To advance research on maritime communications in sixth generation (6G) communication networks, a general three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed in this paper. The proposed model is applicable to a wide range of communication scenarios in maritime internet of things (IoT) communication networks, which involve diverse communication terminals including unmanned aerial vehicles (UAVs), unmanned surface vehicles (USVs)/ships, and shore-side base stations. It employs a comprehensive channel modeling framework to simulate space-time-frequency non-stationary properties in various maritime communication scenarios. Based on the sea spectrum, factors such as sea surface scattering, onboard array rotation, and line-of-sight (LoS) communication probability are jointly considered. Moreover, the proposed channel model incorporates distinct features of maritime channels, including UAV rotation, coastal building interference, and evaporation duct signal propagation, as observed in diverse scenarios. Utilizing the proposed channel model, we derive and simulate the channel statistical properties, such as space-time-frequency correlation function (STF-CF), angular power spectrum density (PSD), and root-mean-square (RMS) delay spread, as well as the channel capacity. We conduct an in-depth investigation to analyze effects of different channel parameters on statistical characteristics and channel capacity. Also, the good agreement between the measurement results and corresponding simulation results confirms the model’s applicability.

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.

High-mobility and large-bandwidth applications at high frequencies make the acquisition of doubly-selective channels costly and complex, as the fast fading channel causes a surge in pilot overheads and inter-carrier interference. Accurate estimation of the complex channel gain (CG) and the carrier frequency offset (CFO) is required to support subsequent high-accuracy channel prediction that alleviates the heavy pilot burden. The emerging technology of the wireless channel map (WCM) can approximately reproduce the actual propagation environment digitally with customized channel parameters, offering an opportunity to accurately estimate the complex CG and CFO. In this paper, a WCM-based prior distribution construction method and a joint complex CG and CFO estimation algorithm are proposed. Specifically, a parameterized linear estimation problem for the complex CG is generated based on a nonuniform delay-domain off-grid channel representation, and with more realistic prior distributions constructed by the knowledge from the WCM-provided angular-delay power spectrum density, the joint estimation problem is solved under the Bayesian inference framework. Simulation results demonstrate the superiority of the proposed algorithm, with a better performance in terms of estimation accuracy and bit error rates (BER) than existing baselines. It is also verified that the proposed WCM-based algorithm is highly adaptable to different WCM precision and robust to different user speeds.

This paper investigates near-field extremely large-scale multiple-input multiple-output (XL-MIMO) systems with sparse uniform planar arrays (UPAs). Based on the Green’s function-based channel model, the paper derives closed-form expressions for the signal beam power when the distance coordinate or the angular coordinates varies with respective to the focused position. Based on that, closed-form expressions for the lobe length and the suppressing ratio are obtained, indicating that both the distance-focusing property and the grating lobe behavior can be enhanced as the focal distance decreases or the antenna spacing increases. Furthermore, the paper introduces a crucial constraint on system parameters, under which effective degrees-of-freedom (EDoF) of XL-MIMO systems with sparse UPAs can be precisely estimated. Then, the paper proposes an algorithm to obtain a closed-form expression, which can estimate EDoF with high accuracy and low computational complexity. The numerical results verifies the correctness of the main results of this paper.

This work studies learning-aided indoor fingerprint positioning (FP) for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems, by exploiting their channel response matrices (CRMs). The existing learning methods of CRM-based FP mostly suffer from two main limitations: i) they require retraining when CRM dimensions change, which severely limits their practicality since the number of antennas and subcarriers may vary dynamically during practical implementation; ii) they overlook the temporal correlations in CRMs along the user trajectory, failing to exploit this information to improve positioning accuracy. Motivated by these limitations, we propose a novel deep learning-assisted FP method, named adaptive spatio-temporal neural network (A-STNN). This method consists of two key components: i) an adaptive mechanism that transforms spatial-frequency domain CRMs (SFCRMs) of arbitrary dimensions into truncated angle-delay domain CRMs (T-ADCRMs) with a unified dimension, enabling the neural network to handle varying antenna and subcarrier configurations; and ii) an STNN that exploits attention mechanisms to jointly extract spatial and temporal features embedded in T-ADCRM sequences along the user’s trajectory, thus improving positioning accuracy. Upon examination of simulation and measurement datasets, A-STNN achieves a prominent improvement in positioning accuracy over existing FP methods, in addition to its unique generalization capability across different antenna and subcarrier settings.

Channel models with a good balance of pervasiveness, accuracy, and efficiency are important for the design and optimization of the sixth generation (6G) wireless communication systems. In this paper, a pervasive beam domain channel model (BDCM) capable of modeling all frequency bands and scenarios in 6G is proposed. Unlike traditional geometry-based stochastic models (GBSMs) that describe channels between antenna pairs in the space domain, the pervasive BDCM reformulates the channel in terms of beam pairs to describe special channel characteristics in the beam domain, such as sparsity and Doppler insensibility. The proposed BDCM incorporates essential spatial wideband and spherical wavefront effects for ultra-massive multiple-input multiple-output (MIMO) by considering the nonlinear phase variations across antenna arrays. The pervasive transform matrices for different antenna configurations are derived to enable flexible conversions between the pervasive GBSM and pervasive BDCM. In addition, key statistical properties of the BDCM are derived and analyzed. The proposed pervasive BDCM in different frequency bands and scenarios are validated by measurement data and compared with the GBSM results. The complexity analysis reveals that the proposed pervasive BDCM significantly reduces the computational complexity compared with the pervasive GBSM under different scatterer densities.

To efficiently implement Terahertz (THz) communications in the 6G era, ultra-massive multiple-input multiple-output (UM-MIMO) technique is considered essential. However, effective wideband THz UM-MIMO transmissions necessitate low-cost yet accurate channel estimation (CE) methods. In this article, we investigate the wideband THz UM-MIMO CE problem under hybrid near- and far-field propagation, molecular absorption, and multi-path reflection. The CE problem is reformulated into a compressed sensing (CS)-aided counterpart (CSCE), exploiting the inherent sparsity of THz UM-MIMO channels to reduce pilot overhead. Our key contributions are: 1) after analyzing the inefficiency of conventional Bayesian learning (BL)-based CSCE frameworks in solving this CE task, we propose a deep unfolding (DU)-aided BL (DUBL) CE algorithm, in which the unfolded expectation-maximization (EM) iteration is implemented through a carefully tailored deep neural network (DNN) architecture; 2) we design a staged offline training procedure equipped with a dedicated loss function to ensure efficient DUBL training; and 3) we conduct a detailed complexity analysis that explicitly quantifies the computational cost of each unrolled layer, thereby characterizing the online inference overhead of the proposed DUBL method. Simulation results demonstrate that the DUBL solution offers substantial THz UM-MIMO CE gains over representative baselines, while complexity comparison highlights its enhanced real-time inference.

Traditional geometry-based positioning methods suffer from low accuracy in non-line-of-sight environments, whereas fingerprint-based positioning methods incur high maintenance costs due to the need for continuous database updates. Channel charting-based positioning methods adopt unsupervised learning to infer user positions from channel state information, but often suffer from low accuracy under complex propagation conditions. Channel maps enable the prediction of real-world positions by providing a priori channel information associated with the propagation environment. Building on this concept, channel charting-based positioning methods leverage this prior knowledge to enhance positioning accuracy and reduce reliance on large labeled datasets. In this paper, we propose a novel channel charting-based semi-supervised positioning algorithm with co-training, which utilizes both labeled data from the channel map and unlabeled data from practical communication systems to predict real-world geographical positions. This algorithm leverages a covariance-based channel feature and a corresponding dissimilarity metric to enhance robustness against noise and timing advance interference. Through ray tracing simulations calibrated by real-world measurements, the proposed algorithm is compared with state-of-the-art positioning methods under different noise conditions, demonstrating its effectiveness and superiority.

Conventional wireless channel measurements and modeling typically study channels via discrete spatial sampling. As the sixth-generation (6G) wireless communication places higher demands on the accuracy of channel state information, this discrete approximation becomes insufficient and motivates research on continuous-space channels. In this work, 28 GHz indoor continuous-space channel measurements are conducted, and key channel characteristics are analyzed. Based on the analysis, the necessity of continuous-space channel research is validated, and the role of channel maps is demonstrated. Furthermore, a continuous-space channel map construction method using the Graph SAmple and aggreGatE (GraphSAGE) algorithm is proposed. By the learning on spatial aggregation function rather than performing a passive weighted sum, the proposed GraphSAGE-based map construction method can reduce the performance bias caused by discrete spatial sampling and recover the continuous-space channels accurately. Continuous-space measurements are used as benchmarks to compare the performance of the GraphSAGE-based channel map. Extensive experiments confirm the superiority of the proposed GraphSAGE-based method over existing artificial intelligence (AI) algorithms, providing a robust approach for the 6G continuous-space channel map construction.

Massive multiple-input multiple-output (MIMO) technologies have been greatly developed for the sixth-generation (6G) communication systems. Based on full-digital antenna arrays, asymmetric massive MIMO communication systems can provide distinct beamforming patterns for uplink and downlink chains. In this paper, a terahertz (THz) channel model for asymmetric massive MIMO communication systems considering the influence of different beam patterns is proposed. The proposed model incorporates channel characteristics of massive MIMO based on spherical-wavefront modeling and distance-dependent steering vectors. The asymmetry between uplink and downlink is considered through antenna beam patterns and array configurations. The statistical properties of the channel model are derived. These channel characteristics and system performance, such as channel capacity, are simulated and compared under different beam patterns. The results show that the space-time-frequency (STF) correlations increase while the delay spread and angular spread decrease when the beam patterns are more concentrated. It is also found that the channel capacity can be increased by utilizing high-gain and narrow beam patterns.

To address sparse channel measurement data and inadequate predictive capabilities in conventional channel models, predictive channel modeling employs joint generative and predictive architectures to enhance robustness. In this paper, we propose an enhanced diffusion-driven predictive framework that integrates generative augmentation and prior-aware prediction into a unified learning pipeline. We first introduce a space-time-frequency (STF) coupled diffusion network based on transformers that generates synthetic channel data preserving critical channel statistical properties. Additionally, we compress measured channel state information into a low-dimensional manifold via a latent encoder and introduce an innovative composite training scheme that couples diffusion-driven prior generation with prediction, equipping the predictive module with rich latent features that lift its performance ceiling and markedly improve generalization across diverse scenarios. Extensive experiments confirm the superiority of our algorithm, and its performance is further validated using channel measurement data, thereby demonstrating its robustness for advanced wireless communications in real-world deployment scenarios.

The maritime environment is uniquely challenged by sparse scattering, sea wave movement, and ducting effects, which significantly impede communication efficiency. Anticipating channel dynamics proactively presents a solution to these challenges. Therefore, this paper investigates the problem of predicting wireless channel path loss in the complex maritime environment. First, a holistic framework that accounts for the synergistic effects of meteorological and radio frequency factors on maritime channels is introduced. Then, an evolutionary-enhanced ensemble neural networks approach is developed that dynamically tailors neural network-based models for the accurate prediction of the maritime channel’s path loss. Numerical results demonstrate that the proposed method outperforms existing state-of-the-art techniques in both prediction accuracy and reliability.

As a key paradigm in sixth-generation (6G) systems for enabling physical–virtual mapping and intelligent network management, Digital Twin (DT) networks demand high-precision, low-latency modeling and continuous updates of wireless environments. However, in dynamic urban scenarios, channel conditions are highly non-stationary, and traditional sensing schemes suffer from limitations in coverage, information timeliness, and scheduling efficiency. To address these challenges, this paper proposes an Integrated Communication-Computing-Intelligence-Controlling (ICCIC) network based on layered unmanned aerial vehicles (UAVs). The system comprises mobile edge servers (ESs) with trajectory control and scheduling capabilities, and statically deployed working UAVs responsible for wireless channel measurements. The collected data is offloaded to ESs for edge computing and local model construction, which supports subsequent DT channel modeling. A multi-objective optimization problem is formulated to jointly schedule UAV trajectories and data offloading, with Age of Information (AoI) and service delay as performance metrics. The optimization is subject to constraints including maximum task execution time and service fairness. To capture multi-type interactions among UAVs during sensing and coordination, we build a multi-relational heterogeneous graph and encode it with a graph neural network (GNN) to obtain structured system states. On this basis, we adopt a QMIX-based multi-agent reinforcement learning (MARL) framework under centralized training and decentralized execution (CTDE) is developed to learn cooperative scheduling policies. Simulation results demonstrate that the proposed method achieves superior performance in sensing timeliness, scheduling responsiveness, and policy convergence, providing robust support for efficient and reliable DT channel modeling.

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.

With advancements in wireless communication and localization technologies, cellular networks are evolving towards integrated sensing and communication (ISAC) capabilities. To address the challenges of sensing-assisted communication, we introduce the digital twin of channel (DToC). Specifically, locations of user terminals (UTs) and their statistical channel state information (sCSI) are treated as physical objects and virtual counterparts in the concept of digital twin (DT), respectively. In this work, we establish a probabilistic model that characterizes sCSI as a location-conditioned distribution. To enable precise sCSI generation, we enhance the latent diffusion model (LDM) and propose an improved latent diffusion model (ILDM) with deterministic sampling. We further propose an accelerated LDM method to speed up the generation process by skipping certain sampling steps. Simulation results demonstrate that the proposed ILDM achieves high accuracy in generating sCSI, while the accelerated LDM delivers significant speedups with minor performance degradation. Our results also validate that the DToC framework can effectively generate sCSI without pilot overhead.

For future sixth generation (6G) space-air-ground-sea integrated networks, leveraging troposcatter for beyond line-of-sight (BLoS) propagation offers a promising solution to achieving long-range unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) multiple-input multiple-output (MIMO) geometry based stochastic model (GBSM) is proposed for long-range UAV-to-ground (U2G) communications. The proposed channel model incorporates UAV 3D mobility and troposcatter characteristics, enabling a distance-dependent transition from visual-range to BLoS communications. A path loss model that accounts for stochastic obstacle deployments and troposcatter characteristics is developed, and the corresponding link budget is calculated to evaluate the feasibility of BLoS communications. Furthermore, troposcatter clusters are modeled and their space-time evolution is characterized to capture channel non-stationarity. Statistical properties, including space-time-frequency correlation function (STF-CF), delay/Doppler power spectral densities (PSDs), root mean square (RMS) delay/Doppler spreads, and coherence bandwidth/time, are also derived and analyzed. The proposed channel model provides valuable guidance for the design of long-range U2G communications.

The emergence of extremely large-scale antenna arrays (ELAA) in millimeter-wave (mmWave) communications, particularly in high-mobility scenarios, highlights the importance of near-field beam prediction. Unlike the conventional far-field assumption, near-field beam prediction requires codebooks that jointly sample the angular and distance domains, which leads to a dramatic increase in pilot overhead. Moreover, unlike the farfield case where the optimal beam evolution is temporally smooth, the optimal near-field beam index exhibits abrupt and nonlinear dynamics due to its joint dependence on user angle and distance, posing significant challenges for temporal modeling. To address these challenges, we propose a novel Convolutional Neural Network– Generative Pre-trained Transformer 2 (CNN–GPT2) based near-field beam prediction framework. Specifically, an uplink pilot transmission strategy is designed to enable efficient channel probing through widebeam analog precoding and frequency-varying digital precoding. The received pilot signals are preprocessed and passed through a CNN-based feature extractor, followed by a GPT-2 model that captures temporal dependencies across multiple frames and directly predicts the near-field beam index in an end-to-end manner. A pretraining–finetuning strategy is further adopted, where the model is first pretrained via masked prediction and then finetuned for the downstream beam prediction task, significantly improving training efficiency and accuracy. Simulation results under the 3GPP TR 38.901 channel model demonstrate that the proposed method achieves higher beam prediction accuracy than conventional recurrent models, while maintaining competitive performance in normalized beam-forming gain. These results confirm the feasibility of employing large-model AI for robust and low-overhead near-field beam management in future 6G systems.

With over 80% of mobile traffic occurring indoors, optimizing indoor wireless communication performance is crucial. For indoor communication systems, interior walls play a significant role in shaping system performance, yet their impacts on wireless communication performance at the link level remain insufficiently explored. In this paper, we propose an evaluation method for indoor wireless performance, considering wall materials and locations. Based on the evaluation method, the relationships between interior walls and signal propagation are investigated, considering line-of-sight (LOS) probability, blockage height, blockage density, and wall materials. The signal propagations are divided into four cases, including the direct path, the reflection path and/or penetration path through walls, and the multipath from scatterers. Moreover, spatial cross-correlation function (SCCF), spectrum efficiency (SE), and energy efficiency (EE) are analyzed to unveil the impacts of walls on channel characteristics and system performance. Our findings reveal that strategically adjusting the placement and materials of walls, while keeping network settings unchanged, can significantly enhance system performance.

The advancement of 6G mobile communication and positioning technologies has amplified the significance of location-aware tools, such as location-indexed channel fingerprints (CFs) and channel charting, which are becoming key enablers for massive MIMO-OFDM systems. In this paper, we propose a novel channel charting with physical CFs (PCFs) and demonstrate its effectiveness in channel state information (CSI) acquisition. First, we define the PCF based on a cluster-based geometric stochastic channel model (GBSM), enabling a comprehensive representation of physical channel characteristics using a compact set of parameters. We then develop a methodology for PCF acquisition in massive MIMO-OFDM systems. By exploiting the relationship between PCFs and the space-frequency-time (SFT) domain channel, the proposed method extracts PCFs from multi-location channel measurements and constructs a structured channel charting with location-indexed PCFs. Furthermore, we propose a low-complexity algorithm to acquire beam domain statistical CSI (sCSI) using the PCFs in the channel charting. The resulting sCSI can be directly employed as prior information for channel estimation. Simulation results show that the proposed method delivers sCSI performance comparable to traditional online probing techniques, and the generated sCSI can serve as reliable prior knowledge to significantly enhance the accuracy of channel estimation. These results validate the proposed PCF as a powerful and versatile tool for channel acquisition and system design of the next-generation mobile communication.

Cooperation between satellite and terrestrial wireless networks promises great potential in meeting fast-growing demands for ubiquitous communications coverage. To tackle spectrum scarcity, spectrum sharing is studied for a hybrid satellite-terrestrial network where satellite links share the same group of time-slotted subcarriers with terrestrial links opportunistically. In particular, with coarse network-wide time synchronization, a time-scale-adaptable spectrum sharing framework is proposed based on a satellite-terrestrial cooperation time scale that can be flexibly adjusted according to practical requirements. For generality, it is assumed that both full and partial frequency reuse could be adopted among the base stations (BSs) and satellite selection is supported when multiple satellites are available. Relying on only statistical channel state information (CSI), joint link scheduling and power control are explored to maximize the average sum rate of the network while ensuring quality of service (QoS) for users. To solve the complicated mixed integer programming (MIP) problem, a low-complexity spectrum sharing scheme is presented based on link-feature-sketching-aided hierarchical link clustering and Monte-Carlo-and-successive-approximation-aided transmit power optimization. Simulation results demonstrate that by link feature sketching, diversity of the links brought by the spatial distribution of the users could be well utilized. The proposed scheme promises a significant performance gain even under strict inter-link interference constraints.

The integration of movable antenna (MA) technique into unmanned aerial vehicle (UAV) communications offers a promising solution to reliable and energy-efficient non-terrestrial networking for low-altitude economy. To characterize multi-MA-assisted UAV-to-UAV wideband fading channels, we propose a three-dimensional arbitrary-elevation two-concentric-cylinders reference model. Based on this model, we derive the space-time-frequency correlation function (STF-CF) in closed form. From the STF-CF, we also obtain the space-Doppler power spectral density (SD-PSD) and the power space-delay spectrum (PSDS). The excellent agreement between the theoretical PSDS and some previously reported measurement data demonstrates the utility of the reference model. We then establish corresponding simulation models, which produce consistent results with the derived expressions. By leveraging the closed-form correlation function, the gradient of the log-determinant of spatial correlation matrix with respect to the MA positions can be conveniently obtained, which may serve as a method to maximize the ergodic capacity of the multi-MA assisted wideband UAV-to-UAV channels.

The integration of sub-6 GHz, centimeter-wave, and millimeter-wave bands through cross-band coordination is recognized as a pivotal approach to optimize system performance in the sixth generation (6G) research. The existing standard channel models, e.g., 3GPP TR 38.901 and QuaDRiGa, support cross-band channel simulations. However, both models assume that multipath components observed at different bands are identical, which is inconsistent with channel measurements. In this paper, we propose a general cross-band channel model, which considers the birth and death of clusters across different bands. The evolution of clusters in temporal and spatial domains is also incorporated, where the common clusters observed at different bands are modeled to undergo identical birth-death processes. Furthermore, the frequency-dependent blockage losses caused by obstacles are embedded in the proposed model. A comprehensive analysis of channel statistical properties is conducted, including the delay/angular/Doppler spreads and stationary times/distances. Moreover, the similarities of cross-band channels in angular and delay domains are analyzed. To validate the accuracy of the proposed model, cross-band channel measurements across 0.7 to 39 GHz are conducted by time-domain channel sounders, which are capable of simultaneously measuring channels at two bands. Comparisons of simulation results from the proposed model, enhanced 6G pervasive channel model, and 3GPP TR 38.901 with measurement data reveal that the proposed model demonstrates the best alignment with measurements.

Orbital angular momentum (OAM) technology can provide an additional degree of freedom in the spatial domain and solve the problem of spectrum shortage in the sixth generation (6G) networks. In this paper, a near-field OAM channel model based on the electromagnetic information (EMI) theory is proposed, in which the dyadic Green’s function approach and discrete Fourier basis functions are utilized to accurately describe the characteristics of near-field channel and OAM signal propagation in practical scenarios, respectively. To improve the performance of misaligned transmission, a near-field OAM misalignment precoding scheme is presented according to the characteristics of the proposed channel model. Specifically, phase errors are compensated and the optimal power is assigned according to the channel condition, which can mitigate the channel capacity loss caused by the the inter-mode interference resulting from the misalignment. Numerical results show that the benefit brought by the polarization along the propagation direction is predominantly significant at short distances, and decreases rapidly to zero as the distance increases. Results also show that the presented scheme outperforms the conventional fixed parameter scheme and effectively reduces the effects of the misalignment in practical scenarios.

This work investigates deep learning (DL)-enabled fingerprint positioning (FP) in a single access point (AP) indoor scenario. Compared to existing FP methods that mostly rely on multiple APs, single AP-based FP faces the tricky issue of substantially reduced spatial information, making those methods less effective or even invalid. To address this issue, we propose a novel DL-enabled FP method to exploit the spatio-temporal features of the angular domain channel matrices (ADCMs) along the user trajectory in single-AP multiple-input multiple-output (MIMO) systems. Specifically, multi-layer convolutional neural networks (CNNs) are designed to extract the spatial features of each ADCM, which reflects position-specific angular distribution of signal propagation. Then a long short-term memory (LSTM) with channel attention (CA) is utilized to capture the temporal dependencies of the sequential ADCMs. Furthermore, a data augmentation method by flipping the ADCM sequence is proposed to reduce the reliance on labor-intensive large-scale data collection for model training. The simulation results show that, in the same single-AP indoor scenario, the proposed approach can significantly increase the positioning accuracy over existing CNNbased and LSTM-based methods, such as ABPN and SIABR, with an improvement of up to 41.7% and 70.8%, respectively.

Cell-free (CF) massive multiple-input multiple-output (M-MIMO) provides an almost uniformly high data rate for all user equipment (UE) through multiple access points (APs), with a non-negligible signal processing burden. Angle domain transmission and channel maps promise to alleviate this burden by reducing channel dimensions in the angle domain and providing$a$priori channel information, respectively. In this paper, we propose a channel map-based angle domain multiple access scheme for uplink CF M-MIMO communications. First, we propose a twostage data reception and pilot assignment scheme constituting receive combining and large-scale fading decoding (LSFD) to reduce overall interference and maximize spectral efficiency (SE). Furthermore, we construct two channel map-based transmission mechanisms by wielding different levels of channel information, where a tailored data reception scheme with a newly derived SE upper bound is also proposed for quantitative evaluation. Simulation results show that the proposed schemes outperform both their space domain alternatives and those without using channel maps in terms of SE.

The current propagation scenario classification methods of standardized channel models are rough and the definitions are unclear, despite the diverse propagation scenarios in the sixth-generation (6G) communications technology era. This paper proposes an unsupervised classification algorithm based on the self-organizing map (SOM) to establish a more refined propagation scenario classification for the 6G era. First, the physical environment information of the actual scenario is extracted based on digital maps. Then, the SOM neural network parameters are determined and the propagation scenario classification is implemented. Finally, the effectiveness of the proposed propagation scenario classification is verified by the data collected from the practical communication network in the following three aspects: physical environment characteristics, evaluation indicators, and channel characteristics. The results show that the proposed SOM scenario classification algorithm outperforms K-means, DBSCAN, and the Gaussian mixture model (GMM), among other algorithms, providing an effective solution for the detailed classification of 6G propagation scenarios.

Millimeter-wave (mmWave) communications have a wide range of applications in indoor scenarios. However, the mmWave channel characteristics such as high path loss (PL), blockage effect, and temporal non-stationarity for indoor multiscenarios are not fully studied by using the same channel sounder, especially in the moving conditions. In this paper, indoor mmWave channel measurements are conducted at 28 GHz band in an office lounge and a lobby, considering both stationary and moving states of transmitter. Channel characteristics including PL, delay power spectral density (PSD), angular PSD, root mean square (RMS) delay spread (DS), and RMS angular spread (AS) for different scenarios are compared and analyzed. The relationships between channel characteristics and environments are thoroughly investigated. Measurement results confirm the clusters birth-death process and the channel temporal nonstationary properties. Multipath components (MPCs) caused by multiple-order reflections from walls are observed to show a significant impact on channel characteristics in the lobby.

Reconfigurable intelligent surfaces (RISs) are considered as one of the potential technologies for the sixth generation (6 G) wireless communication systems. Research on RIS channels is crucial for future RIS-assisted communication systems. However, current RIS channel measurements are only conducted when the receiver (Rx) is stationary, lacking moving channel measurements. In this paper, RIS is used as the transmitter (Tx) antenna in a hall environment to measure the 28 GHz RIS channel. We use near-field coding and far-field coding to measure the RIS channel during the movement process, respectively. The channel characteristics, including path loss, delay spread (DS), Ricean K-factor (KF), temporal autocorrelation function (TACF), and channel capacity are analyzed. It is found that in the hall environment, the coding mode of RIS exerted significant influences on path loss and channel capacity, whereas their impacts on DS, KF, and TACF were relatively minor.

Traditional geometry-based positioning methods (GPMs) suffer from low accuracy in non-line-of-sight (NLOS) environments, while fingerprint-based methods (FPMs) encounter high maintenance costs due to the need for continuous database updates. As an emerging unsupervised technique, channel charting can address these challenges by mapping channel state information (CSI) into a low-dimensional virtual space that represents pseudo-positions of user equipments (UEs). In this paper, a channel charting-based positioning algorithm that leverages a covariance-based channel feature and a corresponding dissimilarity metric is proposed to enhance robustness against noise and timing advance (TA) interference. Additionally, an improved Triplet neural network algorithm is introduced, which enables channel charting to directly and accurately predict real geographical positions. Through ray tracing simulations, the proposed algorithm is compared with state-of-the-art channel charting-based positioning methods, demonstrating its effectiveness and superiority.

