroma Cr components, respectively, compared with a recent G-PCC reference software.

In the next-generation wireless networks, unmanned aerial vehicles (UAVs) equipped with aerial base stations (ABSs) can serve as the supplement of traditional terrestrial networks and provide high-speed access for ground users. In this paper, an extended multi-user massive multiple-input multiple output (MIMO) geometry-based stochastic channel model (GBSM) is proposed for UAV-aided communication systems. The proposed model is the extension of a non-stationary UAV channel model by taking the different antenna array layouts and spatial correlation between ground users into account. Based on the simulation, the effects of UAV height, user density, and antenna configuration on inter-user correlation and on channel capacity are thoroughly investigated. The extension of spatial correlation will enable UAV channel simulator to generate realistic spatial correlated channel impulse responses (CIRs) for multiple users that are relatively close to one another.

In order to provide reliable and efficient connections between unmanned aerial vehicles (UAVs) and ground stations (GSs), realistic UAV-to-ground channel models are indispensable. In this article, we propose a novel 3-D nonstationary geometry-based stochastic model (GBSM) for UAV-to-ground multiple-input-multiple-output (MIMO) channels. Distinctive UAV-to-ground channel characteristics, such as time-domain nonstationarity, distinctions between different altitudes, spatial consistency, and 3-D arbitrary UAV movement trajectories, are taken into account. By adjusting parameter settings, the proposed channel model framework is sufficiently general to support multiple frequency bands and multiple scenarios, including millimeter wave (mmWave) and massive MIMO configurations. Statistical properties, including power delay profile (PDP), stationary interval, space-time correlation function (STCF), and root-mean-square (RMS) delay spread are derived and analyzed for different frequencies and scenarios. The accuracy of the proposed model is validated by comparing its statistical properties with corresponding available channel measurements. The proposed channel model will provide a fundamental support for the design, performance evaluation, and optimization of future UAV integrated sixth-generation (6G) wireless networks.

Sub-1 GHz bands have been used for many years and now some of them will be reallocated for new applications, including the fifth generation (5G) wireless communication systems and beyond, Internet of Things (IoT), smart grid, etc. As the well-known path-loss (PL) models are mainly applicable in 2-6-GHz frequency range, a new channel measurement campaign is needed to study the propagation characteristics at sub-1 GHz bands. In this article, we conduct fixed-to-mobile wideband channel measurements at 400-600-MHz bands in urban and suburban scenarios using the time domain channel sounder. As the interference and noise signals are severe, the transmitted waveform is carefully designed to enlarge the system dynamic range. Meanwhile, ray tracing simulation is applied to construct the measurement environments and do the mutual verification with measurement results. The two-slope PL model and lognormal shadowing fading model are proposed for large-scale fading channel modeling. The root mean square (RMS) delay spread (DS), number of paths, and diffraction characteristics are also analyzed. The results will have great importance for the coming new applications at sub-1 GHz bands.

In recent years, the application potential of visible light communication (VLC) technology as an alternative and supplement to radio frequency (RF) technology has attracted people's attention. The study of the underlying VLC channel is the basis for designing the VLC communication system. In this paper, a new non-stationary geometric street corner model is proposed for vehicular VLC (VVLC) multiple-input multiple-output (MIMO) channel. The proposed model takes into account changes in vehicle speed and direction. The category of scatterers includes fixed scatterers and mobile scatterers (MS). Based on the proposed model, we derive the channel impulse response (CIR) and explore the statistical characteristics of the VVLC channel. The channel gain and root mean square (RMS) delay spread of the VVLC channel are studied. In addition, the influence of velocity change on the statistical characteristics of the model is also investigated. The proposed channel model can guide future vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) optical communication system design.

