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

In this paper, a novel vision transformer (ViT) network is proposed to adaptively construct radio multi-user channels via reconfigurable intelligent surface (RIS), with objective of enhancing received power at the required users’ locations. Different from deep learning scheme that solves RIS phase by learning relationship between the multi-user channel matrix and the RIS phase, channel impulse response (CIR) image is used as input for ViT network, which unifies representation of channels with arbitrary numbers and positions of users. Thus, the network can adaptively solve RIS phase for arbitrary channel scenarios with random user positions and different numbers of users, realizing to reconstruct channels with the required characteristics. Technically, in train stage, local CIR image sets are used as input and RIS phase sets are used as output to train ViT network. Herein, RIS phase sets are determined by closed-form solution and deep neural network for single- and multi-user scenarios, respectively. The CIR image sets are generated by a geometry-based channel model and solved RIS phase. In prediction stage, the desired CIR images, generated based on users’ locations and a mask model, serve as inputs for the trained ViT network to predict RIS phase. Functional verification of ViT network is conducted by comparing differences between CIR image generated by predicted RIS phase and original CIR images input into network. At the same time, different optimization schemes, including convolutional neural network, deep neural network, projected gradient ascent algorithm, and coordinate descent algorithm are compared with ViT network to evaluate prediction performance. The results show that the solved RIS phase by ViT network can achieve the largest coverage probability and magnitude of CIR among the other compared algorithms. At the same time, generalization capability of the above optimization schemes is evaluated under different initial user numbers. The Furthermore, impacts of some parameters related to the desired CIR image generation, such as noise range and bound, on ViT network performance are investigated. These observations can present a reference for intelligent design of communication environment via RIS.

As a novel technology in the sixth-generation (6G) wireless communication systems, integrated sensing and communication (ISAC) enables intelligent agents to perceive, predict, and interact with the environment. It is crucial for ISAC to acquire environmental information based on electromagnetic propagation, referred to as channel semantics, to facilitate tasks such as decision-making and beamforming. However, channel models that focus on physical characteristics face challenges in representing the semantics embedded in the channel, thereby limiting the performance evaluation of ISAC systems. To tackle this, we present a novel unified framework for channel modeling from the conceptual event perspective. By leveraging a multi-level semantic structure and characterized knowledge libraries, the framework decomposes complex channel characteristics into composable and extensible high-level semantic characterization, thereby better capturing the relationship between the environment and channel, and enabling more flexible adjustments of channel models for different events without requiring a complete reset. Specifically, we define channel semantics from three levels: status semantics, behavior semantics, and event semantics, corresponding to channel transient multipaths, channel time-varying trajectories, and channel topology, respectively. Taking a realistic vehicular ISAC scenario as an example, we perform semantic clustering through depth estimation and semantic segmentation of environmental images, and further characterize the channel status semantics by fitting multipath statistical distributions; behavior semantics are modeled using Markov chains to capture time-varying characteristics; event semantics are characterized by employing a co-occurrence matrix. The results indicate that the proposed model can generate accurate channels whereas representing rich semantic information. Additionally, generalization of the model for customized semantics is demonstrated.

5G-for-Railway (5G-R), the next-generation railway dedicated mobile communication system, has a leap-forward improvement compared with the previous system. Accompanied by the differences in frequency, bandwidth and scenario, it brings new challenges to channel characterization and modeling. This paper presents our latest research in this field. Specifically, the 5G-R professional channel measurement of a real railway scenario is carried out. Based on the measured data, the multidimensional channel characteristics such as large-scale fading, delay spread, stationarity, and spatial distribution are evaluated. Furthermore, key cluster parameters such as lifetime, cluster power, delay, and their correlation are analyzed to support the mainstream cluster-based channel model. These pioneering measurement and modeling work in this field can provide a basis for forming reliable and accurate 5G-R channel models.