Massive multiple-input multiple-output (MIMO) is considered as a key technology in the sixth generation (6 G) communication systems, bringing unique channel characteristics such as spherical wavefront and spatial non-stationarity effects. In this paper, we propose a super-resolution ray launching (SRRL) method to efficiently and accurately model the channel characteristics of massive MIMO scenarios. SR-RL simplifies simulations by performing RL only on the selected antenna elements based on the coherence distance and ignoring other elements. Ray paths traced from the selected elements are distributed to the ignored elements using the reflecting planes and possible obstacles along the ray trajectory. The obtained ray paths of both selected and ignored elements are recorded in a table, revealing a pattern of changes in multipath components across the antenna array. Compared to traditional brute-force methods, SR-RL can reduce computational time significantly, and captures spherical wavefront and spatial non-stationarity effects without sacrificing accuracy.

Reconfigurable intelligent surface (RIS) technology has emerged as a key direction for 6 G mobile communication systems due to its revolutionary potential in channel control, coverage enhancement, and energy efficiency optimization. In this paper, a novel ray tracing (RT) channel model for RIS-based beamforming at the transmitter (Tx) side is proposed. The model considers line-of-sight (LoS) conditions for both the Tx and receiver (Rx). At the Tx side, the feed antenna directs the beam toward the RIS, ensuring that at least one reflected ray from the center of the RIS reaches the Rx. The Rx captures the reflected rays arriving from the direction of the RIS. The statistical properties of the channel model for RIS-based beamforming in indoor scenarios are analyzed including path loss, delay power spectral density (PSD), and angular PSD. The statistical properties of the proposed channel model can well matched the channel measurements.

The explosive growth of the sixth-generation ( 6 G ) wireless system necessitates efficient base station (BS) deployment to balance coverage, data rates, and economic costs. In this paper, we propose a novel BS deployment scheme that jointly optimizes the number and locations of the deployed BSs, considering limited BS throughputs and practical non-uniform user distributions. A variant multi-dimensional knapsack problem is formulated and then solved by proposing a novel depth-limited backtracking dynamic programming (DLB-DP) algorithm with a BS-user association (BSUA) algorithm. We compare the proposed DLB-DP algorithm with three state-of-the-art benchmark algorithms in a hotspot scenario. The results demonstrate that the proposed algorithm outperforms the considered alternatives in terms of coverage, data rates, and robustness under varying user densities.

To achieve the ambitious goals of global-coverage, complete applications, and all-spectra in the sixth generation (6G) mobile networks, channel modeling and characteristics analysis are crucial. This paper proposes an unmanned aerial vehicle (UAV)-to-ground channel model with massive multipleinput multiple-output (MIMO) configuration. Different types of three-dimensional (3-D) UAV trajectories are modeled based on an aeronautic smooth turn random mobility model (ST-RMM). Various types of antenna configurations are also considered in the model. This model is a 3-D geometry-based stochastic model (GBSM) founded on the 6G pervasive channel model (6GPC) to satisfy the requirements of 6G wireless communication systems. The generation method of large-scale parameters (LSPs) and small-scale parameters (SSPs) of the model is introduced, and the space-time cluster evolution process is modeled. Statistical properties including root-mean-square (RMS) delay spread, spacetime correlation function (STCF), stationary interval (SI), and channel capacity are also simulated and analyzed.

The sixth-generation (6G) wireless system will involve large-scale wireless channels, but traditional channel acquisition methods are resource-consuming. Digital twin online channel models have been proposed to facilitate efficient 6G network optimization, which maps physical systems to the virtual world, thereby enabling the prediction of physical data. In this paper, we built a Swin Transformer Aided Urban Digital Twin Online Channel Modeling Platform (SmarTwin-OCMP). The platform begins by importing data from the geographic information system (GIS) to obtain the original static urban models based on Unity, then develops a user-friendly interface functionality, capable of providing the channel characteristics of the corresponding communication scenarios. Swin Transformer is adopted to segment various urban communication scenarios on our remote sensing imagery dataset and achieves satisfactory recognition accuracy. Subsequently, we perform electromagnetic parameter matching and prediction of real-time channel characteristics based on the 6G pervasive channel model (6GPCM) according to the recognition and segmentation results. Finally, we integrate all these functionalities into our Unity platform to enable users to interactively explore urban channel information in a more flexible and intuitive manner.

With the rapid evolution of 6G wireless communication technology, the granular classification of communication scenarios becomes increasingly sophisticated. This necessitates a deeper exploration of the intrinsic relationships between environmental information and channel characteristics. Consequently, the development of efficient and accurate methods for communication scenario reconstruction emerges as a critical imperative. Leveraging comprehensive three-dimensional (3D) spatial information from point cloud data, we propose a two-stage workflow for processing massive unstructured point cloud data to generate triangular mesh models of large indoor communication environments. The first stage implements an enhanced RANdom SAmple Consensus (RANSAC) algorithm with adaptive thresholding for robust wall structure extraction. Subsequently, we employ a hybrid reconstruction method combining template-based deformation for furniture elements with a zero-shot semantic segmentation network for wall opening detection. The geometric information extracted through the aforementioned process is utilized to generate mesh models, and ray-tracing (RT) is adopted to simulate channel characteristics. Finally, the efficiency and accuracy of the proposed scenario reconstruction and channel modeling method is demonstrated by comparing its simulated channel characteristics with those of channel measurements.

The separation of antenna design and channel modeling has made it difficult to capture the intricate interplay between antenna parameters and channel dynamics, resulting in suboptimal performance in real-world environments. To address this challenge, this study introduces a stochastic framework that integrates random antenna array, modeled as one-dimensional Wiener process, with a realistic wireless propagation channel model, i.e., the sixth-generation pervasive channel model (6 GPCM). This framework is grounded in rigorous derivations and incorporates reasonable approximations. It provides a detailed analysis of how antenna mobility, characterized by the increasing variance of the Wiener process over the same time interval, influences the spatial cross-correlation function (SCCF), temporal autocorrelation function (TACF), and channel capacity. Finally, based on the aforementioned derivations, simulations are conducted to validate the theoretical findings, and the results are analyzed to demonstrate their implications for wireless communication system design.

Partition-based clustering algorithms like K-Means, KPowerMeans (KPM) and fuzzy c-means (FCM) have drawbacks for identifying arbitrarily sized non-spherical clusters in supporting wireless channel modeling. In this paper, we improve the Mahalanobis distance metric and propose a novel space-transformed and weighted fuzzy c-means (STW-FCM) algorithm for clustering multipath components (MPCs). The proposed STW-FCM algorithm imposes more relaxed assumptions on cluster shapes, allowing better accommodation of irregularly shaped clusters. Both synthetic and channel measurement data are utilized for validating the proposed STW-FCM algorithm, demonstrating its superior performance compared to existing clustering algorithms.

The sixth generation (6 G) communications will achieve broader coverage and support more communication scenarios. Satellite communications, which are important complements to ground communications, are expected to realize global coverage. This paper aims to propose a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for satellite-to-maritime channel. The proposed model consists of line-of-sight (LoS) component, single-bounce (SB) components caused by the sea surface and ship body, and multi-bounce (MB) components due to ducting effect. The satellite trajectory and arbitrary movement of ship are also taken into consideration. The model considers atmosphere effect and rain effect, which are not negligible in satellite communication systems. Finally, statistical properties such as space-time-frequency (STF) correlation function, root mean square (RMS) delay spread, and Doppler power spectral density (PSD) are derived and analyzed.

The Industrial Internet of Things (IIoT) is a promising scenario for Industry 4.0, where the existence of rich scatterers can affect the quality of communication in the factory. In this paper, we investigate the multiple base stations (BSs) deployment problem based on the ray tracing (RT) method in IIoT scenarios considering coverage. We simplify the construction of IIoT scenarios by considering the geometric and channel characteristics to improve computational efficiency in RT simulation. Then, the BS deployment problem is formulated subject to the path loss and energy efficiency. The particle swarm optimization (PSO) algorithm with the surrogate model from RT simulation is proposed to solve the formulated problem. Moreover, the surrogate model is used to replace the all-time RT simulation in the PSO algorithm. The simulation results demonstrate that the proposed method effectively addresses BS deployment challenges in IIoT scenarios.

Underwater acoustic (UWA) channel models are indispensable for the design of UWA communication systems and technologies. In this paper, a novel non-stationary three-dimensional (3D) twin cluster geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) UWA communication systems in shallow seas. The distribution of non-line-of-sight (NLoS) delays obeys the Nakagami distribution in this model according to the results generated by Bellhop. In addition, the relationship between delay and power is modeled as a negative exponential distribution with different parameters for each NLoS component. The periodic mobility of clusters and distribution of scatterers, caused by the fluctuations of sea surface, are considered to account for the unique UWA environment. Channel statistical properties, such as the space-time-frequency correlation function (STFCF), Doppler power spectrum density (PSD), Doppler spread, coherence time, and coherence distance, are investigated. In particular, temporal autocorrelation function (TACF) and frequency correlation function (FCF) are compared with measurement data to validate the accuracy of this model. Simulation results show that the fluctuating sea surface can cause significant changes in UWA channel characteristics, making it indispensable in channel modeling.

To provide services with various statistical delay quality-of-service (QoS) constraints and minimum performance limitations in future satellite networks, this paper proposes a hybrid access scheme for the satellite networks, where the satellite can transmit with users either in non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) mode. Taking user’s statistical delay QoS constraints into consideration, the effective capacities of two modes are investigated and a maximization problem is formulated by jointly considering multiple access selection and power allocation strategy. Specifically, this jointly problem is decomposed into three steps: Firstly, with K-means based Gaussian mixture model algorithm, users are classified into different clusters according to their delay exponents. Based on which, a roughly multiple access suggestion and three user pairing strategies are proposed, by taking the achieved performance with OMA scheme as constraints. Then, users, who are potentially served in NOMA mode, are carefully decided whether the NOMA model is suitable by a deep neural network. Finally, to find the near optimal power allocation factor for each NOMA group, a deep reinforcement learning (DRL) with varying bounds is proposed, with which the lower and upper action space bounds can be flexible varied according to reward, to optimize the power allocation scheme in the considered system. Numerical simulations are provided to show the impacts of statistical delay QoS requirements on users’ achievable effective capacities, the minimum performance on the superiority of pure NOMA scheme, the user pairing strategy on achieved performance, and the proposed method on further enhancing convergence rate as well as achievable performance in satellite networks.

In recent decades, cyber-physical systems (CPSs) have received great attention due to their broad applications. This paper investigates CPS deployment in remote areas, specifically focusing on a digital twin-empowered unmanned robotic system. The system consists of a multifunctional unmanned aerial vehicle (UAV), sensors, and actuators. The UAV carries communication and computing modules, acting as an edge information hub (EIH) that connects sensors and actuators—forming reflex-arc-like sensing-communication-computing-control (SC3) loops. A digital twin is integrated into the EIH to emulate the system’s behavior and assist in the decision-making. To alleviate resource limitations in remote areas, we propose a goal-oriented closed-loop optimization scheme. The proposed scheme takes the SC3loop as an integrated structure and jointly optimizes uplink and downlink (UL&DL) communication and computing resources to minimize the total linear quadratic regulator (LQR) cost. To address the non-convex optimization problem, we derive the closed-form solution for intra-loop allocation and propose an efficient iterative algorithm for inter-loop optimization. Under the condition of adequate CPU frequency, we derive an approximate closed-form solution for inter-loop bandwidth allocation. Simulation results demonstrate the superiority of the proposed scheme, which achieves a two-tier task-level balance within and across the SC3loops.

Combining millimetre-wave (mmWave) communications with an extremely large-scale antenna array (ELAA) presents a promising avenue for meeting the spectral efficiency demands of future sixth-generation (6G) mobile communications. This technology achieves a high data rate and establishes high-gain directional transmission links. However, beam training for mmWave ELAA systems is challenged by excessive pilot overheads as well as insufficient accuracy, as the huge near-field codebook has to be accounted for. In this paper, inspired by the similarity between far-field sub-6 GHz channels and near-field mmWave channels, we propose to leverage sub-6 GHz uplink pilot signals to directly estimate the optimal near-field mmWave codeword, which aims to reduce pilot overhead and bypass the channel estimation. Moreover, we adopt deep learning to perform this dual mapping function, i.e., sub-6 GHz to mmWave, far-field to near-field, and a novel neural network structure called NMBEnet is designed to enhance the precision of beam training. Specifically, when considering the orthogonal frequency division multiplexing (OFDM) communication scenarios with high user density, correlations arise both between signals from different users and between signals from different subcarriers. Accordingly, the convolutional neural network (CNN) module and graph neural network (GNN) module included in the proposed NMBEnet can leverage these two correlations to further enhance the precision of beam training. To better evaluate the performance of the proposed algorithm, we employ state-of-the-art system simulation software to obtain realistic channel data. Simulation results demonstrate the superior performance of the proposed strategy compared to the exhaustive search scheme and existing deep learning-based schemes.

In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for reconfigurable intelligent surface (RIS) assisted massive multiple-input multiple-out (MIMO) communication systems is proposed. The model includes a more general path loss model and a small-scale fading channel model that considers the RIS electromagnetic (EM) response and RIS polarization adjustment. In the path loss model, a method for generating large-scale parameters with height-dependent is proposed. In the small-scale fading model, four different propagation conditions are given to reflect the diverse link states among base station, RIS, and user equipment, depending on whether the sub-channel is line-of-sight (LOS) or non-line-of-sight (NLOS). Different from the conventional phase shift (PS) model, the RIS response matrix in the small-scale fading channel model can distinguish EM waves scattered from different directions. The accuracy of the proposed channel model is further verified by real channel measurements. The results can well guide the practical deployment of RIS in future sixth generation (6G) wireless communication systems.

Indoor positioning is an important application of integrated sensing and communication technology in the sixth generation (6G) wireless communications. To address the limitations of existing fingerprint-based positioning methods (FPMs) under severe multipath effects, a novel channel state information (CSI)-based channel fingerprint structure and a novel line-of-sight (LoS)/non-LoS (NLoS) identification-assisted positioning method (IAPM) is proposed for 6G indoor multiple-input multiple-output (MIMO) communications. The proposed channel fingerprint structure uses the proposed maximum received power path to enhance the feature discrimination. The proposed IAPM incorporates a LoS/NLoS identification module and an improved weighted random forest (IWRF) positioning algorithm for accurate positioning. Evaluations on both channel measurement data and channel synthetic data generated by ray tracing demonstrate that the proposed method achieves superior accuracy and robustness compared with widely used FPMs.

Due to the agile three-dimensional (3D) mobility and flexibility, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in sixth-generation (6G) wireless networks. In UAV communication systems, 3D beamforming is an effective technique for performance enhancement. To take full advantage of 3D beamforming in UAV communication systems, we need to accurately characterize and efficiently predict highly dynamic UAV channels in the beam domain. This paper first proposes a novel beam domain channel model (BDCM) considering random UAV trajectories and fuselage vibrations. Then, we propose a beam domain channel tracking algorithm to capture variations of multipath component (MPC) parameters in UAV channels. Finally, we put forward a novel beam domain channel prediction scheme for UAV communication systems utilizing channel sparsity and high temporal correlation in the beam domain. The proposed channel prediction scheme can extract channel variation trends utilizing the echo state network (ESN) and predict channel parameters in subsequent time blocks based on the history channel information. Simulation results show that the proposed channel prediction scheme outperforms the conventional prediction scheme based on angular speed estimation in terms of both prediction accuracy and communication system performance.

This paper investigates a reconfigurable intelligent surface (RIS)-aided wideband massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system with low-resolution analog-to-digital converters (ADCs). Frequency-selective Rician fading channels are considered, and the OFDM data transmission process is presented in time domain. This paper derives the closed-form approximate expression of the uplink achievable rate, based on which the asymptotic system performance is analyzed when the number of the antennas at the base station and the number of reflecting elements at the RIS grow to infinity. Besides, the power scaling laws of the considered system are revealed to provide energy-saving insights. Furthermore, this paper proposes a gradient ascent-based algorithm to design the phase shifts of the RIS for maximizing the minimum user rate. Finally, numerical results are presented to verify the correctness of analytical conclusions and draw insights.

With the advancement of communication technology and the improvement of localization accuracy, cellular networks are gradually evolving from communication to perception-integrated networks. Addressing the research challenges of sensing-assisted communication, we propose, for the first time, the concept of Digital Twin of Channel (DToC). Specifically, we regard user terminal (UT) positions as physical objects, and statistical channel state information (CSI) as virtual digital objects. Observing the change trend of UTs’ statistical CSI caused by the changes of UT’s physical position enables predictive analytics for subsequent communication tasks. Then, we establish the relationship between physical and virtual digital objects using a Diffusion Model (DM) to achieve the DToC. Indeed, the DM can generate the desired objects by gradually denoising from noisy data using neural networks. Furthermore, we propose a conditional DM utilizing UTs’ positions, which completes the task of generating the corresponding statistical CSI under known user-specific position conditions, thus mapping UT positions to statistical CSI. Simulation results demonstrate that our DToC framework outperforms previous statistical CSI estimation methods. Without the need of pilots, our method can simultaneously generate statistical CSIs from a large number of UTs’ positions, achieving satisfactory results.

In the increasingly critical field of aerial communication, unmanned aerial vehicles (UAVs) have gained significant attention as prominent representatives, and accurate air-to-air (A2A) channel modeling plays a pivotal role in the design and evaluation of reliable communication systems. This paper presents a A2A channel model for UAV communications. It introduces a quasi-deterministic approach to address limitations in existing modeling frameworks. The proposed model uses a truncated ellipsoid to capture the distribution of scatterers in A2A scenarios and, for the first time, incorporates rooftop specular reflection (RSR). Power correction factors, based on the UAV’s airframe structure, position, and posture, are introduced to provide a comprehensive and realistic depiction of the A2A communication channel. The performance of proposed model is assessed by simulating key statistical channel characteristics and comparing with other alternatives. The simulations illustrate how channel behavior is influenced by factors such as flight level, flight trajectory, and UAV posture. The results show that RSR leads to the channel hardening effect, while airframe occlusion causes the received signal power to vary gradually with changes in UAV’s position and posture. The validity of the model is confirmed through comparison with measurement data and ray-tracing results, proving its accuracy and practical application.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a decisive role in the design, optimization, and evaluation of communication systems between UAV and ground terminal. This paper proposes a three-dimensional (3D) model for U2G communication channels, enhanced with ultra-wideband (UWB) features and frequency non-stationarity. This model integrates large-scale and small-scale fading components, introducing bandwidth-dependent path numbers and the UAV posture matrix for realistic scenario representation. It encompasses specific UWB U2G channel phenomena such as the channel hardening, UAV 3D movements, and posture variation effect. The channel parameters, including spatial large-scale parameters (LSPs), bandwidth-correlated path numbers, delay-posture-correlated path power, and frequency-correlated path phase, are generated to capture channel non-stationary characteristics across time and frequency domains. Employing ray-tracing (RT) for the path number and optimization methods for the path delay, the proposed model ensures reliable parameter evolution. The proposed model is assessed through key statistical properties, including space-time-frequency correlation functions, power delay profile, root-mean-square delay spread, Doppler power spectrum density, and the energy variance. It is demonstrated that both posture and bandwidth variations have crucial effects on channel characteristics. The validity and practicability of this research is demonstrated by comparing the simulated outcomes with the measurement data.

The emergence of 6th generation (6G) mobile networks brings new challenges in supporting high-mobility communications, particularly in addressing the issue of channel aging. While existing channel prediction methods offer improved accuracy at the expense of increased computational complexity, limiting their practical application in mobile networks. To address these challenges, we present LinFormer, an innovative channel prediction framework based on a scalable, all-linear, encoder-only Transformer model. Our approach, inspired by natural language processing (NLP) models such as BERT, adapts an encoder-only architecture specifically for channel prediction tasks. We propose replacing the computationally intensive attention mechanism commonly used in Transformers with a time-aware multi-layer perceptron (TMLP), significantly reducing computational demands. The inherent time awareness of TMLP module makes it particularly suitable for channel prediction tasks. We enhance LinFormer’s training process by employing a weighted mean squared error loss (WMSELoss) function and data augmentation techniques, leveraging larger, readily available communication datasets. Our approach achieves a substantial reduction in computational complexity while maintaining high prediction accuracy, making it more suitable for deployment in cost-effective base stations (BS). Comprehensive experiments using both simulated and measured data demonstrate that LinFormer outperforms existing methods across various mobility scenarios, offering a promising solution for future wireless communication systems.

In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) and a beam domain channel model (BDCM) for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems are proposed. The spherical wavefront and space-time-frequency non-stationarity introduced by massive MIMO, movements of the user and clusters, and large bandwidth of mmWave communications are incorporated. The shared clusters between the sensing channel and communication channel caused by the scattering characteristics are also considered. Based on the proposed channel model, important statistical properties are derived and simulated, including the space-time-frequency correlation function (STF-CF), root mean square (RMS) beam spread, RMS delay spread, RMS Doppler spread, coherence time, and channel capacity. By comparing the statistical properties of the sensing channel and the communication channel, it is found that the sensing channel exhibits more significant temporal non-stationarity. Moreover, the distribution of clusters for the sensing channel and communication channel shows significant difference, which is confirmed by the simulation results of RMS beam spread and RMS delay spread.

In this paper, we study the impacts of user mobility on the average bit error probability (ABEP) and average achievable data rate (ADR) of ultra-reliable and low-latency communication (uRLLC) systems. Specifically, based on the time autocorrelation function (ACF) of the sixth generation pervasive channel model (6GPCM), we derive the expression of the pairwise error probability (PEP) of uRLLC systems adopting the M-ary phase shift keying (MPSK) modulation. In addition, we propose a new framework for the calculation of the average ADR considering the channel estimation period and cyclic prefix (CP) length. Moreover, the calculation methods of maximal data transmission period and transmission latency are given. Numerical results show that the increasing user mobility degrades the ABEP and average ADR. Impacts of user mobility on the ABEP surpass its influences on the average ADR. Furthermore, the tightness of analytical results is validated by Monte Carlo simulations. The performance analysis will guide the flexible designs of transmission blocklength under different reliability constraints.

Urban areas serve as one of the most important scenarios in sixth generation (6G) wireless communications, necessitating comprehensive and in-depth wireless channel characteristics studies. The wireless channels in various 6G urban communication scenarios usually show different characteristics. To provide robust identification across a wide range of 6G urban communication scenarios, we propose a novel intelligent scenario identification algorithm. The proposed algorithm leverages an enhanced multi-layer perceptron (MLP) and autoencoder, utilizing easily accessible environmental physical features as inputs. To enhance identification performance, we employ a series of data pre-processing methods and hyperparameter optimization algorithm. Subsequently, experimental results demonstrate the superior performance of our proposed algorithm compared to the benchmark algorithm. Ultimately, the channel characteristics in all domains, including spatial, temporal, frequency, angle, Doppler, and delay, are studied for these scenarios.

Spectrum sharing among the satellite, unmanned aerial vehicle (UAV), and terrestrial components is crucial to alleviate spectrum scarcity in a hybrid maritime communication network (MCN). In the time domain, spectrum sharing optimization based on fine-grained time slices is widely envisioned. However, fine-grained time synchronization is rather challenging due to the large diversity in the link delay. In this paper, we focus on link-cluster-based spectrum sharing based on coordinated link scheduling in terms of subcarrier and time slice allocation. By link-cluster-based scheduling for the satellite links, time-slice-oriented spectrum sharing is realized with coarse time synchronization at time scales much larger than single time slice duration. Only large-scale channel state information (CSI) is utilized for saving cost. An NP-hard mixed integer programming (MIP) problem is formulated, and with the aid of link clustering, a suboptimal spectrum sharing scheme, with only a small performance gap to the optimal one, is proposed. Simulations show that a significant improvement in both energy efficiency and spectrum efficiency could be achieved by the proposed scheme.

The sixth-generation (6G) systems will adopt space- air-ground-sea integrated communications to achieve ubiqui- tous connectivity. In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) is proposed for satellite-unmanned aerial vehicle (UAV)- ground communications. This model considers low earth orbit (LEO) satellites and UAVs as base and mobile relay stations. Millimeter wave (mmWave) bands, terminal positions, arbitrary movement, and elevation angle are described in the model based on standardized large- scale parameters (LSPs). The channel is further analyzed in space, time, and frequency for the proposed model to describe the channel characteristics for delay spread (DS), angular spread (AS), and Doppler spread. The results show that long propagation distance and rapid mobility exhibit significant Doppler shift effects, while adding UAV relay mobile station (Mx) could mitigate. Also, elevation and arrival angle of Mx are impacted by its aerial position.

Base station (BS) deployment is not a one-time endeavor, as when transitioning to higher frequency bands, coverage holes may arise, and the initial deployment may be unsatisfactory. In such cases, the deployment of additional BSs may be necessary. In this paper, we formulate an optimization problem to deploy additional BSs, attempting to minimize overlap coverage rate while satisfying the limitations of coverage rate and spectral efficiency. An improved genetic algorithm is adopted to solve the optimization problem. Numerical results show that the improved genetic algorithm can effectively minimize the overlap coverage rate and outperforms the original genetic algorithm and the particle swarm optimization (PSO) algorithm in terms of the minimum overlap coverage, spectral efficiency, the number additional BSs, and the convergence speed.

In this paper, a geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) maritime multi-user land-to-ship communication channels. The inter-link correlation between different users is modeled by spatial correlated large scale parameters (LSPs). In addition, the evaporation duct is modeled based on a twin-cluster model, and the signal propagation between two clusters is simulated by several-order reflections to obtain the delay and angle parameters of each ray. Simulations based on the proposed model focus on analyzing the statistical properties that reflect the spatial correlation between multiple users. Effects of the number of antennas, multipath components (MPCs), and different heights of evaporation duct on the correlation between users and the channel capacity are investigated and corresponding conclusions are drawn.

Ray tracing (RT) is an effective deterministic channel modeling method, but the computational burden for dynamic scenarios is high. To address this issue, this study categorizes objects involved in various types of motion within dynamic scenarios based on their motion characteristics, distinguishing between static and dynamic objects. Subsequently, the subinterval of the optimal split plane suitable for bounding volume hierarchies (BVH) construction is calculated, and the simplified interval BVH algorithm (SIBVH) is applied to the vehicle-to-vehicle (V2V) communication scenario. The simulation results indicate that, SIBVH based RT algorithm can trace rays much faster than traditional RT method, and improve computational efficiency significantly. The proposed algorithm can be effectively used in complex high-mobility communication scenarios.

In this paper, a quasi-deterministic (Q-D) model for non-stationary underwater acoustic (UWA) channels is proposed. This model combines the BELLHOP deterministic model and geometry-based stochastic model (GBSM), which provides higher accuracy and flexibility. Different propagation components in shallow water are classified as D-rays, R-rays and F-rays in the proposed model, where D-rays are modeled by BELLHOP while both R-rays and F-rays are modeled by GBSM. Some important channel statistical properties, including time-frequency correlation function (TF-CF), Doppler power spectrum density (PSD), average Doppler shift, and RMS Doppler spread are derived and simulated. Finally, simulation results illustrate the correctness of the proposed model.