Millimeter wave (mmWave) bands have been utilized for the fifth generation (5G) communication systems and will no doubt continue to be deployed for beyond 5G (B5G). However, the underlying channels are not fully investigated at multi-frequency bands and in multi-scenarios by using the same channel sounder, especially for the outdoor, multiple-input multiple-output (MIMO), and vehicle-to-vehicle (V2V) conditions. In this paper, we conduct multi-frequency multi-scenario mmWave MIMO channel measurements with 4 × 4 antennas at 28, 32, and 39 GHz bands for three cases, i.e., the human body and vehicle blockage measurements, outdoor path loss measurements, and V2V measurements. The channel characteristics, including blockage effect, path loss and coverage range, and non-stationarity and spatial consistency, are thoroughly studied. The blockage model, path loss model, and time-varying channel model are proposed for mmWave MIMO channels. The channel measurement and modeling results will be of great importance for further mmWave communication system deployments in indoor hotspot, outdoor, and vehicular network scenarios for B5G.

The standardization process of the fifth generation (5G) wireless communications has recently been accelerated and the first commercial 5G services would be provided as early as in 2018. The increasing of enormous smartphones, new complex scenarios, large frequency bands, massive antenna elements, and dense small cells will generate big datasets and bring 5G communications to the era of big data. This paper investigates various applications of big data analytics, especially machine learning algorithms in wireless communications and channel modeling. We propose a big data and machine learning enabled wireless channel model framework. The proposed channel model is based on artificial neural networks (ANNs), including feed-forward neural network (FNN) and radial basis function neural network (RBF-NN). The input parameters are transmitter (Tx) and receiver (Rx) coordinates, Tx-Rx distance, and carrier frequency, while the output parameters are channel statistical properties, including the received power, root mean square (RMS) delay spread (DS), and RMS angle spreads (ASs). Datasets used to train and test the ANNs are collected from both real channel measurements and a geometry based stochastic model (GBSM). Simulation results show good performance and indicate that machine learning algorithms can be powerful analytical tools for future measurement-based wireless channel modeling.

Neural networks are becoming more and more important for intelligent communications and their theoretical research has become a top priority. Loss surfaces are crucial to understand and improve performance in neural networks. In this paper, the Hessian matrix of second order optimization method is analyzed through the analytical framework of random matrix theory (RMT) in order to understand the geometry of loss surfaces. The limited spectrum distribution, extreme eigenvalue distribution, and standard condition number (SCN) of Hessian matrix are analyzed to understand their asymptotic characteristics. Moreover, the relationships among the extreme eigenvalue distribution, SCN, and the convergence of loss surfaces are investigated. The above analyses give insight into utilizing RMT to analyze the neural network theory.

Air-to-ground (A2G) communication plays an important role in ensuring reliable communication links between unmanned aerial vehicles (UAVs) and ground terminals. This paper presents a wideband truncated ellipsoidal shaped scattering region (TESR) geometry based stochastic model (GBSM) for Multiple-Input-Multiple-Output (MIMO) A2G channels. The proposed model contains a line-of-sight (LoS) component, a ground reflection component, and truncated ellipsoid scattering components. Based on the proposed GBSM, some important statistical properties like space-time-correlation-function (STCF) and Doppler power spectrum density (PSD) are derived. The impacts of elevation angle and UAV altitude on A2G channel characteristics are analyzed. Finally, excellent agreement is achieved between measurement data and simulation results of temporal auto correlation functions (ACFs), demonstrating applicability of the proposed model.

This paper proposes 3D non-stationary multipleinput multiple-output (MIMO) geometry-based stochastic models (GBSMs) for high-speed train (HST) tunnel channels. Considering the line-of-sight (LoS), single-bounced (SB), and double-bounced (DB) components from the geometrical tunnel scattering model, a reference HST tunnel channel model under the assumption that scatterers are uniformly distributed on the tunnel walls is first derived. Then, by using the modified method of equal areas (MMEA), the corresponding simulation model is developed. Based on the proposed tunnel channel models, the correlation properties in time and space domains are investigated. A good agreement of statistical properties between the reference model and simulation model can be obtained. Furthermore, the simulation results show that the proposed model can be applied to mimic the nonstationarity of HST tunnel channels.