The rapid proliferation of cellular vehicle-to-everything (C-V2X) communications calls for efficient radio resource management (RRM). Effective C-V2X systems require both ultra-low latency for V2V safety communications and high throughput for V2I services. Current systems struggle with coordinating heterogeneous requirements under rapidly changing channel conditions. To address this challenge, we propose a new framework that integrates Graph Neural Networks (GNNs) with decentralized federated learning(DFL). Our approach embeds a dynamic graph representation into the vehicular network, where communication links are modeled as graph nodes. To adapt to rapidly changing topologies, we adopt the enhanced Graph Sample and Aggregation (GraphSAGE) for scalable neighborhood aggregation. Additionally, we apply unsupervised primal-dual learning to parameterize RRM policies based on instantaneous channel conditions. To improve generalization across network, we introduce a federated model aggregation strategy that employs channel-state-aligned isomorphic GNNs. Experimental results demonstrate that, compared to traditional GNN-based benchmark solutions, our framework achieves near-optimal system throughput while maintaining low computational complexity.

Using Clustered Vehicular Federated Learning (CVFL) in vehicular networks can enhance model intelligence through distributed collaboration while preserving data privacy. CVFL groups clients with similar data distributions to reduce the impact of non-independent and identically distributed (non-iid) data on federated learning efficiency, thereby supporting realtime and dynamic traffic decision-making. However, in practical applications, the high mobility of vehicles and the intermittent connectivity of communication links make it challenging to flexibly determine the number of clusters and the vehicles within each cluster. To address these issues, we propose a Graph Neural Network(GNN)-based clustering scheme called GNN-CVFL. Our proposed scheme includes two modules: clustering and resource allocation. In the clustering module, we treat the clustering problem of vehicle clients as a node classification problem in GNN, using a GNN-based method to cluster clients accurately based on their data distribution. The proposed method can automatically determine the number of clusters and the vehicles within each cluster in real-time. Moreover, based on the clustering results, We use the Lagrangian relaxation method to dynamically allocate available bandwidth resources and CPU frequencies to minimize system latency, ensuring efficient and flexible service for all vehicles. Numerical results demonstrate the feasibility and efficiency of our proposed scheme.

In the high-speed railway (HSR) scenario, handover (HO) reliability is constrained by the high-speed movement and weak coverage at cell edges, posing a threat to the "always online" transmission requirement. To address this, we propose a dynamic beamforming scheme to mitigate HO failures and improve data rates. First, a beamforming-based HO model is established, quantifying the impact of beam direction on data rate and HO reliability. Based on this, an optimization problem is formulated, aiming to enhance data rate, reduce HO failure probability, and minimize beam adjustment overhead. Subsequently, a dynamic beam adjustment algorithm based on deep reinforcement learning is designed, leveraging its real-time decision-making capability to adaptively optimize the beam direction under rapidly changing channel conditions. Simulation results demonstrate that the proposed scheme requires only 23% of the beam adjustment overhead of the ideal real-time precise beamforming, while achieves nearly identical HO performance and 98.9% of its data rate.

Accurate path loss prediction is crucial to evaluation of wireless coverage, and deep learning based path loss prediction has recently received a lot of attention in complex scenarios. Previous studies based on deep learning methods often overlook impacts of radio propagation. Therefore, this paper proposes a deep learning framework enhanced by empirical propagation models, which guides the network learning process through physical constraints to enhance the prediction model. The model uses a convolutional neural network to extract environmental features and analyzes impacts of different empirical models at various network locations on path loss prediction performance. Experimental results show that the deep learning network using the close-in free space reference distance path loss model achieves fairly high prediction accuracy on a 5.9 GHz urban scenario dataset, with a root mean square error as low as 5.63 dB.

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

Smart railways integrate advanced information technologies into railway operating systems to improve efficiency and reliability. Although the development of the fifth generation (5G) has enhanced railway services, future smart railways require ultra-high speeds, ultra-low latency, ultra-high security, full coverage, and ultra-high positioning accuracy, which 5G cannot fully meet. Therefore, the sixth generation (6G) is envisioned to provide green and efficient all-day operations, strong information security, fully automatic driving, and low-cost intelligent maintenance. To achieve these requirements, we propose an integrated network architecture leveraging communications, computing, edge intelligence, and caching in railway systems. We have conducted in-depth investigations on key enabling technologies for reliable transmissions and wireless coverage. For high-speed mobile scenarios, we propose an artificial intelligence (AI)-enabled cross-domain channel modeling and orthogonal time–frequency space–time spread multiple access mechanism to alleviate the conflict between limited spectrum availability and massive user access. The roles of blockchain, edge intelligence, and privacy technologies in endogenously secure rail communications are also evaluated. We further explore the application of emerging paradigms such as integrated sensing and communications (SACs), AI-assisted Internet of Things (IoT), semantic communications (SCs), and digital twin (DT) networks for railway maintenance, monitoring, prediction, and accident warning. Finally, possible future research and development directions are discussed. © 2026 IEEE