In massive multiple-input multiple-output (MIMO) systems, the channel estimation scheme is subject to the spatial non-stationarity and inevitably power leakage in the beam domain. In this paper, a beam domain channel estimation scheme is investigated for spatial non-stationary (SNS) massive MIMO systems considering power leakage. Specifically, a realistic mas-sive MIMO beam domain channel model (BDCM) is introduced to capture the spatial non-stationarity considering power leakage by introducing the illustration of visibility region (VR). Then, a beam domain structure-based sparsity adaptive matching pursuit (BDS-SAMP) scheme is proposed based on the cross-block sparse structure and power ratio threshold of beam domain channel. Finally, the simulation results validate the accuracy of proposed BDS-SAMP scheme with low pilot overhead and reasonable complexity by comparing with conventional schemes.

In the sixth generation (6G) wireless communication systems, the communication coverage range becomes broader, where transmitters (Tx) and receivers (Rx) can be randomly distributed in continuous space. The continuous-space electromagnetic (EM) channel is different from the conventional propagation channel. Its input is continuous current density and its output is continuous electric field so that channel characteristics at any point can be given under the guidance of EM information theory (EIT). EIT is a theory integrating traditional theories applied in communications, such as EM theory and information theory. In this paper, a novel 6G continuous-space EM channel model jointly considering antenna response (AR), mutual coupling (MC), and small-scale fading is proposed. AR at the Tx and Rx sides is modeled using an EM calculation method. MC is obtained using antenna theory. Small-scale fading is described using a geometry-based stochastic model (GBSM). Important statistical properties and channel capacity are analyzed to reveal characteristics of the proposed channel model. The results illustrate that the antenna array has non-negligible impacts on statistical properties of the channel model and channel capacity.

The performance evaluation of sixth generation (6G) communication systems is anticipated to be a controlled and repeatable process in the lab, which brings up the demand for wireless channel emulators. However, channel emulation for 6G space-time-frequency (STF) non-stationary channels is missing currently. In this paper, a non-stationary multiple-input multiple-output (MIMO) geometry-based stochastic model (GBSM) that accurately characterizes the channel STF properties is introduced firstly. Then, a subspace-based method is proposed for reconstructing the channel fading obtained from the GBSM and a channel emulator architecture with frequency domain processing is presented for 6G MIMO systems. Moreover, the spatial time-varying channel transfer functions (CTFs) of the channel simulation and the channel emulation are compared and analyzed. The Doppler power spectral density (PSD) and delay PSD are further derived and compared between the channel model simulation and subspace-based emulation. The results demonstrate that the proposed channel emulator is capable of reproducing the non-stationary channel characteristics.

The sixth generation (6G) wireless communication envisions global coverage, all spectra, and full applications, which correspondingly creates many new communication scenarios. As the foundation of 6G communication system design, network planning, and optimization, more intelligent scenario identification algorithms are necessitated in wireless channel modeling to automatically match suitable parameters for various scenarios. With channel statistics and the efficient channel attention (ECA) mechanism, we propose an improved residual network (ResNet) to identify scenarios in the 6G space–air–ground–sea framework. Datasets from both channel measurements and 6G pervasive channel model (6GPCM) simulations are collected to establish a scenario channel characteristic database, including the numbered scenarios and channel statistical properties such as root mean square (RMS) delay spread (DS), RMS angle spread (AS), and stationary distance/time/bandwidth, etc. During the training and verification process, the proposed algorithm is optimized for 29 scenarios, and the identification accuracy of the proposed ECA–ResNet is higher than the convolutional neural network (CNN) and recurrent neural network (RNN). Finally, the cumulative distribution functions (CDFs) of RMS AS and RMS DS for interoffice main road, office outdoor, office, and industrial Internet of Things (IIoT) scenarios are verified according to the measurement data.

The Industrial Internet of Things (IIoT) is one of the typical application scenarios in the beyond fifth generation (B5G) wireless communication systems. Due to numerous metal obstacles and machines, the industrial channel, especially at the millimeter-wave (mmWave) bands, exhibits complex characteristics that have not been considered in existing literature. This article proposes an innovative 3-D nonstationary geometry-based stochastic model (GBSM) for IIoT scenarios at mmWave bands. In the proposed model, device reflections (DRs) caused by massive metal machines are modeled based on geometrical optics. Furthermore, the generalized extreme value (GEV) distribution and generalized Pareto (GP) distribution are used to parameterize the number of clusters and rays within a cluster, respectively. Further, the Doppler shift is modeled and analyzed using the Gaussian distribution. Some channel statistical characteristics are captured by the proposed model, such as the power delay profile, root-mean-square delay spread, root-mean-square angle spread, intercluster delay, and space–time–frequency correlation function. Then, these channel statistical characteristics are well fitted to the ray-tracing simulations and the channel measurements. The excellent fitting results demonstrate the high accuracy of the proposed model, which is crucial for future IIoT communication system design. What is more, this article shows the antenna height and propagation scenarios can significantly affect the DR ratio, which should adapt to various IIoT communication scenarios.

Among many envisaged drivers for sixth generation (6G), one is from the United Nation’s Sustainability Development Goals 2030 to eliminate digital inequality. Remote coverage in sparsely populated areas, difficult terrains or emergency scenarios requires on-demand access and flexible deployment with minimal capex and opex. In this context, network-in-a-box (NIB) is an exciting solution which packs the whole wireless network into a single portable and reconfigurable box to support multiple access technologies, such as WiFi, 2G–5G, etc. In this article, we propose low-altitude platform station (LAPS)-based NIBs with stratospheric high-altitude platform station (HAPS) as backhaul. Specifically, backhaul employs nonorthogonal multiple access (NOMA) with superposition coding at the transmitting HAPS and successive interference cancellation (SIC) at the receiving NIBs, whereas the access link (AL) employs superposition coding along with the regularized zero-forcing (RZF) precoding at the NIB in order to elevate the computational overhead from the ground users (GUs). The required number of airborne NIBs to serve a desired coverage area, their optimal placement, user association (UA), beam optimization, and resource allocation are optimized by maximizing the sum rate of the AL while maintaining the quality of service. Our findings reveal the significance of thorough system planning and communication parameters optimization for enhanced system performance and best coverage under limited resources.

To provide customized high-quality services for all users in the sixth-generation (6G) wireless communication systems, it is fundamental to study all 6G channel scenarios and establish accurate channel models for these scenarios correspondingly. However, the absence of comprehensive 6G scenario categorization and the difficulties of modeling the channels for all scenarios bring huge challenges. In this article, we aim to give a thorough overview of channel scenarios, identification algorithms, and intelligent channel modeling theories. First, different standardized scenario categorization principles are reviewed. A unified and exclusive scenario categorization method is elaborated with detailed 6G scenario definitions. Second, scenario features, feature selection principles, ML-based identification algorithms, as well as data preprocessing methods are surveyed for the benefit of accurate scenario identification. Third, the intelligent scenario adaptive channel modeling theory based on 6GPCM is specified. Statistical properties for industrial IoT and HST scenarios are simulated and compared with those from measurements. Finally, future research directions and challenges are addressed.

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

In the sixth generation (6G) wireless communication networks, ultra-massive multiple-input multiple-output (MIMO) communication is one of the most promising technologies. In ultra-massive MIMO channels, the mutual coupling (MC) effect is more obvious when antenna elements are more closely spaced. In this paper, a novel 6G space-time-frequency (STF) non-stationary massive MIMO channel model is proposed, which jointly considers MC, antenna efficiency, and near-field steering vectors of different antenna topologies. As the Shannon capacity theorem is based on the wide-sense stationary (WSS) channel assumption and cannot be applied to non-stationary channels, we propose a novel non-stationary channel capacity calculation method that divides the non-stationary channel into WSS sub-channels. Important statistical properties and channel capacities of the proposed channel model are derived and verified by ultra-massive MIMO channel measurements and data post-processing. The results show that the simulated spatial cross-correlation function (CCF) and channel capacity considering MC and antenna efficiency are closer to measured results. It also shows that antenna topologies have an impact on channel capacities. Furthermore, channel capacities using the proposed novel calculation method match the measured channel capacities in non-stationary channels.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

This paper investigates the performance of simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted massive multiple-input multiple-output (MIMO) systems with Rician fading channels and channel estimation errors. We adopt the two-timescale scheme to design the systems, namely, applying the instantaneous channel state information (CSI) to design the base station (BS) beamforming and leveraging the statistical CSI to design the phase shifts of the STAR-RIS. Specifically, we estimate the overall channels based on the linear minimum mean-squared error (LMMSE) estimator and derive the closed-form expression of the average achievable rate. Based on the derived rate, we analyze the power scaling laws in which the transmit power is respectively reduced inversely proportional to the number of BS antennas and STAR-RIS elements. Besides, we draw insights from the comparison between STAR-RIS and conventional RIS under the same condition and the power scaling laws of STAR-RIS and optimize the phase shifts of the STAR-RIS to maximize the sum rate using an accelerated gradient ascent-based algorithm. Finally, numerical results are provided to validate our theoretical insights. In particular, we also compare the two-timescale scheme with the instantaneous CSI scheme in the simulation. We show that STAR-RIS outperforms conventional RIS, and the two-timescale-based scheme outperforms the instantaneous CSI-based scheme. Furthermore, we draw insight into this phenomenon.

The development of sixth-generation (6G) wireless communication systems brings significant challenges in channel modeling. Conducting channel measurements for 6G communications is highly expensive and cannot cover all scenarios and frequency bands. Moreover, existing conventional channel models fail to accurately predict channel characteristics in unknown frequency band. As a result, predictive channel modeling has emerged as a promising solution for addressing these challenges in 6G channel modeling. In this study, we propose a frequency domain predictive channel model that combines an autoencoder with a coupling Convolution Gated Recurrent Unit (Conv-GRU) cells. The proposed model aims to predict channel characteristics in unknown frequency bands. The proposed predictive channel model is validated by using data collected from multiple frequency bands channel measurements. To evaluate its performance, several commonly used prediction networks, i.e., a general LSTM network, a GRU-based predictive network, and a Conv-LSTM-based predictive network, are conducted as benchmarks for comparison. Based on evaluation results, our proposed predictive channel model achieves the highest level of accuracy in predicting channels. Additionally, we provide a performance bound for extrapolation predictability using a Ray Tracing simulator.

The statistical gap between existing correlation-based stochastic channel models (CBSMs) and practical propagation is a longstanding issue, especially for massive multiple-input multiple-output (MIMO) transmission. To address this problem, a pervasively correlated channel model (PCCM) applicable to MIMO channels with arbitrary antenna configurations and scenarios is proposed. Unlike the conventional jointly correlated channel model (JCCM) based on an independently and identically distributed (i.i.d.) random matrix, a new random matrix whose elements are independent and nonidentically distributed (i.n.d.) generalized Gamma complex Gaussian mixture (GGCGM) variables with correlated envelopes is used to approximate the real channel statistics better. Moreover, the PCCM can be simplified via the Rayleigh fading assumption for rapid evaluation of channel performance and supports backward compatibility with existing CBSMs under specific assumptions. Demonstrative numerical experiments for MIMO channels are conducted based on the 3GPP TR 38.901 model considering various scenarios, frequencies, and antenna configurations. The channel capacity distributions are obtained based on random samples generated using the geometry-based stochastic channel model (GBSM), the JCCM, and the proposed PCCM. The numerical results show that the PCCM is more flexible than the JCCM with respect to high-order statistics, enabling more accurate estimation of massive MIMO transmission channel performance.

In this paper, beam training and beam tracking are investigated for extremely large-scale multiple-input-multiple-output communication systems with partially-connected hybrid combining structures. Firstly, we propose a two-stage hybrid-field beam training scheme for both the near field and the far field. In the first stage, each subarray independently uses multiple far-field channel steering vectors to approximate near-field ones for analog combining. To find the codeword best fitting for the channel, digital combiners in the second stage are designed to combine the outputs of the analog combiners from the first stage. Then, based on the principle of stationary phase and the time-frequency duality, the expressions of subarray signals after analog combining are analytically derived and a beam refinement based on phase shifts of subarrays (BRPSS) scheme with closed-form solutions is proposed for high-resolution channel parameter estimation. Moreover, a low-complexity near-field beam tracking scheme is developed, where the kinematic model is adopted to characterize the channel variations and the extended Kalman filter is exploited for beam tracking. Simulation results verify the effectiveness of the proposed schemes.

Spectrum sharing among the satellite, unmanned aerial vehicle (UAV), and terrestrial components is crucial to alleviate spectrum scarcity in a hybrid maritime communication network (MCN). Fine-grained spectrum sharing based on ms-level time-domain slices is widely envisioned. However, ms-level time synchronization is challenging in the hybrid MCN due to the large diversity in the link delay. To tackle this challenge, we propose a fine-over-coarse spectrum sharing framework based on coordinated link scheduling, which is realized by joint subcarrier and time slice allocation. Specially, by link-cluster-based scheduling with grouped time slice allocation for the satellite links, time-slice-oriented spectrum sharing is realized with coarse time synchronization at time scales much larger than a single time slice duration. In the framework, only large-scale channel state information (CSI) is utilized for saving cost. A worst-case model is introduced to depict interference caused by satellite link clusters, and an NP-hard mixed integer programming (MIP) problem is formulated. Based on analysis on the characteristics of the optimal solution, a novel link clustering algorithm is proposed to form a group of shaped virtual cells within the coverage area of the MCN. A suboptimal spectrum sharing scheme with only a small performance gap to the optimal one is then proposed. Simulations show that a significant improvement in both energy efficiency and spectrum efficiency can be achieved by the proposed framework.

It is envisioned that the sixth generation (6G) and beyond 6G (B6G) wireless communication networks will enable global coverage in space, air, ground, and sea. In such networks, both base stations and users can be mobile and will tend to move continuously in three-dimensional (3D) space. Therefore, obtaining channel state information (CSI) in 3D continuous-space is crucial for the design and performance evaluation of future 6G and B6G wireless systems. On the other hand, new 6G technologies such as integrated sensing and communications (ISAC) will also require prior knowledge of CSI in 3D continuous-space. In this paper, a 3D continuous-space electromagnetic channel model is proposed for tri-polarized multi-user communications, taking into account scatterers and spherical wavefronts. Scattered fields are calculated using the method of moments (MoM) with high accuracy. Spherical wave functions are utilized to decompose the dyadic Green’s functions that connect the transmitted source currents and the received electric fields. Simulation results demonstrate that transmit power, apertures, scatterers, and sample intervals have significant impacts on statistical properties and channel capacities, providing insights into the performance of continuous-space electromagnetic channel models and the design of future wireless systems.

Non-reciprocal antenna beam patterns are promising to be utilized in asymmetric massive multiple-input multiple-output (MIMO) systems for future sixth-generation communications. The inconsistency of uplink (UL) and downlink (DL) channels makes channel modeling in this scenario challenging. In this paper, a novel geometry-based stochastic model (GBSM) is proposed for non-reciprocal terahertz (THz) channels. A directional effective scatterer generation algorithm is designed to depict the inconsistency of bidirectional propagation conditions. The correlation function between UL and DL is derived and analyzed, which validates the ability to characterize the non-reciprocal channels. To mimic THz propagation features, molecular absorption and diffuse scattering are introduced to the model, which is verified by measured data. In addition, the non-stationarities in space, time, and frequency domains are characterized, respectively. Statistical properties are compared between analytical and simulation results, and good agreements are shown. Finally, the accuracy of the model is verified by comparing with the ray tracing data.

In this paper, a novel beam domain channel model (BDCM) for sixth generation (6G) ultra-massive multiple-input multiple-output (MIMO) wireless communication systems is proposed by transforming from an existing geometry-based stochastic model (GBSM). Both the GBSM and BDCM consider the special channel characteristics of ultra-massive MIMO, including spherical wavefront and spatial non-stationarity properties. Steering vectors of the spherical wavefront are derived by higher-order Taylor expansion. By sampling the angle and distance rings, steering matrices of the spherical wavefront can be transformed to unitary matrices. This helps to achieve a perfect transformation from the GBSM to the BDCM in the near-field condition. Meanwhile, it opens up a view of beam domain for channel characteristic analysis to reduce the model complexity. Common statistical properties of the GBSM and BDCM in the near-field and simplified far-field steering vector conditions are studied, including spatial cross-correlation functions (SCCFs), temporal autocorrelation functions (TACFs), frequency correlation functions (FCFs), etc. Specific statistical properties, such as the root mean square (RMS) angular spread of the GBSM and RMS beam spread of the BDCM, are studied. Channel capacities of the GBSM and BDCM are investigated and compared with measurement data. It turns out that simulated capacities considering near-field steering vector conditions show good agreements with measured capacities. On the other hand, simulated capacities considering far-field steering vector conditions show a large discrepancy with measured capacities. This indicates that near-field effects need to be included in the channel modeling and performance evaluation of 6G ultra-massive MIMO communication systems.

In this paper, a novel ultra-massive multiple-input multiple-output (MIMO) ship-to-ship beam domain channel model (BDCM) for maritime communications is proposed, which is transformed from a geometry-based stochastic model (GBSM). The location-dependent property and sparse user and scattering distribution property for maritime communication channel are considered. In addition, the scattering effect of the sea surface and the limited range of angle for the evaporation duct propagation are modeled by the scattering masking function and angle filtering function, respectively. Important statistical properties are studied, including the spatial cross-correlation function (SCCF) and temporal autocorrelation function (TACF), etc. The channel capacity is studied. Simulation results show that the ship speed, wind speed, and antenna configuration have great influence on channel characteristics and system performance. In addition, the simulation result of the root mean square (RMS) delay spread agrees with the measurement data, which proves the correctness of the model.

In this work, we propose low-complexity designs for XL-MIMO in the near-field with spatial non-stationarities. We first introduce a notion of visibility region (VR) and propose a VR detection algorithm. Then, we exploit the acquired VR information to design a low-complexity detection scheme for XL-MIMO systems. To further reduce the complexity, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Then, partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the effectiveness of the proposed algorithms which approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

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.

This paper presents an effective few-shot point cloud semantic segmentation approach for real-world applications. Existing few-shot segmentation methods on point cloud heavily rely on the fully-supervised pretrain with large annotated datasets, which causes the learned feature extraction bias to those pretrained classes. However, as the purpose of few-shot learning is to handle unknown/unseen classes, such class-specific feature extraction in pretrain is not ideal to generalize into new classes for few-shot learning. Moreover, point cloud datasets hardly have a large number of classes due to the annotation difficulty. To address these issues, we propose a contrastive self-supervision framework for few-shot learning pretrain, which aims to eliminate the feature extraction bias through class-agnostic contrastive supervision. Specifically, we implement a novel contrastive learning approach with a learnable augmentor for a 3D point cloud to achieve point-wise differentiation, so that to enhance the pretrain with managed overfitting through the self-supervision. Furthermore, we develop a multi-resolution attention module using both the nearest and farthest points to extract the local and global point information more effectively, and a center-concentrated multi-prototype is adopted to mitigate the intra-class sparsity. Comprehensive experiments are conducted to evaluate the proposed approach, which shows our approach achieves state-of-the-art performance. Moreover, a case study on practical CAM/CAD segmentation is presented to demonstrate the effectiveness of our approach for real-world applications.

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.

The research on 6G wireless communications has become a focal point in the global technological competition. For 6G wireless systems, channel modeling is the foundation for the design and deployment of wireless communication systems. With unexplored channel characteristics expected to emerge from new frequency bands and scenarios in 6G wireless channels, predictive channel modeling becomes particularly important. This paper proposes an image-based real-time predictive channel modeling using image processing and machine learning (ML) algorithms, which extracts effective channel information from images and has been validated through channel measurement data and synthetic channel data. The proposed channel model considers large-scale identification and small-scale feature extraction. The former aspect is accomplished by training DeepLabv3+ on our image dataset and classifying the scenarios. For feature extraction, we use VGG-16 as the backbone network and validate it with the 3GPP Uma path loss model for comparison. The results show that predictive channel modeling using segmented images is superior to that of the 3GPP Uma path loss model. Even more, it performs better than the original image-based prediction.

Spatial domain is explored to improve system performance in the sixth generation (6G) wireless communication system. Massive multiple-input multiple-output (MIMO) and orbital angular momentum (OAM) are key technologies to achieve this purpose. In this paper, a novel beam domain channel model (BDCM) for OAM communication systems with massive uniform circular array (UCA) is proposed. The generation of OAM waves proves feasible for UCA. Frequency invariant beamforming (FIBF) is applied to transform OAM basis to plane-wave or spherical-wave basis to make full use of the spatial domain in the multipath scenario. Furthermore, near-field characteristic and mutual coupling (MC) are considered in this model. Statistical properties like temporal autocorrelation function (TACF) and beam spread are analyzed and simulated. Complexity of the proposed BDCM is evaluated. Non-negligible impacts caused by the antenna like configuration and MC on TACF are revealed.

As one of the most promising technologies to fulfill the vision for the sixth-generation (6G) networks, holographic multiple-input multiple-output (MIMO) is a significant technology under research. In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for holographic MIMO communications that includes mutual coupling (MC) is proposed. The holographic MIMO is modeled as an antenna array in compact area with limited element spacing. With comprehensive considerations of the factors of scatterers distribution, geometry relationships, and coupling coefficients, the channel impulse response (CIR) and statistical properties of the proposed channel model including the space-time correlation function (STCF), level crossing rate (LCR), average fade duration (AFD), and instantaneous channel capacity are investigated. Compared to the modified method of the equal area (MMEA) based simulation model, the simulation results demonstrate that the statistical properties of our proposed model confirms the theoretical results well and holographic MIMO has the potential to increase channel capacity.

Artificial intelligence (AI) is a promising solution to achieve channel prediction under limited channel data. In this paper, a novel scatterer density-based predictive channel model is proposed to predict channels in multiple scenarios. By exploring the graph attention networks (GAT) and gated recurrent unit (GRU), the proposed model captures multi-domain information in dynamic scenarios. Besides, it extracts highly space-time correlated data characteristics, captures channel dynamic evolutional patterns, and predicts channels in different scenarios. The space-time graph channel datasets are constructed based on the ray tracing (RT) simulation channels. In the prediction experiments, the proposed method is validated on the datasets to predict channels with good performance. Compared with the 3GPP TR 38.901 channel model, the proposed model obtains more accurate channel statistical properties in different scenarios.

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.

Urban wireless communications are one of most important scenarios in the sixth generation (6G) global communication networks. To provide customized high-quality services for all users in various 6G urban wireless communication scenarios, it is necessary and fundamental to study all kinds of 6G urban wireless channel scenarios and establish corresponding channel models for each scenario. However, existing standardized channel models are insufficient to cover all 6G urban communication scenarios. This paper aims to extend the conventional urban communication scenarios with detailed definitions and environmental parameters to accurately establish corresponding channel models. Specifically, the statistical properties of high speed train (HST) scenario are simulated and compared with those of measurement data. The channel model simulation results match well with the channel measurement data, which demonstrates the correctness of the channel model. Then, by applying the proposed precise scenarios and the corresponding model parameters to the channel model, corresponding channel characteristics can be quickly provided. Finally, the channel characteristics of several urban scenarios are simulated and analyzed.

With the improvement of hardware performance, graphics processing unit (GPU) has been applied to the acceleration of ray tracing (RT) wireless channel modeling. In this paper, a new RT acceleration algorithm based on message passing interface (MPI) and GPU called MPI fused with GPU tree algorithm (MGTree) is proposed. Moreover, the indoor conference room is modeled, and the forward algorithm shooting and bouncing ray (SBR) is used for RT. Considering the conventional reflection and diffraction, the three-dimensional (3D) RT acceleration algorithm based on the unified computing device architecture (CUDA) is used to optimize the conventional algorithm, which combines GPU with MPI to form multi-core distributed computing. Then field information is extracted at the receiving side and the wireless channel simulations are conducted. Through experiments, the new algorithm can effectively improve the upper limit of the acceleration ratio, support more rays, and alleviate the pressure of a single GPU kernel.

Vehicle-to-vehicle (V2V) is an important sixth generation (6G) communication scenario, and we need to measure the performance of V2V communication systems in a controlled and repeatable channel environment. Hence, a corresponding channel emulator is required. In this paper, we first introduce a non-stationary V2V geometry-based stochastic model (GBSM) that takes into account the birth-death process of clusters and adapts to changes of the V2V scenario. Furthermore, the adaptive non-stationary V2V channel emulator with the birth-death process of clusters is constructed on a field-programmable gate array (FPGA) hardware platform. This channel emulator uses the fading channel generation algorithm of sum-of-frequency-modulation (SoFM) and the sine wave generation algorithm of the look-up table (LUT). Moreover, the important statistical properties of the channel under the theoretical simulation and hardware platform emulation are studied through simulations, e.g., temporal autocorrelation function (TACF), spatial cross-correlation function (SCCF), and delay power spectral density (delay PSD). Results show that the proposed channel emulator and the corresponding theoretical channel model fit well.

Nowadays, Reconfigurable intelligent surface (RIS) is regarded as one of the key technology of the sixth generation (6G) wireless communication systems. However, most of the current researches are based on single RIS. In this paper, we propose a three-dimensional (3D) double-RIS-assisted geometry-based stochastic model (GBSM) for massive multiple-input multiple-output (MIMO) communication systems. The channel model also supports the movements of transmitter, receiver, and clusters. For RIS, a new method is proposed for the joint design of reflection coefficients in MIMO channels based on cascaded RISs. In addition, different channel properties in the spatial domain, time domain, and frequency domain are studied to verify the validity and non-stationary properties of the model and explore the improvement of channel performance by double RISs.

Integrated sensing and communication (ISAC) has attracted wide attention as an emerging application scenario for the sixth generation (6G) wireless communication system. In this paper, a novel three-dimensional (3D) non-stationary localization-assisted ISAC geometry-based stochastic model (GBSM) is proposed. The locations of the first-bounce scatterer and last-bounce scatterer in the communication channel can be estimated by the particle filter with the assistance of backscattering sensing. The important channel statistical properties of the proposed channel model are simulated and compared with the ray tracing (RT) results, including the delay spread, azimuth angle of departure/arrival (AAoD/AAoA) spread, and elevation angle of departure/arrival (EAoD/EAoA) spread. The simulation results of the proposed channel model show a good agreement with the RT results, which proves the correctness of the proposed channel model. Utilizing the localization parameters of scatterers, the proposed ISAC channel model can better map the real environment.