In this paper, a new non-stationary regular-shaped geometry-based stochastic model (RS-GBSM) is proposed for vehicular visible light communications (VVLC) channels. The proposed model utilizes a combined two-ring model and a confocal ellipse model, in which the received optical power is constructed as a sum of single-bounced (SB) and double-bounced (DB) components, in addition to the line-of-sight (LoS) component. Using the proposed RS-GBSM, the channel impulse response is generated and utilized to investigate VVLC channel characteristics, such as channel gain and root-mean-square (RMS) delay spread. The received optical power is computed considering the distance between the optical transmitter (Tx) and the optical receiver (Rx). Moreover, the impact of the Rx height on the received power is considered for the LoS scenario. The results show that the LoS power highly depends on the distance, Rx height, and the optical source pattern. For the SB components, it is confirmed that the channel gain in dB and the RMS delay spread follow Gaussian distributions. Finally, the results indicate that the detected optical power from the DB components is low enough to be overlooked.

In this paper, a three-dimensional (3D) non-stationary wideband multiple-input multiple-output (MIMO) channel model based on the WINNER+ channel model is proposed. The angular distributions of clusters in both the horizontal and vertical planes are jointly considered. The receiver and clusters can be moving, which makes the model more general. Parameters, including number of clusters, powers, delays, azimuth angles of departure (AAoDs), azimuth angles of arrival (AAoAs), elevation angles of departure (EAoDs), and elevation angles of arrival (EAoAs) are time-variant. The cluster time evolution is modeled using a birth-death process. Statistical properties, including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), Doppler power spectrum density (PSD), level-crossing rate (LCR), average fading duration (AFD), and stationary interval are investigated and analyzed. The LCR, AFD, and stationary interval of the proposed channel model are validated against the measurement data. Numerical and simulation results show that the proposed channel model has the ability to reproduce the main properties of real non-stationary channels. Furthermore, the proposed channel model can be adapted to various communication scenarios by adjusting different parameter values.

This paper proposes a procedure of predicting channel characteristics based on a well‐known machine learning (ML) algorithm and convolutional neural network (CNN), for three‐dimensional (3D) millimetre wave (mmWave) massive multiple‐input multiple‐output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above‐mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.

Channel modeling is of vital importance to the development and performance evaluation of wireless communication systems. Though many millimeter-wave (mmWave) channel models have been proposed, few of them concern about staircase environments. This paper analyzes the statistical characteristics of 60 GHz channels in a staircase environment with the transmitter (Tx) side fixed and the receiver (Rx) side moving, especially the variation of characteristics arising from the motion of receiver Rx. Fuzzy c-means (FCM) algorithm is applied in clustering procedure and the Kim-Park (K-P) index combined with the multipath component distance (MCD) are utilized to obtain the optimal cluster number. Simulation results show that almost all of the channel characteristics are related to the Euclidean distance between the Tx and Rx. Also, they are affected by building structures, which will provide guidance on the layout of communication devices.

Massive multiple-input multiple-output (MIMO) technology and millimeter wave (mmWave) communication are key technologies for the fifth generation (5G) wireless communications. The combination of mmWave and massive MIMO has the potential to dramatically improve wireless access and throughput performance. Such systems benefit from large available signal bandwidths and small antenna form factor. In the literature, most of the massive MIMO channel measurements are carried out at sub-6 GHz frequency bands, while the effects caused by large antenna arrays at mmWave bands have not been studied yet. In this paper, we conduct channel measurements at 11, 16, 28, and 38 GHz frequency bands combined with large antenna arrays in an indoor office environment. The space-alternating generalized expectation-maximization (SAGE) algorithm is applied to obtain the multipath component (MPC) parameters. New propagation characteristics like spherical wavefront, cluster birth-death, and non-stationarity over antenna array axis are validated for the four mmWave bands by investigating the temporal-spatial channel characteristics like power delay profile (PDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS). The results indicate that massive MIMO effects should be fully considered for mmWave channel models under systems with large antenna arrays.

This paper investigates the blockage loss caused by human body at 11, 16, 28, and 32 GHz by measurements and modeling. The measurements are carried out in an office environment by using a vector network analyzer (VNA) and two horn antennas, with one or two persons walking along or across the line connecting the transmitter (Tx) and receiver (Rx). The METIS knife-edge diffraction (KED) model, Kirchhoff KED model, and geometrical theory of diffraction (GTD) model are used to simulate the human blockage effects. The Gaussian model is also used to fit the measurement data. The human blockage effects are compared for the four millimeter wave (mmWave) bands. The results have shown that as the frequency increases, there is no obvious increasing trend of the losses. The METIS KED model, Kirchhoff KED model, and G TD model can simulate the human blockage effects well.