In high-speed railway (HSR) scenarios, it is essential to ensure reliable handover for sustaining always-online communications of trains. However, this reliability is challenged by limited wireless coverage at cell edges and frequent handovers. To tackle these challenges, this paper explores the potential of dynamic beamforming to simultaneously improve the probability of successful handover and mitigate communication disruptions. First, we establish a beamforming-based transmission model for trains during handovers. Based on this model, we derive the impact of the beam directions of the serving and target cells on communication performance. Second, we formulate an optimization problem aiming at maximizing the conditional data rate of the train within handover regions, where the impacts of handover failure, rapid mobility, and beamforming overhead are considered. Third, to solve this optimization problem, we propose a dynamic beam direction adjustment algorithm by leveraging the property of deep reinforcement learning. The algorithm efficiently determines the optimal beam direction adjustment strategy based on the dynamic channel conditions. Finally, compared to state-of-the-art deep learning methods and beamforming strategies, simulation results demonstrate that the proposed method achieves superiority in communication quality at cell edges and handover performance, providing an efficient and reliable technical solution for HSR communications.

The orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has been recently proposed as a promising modulation scheme for next-generation communication systems with high mobility. Despite its benefits, ODDM modulation and other DD domain modulation schemes face the challenge of excessive equalization complexity. To address this challenge, we propose time domain Tomlinson-Harashima precoding (THP) for the ODDM transmitter, to make the DD domain single-tap equalizer feasible, thereby reducing the equalization complexity. In our design, we first pre-cancel the inter-symbol-interference (ISI) using the linear time-varying (LTV) channel information. Second, different from classical THP designs, we introduce a modified modulo operation with an adaptive modulus, by which the joint DD domain data multiplexing and time-domain ISI pre-cancellation can be realized without excessively increasing the bit errors. We then analytically study the losses encountered in this design, namely the power loss, the modulo noise loss, and the modulo signal loss. Based on this analysis, BER lower bounds of the ODDM system with time domain THP are derived when 4-QAM or 16-QAM modulations are adopted for symbol mapping in the DD domain. Finally, through numerical results, we validate our analysis and then demonstrate that the ODDM system with time domain THP is a promising solution to realize better BER performance over LTV channels compared to orthogonal frequency division multiplexing systems with single-tap equalizer and ODDM systems with maximum ratio combining.

This paper investigates the hybrid beamforming design for the reconfigurable intelligent surface (RIS)-aided full-duplex (FD) cell-free networks, where the access points (APs) connected to the central processing unit serve multiple users cooperatively. The weighted sum rate of uplink and downlink transmissions is maximized by jointly optimizing the digital and analog beamformers, the phase-shift coefficients of RISs and the uplink transmit power while satisfying the power budget constraints of the APs and users. For solving the problem, the objective function is reformulated using Lagrangian dual transform and fractional programming. Then, to tackle the variables coupling, the problem is divided into five subproblems that are solved iteratively via a proposed block coordinate descent (BCD)-based algorithm. To handle the unit-modulus constraint on analog beamformers and phase-shift coefficients, a monotonic fast proximal gradient (mFPG)-based method is proposed under the alternating direction method of multipliers (ADMM) framework. Numerical results demonstrate the effectiveness and efficiency of the proposed algorithm compared to three baseline algorithms. The impacts of the numbers of radio frequency chains and reflecting elements on the uplink and downlink transmissions are presented, respectively. The performance comparison between the FD and half-duplex schemes is provided.