Recently, blockchain radio access network (B-RAN) arises as an innovative paradigm for the sixth-generation (6G) wireless communications to build cooperative trust, aggregate wireless resources, and schedule inter- and intra-network tasks among independent network entities. It establishes an open platform based on blockchain to provide diverse wireless services and applications, such as radio access, Internet of Things (IoT), and mobile-edge computing, via trusted interactions with enhanced security and efficiency. As a distinctive feature, B-RAN enables secure and efficient resource sharing and trading by aggregating, pooling, and coordinating resources from multiple resource hosts and owners across subnetworks. Therefore, an implementable architecture along with various functional modules shall be delicately designed. This work aims to establish a unified architecture with enhanced efficiency, security, compatibility, and flexibility for resource sharing and trading in B-RAN. Specifically, we develop a six-layer architecture that incorporates a number of novel features, such as enhanced blockchain structures, secure interaction methods, efficient service mechanisms, and scalable transaction patterns. We design a number of pluggable functional modules in each layer to support diverse functions, services, and applications of resource sharing and trading. Finally, we implement a practical prototype based on the layered architecture for resource-limited devices. Multiple experiments are presented to verify the performance of the proposed architecture from different aspects.

Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications. The occurrence probability of each path is complex and time variant due to fast changeable altitudes of unmanned aerial vehicles and scattering environments. Considering the A2G channels under urban scenarios, this article presents three novel stochastic probability models for the Line-of-Sight (LoS) path, ground specular (GS) path, and building scattering (BS) path, respectively. By analyzing the geometric stochastic information of 3-D scattering environments, the proposed models are derived with respect to the width, height, and distribution of buildings. The effect of the Fresnel zone and altitudes of transceivers are also taken into account. Simulation results show that the proposed LoS path probability model has good performance at different frequencies and altitudes and is also consistent with existing models at the low or high altitude. Moreover, the proposed LoS and non-LoS path probability models show good agreement with the ray-tracing (RT) simulation method.

Progressive deep image compression (DIC) with hybrid contexts is an under-investigated problem that aims to jointly maximize the utility of a compressed image for multiple contexts or tasks under variable rates. In this paper, we consider the contexts of image reconstruction and classification. We propose a DIC framework, called residual-enhanced mask-based progressive generative coding (RMPGC), designed for explicit control of the performance within the rate-distortion-classification-perception (RDCP) trade-off. Three independent mechanisms are introduced to yield a semantically structured latent representation that can support parameterized control of rate and context adaptation. Experimental results show that the proposed RMPGC outperforms a benchmark DIC scheme using the same generative adversarial nets (GANs) backbone in all six metrics related to classification, distortion, and perception. Moreover, RMPGC is a flexible framework that can be applied to different neural network backbones. Some typical implementations are given and shown to outperform the classic BPG codec and four state-of-the-art DIC schemes in classification and perception metrics, with a slight degradation in distortion metrics. Our proposal of a nonlinear-neural-coded and richly structured latent space makes the proposed DIC scheme well suited for image compression in wireless communications, multi-user broadcasting, and multi-tasking applications.

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.

Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal role in reliable communications between UAV and ground terminal. This paper proposes a three-dimensional (3D) non-stationary hybrid model including large-scale and small-scale fading for U2G multiple-input-multiple-output (MIMO) channels. Distinctive channel characteristics under U2G scenarios, i.e., 3D trajectory and posture of UAV, fuselage scattering effect (FSE), and posture variation fading (PVF) are incorporated into the proposed model. The channel parameters, i.e., path loss (PL), shadow fading (SF), path delay, and path angle, are generated incorporating machine learning (ML) and ray tracing (RT) techniques to capture the structure-related characteristics. In order to guarantee the physical continuity of channel parameters such as Doppler phase and path power, the time evolution methods of inter- and intra- stationary intervals are proposed. Key statistical properties, including temporal auto-correction function (ACF), power delay profile (PDP), level crossing rate (LCR), average fading duration (AFD), and stationary interval (SI), are analyzed with the impact of the change of fuselage and posture variation. It is demonstrated that both posture variation and fuselage scattering have crucial effects on channel characteristics. The validity and practicability of the proposed model are verified by comparing the simulation results with the measured ones.

In this paper, a novel three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) channel model for shortwave communication systems is proposed. Three transmission modes, i.e., groundwave, near vertical incident skywave (NVIS), and long-distance skywave are considered to eliminate the blind area and realize the full-coverage for shortwave communication. The ionospheric absorption loss and surface reflection loss during multi-hop transmissions are explored in the proposed channel model. In addition, new massive MIMO channel characteristics including the near-field spherical wavefront effect and spatial non-stationarity are considered. Temporal and frequency non-stationarities are also modeled due to the receiver (Rx) mobility and large relative bandwidth, respectively. The analytical and simulated space cross-correlation function (SCCF), time autocorrelation function (TACF), and frequency correlation function (FCF) of the proposed model are compared. The simulated path loss and singular value spread (SVS) are compared with those of the corresponding channel measurements, illustrating good fittings. In addition, the delay power spectral density (PSD) and Doppler PSD, and channel capacity are also simulated and analyzed. The proposed model can be used as a basis for the design and construction of shortwave communication systems.

The sixth generation (6G) pervasive channel model (6GPCM) can characterize channels for all spectra from the sub-6 GHz band to the visible light communication (VLC) band and all scenarios, such as maritime, (ultra-)massive multiple-input multiple-output (MIMO), and industrial Internet of things (IIoT) communication scenarios in 6G wireless systems. The unified channel model can enable us to analyze channel statistical properties in systems using different scales of antenna arrays, different frequency bands, and different scenarios with different movement speeds. In this paper, we conduct a complete study on space-time-frequency (STF) statistical properties of the 6GPCM. Mathematical derivations and simulations are provided, including STF correlation function (STFCF), spatial/temporal/frequency correlation functions, angular/Doppler/delay power spectral densities (PSDs), root mean square (RMS) angular/Doppler/delay spreads, coherence distance/time/bandwidth, stationary distance/time/bandwidth, and level-crossing rates (LCRs)/average fade durations (AFDs) in STF domains. In addition, we classify these statistical properties according to their definitions and then reveal the complex relationships between them and channel model parameters. This work will lay a solid foundation and offer useful guidelines for research on 6G wireless communication systems.

Massive multiple-input multiple-output (MIMO) channels are distinctly characterized by their array non-stationarity, which has not been considered in the existing beam domain channel models (BDCMs). In this paper, the array non-stationarity of massive MIMO channels is modeled by the spatially consistent visibility regions (VRs) over a large uniform planar array (UPA) in terms of individual multipath components (MPCs). Based on this, a novel three-dimensional (3D) BDCM incorporating the effects of array non-stationarity is proposed. Statistical properties of the proposed BDCM including channel power, power leakage, space-time-frequency correlation function (STF-CF), and beam spread are derived. The ergodic and outage capacities are evaluated. The impacts of array non-stationarity on those statistics and channel capacity are analyzed. Results suggest that the beamwidths or spatial resolutions of the BDCM for different directions are not equal due to the array non-stationarity. This in turn increases the power leakage and correlation between channel elements and reduces the beam domain channel capacity.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications in the next-generation wireless networks. Considering the massive multiple-input multiple-output (MIMO) configuration, this paper proposes a novel three-dimensional (3D) beam domain channel model (BDCM) for UAV communications. Through dividing the large antenna array into several sub-arrays and classifying multipath components as near-field and far-field components, the proposed BDCM takes the spherical wave front (SWF) and array non-stationarity into account. Channel statistical properties including spatial-temporal-frequency correlation function (STF-CF), root-mean-squared (RMS) Doppler spread, beam spread, channel matrix collinearity (CMC), and stationary time interval are derived and simulated for the proposed BDCM. Influences of SFW and non-stationary properties on the statistical properties and system performance are analyzed. Simulation results show that, compared with the equivalent geometry-based stochastic model (GBSM), the proposed BDCM has better temporal correlation, while BDCM and GBSM are equivalent in the system performance evaluation. Furthermore, the performance of the proposed BDCM is evaluated in terms of accuracy, complexity, and pervasiveness. The results show that the proposed BDCM can represent massive MIMO channel properties accurately with low complexity and good compatibility.

In this paper, we study the spectrum efficiency (SE), energy efficiency (EE), and economic efficiency (ECE) for a heterogeneous cellular architecture that separates the indoor and outdoor scenarios for beyond 5G (B5G) wireless communication systems. For outdoor scenarios, massive multiple-input-multiple-output (MIMO) technologies and distributed antenna systems (DASs) at sub-6 GHz frequency bands are used for long-distance communications. For indoor scenarios, millimeter-wave (mmWave) and beamforming communication technologies are deployed at wireless indoor access points (IAPs) to provide high-speed short-range services to indoor users. Mathematical expressions for the system capacity, SE, EE, and ECE are derived using a proposed realistic power consumption model. The results shed light on the fact that the proposed network architecture is able to improve SE and EE by more than three times compared to those conventional network architectures. The analysis of system performance in terms of SE, EE, ECE, and their trade-off results in the observation that the proposed network architecture offers a promising solution for future B5G communication systems.

In this paper, we investigate channel acquisition for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead and increases the number of UTs that can be served. Moreover, we propose a channel prediction method for data segments based on the estimated TB domain channel. To reduce the complexity, we formulate the channel estimation as a statistical inference problem and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm, which can be implemented with low complexity by exploiting the structure of the TB matrix together with the chirp z-transform (CZT). Simulation results demonstrate the superior performance of the proposed channel acquisition approach.

In millimeter wave (mmWave) communication systems with massive multiple-input multiple-output (MIMO) architecture, selecting the antennas contributing most from the candidate array to transmit/receive signals is one of the effective solutions to reduce hardware cost and power consumption while maintaining high spectral efficiency. In this paper, for the communication systems where the base station (BS) equipped with massive MIMO antenna array communicates with multiple single-antenna users, the impact of limited-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) on system capacity is investigated, and two antenna selection (AS) algorithms, namely quantization-aware greedy with square maximum-volume (QAG-SMV) and group-selection (GS) schemes, are proposed to enhance system capacity for the uplink and downlink transmission, respectively. Specifically, after the quantization noise caused by limited-resolution ADCs/DACs is converted to independent additive noise, the problem of maximizing system capacity is formulated. Then, two novel AS schemes are proposed to improve system capacity. Simulation results show that the proposed AS algorithms can obtain higher average system capacity, and the computational complexity is reduced as well.

Benefiting from the fusion of communication and intelligent technologies, network-enabled robots have become important to support future machine-assisted and unmanned applications. To provide high-quality services for robots in wide areas, hybrid satellite-terrestrial networks are a key technology. Through hybrid networks, computation-intensive and latency-sensitive tasks can be offloaded to mobile edge computing (MEC) servers. However, due to the mobility of mobile robots and unreliable wireless network environments, excessive local computations and frequent service migrations may significantly increase the service delay. To address this issue, this paper aims to minimize the average task completion time for MEC-based offloading initiated by satellite-terrestrial-network-enabled robots. Different from conventional mobility-aware schemes, the proposed scheme makes the offloading decision by jointly considering the mobility control of robots. A joint optimization problem of task offloading and velocity control is formulated. Using Lyapunov optimization, the original optimization is decomposed into a velocity control subproblem and a task offloading subproblem. Then, based on the Markov decision process (MDP), a dual-agent reinforcement learning (RL) algorithm is proposed. The convergence and complexity of the improved RL algorithm are theoretically analyzed, and the simulation results show that the proposed scheme can effectively reduce the offloading delay.

The integrated sensing and communication (ISAC) refers to the integration of radio sensing and wireless communications to realize the multiplexing of space, time, and frequency resources. In the sixth generation (6G) wireless networks, ISAC is considered as one of the most promising technologies. In this paper, a novel ISAC channel model combining forward and backward scattering is proposed. In addition to the non-stationarity caused by motions, the correlations between sensing and communication channels are investigated. The channel characteristics of sensing such as forward scattering and backward scattering are introduced into the communication channel model. Utilizing the correlations between sensing and communication channels, the communication channel model is divided into line-of-sight (LOS), forward scattering, and backward scattering components. These three components are summed according to probability weighting to obtain a more accurate channel model for sensing assisted communication systems. Moreover, important statistical properties of the proposed ISAC channel model are derived and simulated. The analytical and simulation results match well, demonstrating the correctness of derivations and simulations. Some derived/simulated statistical properties are verified by corresponding measurement data, which indicates the utility of the proposed ISAC channel model.

In this paper, we investigate channel estimation for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead. To reduce the complexity, we are able to formulate the channel estimation as a sparse signal recovery problem due to the channel sparsity in the TB domain and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm. Simulation results demonstrate the superior performance of the proposed channel estimation approach.

In the sixth generation (6G) wireless communication systems, the employment of ultra-massive multiple-input multiple-output (MIMO) technology can provide significant performance improvement. Meanwhile, it introduces some new channel propagation characteristics. For 6G system design and evaluation, a comprehensive ultra-massive MIMO channel characteristic analysis and an accurate channel modeling methodology are indispensable. In this paper, we emphasize on the joint correlation property analysis between the transmitter (Tx) and receiver (Rx) sides of ultra-massive MIMO channels. Firstly, the Weichselberger model generated based on the mutual coupling matrix of Tx and Rx eigenvectors is introduced and a geometrical based stochastic model (GBSM) is presented. Secondly, an urban ultra-massive MIMO channel measurement campaign at 5.3 GHz is conducted. By splitting the ultramassive array into several subarrays, the variances of mutual coupling matrices and corresponding power angular spectrums (PASs) along different subarrays are observed. They show great consistency and exhibit evident spatial non-stationarity. Lastly, channel capacities simulated by the Weichselberger model and twin cluster GBSM are compared with that calculated using the sub-channel measurement data. Through the joint optimization of mutual coupling matrix and capacity, GBSM can provide better fitness of real channel measurement data. We also verify that new channel characteristics at Tx/Rx side and between both sides should all be included in future ultra-massive MIMO channel modeling.

Beamforming can flexibly change the beamwidth and beam direction to achieve the best system performance in the sixth generation (6G) mobile communication. The asymmetric communication systems based on all-digital beamforming have huge advantages, such as more user access in uplink and better signal-to-noise ratio (SNR) in downlink. For asymmetric communication systems, accurate channel models are especially important. In this paper, we apply the space-time-frequency non-stationary channel model to the asymmetric channels by generating correlated parameters in the uplink and downlink with different antenna patterns. Then we investigate the scattering distributions and statistical properties with different antenna array patterns. The simulation results indicate that the scattering distributions and correlation functions are greatly affected by antenna patterns.

As a promising key technology for the sixth generation (6G) wireless communication systems, the reconfigurable intelligent surface (RIS) is able to intelligently control the propagation environments. In order to investigate the properties of RIS-assisted channels, related channel measurements are necessary. However, the channel parameter estimation (CPE) work in RIS-assisted channel measurements is challenging since the RIS can only tune part of the channel. In this paper, we present a novel channel sounding procedure firstly where channels are observed through different RIS transmission modes. Then a space-alternating generalized expectation-maximization (SAGE) based CPE scheme for 6G RIS-assisted channel measurements is proposed, which is realized by the multipath components (MPCs) tracking and detection, the maximum likelihood (ML) estimation, and other procedures. It is capable of detecting whether MPCs interact with RIS and estimating all important channel parameters, including the angle parameters at the RIS side. Finally, the proposed scheme is evaluated through synthetic channels. The impacts of the RIS size, the number of transmission modes, and the phase quantization on its performance are investigated. Simulation results show a good performance of the proposed scheme.

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.

Reconfigurable intelligent surface (RIS) is a potential solution to cost-effectively and energy-efficiently improve the performance of the sixth generation (6G) wireless communication system. In this paper, a method to realize the function of RIS in three dimensional (3D) ray tracing simulator is proposed and applied to two indoor scenarios where there is no line-of-sight (LoS) path between transmitter (Tx) and receiver (Rx). To verify the effectiveness of the proposed method, channel characteristics including received power, channel capacity, and angular power spectral density (PSD) are studied. To analyze the influence of different RIS deployment locations on coverage enhancement, simulations with different setups are carried out at 5.4 GHz. Besides, applicability of RIS in millimeter wave (mm Wave) is also demonstrated in ray tracing simulator. Numerical results show a significant coverage improvement by adding RIS into current wireless communication system and reveal useful insights for optimal RIS deployment in indoor scenarios.

With the development and popularization of massive multiple-input multiple-output (MIMO) technology, the spatial non-stationarity and mutual coupling (MC) of massive MIMO channels are significant. Shannon channel capacity formula assumes that the signal is wide-sense stationary (WSS), which is not applicable in non-stationary channel. In this paper, we investigate the spatial non-stationary channel capacity of massive MIMO channel model with MC. The spatial statistical properties and stationary interval (SI) are simulated and the channel capacity is derived. Compared to the WSS channel capacity, the non-stationary channel capacity is greatly improved. There is also capacity increase in channel with MC due to the decorrelation of channel coefficients. The massive MIMO channel measurement can verify the accuracy of the channel model and the correctness of the spatial non-stationary channel capacity calculation.

Underground space is relative to above-ground space, mainly including underground shopping malls, underground parking lots, underground mines, etc. This paper focuses on the characteristics of wireless transmission in underground mines. In this paper, a new hybrid multimode waveguide model is proposed. Considering the movement of the receiving antenna, the proposed model combines the roughness loss and tilt loss of walls, and provides an analytical expression for the received power at any position in a tunnel. Using the proposed model, various tunnel factors are analyzed, such as the operating frequency, the cross section size, etc. It is found that, in underground mine, the curve of signal power can be divided into near field and far field. The operating frequency and the cross section size can affect the received signal power significantly. For different transmitting antenna positions, the same change of the receiving antenna position has different effects. The dielectric constant has little effect on received signal power.

Compared with conventional passive channel modeling, artificial intelligence (AI) based channel models show great advantages in solving real-time prediction problems in wireless communications. In this paper, a generative adversarial network (GAN) and long short-term memory (LSTM) based channel prediction framework is proposed to model indoor wireless channels. By using GAN and LSTM, the model not only enriches the channel data but also achieves the sequence prediction, which can solve the problem of the shortage of training data and prediction channels in the space domain. The prediction performance is evaluated by comparing the root mean square error (RMSE) and mean absolute percentage error (MAPE) of measured data and predicted data. By comparing the statistical properties of the channel measurement data and of the synthetic data, it can be found that the proposed model can predict unknown information in the space domain.

Industrial Internet of things (IIoT) is a typical application scenario in the sixth generation (6G) mobile networks. IIoT scenarios involve dense multipath components (MPCs) and nonnegligible scattering components caused by many moving objects. In this paper, image method (IM) is applied and extended to analyze the channel properties of IIoT. Directive model is modified to adapt to IM. The moving patterns of objects are defined and their snapshots are established along the time axis. Multiple-input multiple-output (MIMO) is supported as it is widely applied in IIoT. A smart warehouse scenario equipped with moving handcars is selected to analyze the channel of IIoT scenario. Parameters such as azimuth angle, elevation angle, angular spread, power, and delay spread of received rays are calculated and compared with those generated by quasi-deterministic (Q-D) model traditionally used in IM. Maximum and minimum Doppler shifts, received power, and delay spread are calculated along the time axis to analyze the influence of mobility to channel properties. The results show that directive model generates scattering components more realistically compared with Q-D model, and that the channel properties may experience sudden changes due to the line-of-sight (LoS) component being obstructed.

In this paper, we consider several kinds of fading channels which are used to describe the multipath effect, including Rayleigh, Rice, Nakagami, Nakagami-Hoyt, and $\kappa - \mu$. The communication performance indicators for each kind of channel, such as instantaneous signal-to-noise rate (SNR), outage probability (OP), and average error rate are analyzed and some novel expressions are derived. Among them, the OP is derived by calculating the cumulative distribution function (CDF) of the instantaneous SNR, and the average error rate is derived from the probability density function (PDF) of the instantaneous SNR with integral operation, by using the direct method or the moment generating function method. The most obvious result is that the increase of channel parameters $m, \kappa, \mu$, and q leads to lower OP and lower average error rate. Moreover, the same results can be obtained by taking the same special case of different generalized fading channels.

Terahertz (THz) communication and the application of massive multiple-input multiple-output (MIMO) technology are significant for the sixth generation (6G) communication systems. In this paper, we employ the shooting and bouncing ray (SBR) method integrated with GPU acceleration technology to model THz and massive MIMO channel. The results of ray tracing (RT) simulation in this paper, i.e., angle of departure (AoD), angle of arrival (AoA), and power delay profile (PDP) under the frequency band supported by the commercial RT software Wireless Insite (WI) are in agreement with those produced by WI. Based on the Kirchhoff scattering effect on material surfaces and atmospheric absorption loss showing at THz frequency band, the modified propagation models of Fresnel reflection coefficients and free-space attenuation are consistent with the measured results. For massive MIMO, the channel capacity and the stochastic power distribution are analyzed. The results indicate the applicability of SBR method for building deterministic models of THz and massive MIMO channels with extensive functions and acceptable accuracy.

Extremely large antenna array (ELAA) is one of the most promising technologies in 5G-Advanced (5.5G), which is equipped with hundreds or even thousands of antennas at base station (BS), to meet the requirements of higher rate and spectral efficiency. To verify the characteristics of ELAA channel, including spatial non-stationary, near-field spherical wavefront effect and channel hardening, a measurement campaign is performed, which is equipped with 128-element antenna array at BS on 5.3 GHz with 160 MHz bandwidth in a typical urban macro (UMa) scenario. Based on this, the correlation of power angular spectrums (PAS) between sliding windows of antenna array and the standard deviation (STD) of angular spreads (AS) are defined as the metrics to measure the level of variations of ELAA channel along with the antenna array. The correlation goes slightly down while the distance of two subarrays of the BS is larger than 0.6 m and the STD of AS changes in the range from 1° to 8°. The STD of normalized channel gain is also given to measure the channel hardening. Due to the power imbalance along with the antenna array, it decreases non-linearly with the increasing number of antennas. Meanwhile, the comparisons between measurement and simulations by 3GPP TR 3S. 901 and modified COST 2100 are given to show the necessity of the modifications on the current channel model. All of above analyses show that the modified COST 2100 channel model has a better match with the ELAA measurement.

Ray tracing is an efficient channel modeling method. However, the traditional ray tracing method has high computation complexity. To solve this problem, an improved bounding volume hierarchies (BVH) algorithm is proposed in this paper. Based on surface area heuristic (SAH) and spatial distance, the proposed algorithm can effectively reduce the number of unnecessary intersection tests between ray and triangular facets. In addition, the algorithm fully considers the influence of ray action range, which can not only make up for the defects of spatial division based on uniform grid method and k-dimensional (KD) tree, but also solve the problem of unsatisfactory spatial division based on traditional BVH algorithm. The simulation results show that compared with the traditional BVH algorithm, the proposed algorithm can improve the computation efficiency by 20% to 35% while ensuring the computation accuracy.

Due to the indoor none-line-of-sight (NLoS) propagation and multi-access interference (MAI), it is a great challenge to achieve centimeter-level positioning accuracy in indoor scenarios. However, the sixth generation (6G) wireless communications provide a good opportunity for the centimeter-level positioning. In 6G, the millimeter wave (mmWave) and terahertz (THz) communications have ultra-broad bandwidth so that the channel state information (CSI) will have a high resolution. In this paper, a weighted random forest (WRF) based indoor positioning algorithm using CSI-based channel fingerprint feature is proposed to achieve high-precision positioning for 6G indoor communications. In addition, ray-tracing (RT) is used to improve the efficiency of establishing channel fingerprint database. The simulation results demonstrate the accuracy and robustness of the proposed algorithm. It is shown that the positioning accuracy of the algorithm is stable within 6 cm in different indoor scenarios when the channel fingerprint database is established at 0.2 m intervals.

Three channel models in the fifth generation (5G) and beyond fifth generation (B5G) communication systems, i.e., QuaDRiGa, more general 5G channel model (MG5GCM), and B5G channel model (B5GCM) are investigated in this paper. They are geometry-based stochastic models (GBSMs) and can be applied to various frequency bands, including sub-6 GHz, millimeter wave (mmWave) bands, and terahertz (THz) bands. We analyze time evolution, space-time-frequency non-stationarity, and spherical wave-front in channel modeling. Based on channel impulse responses (CIRs), temporal autocorrelation functions (ACFs), spatial cross-correlation function (CCFs), and non-stationary multiple-input multiple-output (MIMO) channel capacities are simulated. The results demonstrate that the statistical properties of the simulation model are in good agreement with the statistical properties of the theoretical model, indicating the correctness of three channel models. The proposed novel channel capacity method is closer to channel measurement data than traditional capacity in non-stationary channel. The capacity of non-stationary MIMO channel is higher than the capacity of a WSS channel.

Nowadays, the demand for data traffic increases dramatically, which has posed significant challenge on the current infrastructures of cellular networks. To tackle the challenge, the massive multiple-input-multiple-output (MIMO) technology has been proposed, which can improve spectral efficiency greatly, and attract a lot of attentions in industry and academic. In spite of the great potential benefits of massive MIMO, the property of massive MIMO channel with hundreds or thousands of antennas is still an open problem. In this paper, we try to look into deeply the spatial correlation of the channels for extremely large aperture array (ELAA) with some measurements. Specifically, we analyze the correlation between two users’ channels. We also propose a novel metric to characterize the performance of multiuser MIMO (MU-MIMO), which will be used to analyze the MU-MIMO channel later. At last, we study the sinlge-user MIMO (SU-MIMO) channel in terms of the distribution of singular values and also establish the connection between the spread of singular values and UE correlation under special case.

Compared with image method (IM) based ray tracing (RT), shooting and bouncing ray (SBR) method is characterized by fast speed but low accuracy. In this paper, an iterative precise algorithm based on equiangular division is proposed to make rough paths accurate, allowing SBR to calculate exact channel information. Different ray launching methods are compared to obtain a better launching method. By using equiangular division, rays are launched more uniformly from transmitter (Tx) compared with the current equidistant division method. With the proposed iterative precise algorithm, error of angle of departure (AOD) and angle of arrival (AOA) is below 0.01 degrees. The relationship between the number of iterations and error reduction is also given. It is illustrated that the proposed method has the same accuracy as IM by comparing the power delay profile (PDP) and angle distribution of paths. This can solve the problem of low accuracy brought by SBR.

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.

Orbital angular momentum (OAM) is regarded as a key technology of the sixth generation (6G) wireless communication systems. The vortex wave is a typical application of OAM. In this paper, a novel time-varying three-dimensional (3D) geometry-based stochastic model (GBSM) for a multiple-input multiple-output (MIMO) system employing the vortex wave is proposed. This model supports all kinds of OAM antennas that generate the vortex wave and introduces a spiral phase term to represent the helical wavefront of the vortex wave. To better reveal characteristics of the OAM-based MIMO system, some important statistical properties like temporal autocorrelation function (ACF) and spatial cross-correlation function (CCF) are derived and analyzed. Non-negligible impacts on the temporal ACF caused by OAM antennas are revealed. The results illustrate that the OAM-based MIMO system can help decrease spatial CCF, especially in the environment that angular spreads are small. Characteristics analyzed in this paper are important for future OAM communication systems.