This paper first proposes a three dimensional (3-D) non-stationary wideband multi-confocal ellipsoid channel model with uniform planar antenna array (UPA) for massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed 3-D geometry-based stochastic model (GBSM) not only considers the non-stationary channel characteristics in time domain but also describes the non-stationary channel characteristics in array domain by adopting a birth-death (BD) process and seed algorithm on UPA for the first time. At the meanwhile, selective cluster evolution and cluster evolution areas (CEAs) are first proposed in this paper. Only clusters in CEAs go through cluster evolution because near-field effect and other clusters can be observed by all antennas. Channel parameters, including delay, Doppler frequency, angle of departure (AoD), and angle of arrive (AoA), are considered in both the azimuth direction and elevation direction. This paper also considers rotations of UPAs. Based on the proposed theoretical reference model, the relevant simulation model is also obtained. The statistical properties of theoretical reference model and simulation model can match well with numerical results.

Millimeter wave (mmWave) communication has been a key technology for the fifth generation (5G) wireless communications. There have been various mmWave channel measurements. However, many measurements in the literature are conducted with different configurations, which may have large impacts on the propagation channel characteristics, and make the comparison of propagation channel characteristics for different mmWave bands impossible. In this paper, we carry out channel measurements at 11, 16, 28, and 38 GHz bands in an indoor environment using a vector network analyzer (VNA). The space-alternating generalized expectation-maximization (SAGE) algorithm is used to obtain the multipath component (MPC) parameters including three dimensional (3D) angular domain information. The propagation characteristics like average power delay profile (APDP), power azimuth profile (PAP), power elevation profile (PEP), root mean square (RMS) delay spread (DS), and azimuth and elevation angular spread (AS) are shown and compared for the four frequency bands. The results show similar properties for different bands and indicate the possibility of the derivation of a unified channel model framework for 10-40 GHz bands.

In this paper, we investigate the impact of different parameters on channel characteristics in a high-speed train (HST) ray tracing tunnel channel model. Signal propagation in HST tunnel scenarios differs much from that of other HST scenarios due to the unique construction of tunnels. Ray-tracing method is applied to analyze the received power and power delay profile (PDP) of tunnel channel models. Different parameters, i.e., carrier frequency, tunnel shape, tunnel dimension, the distance between the transmitter (Tx) and receiver (Rx), and antenna polarization, are studied via simulation results.

Most millimeter wave (mmWave) channel measurements are conducted with different configurations, which may have large impacts on propagation channel characteristics. In addition, the comparison of different mmWave bands is scarce. Moreover, mmWave massive multiple-input multiple-output (MIMO) channel measurements are absent, and new propagation properties caused by large antenna arrays have rarely been studied yet. In this paper, we carry out mmWave massive MIMO channel measurements at 11-, 16-, 28-, and 38-GHz bands in indoor environments. The space-alternating generalized expectation-maximization algorithm is applied to process the measurement data. Important statistical properties, such as average power delay profile, power azimuth profile, power elevation profile, root mean square delay spread, azimuth angular spread, elevation angular spread, and their cumulative distribution functions and correlation properties, are obtained and compared for different bands. New massive MIMO propagation properties, such as spherical wavefront, cluster birth-death, and non-stationarity over the antenna array, are validated for the four mmWave bands by investigating the variations of channel parameters. Two channel models are used to verify the measurements. The results indicate that massive MIMO effects should be fully characterized for mmWave massive MIMO systems.

A new joint eigenvalue distribution (JED) based on dual extreme eigenvalues of finite random matrix is proposed in this study. Different from conventional JED based on K ( K ≥ 2) variables, only two variables are included in the proposed formulation. The upper and lower bounds of the new JED are determined. The new JED provides a simple and efficient way to deduce the distributions of key characteristics of finite random matrix, such as the extreme (largest and smallest) eigenvalues, standard condition number, and scaled largest eigenvalue. Moreover, a novel cooperative spectrum sensing (CSS) scheme based on the new JED is proposed for cognitive radio networks. The simulation results verify the proposed JED and the proposed CSS scheme can improve sensing performance.