Considering great benefits brought by massive multiple-input–multiple-output (MIMO) technologies in the Internet of Things (IoT), it is of vital importance to analyze new massive MIMO channel characteristics and develop corresponding channel models. In the literature, various massive MIMO channel models have been proposed and classified with different but confusing methods, i.e., physical versus analytical method and deterministic versus stochastic method. To have a better understanding and usage of massive MIMO channel models, this work summarizes different classification methods and presents an up-to-date unified classification framework, i.e., artificial intelligence (AI)-based predictive channel models and classical nonpredictive channel models, which further clarify and combine the deterministic versus stochastic and physical versus analytical methods. Furthermore, massive MIMO channel measurement campaigns are reviewed to summarize new massive MIMO channel characteristics. Recent advances in massive MIMO channel modeling are surveyed. In addition, typical nonpredictive massive MIMO channel models are elaborated and compared, i.e., deterministic models and stochastic models, which include the correlation-based stochastic model (CBSM), geometry-based stochastic model (GBSM), and beam-domain channel model (BDCM). Finally, future challenges in massive MIMO channel modeling are given.

Reconfigurable intelligent surfaces (RISs) are 2-D metasurfaces, which can intelligently manipulate electromagnetic waves by low-cost near passive reflecting elements. RIS is viewed as a potential key technology for the sixth-generation (6G) wireless communication systems mainly due to its advantages in tuning wireless signals, thus smartly controlling propagation environments. In this article, we aim at addressing channel characterization and modeling issues of RIS-assisted wireless communication systems. First, the concept, principle, and potential applications of RIS are given. An overview of RIS-based channel measurements and experiments is presented by classifying frequency bands, scenarios, system configurations, RIS constructions, experiment purposes, and channel observations. Then, RIS-based channel characteristics are studied, including reflection and transmission, the Doppler effect and multipath fading mitigation, channel reciprocity, channel hardening, rank improvement, far field, near field, and so on. RIS-based channel modeling works are investigated, including large-scale path loss models and small-scale multipath fading models. Finally, future research directions related to RIS-assisted channels are also discussed.

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.

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then determine the cluster types based on their evolution patterns. We classify the determined clusters into line-of-sight, single-bounce reflections from static interaction objects (IOs), single-bounce reflections from mobile IOs, multiple-bounce reflections, and density multipath components (DMCs). Particularly, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This paper presents the complete parameterization of the channel model. We validate this model by comparing the delay spread and the angular spreads of arrival/departure obtained from the proposed model with the measurement data.

As the requirements related to resolution or the data rate increase, most angle-of-arrival (AoA) estimation problems can be assumed to be under a broadband signal model. In this paper, AoA estimation using a nonuniform planar array (NUPA), which aims to resolve more signals and reduce mutual coupling, is considered under a broadband signal model. The analysis is conducted in both the space and frequency domains, and a broadband co-array is proposed to improve the performance of the AoA estimation. The broadband co-array is generated as follows: First, the received broadband signal is decomposed into several narrowband signals, and the samples from different frequency bins are transformed to space-domain samples to generate a virtual array. Then, the second-order statistics of the virtual array are used to generate the broadband co-array. The virtual array can decrease the number of element pairs with small interelement spacing to reduce mutual coupling in AoA estimations. With the help of the virtual array, the following broadband co-array can greatly increase the degrees of freedom, resulting in more resolvable signals and lower Cramer-Rao bounds of AoA estimation. An optimization method based on sparse recovery is proposed to locate the array. The simulation results confirm the AoA estimation performance achieved by the designed NUPA.

In this paper, a novel space-time non-stationary three-dimensional (3D) wideband massive multiple-input multiple-output (MIMO) channel model is proposed. We then propose a ray-level process to model the spatial-temporal evolution of individual multipath components (MPCs), including near-field effects and (dis)appearance, and cluster-level large-scale fading. The proposed evolution process can flexibly control rays’ lifespans and smoothness of (dis)appearance in both space and time domains. In addition, we propose an improved Rayleigh-distance criterion to determine the most adequate wavefront for each cluster and ray. Existing models can easily implement the proposed criterion and make a more efficient use of computation resources. Also, a Gamma-Poisson mixture distribution is introduced to model the distribution of the number of clusters when multiple locations of the mobile station are considered. Key statistical properties of the channel, including the autocorrelation function (ACF), Doppler power spectral density (PSD), spatial cross-correlation function (S-CCF), and frequency correlation function (FCF), are derived and the impact of the ray-level evolution process on them is analyzed. We demonstrate the correctness of the derived statistical properties through numerical and simulation results.

This paper investigates robust downlink precoding for three-dimensional (3D) massive multi-input multi-output (MIMO) configuration with matrix manifold optimization. Starting witha posteriorichannel model, we formulate the robust precoder design to maximize an upper bound of ergodic weighted sum-rate under a total power budget. We derive the generalized eigenvector structure for optimal precoder with matrix manifold optimization. However, since the precoding of multiple users is coupled in the structure, we maximize the objective function for each user in alternation and prove the solution of each individual problem is the generalized eigenvector corresponding to the maximum generalized eigenvalue. In accordance with this, we design an iterative algorithm and present its convergence analysis. Furthermore, we propose a Riemannian conjugate gradient (RCG) method to solve the generalized eigenvalue problem (GEP) for higher efficiency in the precoder design algorithm.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

The visible light communication (VLC) technology has attracted much attention in the research of the sixth generation (6G) communication systems. In this paper, a novel three dimensional (3D) space-time-frequency non-stationary geometry-based stochastic model (GBSM) is proposed for indoor VLC channels. The proposed VLC GBSM can capture unique indoor VLC channel characteristics such as the space-time-frequency non-stationarity caused by large light-emitting diode (LED) arrays in indoor scenarios, long travelling paths, and large bandwidths of visible light waves, respectively. In addition, the proposed model can support special radiation patterns of LEDs, 3D translational and rotational motions of the optical receiver (Rx), and can be applied to angle diversity receivers (ADRs). Key channel properties are simulated and analyzed, including the space-time-frequency correlation function (STFCF), received power, root mean square (RMS) delay spread, and path loss (PL). Simulation results verify the space-time-frequency non-stationarity in indoor VLC channels. In addition, the influence of light source radiation patterns, receiver rotations, and ADRs on channel characteristics have been investigated. Finally, the accuracy and practicality of the proposed model are validated by comparing the simulation result of channel 3dB bandwidth with the existing measurement data. The proposed channel model will play a supporting role in the design of future 6G VLC systems.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. The steering vectors vary across different subcarriers due to the effect of the propagation delay across the largescale antenna array. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

Industrial automation is one of the key application scenarios of the fifth generation (5G) wireless communication network. The high requirements of industrial communication systems for latency and reliability lead to the need for industrial channel models to support massive multiple-input multiple-output (MIMO) and millimeter wave communication. In addition, due to the complex environment, huge communication equipment, and numerous metal scatterers, industrial channels have special rich dense multipath components (DMCs). Considering these characteristics, a novel three dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for industrial automation wireless channel is proposed in this paper. Channel characteristics including the transfer function, time-varying space-time-frequency correlation function (STFCF), and root mean square (RMS) delay spread, model parameters including delay scaling factor and power decay factor are studied and analyzed. Besides, according to the indoor factory scenario classification of the 3rd Generation Partnership Project (3GPP) TR 38.901, two sub-scenarios considering the clutter density are simulated. Simulated cumulative distribution functions (CDFs) of RMS delay spread show a good consistency with the measurement data.

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 this paper, we conduct wireless channel measurements in indoor corridor scenarios at 2.4, 5 and 6 GHz bands with bandwidth of 320 MHz. The measurement results of channel characteristics at different frequency bands such as average power delay profile (APDP), path loss (PL), delay spread (DS), and Ricean K factor (KF) are presented and analyzed. It is found that the PL exponent (PLE) and PL offset 3 in the floating-intercept (FT) model tend to increase with the increase of frequency. The DS and KF values of the three frequency bands in line of sight (LOS) scenario are basically the same. These results are significant for the design of communication systems.

Underwater acoustic (UWA) communication plays a key role in the process of exploring and studying the ocean. In this paper, a modified non-stationary wideband channel model for UWA communication in shallow water scenarios is proposed. In this geometry-based stochastic model (GBSM), multiple motion effects, time-varying angles, distances, clusters' locations with the channel geometry, and the ultra-wideband property are considered, which makes the proposed model more realistic and capable of supporting long time/distance simulations. Some key statistical properties are investigated, including temporal autocorrelation function (ACF), power delay profile (PDP), average delay, and root mean square (RMS) delay spread. The impacts of multiple motion factors on temporal ACFs are analyzed. Simulation results show that the proposed model can mimic the non-stationarity of UWA channels. Finally, the proposed model is validated with measurement data.

As a potential development direction of future transportation, the vacuum tube ultra-high-speed train (UHST) wireless communication systems have newly different channel characteristics from existing high-speed train (HST) scenarios. In this paper, a three-dimensional non-stationary millimeter wave (mmWave) geometry-based stochastic model (GBSM) is proposed to investigate the channel characteristics of UHST channels in vacuum tube scenarios, taking into account the waveguide effect and the impact of tube wall roughness on channel. Then, based on the proposed model, some important time-variant channel statistical properties are studied and compared with those in existing HST and tunnel channels. The results obtained show that the multipath effect in vacuum tube scenarios will be more obvious than tunnel scenarios but less than existing HST scenarios, which will provide some insights for future research on vacuum tube UHST wireless communications.

A general three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) geometry-based stochastic model (GBSM) for the sixth generation (6G) communication systems is proposed in the paper. The novelty of the model is that the model is designed to cover a variety of channel characteristics, including space-time-frequency (STF) non-stationarity, spherical wavefront, spatial consistency, channel hardening, etc. Firstly, the introduction of the twin-cluster channel model is given in detail. Secondly, the key statistical properties such as space-time-frequency correlation function (STFCF), space cross-correlation function (CCF), temporal autocorrelation function (ACF), frequency correlation function (FCF), and performance indicators, e.g., singular value spread (SVS), and channel capacity are derived. Finally, the simulation results are given and consistent with some measurements in relevant literatures, which validate that the proposed channel model has a certain value as a reference to model massive MIMO channel characteristics.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

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.

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose an HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions toward establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation ubiquitous Internet of Things.

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.

With the rapid development of marine activities, there has been an increasing number of Internet-of-Things (IoT) devices on the ocean. This leads to a growing demand for high-speed and ultrareliable maritime communications. It has been reported that a large performance loss is often inevitable if the existing fourth-generation (4G), fifth-generation (5G), or satellite communication technologies are used directly on the ocean. Hence, conventional theories and methods need to be tailored to this maritime scenario to match its unique characteristics, such as dynamic electromagnetic propagation environments, geometrically limited available base station (BS) sites and rigorous service demands from mission-critical applications. Toward this end, we provide a survey on the demand for maritime communications enabled by state-of-the-art hybrid satellite-terrestrial maritime communication networks (MCNs). We categorize the enabling technologies into three types based on their aims: 1) enhancing transmission efficiency; 2) extending network coverage; and 3) provisioning maritime-specific services. Future developments and open issues are also discussed. Based on this discussion, we envision the use of external auxiliary information, such as sea state and atmosphere conditions, to build up an environment-aware, service-driven, and integrated satellite-air-ground MCN.

In fifth generation (5G) and beyond Internet of Things (IoT), it becomes increasingly important to serve a massive number of IoT devices outside the coverage of terrestrial cellular networks. Due to their own limitations, unmanned aerial vehicles (UAVs) and satellites need to coordinate with each other in the coverage holes of 5G, leading to a cognitive satellite-UAV network (CSUN). In this paper, we investigate multi-domain resource allocation for CSUNs consisting of a satellite and a swarm of UAVs, so as to improve the efficiency of massive access in wide areas. Particularly, the cell-free on-demand coverage is established to overcome the cost-ineffectiveness of conventional cellular architecture. Opportunistic spectrum sharing is also implemented to cope with the spectrum scarcity problem. To this end, a process-oriented optimization framework is proposed for jointly allocating subchannels, transmit power and hovering times, which considers the whole flight process of UAVs and uses only the slowly-varying large-scale channel state information (CSI). Under the on-board energy constraints of UAVs and interference temperature constraints from UAV swarm to satellite users, we present iterative multi-domain resource allocation algorithms to improve network efficiency with guaranteed user fairness. Simulation results demonstrate the superiority of the proposed algorithms. Moreover, the adaptive cell-free coverage pattern is observed, which implies a promising way to efficiently serve wide-area IoT devices in the upcoming sixth generation (6G) era.

To achieve space-air-ground-sea integrated communication networks for future sixth generation (6G) communications, unmanned aerial vehicle (UAV) communications applying to maritime scenarios serving as mobile base stations have recently attracted more attentions. The UAV-to-ship channel modeling is the fundamental for the system design, testing, and performance evaluation of UAV communication systems in maritime scenarios. In this paper, a novel non-stationary multi-mobility UAV-to-ship channel model is proposed, consisting of three kinds of components, i.e., the line-of-sight (LoS) component, the single-bounce (SB) components resulting from the fluctuation of sea water, and multi-bounce (MB) components introduced by the waveguide effect over the sea surface. In the proposed model, the UAV as the transmitter (Tx), the ship as the receiver (Rx), and the clusters between the Tx and Rx, can be seen as moving with arbitrary velocities and arbitrary directions. Then, some typical statistical properties of the proposed UAV-to-ship channel model, including the temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectrum density (PSD), delay PSD, angular PSD, stationary interval, and root mean square (RMS) delay spread, are derived and investigated. Finally, by comparing with the available measurement data, the accuracy of proposed channel model is validated.

In this paper, a novel three-dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) is proposed for the sixth generation (6G) terahertz (THz) wireless communication systems. The proposed THz channel model is very general having the capability to capture different channel characteristics in multiple THz application scenarios such as indoor scenarios, device-to-device (D2D) communications, ultra-massive multiple-input multiple-output (MIMO) communications, and long traveling paths of users. Also, the generality of the proposed channel model is demonstrated by the fact that it can easily be reduced to different simplified channel models to fit specific scenarios by properly adjusting model parameters. The proposed general channel model takes into consideration the non-stationarities in space, time, and frequency domains caused by ultra-massive MIMO, long traveling paths, and large bandwidths of THz communications, respectively. Statistical properties of the proposed general THz channel model are investigated. The accuracy and generality of the proposed channel model are verified by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding channel measurements.

In this study, we consider a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system with a mixed analog-to-digital converter (mixed-ADC) and hybrid beamforming architecture, in which antenna selection is applied to achieve intelligent assignment of high- and low-resolution ADCs. Both exact and approximate closed-form expressions for the uplink achievable rate are derived in the case of maximum-ratio combining reception. The impacts on the achievable rate of user transmit power, number of radio frequency chains at a base station, ratio of high-resolution ADCs, number of propagation paths, and number of quantization bits are analyzed. It is shown that the user transmit power can be scaled down inversely proportional to the number of antennas at the base station. We propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, the energy efficiency is investigated, and an optimal tradeoff between the achievable rate and power consumption is discussed. Our results will provide a useful reference for the study of mixed-ADC multi-cell mmWave massive MIMO systems with antenna selection.

The clustered delay line channel model, in which each cluster consists of many rays, is widely used for link-level evaluations in mobile communications. Multiple parameters of each ray, including the delay, amplitude, cross-polarization ratio (XPR), initial phases of four polarization combinations, and the azimuth and elevation angles of arrival and departure, must be known and are measured using a channel sounder. The number of rays in every cluster is usually greater than the number of elements in the antenna array of the channel sounder, which represents a challenging issue in multipolarized channel measurements. Based on the broadband extended array response of an electromagnetic vector antenna array, a new subspace estimation method is proposed to resolve a large number of rays. The inter-element spacing of the array can be greater than half the carrier wavelength, which reduces inter-element coupling and simplifies the array design, especially for millimeter-wave bands. The delay of each cluster is first estimated using the reference antenna element. Next, two-dimensional angles of every ray are estimated using the classic rank-deficient multiple signal classification algorithm. Finally, the initial phases, XPR, and amplitude of every ray are estimated. Simulation results validate the proposed method.

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade the judgment on users' channel state information (CSI) accuracy, thus affecting the user pairing processing and suppressing the superiority of the NOMA scheme. Inspired by the advantages of machine learning (ML) algorithms, we propose an improved support vector machine (SVM) scheme to reduce the inappropriate user pairing risks and enhance the performance of NOMA based satellite networks with imperfect CSI. Particularly, a genetic algorithm (GA) is employed to optimize the regularization and kernel parameters of the SVM, which effectively improves the classification accuracy of the proposed scheme. Simulations are provided to demonstrate that the performance of the proposed method is better than that with random user paring strategy, especially in the scenario with a large number of users.

In this paper, we coin a new concept of tensor-computing, which is based on tensor theory and designed for future sixth generation (6G) wireless communication systems. Two different types of tensors, namely spectrum-tensor and system-tensor, are defined and analysed to develop a new spectrum usage framework for 6G. The spectrum-tensor encapsulates high dimensional spectrum big data into the format of a compact tensor. The system-tensor summarizes key system performance, including data rate, bandwidth, delay, spectral efficiency, and energy efficiency, into a multi-dimension tensor. The concepts of spectrum-tensor and system-tensor enable unique tensor-based computing and analysis with the help of high efficiency tensor-computing tools, such as tensor completion and tensor decomposition. In the new spectrum usage framework, a value-based spectrum fusion scheme is designed. The maximum system value is achieved under the constraint that the individual value of single user should be guaranteed. The proposed tensor-computing framework builds a bridge between 6G wireless functions with real-world high dimension data processing tools, such as TensorFlow and Tensor Processing Unit (TPU). The authors hope this paper will shine a beam of tensor theory in and open a new research field of tensor-computing for future 6G wireless communications.

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.

Based on the geometry and ray tracing (RT) theory, a millimeter wave (mmWave) channel model and parameter computation method for unmanned aerial vehicle (UAV) assisted air-to-ground (A2G) communications are proposed in this paper. In order to speed up the parameter calculation, a reconstruction process of scene database on the original digital map is developed. Moreover, the effects of reconstruction accuracy on the channel parameter and characteristic are analyzed by extensive simulations at 28 GHz under the campus scene. The simulation and analysis results show that the simplified database can save up to 50% time consumption. However, the difference of statistical properties is slight in the campus scenario.

Energy efficiency (EE) is a major performance metric for fifth generation (5G) and beyond 5G(B5G) wireless communication systems, especially for ultra dense networks. This paper proposes an end-to-end (e2e) power consumption model and studies the energy efficiency for a heterogeneous B5G cellular architecture that separates the indoor and outdoor communication scenarios in ultra dense networks. In this work, massive multiple-input-multiple-output (MIMO) technologies at conventional sub-6 GHz frequencies are used for long-distance outdoor communications. Light-Fidelity (LiFi) and millimeter wave (mmWave) technologies are deployed to provide a high data rate service to indoor users. Whereas, in the referenced non-separated system, the indoor users communicate with the outdoor massive MIMO macro base station directly. The performance of these two systems are evaluated and compared in terms of the total power consumption and energy efficiency. The results show that the network architecture which separates indoor and outdoor communication can support a higher data rate transmission for less energy consumption, compared to non-separate communication scenario. In addition, the results show that deploying LiFi and mmWave IAPs can enable users to transmit at a higher data rate and further improve the EE.

A novel beam domain channel model (BDCM) for massive multiple-input multiple-output (MIMO) communication systems has been proposed in this paper. The near-field effect and spherical wavefront are firstly assumed in the proposed model, which is different from the conventional BDCM for MIMO based on the far-field effect and plane wavefront assumption. The proposed novel BDCM is the transformation of an existing geometry-based stochastic model (GBSM) from the antenna domain into beam domain. The space-time non-stationarity is also modeled in the novel BDCM. Moreover, the comparison of computational complexity for both models is studied. Based on the numerical analysis, comparison of cluster-level statistical properties between the proposed BDCM and existing GBSM has shown that there exists little difference in the space, time, and frequency correlation properties for two models. Also, based on the simulation, coherence bandwidths of the two models in different scenarios are almost the same. The computational complexity of the novel BDCM is much lower than the existing GBSM. It can be observed that the proposed novel BDCM has similar statistical properties to the existing GBSM at the cluster-level. The proposed BDCM has less complexity and is therefore more convenient for information theory and signal processing research than the conventional GBSMs.

In this paper, a practical model for non-stationary Vehicle-to-Vehicle (V2V) multiple-input multiple-output (MIMO) channels is proposed. The new model considers more accurate output phase of Doppler frequency and is simplified by the Taylor series expansions. It is also suitable for generating the V2V channel coefficient with arbitrary velocities and trajectories of the mobile transmitter (MT) and mobile receiver (MR). Meanwhile, the channel parameters of path delay and power are investigated and analyzed. The closed-form expressions of statistical properties, i.e., temporal autocorrelation function (TACF) and spatial cross-correlation function (SCCF) are also derived with the angle of arrival (AoA) and angle of departure (AoD) obeying the Von Mises (VM) distribution. In addition, the good agreements between the theoretical, simulated and measured results validate the correctness and usefulness of the proposed model.

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary massive multiple-input multiple-output (MIMO) channel model for 6G THz indoor communication systems is proposed. In this geometry-based stochastic model (GBSM), the initialization and evolution of parameters in time, space, and frequency domains are developed to generate the complete channel transfer function (CTF). Based on the proposed model, the correlation functions including time auto-correlation function (ACF), spatial cross-correlation function (CCF), and frequency correlation function (FCF) are investigated. The results show that the statistical properties of the simulation model match well with those of the theoretical model. The stationary intervals at different frequencies are simulated. The non-stationarity in time, space, and frequency domains is verified by theoretical derivations and simulations.

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.

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.

The papers in this special section focus on wireless big data. Big data, which has been following the exponential growth rates in different commercial areas, has profoundly changed the way we live. It has received considerable attention in both academic and industrial communities, in contexts such as mobile communications, distributed computing, e-health, intelligent transportation systems, wireless sensor networks, etc. In the meantime, the Internet of Things (IoT) scenarios considered in the Fifth Generation (5G) wireless communication systems are expected to create many novel applications and services with various requirements [1]. These new directions bring a dramatic increase and change in the amount and types of wireless data, thus driving wireless communications into a new era. Therefore, an in-depth analysis and understanding of wireless big data can greatly facilitate better system design and performance optimization, which will certainly benefit equipment vendors, network operators and service providers.

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

Due to the agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for on-demand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Using a typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of the UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by using problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.

High-speed train (HST) communications in tunnels have attracted more and more research interests recently, especially within the framework of the fifth generation (5G) wireless networks. In this paper, based on cuboid-shape, three-dimensional (3D) non-stationary wideband geometry-based stochastic models (GBSMs) for HST tunnel scenarios are proposed. By considering the influence of the tunnel walls, a theoretical channel model is first established, which assumes clusters with an infinite number of scatterers randomly distributed on the tunnel walls. The corresponding simulation model is then developed and the method of equal areas is employed to obtain the discrete parameters, such as the azimuth and elevation angles. We derive and investigate the most important channel statistical properties of the proposed 3D GBSMs, including the time-variant autocorrelation function, spatial cross-correlation function, and Doppler power spectrum density. It is indicated that all statistical properties of the simulation model, verified by simulation results, can match very well with those of the theoretical model. Furthermore, a validation is presented by comparing the stationary regions of our proposed tunnel channel model to those of relevant measurement data.

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.

With the vigorous development of vehicular communications in 5G and beyond networks, cognitive satellite-terrestrial networks are expected to support multitudinous services and applications in future intelligent transportation systems and mobile Internet. To this aim, we introduce a cognitive satellite-vehicular network (CSVN) in this paper, where the secondary vehicular communications are featured with mobility. To realize friendly coexistence between satellite and vehicular networks as well as efficient resource utilization, we investigate the tradeoff between energy efficiency (EE) and spectrum efficiency (SE) and analyze the associated power allocation in the CSVN. Specifically, by introducing a preference factor which reflects the priority level of EE/SE, we firstly propose a unified EE-SE tradeoff metric to adapt to dynamic vehicular environments. Based on the formulated EE-SE tradeoff metric, we derive a power allocation strategy under the interference power constraints imposed by primary satellite communications. Finally, simulation results are provided to show the effects of preference factor, interference constraints, and vehicle velocity on the EE-SE tradeoff performance.

On-demand streaming of high-quality video content is a widely anticipated vehicular infotainment service. How to reduce the cost of content streaming is a primary concern of the service providers, but is still an underinvestigated subject in the literature. This paper proposes an integrated mobile streaming and caching scheme that jointly leverages two communication service tiers and on-board caching resource for cost reduction. Algorithms are presented to achieve optimal buffering at the session level and optimal caching at the device level. An analytical framework is established to characterize the average cost as a function of the streaming rate in a large scale network. Numerical results demonstrate how the “cost-streaming rate” function changes with vehicle density, network congestion level, content length, and average packet transmission time. We learn an important insight that there is a minimum cost threshold even when the streaming rate approaches zero. We also show that the proposed protocol can effectively reduce the overall cost when the network is not congested. Our findings can provide useful guidelines for the business planning and operation of vehicular content streaming services.

With the ever increasing spectrum demand of broadband multimedia services, cognitive satellite terrestrial networks have emerged as a promising paradigm for future space information networks. To provide services with diverse delay quality-of-service (QoS) requirements in an energy-limited system, in this paper, we investigate energy efficient power allocation for cognitive satellite terrestrial networks. Employing statistical delay-QoS metric, power allocation schemes are formulated as optimization problems to maximize effective energy efficiency of secondary satellite communications while satisfying interference constraints imposed by primary terrestrial communications. Specifically, allowing for the availability of instantaneous channel state information (CSI) of the secondary transmitter-primary receiver link, optimal transmit powers are derived for both the cases of statistical and instantaneous interference constraints. Moreover, to provide a theoretical insight on the performance of the considered network, we derive closed-form expressions for the outage probability based on the obtained optimal transmit powers. The simulation results demonstrate the validity of the theoretical results and show the impacts of the delay exponent, interference constraint, and aggregate interference from terrestrial networks on the performance of satellite networks.

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.

In this paper, we study the propagation characteristics of air-to-air (A2A) channels in a virtual urban scenario generated with the ITU-R model. Based on ray-tracing simulations, we explore the behavior of large- scale fading when taking the environmental setup into consideration. Our analytical results show that the large-scale fading statistics, e.g., pathloss exponent (PLE) and shadow fading variance, are associated with the receiver height and elevation angle. Furthermore, we derive an approximate expression for line-of-sight probability according to the deployment and distribution of buildings. The expression is characterized as a function of receiver height and elevation angle with the condition of high transmitter height. To efficiently evaluate the performance of unmanned aerial vehicle (UAV) communication systems, we then propose an empirical propagation channel prediction model, which can be easily extended to different scenarios in the ITU-R 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.

Fog computing has recently emerged as a promising technique in content delivery wireless networks to alleviate the heavy bursty traffic burdens on backhaul connections. In order to improve the overall system performance, in terms of network throughput, service delay and fairness, it is very crucial and challenging to jointly optimize node assignments at control tier and resource allocation at access tier under dynamic user requirements and wireless network conditions. To solve this problem, in this paper, a fog-enabled multitier network architecture is proposed to model a typical content delivery wireless network with heterogeneous node capabilities in computing, communication, and storage. Further, based on Lyapunov optimization techniques, a new online low-complexity algorithm, namely fogenabled multitier operations scheduling (FEMOS), is developed to decompose the original complicated problem into two operations across different tiers. Rigorous performance analysis derives the tradeoff relationship between average network throughput and service delay, i.e., [O(1/V), O(V)] with a control parameter V, under FEMOS algorithm in dynamic wireless networks. For different network sizes and traffic loads, extensive simulation results show that FEMOS is a fair and efficient algorithm for all user terminals and, more importantly, it can offer much better performance, in terms of network throughput, service delay, and queue backlog, than traditional node assignment and resource allocation algorithms.

Welcome to the IEEE JSAC special issue onAirborne Communication Networks. The goal of this special issue is to disseminate the contributions in the field of airborne communication networks.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based two-way relay network with secrecy considerations, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of single and multiple eavesdroppers. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to degrade the performance of any potential eavesdropper. Moreover, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. We propose different decoding schemes based on the successive interference cancellation for the legitimate users, relay, and eavesdroppers. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols under both single- and multiple-eavesdropper cases are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

This paper proposes a novel quadrature space-frequency index modulation (QSF-IM) scheme as a promising energy-efficient radio-access technology for the fifth generation (5G) wireless systems. Motivated by the potential energy saving of spatial modulation (SM) with the part of information being carried through antenna indexes, the proposed scheme further leverages the benefits of SM by applying the idea across the spatial and frequency domains. Moreover, by deploying dual antenna constellation for in-phase and quadrature-phase transmission, the proposed scheme can enhance data rate at no extra cost of energy consumption, leading to further improvement in energy efficiency. Theoretical bit error rate and achievable sum-rate of the proposed scheme over frequency-selective correlated Rician and Rayleigh fading channels are derived and are shown to have good agreement with simulations. Furthermore, the effectiveness of the proposed scheme is analyzed through a comprehensive list of performance metrics, including spectral efficiency (SE), energy efficiency (EE), cost efficiency (CE), and economic efficiency. Performance trade-offs between these metrics are thoroughly investigated. Compared with other existing schemes, the proposed QSF-IM scheme is demonstrated to offer better EE-SE and EE-CE tradeoffs, and can therefore be considered as a potential candidate for energy-spectral efficient 5G systems.

A novel unified framework of geometry-based stochastic models for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output, high-speed train, vehicle-to-vehicle, and millimeter wave communications. It is a 3-D non-stationary channel model based on the WINNER II and Saleh-Valenzuela channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.

In this paper, new enhanced cognitive radio networks (E-CRNs) based on spectrum sharing (SS) and spectrum aggregation (SA) are proposed for fifth generation (5G) wireless networks. The E-CRNs jointly exploit the licensed spectrum shared with the primary user (PU) networks and the unlicensed spectrum aggregated from the industrial, scientific, and medical bands. The PU networks include TV systems in TV white space and different incumbent systems in the long term evolution time division duplexing bands. The harmful interference from the E-CRNs to the PU networks are delicately controlled. Furthermore, the coexistence between the E-CRNs and other unlicensed systems, such as WiFi, is studied. The E-CRNs framework including dynamic spectrum management (DSM) is designed for the key parameters of licensed SS and unlicensed SA. The essential tradeoff between sharing efficiency and aggregation efficiency for the E-CRNs is discussed. Based on this tradeoff, a spectrum lean-management scheme is proposed to fulfill the DSM. Moreover, a water-filling algorithm is designed to dynamically access the available spectrum. Numerical results demonstrate that the proposed E-CRNs can significantly improve the system performance in terms of data rate, outage probability, and spectrum efficiency. In particular, the E-CRNs framework provides a spectrum usage prototype for 5G wireless communication networks.

Visible light communication (VLC) is an emerging nascent research area having enormous application prospects. Efficient evaluation methods are critical requirements for implementing high-performance VLC systems. Most research just focus on system analyses from optical channels, modulation methods, or communication theories. However, the basic theoretical analyses of the relationships between hardware circuit current energy and optical power are absent. This paper makes up for this deficiency. Based on a general VLC communication scenario, we theoretically analyze the transferring procedures between circuit current energy and optical power. The current energy transferring calculation model (CETCM) and CET parameters are proposed for the first time. The peak current energy response, current energy gain, threshold of optical power transferring distance, and current signal-to-noise ratio can be calculated by the CETCM and CET parameters. Experiments show that they can comprehensively reflect the communication characteristics of practical VLC system, and are quite important and valuable for VLC system evaluations, implementations, and optimizations.

In this paper, a new WINNER+-based 3-D non-stationary geometry-based stochastic model (GBSM) for multiple-input multiple-output channels is proposed, as well as extended evolving algorithms of time-variant channel parameters. Meanwhile, important statistical properties of the channel model, i.e., time-variant autocorrelation function, time-variant cross-correlation function, and time-variant Doppler power spectrum density are derived and analyzed. Moreover, we propose an efficient hardware implementation method, namely, sum-of-frequency-modulation (SoFM) method, to generate non-stationary channel coefficients. By utilizing compact hardware architecture with SoFM modules, the proposed 3-D non-stationary GBSM is realized on a field-programmable gate array hardware platform. Simulations and hardware measurement results demonstrate that our proposed channel simulator and emulator can get more accurate and realistic Doppler frequency than those of the existing models. In addition, hardware measurements of statistical properties are also well consistent with the corresponding theoretical ones, which verify the correctness of both the hardware emulation scheme and theoretical derivations.

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.

Cellular communication networks are plagued with redundant capacity, which results in low utilization and cost-effectiveness of network capital investments. The redundant capacity can be exploited to deliver secondary traffic that is ultra-elastic and delay-tolerant. In this paper, we propose an analytical framework to study the capacity-delay tradeoff of elastic/secondary traffic in large scale cellular networks with spectrum aggregation. Our framework integrates stochastic geometry and queueing theory models and gives analytical insights into the capacity-delay performance in the interference limited regime. Closed-form results are obtained to characterize the mean delay and delay distribution as functions of per user throughput capacity. The impacts of spectrum aggregation, user and base station densities, traffic session payload, and primary traffic dynamics on the capacity-delay tradeoff relationship are investigated. The fundamental capacity limit is derived and its scaling behavior is revealed. Our analysis shows the feasibility of providing secondary communication services over cellular networks and highlights some critical design issues.

In this paper, a novel 3-D non-stationary wideband geometry-based stochastic theoretical channel model for massive multiple-input multiple-output communication systems is proposed. First, a second-order approximation to the spherical wavefront in space and time domains, i.e., parabolic wavefront, is proposed to efficiently model near-field effects. Second, environment evolution effects are modeled by spatial-temporal cluster (re)appearance and shadowing processes. We propose (re)appearance processes to model the visibility of clusters with enhanced spatial-temporal consistency. Shadowing processes are used to capture smooth spatial-temporal variations of the clusters' average power. In addition, a corresponding simulation model is derived along with a 3-D extension of the Riemann sum method for parameters computation. Key statistical properties of the proposed model, e.g., the spatial-temporal cross-correlation function, are derived and analyzed. Finally, we present numerical and simulation results showing an excellent agreement between the theoretical and simulation models and validating the proposed parameter computation method. The accuracy and flexibility of the proposed simulation model are demonstrated by comparing simulation results and measurements of the delay spread, slope of cluster power variations, and visibility regions' size.

Allowing frequency reuse between satellite and terrestrial networks, the integrated satellite-terrestrial network can spatially optimize the usage of scarce spectrum resource and is thus becoming one of the most promising infrastructures for future multimedia services. Taking the requirements of both efficiency and reliability in satellite communications into account, we propose an adaptive transmission scheme for the integrated network in this paper, where the satellite can communicate with the destination user either in direct mode or in cooperative mode. Specifically, we first investigate the symbol error rate (SER) performance of two transmission modes with co-channel interference under composite multipath/shadowing fading. Taking the derived SERs as constraints, we formulate the adaptive transmission scheme as an optimization problem with the objective of maximizing energy efficiency (EE) and discuss the trade-off among EE, spectral efficiency (SE), and SER. Furthermore, economic efficiency is also analyzed as a complementary performance measure to SE and EE. Simulation results show that the proposed scheme can increase the attainable EE of satellite communications, which indicates that we should choose the transmission mode adaptively according to different interfering scenarios and shadowing degrees, rather than adopting cooperative transmission aggressively.

With a great potential to support multitudinous services and applications, mobile device-to-device (D2D) communications are conceived as a candidate paradigm for the future intelligent transportation systems and mobile Internet. To optimize the performance of underlaying mobile D2D communication systems with mutual interference caused by resource reuse, we propose two scenario-related power allocation schemes and investigate the energy efficiency (EE) and spectral efficiency (SE) trade-off. A 3-D vehicle-to-vehicle channel model is adopted to characterize propagation characteristics in realistic vehicular environments. We observe that a small degradation in EE around its peak value can significantly increase the SE for high vehicular traffic density (VTD) scenarios, while a marginal degradation in SE results in a considerable gain in EE for low VTD scenarios. Therefore, we maximize the SE subject to EE requirement in high VTD scenarios and maximize EE subject to SE requirement in low VTD scenarios. Moreover, to provide comprehensive understanding and further facilitate the practicality of EE-SE trade-off, economic efficiency (ECE) is employed as a general evaluation criterion to assess the efficacy of tradeoff. Finally, extensive simulations are provided to reveal the tradeoff quantitatively and demonstrate the viability that ECE can serve as a general metric for EE–SE trade–off in vehicular environments under different communication conditions.

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.

In this paper, we revisit the idea of using deep neural network for channel equalization to account for nonlinear channel distortions as well as temporal variations of radio signals. Our insight is leveraging the the shift-invariant properties of the convolutional neural network (CNN) to learn matched filters analogous to the tap weights of conventional equalizer. Then we feed the learned filters into a subsequent recurrent neural network (RNN) with long-short-term-memory (LSTM) cells for temporal modeling of the channel. We train our proposed CNN-RNN (CRNN) equalizer based on real testbed collected data and enlarge the generalization ability of the learned network model as much as we can to adapt to different channel conditions. Experimental results show that the SER performance for our designated single-input single-output (SISO) system which utilises quadrature phase shift keying (QPSK) modulation scheme with the proposed CRNN-based channel equalizer outperforms that of other equalizers by average 2 to 5 dB at low signal-to-noise ratio (SNR).

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.

Most channel models in the literature are based on the assumption that the mobile station (MS) moves along a straight line with a constant speed. In a realistic environment, the MS may experience changes in their speeds and trajectories. In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model allowing for velocity variations of the MS is proposed. The parameters are obtained from the WINNER+ channel model to make the simulations more realistic. Statistical properties including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), and Doppler power spectral density (PSD) are derived and analyzed. Our findings show that a variation of the velocity of the MS has a significant impact on the statistical properties of the channel model. Furthermore, the proposed channel model can be used as a basic framework for future non-stationary channel modeling.

With the anticipated increase in the number of low power base stations (BSs) deployed in small cell networks, blockage effects becoming more sensitive on wireless transmissions over high spectrums, variable propagation fading scenarios make it hard to describe coverage of small cell networks. In this paper, we propose a dual-directional path loss model cooperating with Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) transmissions for the fifth generation (5G) fractal small cell networks. Based on the proposed path loss model, a LoS transmission probability is derived as a function of the coordinate azimuth of the BS and the distance between the mobile user (MU) and the BS. Moreover, the coverage probability and the average achievable rate are analyzed for 5G fractal small cell networks. Numerical results imply that the minimum intensity of blockages and the maximum intensity of BSs can not guarantee the maximum average achievable rate in 5G fractal small cell networks. Our results explore the relationship between the anisotropic path loss fading and the small cell coverage in 5G fractal small cell networks.

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.

In this paper, we consider a cognitive satellite-terrestrial network where the satellite communication operates in the microwave frequency bands allocated to terrestrial networks in an underlay mode. Taking the statistical delay quality-of-service (QoS) requirements into account, we investigate the effective capacity of the satellite network while satisfying interference-power limitations imposed by terrestrial networks. Specifically, the primary terrestrial transmitters that would result in aggregate interference at the satellite receiver are modeled as points of a Poisson point process (PPP). By characterizing the aggregate interference as a gamma distribution, we obtain a closed-form expression for the effective capacity of the secondary satellite network. Finally, simulation results are provided to not only demonstrate the validity of the theoretical results, but also show the effects of system parameters such as delay exponent of satellite communications, interference-power limitations of terrestrial networks, and intensity of terrestrial transmitters on the performance of the satellite network.

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.

Millimeter wave (mmWave) communication works in the frequencies above 6 gigahertz (GHz), with the system bandwidth up to 500 megahertz (MHz) or wider. In this case, the channel situations are dramatically different from the existing wireless channels in Third Generation/Fourth Generation (3G/4G) communication systems, and the stationarity of mmWave channels could be very different from our former knowledge. The focus of this paper is to study the frequency and spatial stationarities of mmWave channels. For better understanding, we compare the stationarity regions between mmWave channels and the channels below 6 GHz. First, the general average power delay profile (APDP) method is introduced as a metric to determine the size of the stationarity regions in time, frequency, and spatial domains. Then, the general APDP method is applied in the data analysis of a channel measurement. We find that the frequency stationarity regions of the channels are much larger in mmWave bands above 6 GHz than those in conventional bands below 6 GHz, while the spatial stationarity regions of the channels are much smaller in mmWave bands above 6 GHz than those in conventional bands below 6 GHz.

Due to the frequent dramatic disasters of underground mining collapses, the establishment of a reliable communication system in the underground mining environment is of vital importance. Instead of using the conventional wired and radio communication systems, researchers have considered the use of visible light communication (VLC) for underground mining communications (UMCs). In this paper, we extend the recursive method to model VLC propagation channel and focus on two different parts of underground mining communication, namely the working face and mining roadway. Both the required illuminance and VLC channel characteristics are thoroughly investigated for miner-to-miner (M2M) and infrastructure-to-miner (I2M) communication scenarios. It is shown that the illumination standard requirements can be fulfilled in the two parts and the mining roadway is brighter than the working face environment. Furthermore, VLC channel characteristics, such as the channel gain, mean excess delay, root mean square (RMS) delay spread, are thoroughly investigated. It is found that the proposed channel model is sufficiently accurate when taking the line-of-sight (LoS) and the first order reflections into consideration.

Given the fact that the relay contributes to higher data rate or more reliable transmission at the expense of extra power consumption, we investigate the performance of relay aided Device-to-Device (D2D) communications from the perspective of energy efficiency (EE). Firstly, a closed-form expression for the EE of the considered network over Nakagami-m fading channels is derived. Then, taking into account the effects of practical modulation and coding schemes, we propose a channel quality indicator (CQI) based power control approach to maximize the EE of relay aided D2D communications while guaranteeing the interference limitation. The proposed scheme could avoid unnecessary power increment and is applicable in real time resource allocation. Simulation results demonstrate the validity of the theoretical analysis and illustrate that the CQI based scheme can improve EE.

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.

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.

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.

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.

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.

Unlicensed spectra access holds the promise of alleviating licensed spectra scarcity and providing super high-rate mobile data services, which has been viewed as one of the key technologies of fifth generation (5G) cellular networks. In this paper, we first design a framework for 5G licensed and unlicensed spectra interoperable networks based on the cloud radio access network technology and the control/data decoupled architecture. Then, we investigate network-level cost-reliability tradeoff from two aspects. First, we study a fundamental tradeoff between the cost and the reliability by minimizing cost for a given reliability level. Second, we define a quality of experience efficiency utility as the complementary measure to characterize the cost and the reliability, offering an inherent tradeoff between them. Moreover, an interference power estimation method is proposed to more accurately estimate channel states to guarantee resource allocation effectiveness. Finally, we conduct extensive simulation study and demonstrate the effectiveness of the interference power estimation method.

For future networks [i.e., the fifth generation (5G) wireless networks and beyond], millimeter-wave (mmWave) communication with large available unlicensed spectrum is a promising technology that enables gigabit multimedia applications. Thanks to the short wavelength of mmWave radio, massive antenna arrays can be packed into the limited dimensions of mmWave transceivers. Therefore, with directional beamforming, both mmWave transmitters (MTXs) and mmWave receivers (MRXs) are capable of supporting multiple beams in 5G networks. However, for the transmission between an MTX and an MRX, most works have only considered a single beam, which means that they do not make full potential use of mmWave. Furthermore, the connectivity of single beam transmission can easily be blocked. In this context, we propose a single-user (SU) multi-beam concurrent transmission scheme for future mmWave networks with multiple reflected paths. Based on spatial spectrum reuse, the scheme can be described as a multiple-input multiple-output (MIMO) technique in beamspace (i.e., in the beam-number domain). Moreover, this paper investigates the challenges and potential solutions for implementing this scheme, including multi-beam selection, cooperative beam tracking, multi-beam power allocation, and synchronization. The theoretical and numerical results show that the proposed beamspace SU-MIMO can largely improve the achievable rate of the transmission between an MTX and an MRX and, meanwhile, can maintain the connectivity.

With the recent developments of high-mobility wireless communication systems, e.g., high-speed train (HST) and vehicle-to-vehicle communication systems, the ability of conventional stationary channel models to mimic the underlying channel characteristics has widely been challenged. Measurements have demonstrated that the current standardized channel models, like IMT-Advanced (IMT-A) and WINNER II channel models, offer stationary intervals that are noticeably longer than those in measured HST channels. In this paper, we propose a non-stationary channel model with time-varying parameters, including the number of clusters, the powers, and the delays of the clusters, the angles of departure, and the angles of arrival. Based on the proposed non-stationary IMT-A channel model, important statistical properties, i.e., the local spatial cross-correlation function and local temporal autocorrelation function are derived and analyzed. Simulation results demonstrate that the statistical properties vary with time due to the non-stationarity of the proposed channel model. An excellent agreement is achieved between the stationary interval of the developed non-stationary IMT-A channel model and that of relevant HST measurement data, demonstrating the utility of the proposed channel model.

Wireless caching at users' devices in mobile social network is considered to be a promising solution to alleviate backhaul overload in future wireless networks. However, most of the current works propose caching schemes based on heuristic reasoning and intuition with poor performance or high complexity which are impractical due to individual devices' computing capacity restriction. In this paper, we design a cooperative caching scheme aimed at maximizing hit ratio, incorporating probabilistic modeling of mobility and user interests patterns from mobile social networks. Furthermore, we reformulate this optimization problem into a submodular function maximization and propose a semigradient-based cooperative caching scheme, while this scheme's efficiency is shown to significantly outperform the greedy caching by 99.6%.

This paper first proposes a novel three dimensional (3-D) wideband multi-confocal ellipsoid model for wireless multiple-input multiple-output (MIMO) communication channels. The proposed 3-D geometry-based stochastic model (GBSM) describes the channel in both the azimuth direction and elevation direction, including delay, Doppler frequency, angle of departure (AoD), and angle of arrive (AoA). It can be shown that the ellipsoid model can capture the effect of the clusters more accurately than the previous elliptic-cylinder model. Using the proposed theoretical model as the reference model, the corresponding simulation model is also derived. Numerical results demonstrate that their statistical properties can match well.

In this paper, an effective generalized spatial modulation (GenSM) scheme with transmit antenna grouping is proposed to overcome the performance degradation caused by correlated channels. In the proposed scheme, the transmit antennas are divided into several equal-sized groups, and spatial modulation (SM) is carried out to select one active antenna in each group independently. It is quite different from the conventional GenSM which jointly selects active antenna set. Apart from the straightforward block grouping method, which collects the adjacent antennas to the same group, interleaved grouping is also introduced. It can maximize the average distance between the antennas in the same group, since the channel correlation depends on it. To evaluate the performance, a closed-form expression of the average bit error probability (ABEP) upper bound is derived for all proposed grouping methods and Monte-Carlo simulations are conducted to verify the analysis and reveal the performance gain of the proposed scheme in terms of bit error rate (BER) in comparison with conventional GenSM and SM.

High performance of visible light communication (VLC) systems requires overcoming the limitations imposed by the optical wireless channel distortions resulting from path loss and temporal dispersion. In order to design techniques to combat the effects of channel distortions, an accurate VLC channel model is needed. In this paper, we propose a new regular-shaped geometry-based multiple bounce model (RS-GBMB) for VLC channels. The proposed model employs a combined two-ring model and ellipse model, where the received signal is constructed as a sum of the line-of-sight (LoS), single-, double-, and triple bounced rays of different powers. This makes the model sufficiently generic and adaptable to a variety of indoor scenarios. Based on the proposed RS-GBMB model, statistical properties are then investigated, such as the channel DC gain, mean excess delay, root mean square (RMS) delay spread, and Rician factor.

The performance of visible light communication (VLC) system may potentially be degraded by wireless optical channel distortions resulting from path loss and temporal dispersions. In order to devise techniques to combat the effects of channel distortions, an accurate channel model is needed. In this paper, we propose a new field of view (FOV) geometry-based single bounce (GBSB) model for VLC channels. Statistical properties of the proposed GBSB model are then studied, such as the channel gain, mean excess delay, root mean square (RMS) delay spread, Rician factor, and time correlation. Also, the required illuminance for the proposed scenario and the number of light emitting diodes (LEDs) per LED lamp are estimated.

With the growth of wireless transmission rate on user terminals, the backhaul network capacity becomes a bottleneck for improving the performance of future 5G ultra-dense cellular networks. Based on the wireless multi-hop relay technology, the backhaul network capacity of 5G ultra-dense cellular networks with multi-gateways is analyzed in this paper. Moreover, a minimum average hop number (MAN) algorithm is developed to improve the backhaul network capacity and energy efficiency of wireless backhaul networks for 5G ultra-dense cellular networks. Simulation results indicate there exist a stationary backhaul network capacity and a maximum energy efficiency of wireless backhaul networks when the density of small cell BSs is larger than the specified threshold.

In this paper, we compare the statistical properties of high-speed train (HST) wireless channels in different scenarios using a generic non-stationary HST channel model that has been verified by channel measurements (Ghazal et al., 2015). We mainly focus our comparison and analysis on the three most common HST scenarios, i.e., the rural area, cutting, and viaduct scenarios. Several channel statistical properties such as the temporal autocorrelation function (ACF), space cross-correlation function (CCF), and space- Doppler (SD) power spectrum density (PSD) are investigated. The impacts of different scenario- specific parameters on the channel statistical properties are also studied via numerical analysis.

With the massive multi-input multi-output (MIMO) antennas technology adopted for the fifth generation (5G) wireless communication systems, a large number of radio frequency (RF) chains have to be employed for RF circuits. However, a large number of RF chains not only increase the cost of RF circuits but also consume additional energy in 5G wireless communication systems. In this paper, we investigate energy and cost efficiency optimization solutions for 5G wireless communication systems with a large number of antennas and RF chains. An energy efficiency optimization problem is formulated for 5G wireless communication systems using massive MIMO antennas and millimeter wave technology. Considering the nonconcave feature of the objective function, a suboptimal iterative algorithm, i.e., the energy efficient hybrid precoding (EEHP) algorithm is developed for maximizing the energy efficiency of 5G wireless communication systems. To reduce the cost of RF circuits, the energy efficient hybrid precoding with the minimum number of RF chains (EEHP-MRFC) algorithm is also proposed. Moreover, the critical number of antennas searching (CNAS) and user equipment number optimization (UENO) algorithms are further developed to optimize the energy efficiency of 5G wireless communication systems by the number of transmit antennas and UEs. Compared with the maximum energy efficiency of conventional zero-forcing (ZF) precoding algorithm, numerical results indicate that the maximum energy efficiency of the proposed EEHP and EEHP-MRFC algorithms are improved by 220% and 171%, respectively.

The statistical characteristics of the signal-to-interference plus noise ratio (SINR) are closely related to many performance metrics of cellular networks. In this paper, the downlink average rate and SINR distribution are studied for orthogonal frequency division multiple access (OFDMA)-based cellular networks subject to the distance-dependent path loss and shadow fading (SF). With the analytical approximate mean and moment generating function (MGF) of the SINR in the logarithmic domain, the closed-form approximation for the lower and upper bounds of the average rate is obtained. Then the distribution of the SINR in the logarithmic domain is proposed to be approximated as the normal inverse Gaussian (NIG) distribution whose parameters are computed explicitly through moment matching. Also, the closed-form expression for the cumulative distribution function (CDF) of the SINR based on the NIG approximation is derived. Simulation results not only verify the tightness of the bounds, but also show that the NIG approximation is up to one order of magnitude more accurate than the Pearson type IV approximation and at least one order of magnitude more accurate than the lognormal approximation when the SF correlation coefficient is small or the standard deviations of the SF are large or different.

In this paper, the bit error rate (BER) performance of a new multiple-input-multiple-output technique, named spatial modulation (SM), is studied under a novel non-stationary wideband high-speed train (HST) channel model in different scenarios. Time-varying parameters obtained from measurement results are used to configure the channel model to make all results more realistic. A novel statistic property called the stationary interval in terms of the space-time correlation function is proposed to describe the channel model's time-varying behavior. The accurate theoretical BER expression of SM systems is derived under the time-varying wideband HST channel model with the non-ideal channel estimation assumption. The simulation results demonstrate that the BER performance of SM systems shows a time-varying behavior due to the non-stationary property of the employed HST channel model. The system performance can maintain a relative stationary status within the specified stationary interval. It can also be observed that the BER performance of SM systems under the HST channel model is mainly affected by the correlation between sub-channels, inter-symbol-interference, Doppler shift, and channel estimation errors.

This paper proposes a novel method for ergodic sum rate analysis of spatial modulation (SM) systems with maximum likelihood receiver. This method is developed based on the MT-ary symmetric channel, where MTis the number of transmit antennas. The probability of antenna detection error is approximated by the pair-wise error probability. Then, an approximation to the ergodic sum rate of information transmission via SM with maximum likelihood receiver is computed. It is demonstrated via simulation that the proposed analysis method is able to provide an excellent approximation to the ergodic sum rate of SM.

In this paper, we propose and analyze a new relaying scheme for the three-node cooperative relaying system, named as incremental hybrid decode-amplify-forward relaying (IHDAF), where the relay may choose to keep silent or transmit message in decode-and-forward (DF) or amplify-and-forward (AF) mode based on the qualities of the channels among the source, relay, and destination. Closed-form expressions of the outage probability and bit error rate (BER) of the proposed IHDAF protocol are derived. The optimal relationship of the two important signal-to-noise ratio (SNR) thresholds at the relay and destination to decide whether cooperation is necessary and the cooperative mode has been achieved. Moreover, the effects of the power allocation schemes, SNR thresholds, and relay locations on the outage probability and BER of the IHDAF protocol are studied. Theoretical analysis and simulation results show that the IHDAF relaying scheme outperforms the incremental-selective DF (ISDF), incremental DF (IDF), and cooperative STBC schemes, especially when the relay is close to the destination.

It is a great challenge to evaluate the network performance of cellular mobile communication systems. In this paper, we propose new spatial spectrum and energy efficiency models for Poisson-Voronoi tessellation (PVT) random cellular networks. To evaluate the user access to the network, a Markov chain based wireless channel access model is first proposed for PVT random cellular networks. On that basis, the outage probability and blocking probability of PVT random cellular networks are derived, which can be computed numerically. Furthermore, taking into account the call arrival rate, the path loss exponent and the base station (BS) density in random cellular networks, spatial spectrum and energy efficiency models are proposed and analyzed for PVT random cellular networks. Numerical simulations are conducted to evaluate the network spectrum and energy efficiency in PVT random cellular networks.

The recent development of high-speed trains (HSTs), as a high-mobility intelligent transportation system, and the growing demands of broad-band services for HST users, introduce new challenges to wireless communication systems for HSTs. The deployment of mobile relay stations on top of the train carriages is one of the promising solutions for HST wireless systems. For a proper design and evaluation of HST wireless communication systems, we need accurate channel models that can mimic the underlying channel characteristics for different HST scenarios. In this paper, a novel nonstationary geometry-based stochastic model (GBSM) is proposed for wideband multiple-input multiple-output HST channels in rural macrocell scenarios. The corresponding simulation model is then developed with angle parameters calculated by the modified method of equal areas. Both channel models can also be used to model nonstationary vehicle-to-infrastructure channels in vehicular communication networks. The system functions and statistical properties of the proposed channel models are investigated based on a theoretical framework that describes nonstationary channels. Numerical and simulation results demonstrate that the proposed channel models have the capability to characterize the nonstationarity of HST channels. The statistical properties of the simulation model, verified by the simulation results, can match those of the proposed theoretical GBSM. An excellent agreement is achieved between the stationary intervals of the proposed simulation model and those of relevant measurement data, demonstrating the utility of the proposed channel models.

Cognitive radio (CR) is considered one of the prominent techniques for improving the utilization of the radio spectrum. A CR network (i.e., secondary network) opportunistically shares the radio resources with a licensed network (i.e., primary network). In this work, the spectral-energy efficiency trade-off for CR networks is analyzed at both link and system levels against varying signal-to-noise ratio (SNR) values. At the link level, we analyze the required energy to achieve a specific spectral efficiency for a CR channel under two different types of power constraint in different fading environments. In this aspect, besides the transmit power constraint, interference constraint at the primary receiver (PR) is also considered to protect the PR from a harmful interference. Whereas at the system level, we study the spectral and energy efficiency for a CR network that shares the spectrum with an indoor network. Adopting the extreme-value theory, we are able to derive the average spectral and energy efficiency of the CR network. It is shown that the spectral efficiency depends upon the number of the PRs, the interference threshold, and how far the secondary receivers (SRs) are located. We characterize the impact of the multi-user diversity gain of both kinds of users on the spectral and energy efficiency of the CR network. Our analysis also proves that the interference channels (i.e., channels between the secondary transmitter and PRs) have no impact on the minimum energy efficiency.

Recent literature has suggested the benefits of integrating licensed radio and cognitive radio into a hybrid cooperative communication system. The fundamental properties of such hybrid systems, however, have not been thoroughly investigated. This paper studies the hybrid cognitive Gaussian relay channel (HCGRC), which uses licensed radio resource (RR) and cognitive/unlicensed RR for forward and relay transmissions, respectively. HCGRC fundamentally differs from conventional relay channels in that the licensed and cognitive RRs are not subject to a total resource constraint and that the cognitive RR is opportunistic in nature. With respect to both the upper and lower bounds, we derive the optimal power-bandwidth allocation strategies for the cognitive relay to maximize the capacity, spectrum efficiency (SE), and energy efficiency (EE). The Pareto-optimal EE-SE tradeoff curve is also derived analytically. Our study leads to two key observations. First, the multi-objective power-bandwidth allocation problem is characterized by five regions, each representing a unique performance tradeoff. Second, the reliability of cognitive RR has no impact on the EE-SE tradeoff given unlimited bandwidth and power.

Existing works have addressed the tradeoffs between any two of the three performance metrics: throughput, energy efficiency (EE), and delay. In this paper, we unveil the intertwined relations among these three metrics under a unifying framework and particularly investigate the problem of EE-guaranteed throughput-delay tradeoff in interference-free wireless networks. We first propose two admission control schemes, referred to as the first-out and first-in schemes. We then formulate it as two stochastic optimization problems, aiming at throughput maximization (in the first-out scheme) or dropping rate minimization (in the first-in scheme) subject to requirement of EE (RoE), stability, admission control, and transmit power. To solve the problems, the EE-Guaranteed algorithm for throUghput-delAy tRaDeoff (eGuard), respectively called eGuard-I and eGuard-II in the first-out and first-in schemes, is devised. Moreover, with guaranteed RoE, we theoretically show that the eGuard (I and II) can not only push the throughput arbitrarily close to the optimal with tradeoffs in delay but also quantitatively control the throughput-delay performance on demand. Simulation results consolidate the theoretical analysis and particularly show the pros and cons of the two schemes.

This paper proposes a novel non-stationary wideband multi-confocal ellipse two dimensional (2-D) channel model for massive multiple-input multiple-output (MIMO) communication systems. Spherical wavefront is assumed in the proposed channel model, instead of the plane wavefront assumption used in conventional MIMO channel models. In addition, the birth-death process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Statistical properties of the channel model such as the space-time-frequency correlation function and power imbalance on the antenna array are studied. The impact of the spherical wavefront assumption on the statistical properties of the channel model is investigated. Furthermore, numerical analysis shows that the proposed channel model is able to capture specific characteristics of massive MIMO channel as observed in measurements.

Actual vehicle-to-vehicle (V2V) channel measurements have shown that the wide-sense stationary (WSS) modeling assumption is valid only for very short time intervals. This fact motivates us to develop non-WSS V2V channel models. In this paper, we propose a novel three-dimensional (3D) theoretical non-WSS regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic scattering wideband multiple-input multiple-output (MIMO) V2V fading channels. The movements of the transmitter (Tx), scatterers, and receiver (Rx) result in the time-varying angles of departure (AoDs) and angles of arrival (AoAs) that make our models non-stationary. The proposed RS-GBSMs, combining line-of-sight (LoS) components, a two-sphere model, and multiple confocal elliptic-cylinder models, have the ability to study the impacts of vehicular traffic density (VTD) and non-stationarity on channel statistics, and jointly consider the azimuth and elevation angles by using the von Mises Fisher (VMF) distribution. The proposed RS-GBSMs are sufficiently generic and adaptable to model various V2V scenarios. Based on the proposed 3D non-WSS RS-GBSMs, important local channel statistical properties are derived and thoroughly investigated. The impacts of VTD and non-stationarity on these channel statistical properties are investigated by comparing them with those of the corresponding WSS model. The proposed non-WSS RS-GBSMs are validated by measurements in terms of the channel stationary time. Finally, numerical and simulation results demonstrate that the 3D non-WSS model is more practical to characterize real V2V channels.

Errors encountered in digital wireless channels are not independent but rather form bursts or clusters. Error models aim to investigate the statistical properties of bursty error sequences at either packet level or bit level. Packet-level error models are crucial to the design and performance evaluation of high-layer wireless communication protocols. This paper proposes a general design procedure for a packet-level generative model based on a sampled deterministic process with a threshold detector and two parallel mappers. In order to assess the proposed method, target packet error sequences are derived by computer simulations of a coded enhanced general packet radio service system. The target error sequences are compared with the generated error sequences from the deterministic process-based generative model using some widely used burst error statistics, such as error-free run distribution, error-free burst distribution, error burst distribution, error cluster distribution, gap distribution, block error probability distribution, block burst probability distribution, packet error correlation function, normalized covariance function, gap correlation function, and multigap distribution. The deterministic process-based generative model is observed to outperform the widely used Markov models. Copyright © 2013 John Wiley & Sons, Ltd.

A multiuser massive MIMO system with mutual coupling is investigated in finite-dimensional channel scenarios. The uplink ergodic achievable rate is analytically derived for a multiuser massive MIMO system equipped with a rectangular planar uniform antenna array at the base station (BS). Numerical results show that the mutual coupling effect reduces the uplink achievable rate of multiuser massive MIMO systems when the antenna distance decreases. But when the size of the antenna array is fixed, the uplink achievable rate increases with the growing of antenna number.

This paper deals with the learning and decision making issue for cognitive radio (CR). Two reinforcement-learning algorithms proposed in the literature are compared for opportunistic spectrum access (OSA): Upper Confidence Bound (UCB) algorithm and Weight Driven (WD) algorithm. This paper also introduces two new metrics in order to evaluate the machine learning algorithm performance for CR: effective cumulative regret and percentage of successful trials. They provide a fair evaluation means for CR performance.

In this paper, we investigate the pilot position selection (PPS) problem in the frequency domain pilot multiplexing technique (FDPMT) for channel estimation of single-carrier block transmission with frequency domain equalization (SC-FDE). Unlike the widely accepted point of view that the conventional PPS technique is the optimal one, this paper for the first time questions this viewpoint and demonstrates the suboptimal property of the conventional PPS technique. To further improve the performance of the FDPMT, an optimal PPS technique is proposed based on the minimization of the average bit error rate (BER). Compared with the conventioanl PPS technique, the proposed PPS technique exhibits better performance with similar complexity.

This paper analyzes the capacity of the finite scatterer (FS) multiple-input multiple-output (MIMO) channel model with finite angular resolution. The rank of the channel is bounded by not only the number of antennas but also the finite angular resolution of antenna arrays. The probability mass function (PMF) of rank is studied and derived based on the Stirling number of the second kind. As a result, the closed-form expression of the capacity of a FS MIMO channel model is obtained. Furthermore, asymptotic analyses such as the large-scale system analysis, infinite scatterer analysis, and the multiplexing gain analysis are investigated.

Spatial modulation (SM) is a relatively new multiple-input multiple-output (MIMO) technology that can provide high data rate with reasonable spectral efficiency. In this paper, the bit error rate (BER) performance of SM systems under vehicle-to-vehicle (V2V) channel models is investigated. The theoretical BER expression is given. The impact of some V2V channel model parameters on the underlying space-time correlation function (STCF) and the BER performance of SM systems are also studied. Simulation results indicate that modulation schemes, maximum Doppler frequency, the distance between the transmitter (Tx) and receiver (Rx), and antenna element spacings can affect the performance of SM systems.

This paper proposes a novel theoretical non-stationary three dimensional (3-D) wideband twin-cluster channel model for massive multiple-input multiple-output (MIMO) communication systems with carrier frequencies on the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, near field effects instead of far field effects are considered in the proposed model. These include the spherical wavefront assumption and a birth-death process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Their impacts on massive MIMO channels are investigated via statistical properties including correlation functions, condition numbers, and angular power spectra. Additionally, the impact of elevation angles on correlation functions is discussed. A corresponding simulation model for the theoretical model is also proposed. Finally, numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.

The 15 papers in Part II of this special issue focus on the spectrum and energy efficient design of wireless communication networks.

This paper proposes a generic geometry-based stochastic model for nonisotropic scattering vehicle-to-vehicle (V2V) Ricean fading channels. With the proposed model, the level crossing rate (LCR) and average fade duration (AFD) are derived. The resultant expressions are sufficiently general and subsume many well-known existing LCRs and AFDs as special cases. The derived LCR and AFD are further investigated in terms of some important parameters, e.g., the shape of the scattering region (two-ring or ellipse), mean angle, angle spread, and directions of movement of the Tx and Rx (same or opposite direction). More importantly, in this paper, the impact of the vehicular traffic density on the LCR and AFD for nonisotropic scattering V2V Ricean fading channels is investigated for the first time. Excellent agreement is observed between the theoretical LCRs/AFDs and corresponding measured data, thus demonstrating the validity and utility of the proposed model.

Generative models, which can generate bursty error sequences with similar burst error statistics to those of descriptive models, have an immense impact on the wireless communications industry as they can significantly reduce the computational time of simulating wireless communication links. Adaptive generative models aim to produce any error sequences with any given signal-to-noise ratios (SNRs) by using only two reference error sequences obtained from a reference transmission system with two different SNRs. Compared with traditional generative models, this adaptive technique can further considerably reduce the computational load of generating new error sequences as there is no need to simulate the whole reference transmission system again. In this paper, reference error sequences are provided by computer simulations of a long term evolution (LTE) system. Adaptive generative models are developed from three widely used generative models, namely, the simplified Fritchman model (SFM), the Baum-Welch based hidden Markov model (BWHMM), and the deterministic process based generative model (DPBGM). It is demonstrated that the adaptive DPBGM can provide accurate burst error statistics and bit error rate (BER) performance of the LTE system, while the adaptive SFM and adaptive BWHMM fail to do so.

This paper proposes a novel three-dimensional (3D) theoretical regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) Ricean fading channels. The proposed RS-GBSM, combining line-of-sight (LoS) components, a two-sphere model, and an elliptic-cylinder model, has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics, and jointly considers the azimuth and elevation angles by using the von Mises Fisher distribution. Moreover, a novel parameter computation method is proposed for jointly calculating the azimuth and elevation angles in the SoS channel simulator. Based on the proposed 3D theoretical RS-GBSM and its SoS simulation model, statistical properties are derived and thoroughly investigated. The impact of the elevation angle in the 3D model on key statistical properties is investigated by comparing with those of the corresponding two-dimensional (2D) model. It is demonstrated that the 3D model is more accurate to characterize real V2V channels, in particular for pico cell scenarios. Finally, close agreement is achieved between the theoretical model, SoS simulation model, and simulation results, demonstrating the utility of the proposed models.

Motivated by the deployment of cognitive radio (CR) based relays in cellular networks, this paper studies the fundamental limits of heterogeneous cognitive Gaussian relay channels (HCGRCs). Unlike conventional relay channels, in HCGRC a source transmits to a relay in a licensed band, while the relay transmits to a destination in an unlicensed cognitive spectrum band. The licensed and unlicensed bands are characterized by different power, bandwidth and reliability constraints. Taking an information-theoretic perspective, the fundamental properties of the HCGRC are analyzed thoroughly in terms of capacity, spectral efficiency (SE), and energy efficiency (EE). With regard to each metric, we derive the optimal resource allocation strategy and discuss the impacts of CR spectrum reliability and relay location on the metric. We find that in HCGRC, improving the SE and EE are not necessarily conflicting objectives. Instead, both metrics can be optimized simultaneously with proper resource allocation.

It is of great importance and interests for network operators to evaluate the impact of inter-cell co-channel interference on the achievable data rate in cellular networks. In this paper, a closed-form maximum achievable rate over Nakagami-m fading uplink channels based on multicell linear Wyner model is derived. Moreover, the maximum achievable rates in some special cases are investigated. Numerical results show that the maximum achievable rate decreases with the increase of the inter-cell signal interference factor and is also affected by Nakagami-m fading severity parameters and number of users in a cell.

Hybrid Automatic Retransmission reQuest (HARQ) is an effective technique to improve the reliability of wireless communication systems by detecting, correcting, and retransmitting the erroneous packets. Packet-level error sequences obtained from physical layer wireless communication systems are important for the design and performance evaluation of high layer protocols, e.g., HARQ. In this paper, we utilize the open source Vienna long-term evolution (LTE) simulator to study the impact of HARQ on the burst error statistics of packet-level error sequences. Moreover, we propose a generative model that can generate packet-level error sequences with predicted burst error statistics similar to those of error sequences obtained from wireless systems with HARQ. Simulation results demonstrate that the proposed generative model is accurate and efficient in predicting the behavior of HARQ in terms of a set of burst error statistics rather than predicting the packet error rate (PER) only.

In this paper, for the first time real--world channel measurements are used to analyse the performance of spatial modulation (SM), where a full analysis of the average bit error rate performance (ABER) of SM using measured urban correlated and uncorrelated Rayleigh fading channels is provided. The channel measurements are taken from an outdoor urban multiple input multiple output (MIMO) measurement campaign. Moreover, ABER performance results using simulated Rayleigh fading channels are provided and compared with a derived analytical bound for the ABER of SM, and the ABER results for SM using the measured urban channels. The ABER results using the measured urban channels validate the derived analytical bound and the ABER results using the simulated channels. Finally, the ABER of SM is compared with the performance of spatial multiplexing (SMX) using the measured urban channels for small and large scale MIMO. It is shown that SM offers nearly the same or a slightly better performance than SMX for small scale MIMO. However, SM offers large reduction in ABER for large scale MIMO.

The authors investigate the relay‐aided multicell multiple‐input–multiple‐output (MIMO) cellular network by comparing both the signal forwarding and interference forwarding relaying paradigms, each employing the adaptive MIMO relay scheme. The spectral, energy and economic efficiency values are utilised as key performance metrics. Furthermore, both radio frequency and circuit power consumption are considered in the energy calculation. They demonstrate that there is a tradeoff between spectral and energy efficiency. Thus, economic profitability is used to find a balance in the tradeoff as profitability is important to the long term deployment of a scheme. They introduce the economic efficiency metric which considers the inherent tradeoff as a complementary performance measure to obtain maximum economic profitability while maintaining gains in both spectral and energy efficiency.

In this paper, we propose a new regular-shaped geometry-based stochastic model (RS-GBSM) for non-isotropic scattering wideband multiple-input multiple-output vehicle-to-vehicle (V2V) Ricean fading channels. By correcting the unrealistic assumption widely used in current RS-GBSMs, the proposed model can more practically study the impact of the vehicular traffic density on channel statistics for different time delays. From the proposed model, we derive the Doppler power spectral density (PSD) and find that highly dynamic Doppler spectrum appears for V2V channels. Excellent agreement is achieved between the derived Doppler PSD and measured data, demonstrating the utility of the proposed model. To combat the intercarrier interference (ICI) caused by highly dynamic Doppler spectrum in real orthogonal frequency division multiplexing based V2V systems, this paper proposes a new type of ICI cancellation scheme, named as precoding based cancellation (PBC) scheme. The proposed scheme can be easily implemented into real V2V systems with the same ICI mitigation performance as the current best ICI cancellation scheme that has high complexity. To further improve the performance of the proposed PBC scheme, a new phase rotation aided (PRA) method, namely constant PRA (CPRA) method, is proposed. Compared with the existing PRA method, the CPRA method has better performance and much less implementation complexity. Therefore, the proposed PBC scheme with the CPRA method is the best ICI cancellation scheme for real V2V systems.

Virtual multiple-input multiple-output (V-MIMO) technology promises significant performance enhancements to cellular systems in terms of spectral efficiency (SE) and energy efficiency (EE). How these two conflicting metrics scale up in large cellular V-MIMO networks is unclear. This paper studies the EE-SE trade-off of the uplink of a multi-user cellular V-MIMO system with decode-and-forward type protocols. We first express the trade-off in an implicit function and further derive closed-form formulas of the trade-off in low and high SE regimes. Unlike conventional MIMO systems, the EE-SE trade-off of the V-MIMO system is shown to be susceptible to many factors including protocol design (e.g., resource allocation) and scenario characteristics (e.g., user density). Focusing on the medium and high SE regimes, we propose a heuristic resource allocation algorithm to optimize the EE-SE trade-off. The fundamental performance limits of the optimized V-MIMO system are subsequently investigated and compared with conventional MIMO systems in different scenarios. Numerical results reveal a surprisingly chaotic behavior of V-MIMO systems when the user density scales up. Our analysis indicates that low frequency reuse factor, adaptive resource allocation, and user density control are critical to harness the full benefits of cellular V-MIMO systems.

The fifteen papers in this special issue are devoted to the topic of spectrum and energy efficiency of wireless and mobile communications networks.

A relay cooperation scheme is proposed for the downlink of multicell multiple-input-multiple-output cellular networks. The relay stations (RSs) will cooperatively transmit the signal replicas of all the user equipments obtained during the broadcast phase. We consider different RS decoding strategies during the broadcast phase and joint relay transmission with different degrees of channel state information (CSI) sharing during the relay phase. We also propose the partial semi-orthogonal user selection (PSUS) method designed specifically for relay cooperation. The spectral and energy efficiencies are then evaluated for the relay cooperation scheme. Its cooperative costs for different cooperation levels are also investigated. Simulation results indicate that joint RS decoding outperforms independent RS decoding but a cooperative link with a bit rate of an order of magnitude greater than that achievable by the relay network is required. Compared to the competing user selection methods that require global CSI, the proposed PSUS method utilized for relay phase joint transmission operates at less than half of the cooperative cost while introduces only a slight degradation in system performance.

Energy efficiency has gained its significance when service providers' operational costs burden with the rapidly growing data traffic demand in cellular networks. In this paper, we propose an energy efficiency model for Poisson-Voronoi tessellation (PVT) cellular networks considering spatial distributions of traffic load and power consumption. The spatial distributions of traffic load and power consumption are derived for a typical PVT cell, and can be directly extended to the whole PVT cellular network based on the Palm theory. Furthermore, the energy efficiency of PVT cellular networks is evaluated by taking into account traffic load characteristics, wireless channel effects and interference. Both numerical and Monte Carlo simulations are conducted to evaluate the performance of the energy efficiency model in PVT cellular networks. These simulation results demonstrate that there exist maximal limits for energy efficiency in PVT cellular networks for given wireless channel conditions and user intensity in a cell.

This paper presents a wireless multiple-input/multiple-output (MIMO) testbed for the UK-China Science Bridges Project: R&D on Beyond Fourth Generation (B4G) Wireless Mobile Communications (UC4G). An introductory review of other approaches is first presented and then subsequently the motivation, architecture and specifications of the UC4G testbed are outlined. The flexibility of operation, versatility of function and modular design of the UC4G testbed are shown to be its key benefits.

By observing that the relay stations (RSs) will receive a signal replica of the users' transmissions during the broadcast phase, we propose an adaptive relay cooperation scheme. The RSs will utilize the readily available signal replica and the additional diversity naturally inherent in them to either perform cooperative multipoint (COMP) relaying or remain silent with only the direct transmission being considered. We investigate this scheme in a 7 cell network to demonstrate system level capacity and energy efficiency improvements. Results show that the proposed scheme outperforms the conventional adaptive relay scheme and is able to benefit from the more aggressive full bandwidth frequency reuse plan at low relay transmit power.

This paper presents a green data transmission approach to enhance the energy efficiency of power line communications (PLC) by jointly utilizing signal detection and resource allocation techniques. Due to the awareness of the interference as enabled by the signal detection function, the proposed PLC system can adaptively adjust the transmission parameters. Furthermore, given a power budget, a performance optimization algorithm is proposed that maximizes the energy efficiency of PLC by optimally choosing the signal detection duration and the transmit power. Simulation results show that the proposed system can not only mitigate the effects of interference, but also considerably improve the energy efficiency of PLC when compared with the existing PLC systems.

This paper proposes a non-stationary wideband geometry-based stochastic model (GBSM) for multiple-input multiple-output (MIMO) high-speed train (HST) channels. The proposed model has the ability to investigate the non-stationarity of HST environment caused by the high speed movement of the receiver. Based on the proposed model, the space-time-frequency (STF) correlation function (CF) and STF local scattering function (LSF) are derived for different taps. Numerical results show the non-stationarity of the proposed channel model.

Energy efficiency is becoming increasingly important for wireless communication systems in order to minimize carbon footprint of wireless networks and to increase the battery life of mobile terminals. The spectral-energy efficiency trade-off is of primary significance to determine how much energy per bit is required in a wireless communication system to achieve a specific spectral efficiency. In this paper, we study energy-efficient resource allocation scheme for Orthogonal Frequency- Division Multiple Access (OFDMA) systems with multiple users. The trade-off between spectral and energy efficiencies is analyzed under the constraint of maintaining the fairness among users. We first formulate the energy-efficient optimization problem as an integer fractional programming. We then apply an iterative fractional method to simplify the problem to integer linear programming (ILP) problem. Simulation results demonstrate that the impact of user's quality of service (QoS) is minor on the energy efficiency when a large spectral efficiency is required.

For the design and performance evaluation of advanced wireless communication systems employing multiple-input multiple-output (MIMO) technologies, realistic MIMO channel models with a good tradeoff between accuracy and complexity are indispensable. This paper compares the statistical properties of the two latest standardized MIMO channel models: Long Term Evolution-Advanced (LTE-A) and IMT-Advanced (IMT-A) channel models. Closed-from expressions are derived for the spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), envelope level-crossing rate (LCR), average fading duration (AFD), power delay profile (PDP), and frequency correlation function (FCF) for both models. Simulation results are provided which can match the corresponding theoretical derivations very well, demonstrating the correctness of both theoretical and simulation results. The LTE-A channel model is simple but has significant flaws in terms of the accuracy. It can only support system bandwidths up to 50 MHz, not the claimed 100 MHz, and only describes the average spatial-temporal properties of MIMO channels. The IMT-A channel model is complex with more model parameters but has better accuracy. It allows us to simulate the variations of different MIMO channel realizations and can indeed support system bandwidths up to 100 MHz.

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.

A significant challenge of cognitive radio (CR) is to perform wideband spectrum sensing in a fading environment. In this paper, a novel multi-rate sub-Nyquist spectrum detection(MSSD) system is introduced for cooperative wideband spectrum sensing in a distributed CR network. Using only a few sub- Nyquist samples, MSSD is able to sense the wideband spectrum without full spectrum recovery. Specifically, given the low spectral occupancy, sub-Nyquist sampling is performed in each sampling channel and a test statistic is formed by using sub-Nyquist samples from multiple sampling channels. Furthermore, the use of different sub-Nyquist sampling rates is proposed to improve the system detection performance, and the performance of MSSD over both non-fading and Rayleigh fading channels is analyzed. Numerical results show that MSSD can considerably improve the wideband spectrum sensing performance in a fading scenario, with a relatively low implementation complexity and a low computational complexity.

In this paper, we investigate the interference mitigation from a cross-layer perspective for a cognitive radio (CR) multiple-input multiple-output (MIMO) network coexisting with a primary time-division-duplexing (TDD) system. The channel allocation in the media access control (MAC) layer and a subspace-based precoding scheme in the physical layer of the CR network are jointly considered to minimise the interference to the primary user and maximise the CR throughput. Two distributed cross-layer algorithms, namely, joint iterative channel allocation and precoding (JICAP) and non-iterative channel allocation and precoding (NICAP), are proposed for the cases with and without channel information among CR nodes, respectively. Moreover, a channel estimation scheme is also proposed to enable the NICAP. The effectiveness of the proposed algorithms over non-cross-layer counterpart is demonstrated via simulations.

In this paper, we investigate the impact of receiver interference cancellation (IC) techniques on the base station (BS) downlink transmission power for a multiple-input multiple-output (MIMO) communication system with inter-cell interference. Besides the power amplifier module whose power consumption is determined by the required transmission power, we also consider the signal processing module when characterizing the total power consumption of the BS. Rather than specifying absolute values, we define the power consumption of the signal processing module as a proportion of the power consumption of the power amplifier module. We investigate the influence of both the signal processing module and receiver IC techniques on the total power consumption of the BS. Results show that besides receiver IC techniques, the power consumed at the signal processing module should not be overlooked when evaluating the BS total power consumption. Particularly, when the power consumption of the signal processing module becomes too dominant, it may contribute more to the increase of the BS total power consumption as compared to the additional power needed by the power amplifier module to combat inter-cell interference. Furthermore, any transmission power savings that are potentially obtained from receive diversity gain and receiver IC techniques may not be justified if the signal processing consumes too much power relative to that of the power amplifier.

Characterization and modeling of co-channel interference is critical for the design and performance evaluation of realistic multi-cell cellular networks. In this paper, based on alpha stable processes, an analytical co-channel interference model is proposed for multi-cell multiple-input multi-output (MIMO) cellular networks. The impact of different channel parameters on the new interference model is analyzed numerically. Furthermore, the exact normalized downlink average capacity is derived for a multi-cell MIMO cellular network with co-channel interference. Moreover, the closed-form normalized downlink average capacity is derived for cell-edge users in multi-cell multiple-input single-output (MISO) cooperative cellular networks with co-channel interference. From the new co-channel interference model and capacity formulas, the impact of cooperative antennas and base stations on cell-edge user performance in the multi-cell multi-antenna cellular network is investigated by numerical methods. Numerical results show that cooperative transmission can improve the capacity performance of multi-cell multi-antenna cooperative cellular networks, especially in a scenario with a high density of interfering base stations. The capacity performance gain is degraded with the increased number of cooperative antennas or base stations.

Abstract For the practical simulation and performance evaluation of mobile‐to‐mobile (M2M) communication systems, it is desirable to develop accurate M2M channel simulation models for more realistic scenarios of non‐isotropic scattering. In this paper, by using a ‘double‐ring’ concept to describe M2M non‐isotropic scattering environments, we propose new deterministic and stochastic sum‐of‐sinusoids (SoS) based simulation models. The proposed simulation models extensively consider the distributions of the angle of arrival (AoA) and the angle of departure (AoD), and thus provide a good approximation to the desired statistical properties of the reference model. Copyright © 2009 John Wiley & Sons, Ltd.

How to predict the self-similar network traffic with high burstiness is a great challenge for network management. The covariation orthogonal prediction could effectively capture the burstiness in the network traffic, and the artificial neural network prediction could adapt the network traffic change by self-learning. To improve the prediction accuracy, we propose a new hybrid network traffic prediction method based on the combination of the covariation orthogonal prediction and the artificial neural network prediction. Through empirical study, the accuracy of the new prediction method can be effectively improved seen from the mean and the prediction error.

This paper proposes a novel three-dimensional (3D) two-sphere regular-shaped geometry-based stochastic model (RS-GBSM) with only double-bounced rays for non-isotropic scattering narrowband multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) channels. The proposed 3D model has the ability to investigate the joint impact of both the azimuth angle and elevation angle on channel statistics. Based on the proposed model, the space-time (ST) correlation function (CF) is derived and the impact of some important parameters on the resulting ST CF is investigated. Numerical results show that the 3D model results in lower ST correlations than the corresponding 2D model.

This paper proposes a novel three-dimensional (3D) regular-shaped geometry-based stochastic model (RS-GBSM) for non-isotropic multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) Ricean fading channels. The proposed model, combining a two-sphere model and an elliptic-cylinder model, is the first 3D RS-GBSM that has the ability to investigate the impact of the vehicular traffic density (VTD) on channel statistics and jointly consider the azimuth angle and elevation angle. From the proposed model, the space-time (ST) correlation function (CF) and the corresponding space-Doppler (SD) power spectral density (PSD) are derived. Finally, some numerical results and interesting observations are given.

In this paper, we present an interference model for cognitive radio (CR) networks employing power control or contention control scheme. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are derived for two cases. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a cognitive media access control (MAC) employs carrier sense multiple access with collision avoidance (CSMA/CA) based contention control to coordinate the operation of CR nodes with transmission requests. These two control schemes are compared in terms of their resulting interference distributions. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis for the impact of key parameters on the interference of CR networks is given via numerical studies as well.

With the ever-increasing wireless/wired data applications recently, considerable efforts have focused on the design of distributed explicit rate flow control schemes for multi-input-multi-output service. This paper describes two novel wireless/wired multipoint-to-multipoint multicast flow control schemes, which are based on the distributed self-tuning proportional integrative plus derivative (SPID) controller and distributed self-tuning proportional plus integrative (SPI) controller, respectively. The control parameters can be designed to ensure the stability of the control loop in terms of source rate. The distributed explicit rate SPID and SPI controllers are located at the wireless/wired multipoint-to-multipoint multicast source to regulate the transmission rate. We further analyze the theoretical aspects of the proposed algorithm, and show how the control mechanism can be used to design a controller to support wireless/wired multipoint-to-multipoint multicast transmissions. Simulation results demonstrate the efficiency of the proposed scheme in terms of system stability, fast response, low packet loss, and high scalability, and the results also show SPID scheme has better performance than SPI scheme, however, SPID scheme requires more computing time and CPU resource.

Abstract In this paper, we analyze the impacts of different frame types on the self‐similarity and burstiness characteristics of the aggregated frame traffic in a real 802.11 wireless local area network (WLAN). We find that the impacts of different frame types are related to the mean frame sizes and the proportions of specified frame types in the aggregated frame traffic. Furthermore, we propose an analytical model to capture the relationship of self‐similarity characteristics between the aggregated frame traffic and different frame types. These new results provide an insight of frame traffic characteristics and some practical guidelines for developing new efficient algorithms to improve the common medium utilization and system throughput performance. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, based on the tapped delay line (TDL) structure, we propose a geometry-based stochastic model (GBSM) for wideband multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) Ricean fading channels. The proposed wideband model is the first GBSM that has the ability to study the impact of the vehicular traffic density (VTD) on channel statistics for different time delays, i.e., for every tap in our model. From the proposed model, the space-time (ST) correlation function (CF) and the corresponding space-Doppler (SD) power spectral density (PSD) are derived. Excellent agreement is achieved between the theoretical Doppler PSDs and measured data, demonstrating the utility of the proposed model.

In this paper, the source covariance matrices of multiple-input multiple-output (MIMO) interference channels (IFCs) are investigated from a game-theoretic perspective. It is proved that the requirement of sufficiently small interference-to- noise ratio (INR) is the sufficient condition for the uniqueness of the Nash bargaining (NB) solution. The structure of the source covariance matrices, which constitute the feasible set of NB solution, is analyzed by comparing them with the covariance matrices leading to the Nash equilibrium (NE). The existence of the NB solution and concavity of the rate product for MIMO IFCs are also studied.

In this paper, based on the concept of the tapped delay line (TDL) structure, we first propose a new wideband multiple-ring multiple input multiple output (MIMO) channel reference model for macro-cell scenarios. It is shown that the proposed model can easily match any given or measured power delay profile (PDP) and has the ability to jointly consider the angle of arrival (AoA), angle of departure (AoD), and time of arrival (ToA). From the proposed model, we derive the closed-form expression of the 3-dimensional (3-D) space-time-frequency (STF) correlation function (CF) for each tap, considering the interaction of the temporal, spatial, and frequency correlations. Finally, based on the proposed reference model, a deterministic simulation model is then proposed.

This paper develops a generic geometry-based stochastic model for mobile-to-mobile (M2M) Ricean fading channels. From the generic model, the level crossing rate (LCR) and average fade duration (AFD) are derived. Based on the derived expressions, we for the first time investigate the LCR and AFD for M2M channels with different vehicular traffic densities (VTDs). Excellent agreement is achieved between the theoretical results and measured data, demonstrating the utility of the proposed model.

Generative models are created to be used in the design and performance assessment of high layer wireless communication protocols and some error control strategies. Generative models can replace real digital wireless channels to significantly reduce the time and complexity of system simulation. The errors occurring in digital wireless channels are not independent but form clusters or bursts. Generative models have to produce error sequences having similar burst error statistics to those of original error sequences obtained from real digital systems. In this paper, we propose a generative hidden Markov model (HMM) with three layers. It is shown that the proposed three layered HMM can generate error sequences that have statistics compatible with those of original error sequences derived from an enhanced general packet radio service (EGPRS) transmission system.

In this paper, we propose a cross-layer design (CLD) scheme combining rate adaptation and four types of hybrid automatic repeat request (HARQ) for multiband orthogonal frequency division multiplexing (MB-OFDM) ultra wideband (UWB) systems following the ECMA-368 standard. The time varying property is incorporated into standard UWB channel models for the purpose of investigating rate adaptation. To accurately accommodate fast time-varying channel conditions, we propose to embed the selected rate information into the acknowledgement (ACK) frames. It is shown that the proposed CLD scheme combining rate adaptation and HARQ can provide higher throughput than the HARQ-only schemes. Among the four types of HARQ schemes, HARQ Type-III has the best throughput performance while Type I has the worst.

For realistic simulation of multiple-input multiple -output (MIMO) channels under more realistic scenario of non-isotropic scattering, we propose new deterministic and stochastic sum-of-sinusoids (SoS) based simulation models. The proposed models comprehensively consider the distributions of the angle of arrival (AoA), and thus show a good approximation to the desired statistical properties of the reference model.

To simulate mobile-to-mobile (M2M) Rayleigh fading channels under more realistic scenario of non-isotropic scattering, we propose one deterministic and one stochastic sum-of-sinusoids (SoS) based simulation models. The proposed models extensively consider the distributions of the angle of arrival (AoA) and the angle of departure (AoD), and thus show a good approximation to the desired statistical properties of the reference model.

Vehicular Communication Networks and Systems (VCNS) and Intelligent Transportation Systems (ITS) are one of the most attractive and challenging topics in recent days since a well efficient protocol for vehicular communication can facilitate the reduction of traffic congestion and can provide us with many more promising applications. In this paper, we propose a protocol for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. As one of the challenging parts of this paper, we present an experimental testbed in which two major applications of V2I & V2V communication (i.e. traffic congestion detection and emergency warning) is implemented. Based on careful analysis, we also calculate some key system parameters which reflect the efficiency of the protocol in different applications.

Soft decision decoding is currently used in many wireless communications systems because of its capability for increasing the coding gain. Soft generative models produce soft error sequences in order to help in the design and evaluation of the performance of many coding schemes and high layer wireless communication protocols. Errors in wireless fading channels are not independent but appear in bursts or clusters. In this paper, we design a soft Markovian based generative model with two embedded processes. The first process combines produced soft error bursts with soft error-free bursts. Whereas, in the second process new error bursts are created according to the number of soft error clusters in each soft error burst of a reference soft error sequence. The reference soft error sequence is obtained from an enhanced general packet radio service (EGPRS) transmission system. The achieved results show a satisfactory behavior compared with the reference soft error sequence.

In this paper, a generic and adaptive geometrybased stochastic model (GBSM) is proposed for non-isotropic multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) Ricean fading channels. The proposed model employs a combined two-ring model and ellipse model, where the received signal is constructed as a sum of the line-of-sight, single-, and doublebounced rays with different energies. This makes the model sufficiently generic and adaptable to a variety of M2M scenarios (macro-, micro-, and pico-cells). More importantly, our model is the first GBSM that has the ability to study the impact of the vehicular traffic density on channel characteristics. From the proposed model, the space-time-frequency correlation function and the corresponding space-Doppler-frequency power spectral density (PSD) of any two sub-channels are derived for a non-isotropic scattering environment. Based on the detailed investigation of correlations and PSDs, some interesting observations and useful conclusions are obtained. These observations and conclusions can be considered as a guidance for setting important parameters of our model appropriately and building up more purposeful measurement campaigns in the future. Finally, close agreement is achieved between the theoretical results and measured data, demonstrating the utility of the proposed model.

In this paper, we explore the statistical characterization of Multiple-Input Multiple-Output (MIMO) channel correlation matrices with the main focus being on their condition number statistics. More specifically, novel expressions are derived for the probability density function (PDF) and cumulative distribution function (CDF) of the MIMO condition number. Contrary to the majority of related studies, where only the common case of Rayleigh fading was considered, our investigation is extended to account for the generalized case of Ricean fading where a deterministic Line-Of-Sight (LoS) component exists in the communication link. The overall analysis is based on the principles of random matrix theory and particularly of dual complex non-central Wishart matrices; the latter represent a practical class of MIMO systems, namely dual-branch systems which are equipped with two transmit and receive antenna elements. All the derived formulae are validated through extensive simulations with the attained accuracy being remarkably good.

This paper deals with the design of a set of multiple uncorrelated Rayleigh fading waveforms. The Rayleigh fading waveforms are mutually uncorrelated, but each waveform is correlated in time. The waveforms are generated by using the deterministic sum-of-sinusoids (SOS) channel modeling principle. Two new closed-form solutions are presented for the computation of the model parameters. Analytical and numerical results show that the resulting deterministic SOS-based channel simulator fulfills all main requirements imposed by the reference model with given correlation properties derived under two-dimensional isotropic scattering conditions. The proposed methods are useful for the design of simulation models for diversity-combined Rayleigh fading channels, relay fading channels, frequency-selective channels, and multiple-input multiple-output (MIMO) channels.

Cognitive radio (CR) has been considered as a promising technology to improve the spectrum utilization. In this paper we analyze the capacity of a CR network with average received interference power constraints. Under the assumptions of uniform node placements and a simple power control scheme, the maximum transmit power of a target CR transmitter is characterized by its cumulative distribution function (CDF). We study two CR scenarios for future applications. The first scenario is called the CR based central access network, which aims at providing broadband access to CR devices. In the second scenario, the so-called CR assisted virtual multiple-input multiple-output (MIMO) network, CR is used to improve the access capability of a cellular system. The uplink ergodic channel capacities of both scenarios are derived and analyzed with an emphasis on understanding the impact of numbers of primary users and CR users on the capacity. Numerical and simulation results suggest that the CR based central access network is more suitable for less-populated rural areas where a relatively low density of primary receivers is expected; while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users.

In this paper we study the system level performance of cognitive radio (CR) networks under average received interference power constraints. Under the assumption of uniform node placements and a simple power control scheme, we derive the closed-form expression for the cumulative distribution function (CDF) of the maximum allowable transmit power of the target CR transmitter. We further study two CR network scenarios: a CR based central access network and a CR assisted virtual multiple-input multiple-output (MIMO) network. The average uplink capacities of both networks are derived and analyzed, with an emphasis on understanding the effect of the numbers of primary users and CR users on the capacity. Numerical and simulation results demonstrate that the CR based central access network is more suitable for less-populated rural areas where a lower density of primary receivers is expected, while the CR assisted virtual MIMO network performs better in urban environments with a dense population of mobile CR users.

Spectrum sensing plays a paramount role in cognitive radio (CR), which is widely agreed to be the most promising method for alleviating the symptom of RF spectral scarcity. In this paper, we first analyze the effects of multipath/shadowing on a CR system. Furthermore, we build models for the primary user system and the CR user system separately. For the former model, we develop the possible conversation zone to represent the CR cooperative detectable zone. For the latter model, we construct a novel centralized network, called the spider-net sensing network (SNSN), to implement controllable sensing resolution and reliable sensing while avoiding harmful interference from other CR users. Finally, the numerical results show that our proposed SNSN outperforms one without a grid structure using the same combining scheme.

Cognitive radio (secondary) networks have been proposed as means to improve the spectrum utilization. A secondary network can reuse the spectrum of a primary network under the condition that the primary services are not harmfully interrupted. In this paper, we study the distribution of the interference power at a primary receiver when the interfering secondary terminals are distributed in a Poisson field. We assume that a secondary terminal is able to cease its transmission if it is within a distance of R to the primary receiver. We derive a general formula for the characteristic function of the random interference generated by such a secondary network. With this general formula we investigate the impacts of R, shadowing, and small scale fading on the probability density function (PDF) of the interference power. We find that when there is no interference region (R = 0), the interference PDFs follow heavy-tailed alpha-stable distributions. In case that a proper interference region is defined by a positive value of R, the tails of the interference power PDFs can be significantly shortened. Moreover, the impacts of shadowing and small scale fading on the interference PDFs are studied and the small scale fading is found to be beneficial in terms of reducing the mean value and outage probability of the interference power.

In this paper, the performance of a multiple-input multiple-output (MIMO) system is assessed in the case of vehicle-to-roadside communications. We investigate a line-of- sight (LoS) scenario where a specular wavefront impinges on the receive side. Under these conditions, the channel response is usually rank deficient due to the high correlation between the spatial LoS responses, unless specific antenna geometries are employed in order to achieve subchannel orthogonality. For our investigation, a recently proposed criterion, which maximizes the LoS-channel rank and eventually the channel capacity, is revised for different array configurations.

The generation of multiple uncorrelated Rayleigh fading waveforms is often demanded for simulating wideband fading channels, multiple-input multiple-output (MIMO) channels, and diversity-combined fading channels. In this letter, an improved deterministic sum-of-sinusoids (SoS) channel simulator with a new parameter computation method is proposed to simulate a large number of uncorrelated Rayleigh fading processes. Compared with the existing SoS channel simulators, the proposed deterministic SoS model yields a much better simulation efficiency while still preserving satisfactory approximations to the desired statistical properties of the reference model.

Orthogonal frequency division multiplexing (OFDM) signals are very sensitive to radio frequency (RF) impairments. One of the major impairments is the mismatching errors between the in-phase (I) and quadrature (Q) branches in the up and down-conversions. The I/Q mismatching errors can include phase and amplitude mismatching errors. These mismatching errors make the signal constellation expand and rotate, which severely degrades the performance of OFDM systems. In this paper, a closed-form expression for the bit error rate (BER) of the OFDM system in I/Q mismatching error environments is derived as a function of phase and amplitude mismatching error parameters. This enables a convenient and useful evaluation of the performance of OFDM communication systems with I/Q mismatching errors. The derived analytical expression is validated by the fact that computer simulation results closely match with the analytical derivation results.

A number of turbo equalization (TE) methods with reduced complexity have recently been introduced, in which the maximum a posteriori probability (MAP) equalizer is replaced by suboptimal and low complexity ones. These methods can save the computational complexity significantly, but with sacrificed system performance. In this paper, the authors first use the extrinsic information transfer (EXIT) chart tool to visually explain why these suboptimal methods have the worse performance. Then, the authors propose a novel iterative method for TE which takes advantage of both parallel and serial concatenation turbo-like schemes. In the novel iterative method, the EXIT chart switches between the two schemes as a balance. The transmitter side is also changed correspondingly by utilizing the concept of repetition code to acquire a structure similar to turbo codes. It is shown from both analytical and simulation results that the proposed iterative method for TE results in the excellent system performance while its realization complexity is kept relatively low.

Accurate and efficient generative models are significant for the design and performance evaluation of wireless communication protocols as well as error-control schemes. In this paper, deterministic processes are used to derive a new class of hard and soft generative models for simulation of digital wireless channels with hard and soft decision outputs, respectively. The proposed deterministic-process-based generative models (DPBGMs) are all based on a properly parameterized and sampled deterministic process followed by a threshold detector and two parallel mappers. The target hard and soft error sequences are provided by computer simulations of uncoded enhanced general packet radio service (EGPRS) systems with typical urban and rural area channels. Simulation results indicate that the proposed DPBGMs enable us to approximate very closely all the interested burst-error statistics of the target hard and soft error sequences. The validity of the suggested DPBGMs is further confirmed by the excellent match of the simulated frame-error rates and residual bit-error rates of coded EGPRS systems obtained from the target and generated error sequences

Simulating wideband fading channels, multiple-input multiple-output (MIMO) channels, and diversity-combined fading channels often demands the generation of multiple uncorrelated Rayleigh fading waveforms. In this letter, two appropriate parameter computation methods, namely the method of exact Doppler spread (MEDS) and Lp-norm method (LPNM), for deterministic sum-of-sinusoids (SoS) channel simulators are investigated to guarantee the uncorrelatedness between different simulated Rayleigh fading processes. Numerical and simulation results show that the resulting deterministic SoS channel simulator can accurately and efficiently reproduce all the desired statistical properties of the reference model.

Abstract This paper studies the peak‐to‐average power ratios (PAPRs) in multicarrier modulation (MCM) systems with seven different orthogonal bases, one Fourier base and six wavelet bases. It is shown by simulation results that the PAPRs of the Fourier‐based MCM system are lower than those of all wavelet‐based MCM (WMCM) systems. A novel threshold‐based PAPR reduction method is then proposed to reduce the PAPRs in WMCM systems. Both numerical and simulation results indicate that the proposed PAPR reduction method works very effectively in WMCM systems. Copyright © 2006 John Wiley & Sons, Ltd.

Packet-level error models have great significance for the design and performance evaluation of higher layer wireless communication protocols. These models aim to characterize the statistical behavior of bursty packet error sequences encountered in digital wireless channels. In this paper, target packet error sequences are generated by computer simulations of a coded General Packet Radio Service (GPRS) system with a typical urban (TU) channel and co-channel interference. A general design procedure of a generative model is then proposed by using a properly parameterized and sampled deterministic process with a threshold detector and two parallel mappers. Simulation results indicate that the proposed deterministic process based generative model (DPBGM) allows us to approximate very well the characteristics of the target packet error sequences with respect to the gap distribution (GD), error-free run distribution (EFRD), error cluster distribution (ECD), error burst distribution (EBD), error-free burst distribution (EFBD), block error probability distribution (BEPD), and packet error correlation function (PECF). Importantly, it is shown that both the accuracy and the efficiency of the suggested DPBGM outperform those of a widely used Markov model.

In order to evaluate the performance of wireless communication protocols and design good error control schemes for bursty channels, it is important to develop accurate and simple generative models. In this paper, a novel generative deterministic model (GDM) is proposed for the simulation of bursty error sequences encountered in digital mobile fading channels. The proposed GDM is simply a properly parameterized and sampled deterministic process followed by a threshold detector and two parallel mappers. Simulation results show that this generative model enables us to match very closely any given error-free run distribution (EFRD), gap distribution (GD), and error cluster distribution (ECD) of the underlying descriptive model.

Efficient and accurate generative models are of great importance for the design and performance evaluation of wireless communication protocols as well as error control schemes. In this paper, deterministic processes are utilized to derive a new generative model for the simulation of bursty error sequences encountered in digital mobile fading channels. The proposed deterministic process based generative model (DPBGM) is simply a properly parameterized and sampled deterministic process followed by a threshold detector and two parallel mappers. The target error sequence is generated by a computer simulation of a frequency hopping (FH) convolutionally coded Gaussian minimum shift keying (GMSK) transmission system with Rayleigh fading. Simulation results show that this generative model enables us to match very closely any given gap distribution (GD), error-free run distribution (EFRD), error cluster distribution (ECD), error burst distribution (EBD), error-free burst distribution (EFBD), block error probability distribution (BEPD), and bit error correlation function (BECF) of the underlying descriptive model.

Generative models have significant applications in the design and performance evaluation of communication protocols as well as coding systems. In this paper, a novel deterministic process based generative model (DPBGM) is proposed for the characterization of burst errors in Rayleigh fading channels. A simple procedure has been developed for generating an error sequence with desired burst error statistics from a properly sampled and parameterized deterministic process followed by a threshold. The proposed generative model enables us to perfectly match any given error burst distribution (EBD) and error-free burst distribution (EFBD) of the underlying descriptive model. The gap distribution (GD) and the error-free run distribution (EFRD) obtained from the generated error sequence can also be fitted to those of the target error sequence with good accuracy.

For the simulation of practical diversity combined fading channels, multiple-input multiple-output (MIMO) channels, and space-time-selective fading channels, it is desirable to produce multiple Rayleigh fading processes with specified cross-correlations. Based on the deterministic sum-of-sinusoids (SOS) channel modeling approach, we have developed a simple efficient procedure for generating an arbitrary number of cross-correlated Rayleigh fading processes. It is demonstrated by numerical and simulation results that the resulting deterministic channel simulator can accurately reproduce all of the desired statistical properties, such as the autocorrelation functions (ACFs), the cross-correlation functions (CCFs), and the phase properties.

Abstract For the narrow band Wireless Code Division Multiple Access (WCDMA) system, there are some channel coding schemes proposed and applied like Turbo code and convolutional codes. But for the 4G Code Division Multiple Access (CDMA) wideband systems, we have to use new channel coding schemes with high bandwidth efficiency. In this case, multilevel coding (MLC) scheme is easy to map to Multiple Quardrature Amplitude Modulation (MQAM) modulation strategy to be used for 4G, and MLC+MQAM will be a potential channel coding scheme for the error correcting of next generation of mobile systems. A novel criterion, that is ‘capacity rule’ plus ‘mapping rule’, for the design of the optimum MLC scheme for radio systems over Rayleigh fading channels is proposed in this paper. Based on this theory, a few of key issues related to design an optimum MLC system are investigated. These include a novel optimum design criterion proposed, different mapping strategies, different decoding methods of MLC/MSD and MLC/Parallel Decoding on Levels (PDL) and their performance comparison over Additive White Gaussian Noise (AWGN) and Rayleigh fading channels respectively. Copyright © 2003 John Wiley & Sons, Ltd.

A channel simulator that models accurately the physical channel statistics determined by the frequency separation between two carriers in frequency hopping (FH) systems is called a FH channel simulator. In this paper, we propose a novel deterministic FH simulation model for Rayleigh fading channels, which can easily be implemented and is very useful for investigating the frequency diversity gain of low speed mobiles in practical FH systems. The optimization technique based on the L/sub p/-norm method is exploited to calculate some simulation model parameters. Moreover, the performance evaluation of the proposed channel simulator is performed for typical GSM FH mobile radio data transmission scenarios. A pretty good agreement between the statistical properties of the simulation model and those of the underlying analytical model is observed in all cases.

Multilevel coding (MLC) schemes based on channel capacity with multistage decoding (MSD) and parallel decoding on levels (PDL) are considered and compared. The channel models AWGN and Rayleigh fading are used in order to study the performance of MLC systems under different conditions. The investigation is done for 8ASK modulation and three set partitioning strategies. In each scheme BCH codes with different code lengths are used as component codes. Numerical results indicate that MSD is a sub-optimal decoding method of MLC for both channels, while PDL is most robust to varying channels if block partitioning (BP) is used. For Ungerboeck partitioning (UP) and mixed partitioning (MP) strategy, the MSD method is strongly recommended to use for the MLC system, while for the BP strategy, PDL is suggested to use as a simple decoding method compared with MSD.

According to the "capacity rule", the performance of multilevel coding (MLC) schemes with 8ASK modulation and three set partitioning strategies over AWGN channels is investigated. Two different decoding methods, which are multistage decoding (MSD) and parallel decoding on level (PDL), are used. In each scheme BCH codes with code lengths of 127 are used as component codes. Numerical results indicate that MSD is a sub-optimal decoding method of MLC for AWGN channels. For Ungerboeck partitioning (UP) and mixed partitioning (MP), the MSD method is strongly recommended to use for the MLC system, while for block partitioning (BP), PDL is suggested to use as a simple decoding method compared with MSD.

The performance of an improved multilevel coding (MLC) system with intralevel interleaving and iterative multilevel decoding in Rayleigh fading channels is studied. BCH codes are selected as component codes and code rates are distributed according to "capacity rule". The Ungerboeck partitioning scheme and 8ASK modulation are used. The simulation results indicate that interleaving technique and iterative decoding can improve the performance of MLC system greatly.

Two novel pseudonym random interleaving schemes, whose interleaving degrees obey a uniform distribution and a Rayleigh distribution respectively, are proposed in this paper. The performance of BCH (63,39,4) and BCH (127,78,7) codes using these two schemes as well as a periodic interleaving scheme in Rayleigh fading channels are obtained by simulation. The results show that pseudonym random interleaving can decrease the average time delay on some occasions and it is also a good cipher scheme.