This paper investigates the rate outage constrained (ROC) energy efficiency (EE) under multiple-input single-output (MISO) interference channels, where only channel distribution information (CDI) is available at base stations (BSs). An EE maximization problem is formulated under the constraints of tolerable rate outage probability of each user and the power budget of each BS. Due to the computationally intractable form of the considered problem, two equivalent formulations are derived and four algorithms are proposed, where two are based on the block successive upper bound minimization (BSUM) method and the other two are based on the successive convex approximation (SCA) method. Further, the philosophies behind the BSUM-based algorithms and the SCA-based algorithms are analyzed. Numerical results demonstrate the efficacy of the proposed algorithms, while the BSUM-based algorithms are much more computationally efficient than all the state-of-the-art algorithms, as long as all the coupling requirements on quality of service (like ROC transmission) can be fused into EE. Besides, the impacts of power budget, circuit power and tolerable outage probability on the ROC EE are revealed and discussed.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently emerged as a promising candidate for ensuring reliable communications over high mobility channels. One of the key challenges faced by systems based on ODDM modulation and other DD domain modulation schemes (e.g., orthogonal time frequency space modulation), is the excessive receiver complexity. To address this challenge, we propose time domain Tomlinson-Harashima precoding (THP) for the ODDM systems to make the single-tap equalizer feasible, thereby significantly reducing the receiver complexity. Different from previous THP designs, we first propose intersymbol-interference (ISI) pre-cancellation based on the linear time-varying (LTV) channel information. Second, we propose a modified modulo operation with an adaptive modulus to realize DD domain data modulation and time domain ISI precancellation. We analytically investigate the bit error rate (BER) performance of our proposed ODDM system with time domain THP. Particularly, we investigate three types of losses that can degrade the performance, namely the modulo signal loss, the power loss, and the modulo noise loss. Based on these, a lower bound for the BER of our proposed ODDM system with time domain THP under the DD domain single-tap equalizer is derived. Through numerical simulations, our analysis is first validated and finally, we show the significance of our design to attain a low complex receiver and superior BER performance over LTV channels.

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

In this paper, a 3D geometry-based stochastic channel model (GBSM) is proposed for RIS-assisted multi-user communications. The proposed GBSM is divided into two sub-channels, that is, BS-RIS and RIS-Rx links, and propagation distances and angles of multipath components are derived to describe multi-user channels. In addition, optimization objective for multi-user channel is proposed, and deep reinforcement learning is introduced to solve high-dimensional RIS phase problem. Based on the proposed model and solved RIS phase, channel capacity and root mean square delay spread are derived. The simulation results show that RIS optimization parameters and channel parameters have major impact on channel characteristics. The conclusions can provide a reference for designing and developing of RIS-assisted multi-user systems.

The cellular-vehicle-to-everything (C-V2X) communication network is constantly evolving and changing the way people travel. To realize connected automated vehicles, both precise positioning and reliable communications of vehicles and associated terminals are demanding. Since the orthogonal frequency division multiplexing (OFDM) scheme is vulnerable to Doppler spread under high-mobility, the orthogonal time frequency space (OTFS) modulation is proposed recently to tackle this challenge based on the sparsity and stability of the channel spreading function. To this end, this article proposes a waveform design for V2X based on OTFS modulation, named orthogonal time frequency code space modulated waveform (OTFCSMW). The waveform design is able to realize random access and positioning simultaneously. In detail, the transceiver design of OTFCSMW is introduced, where orthogonal spreading sequences are utilized to provide spreading gain and represent terminal identifications based on the proposed orthogonal spreading combinations. Then a joint time-of-arrival (ToA) estimation and channel estimation strategy is proposed. The ToAs of terminals are estimated based on the sparsity of taps in the channel spreading function, and remaining unknown channel parameters are estimated based on the minimum-mean-square-error (MMSE) principle. Finally, the equalization scheme for OTFCSMW based on MMSE principle is proposed. Simulation results demonstrate that OTFCSMW can realize similar positioning performance to OFDM and outperforms the orthogonal-spreading-based-OFDM-waveform (S-OFDMW) scheme on bit error rate (BER) in different V2X channel environments.

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

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

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

Reconfigurable intelligent surface (RIS) has been proposed as a wireless coverage enhancement enabler. However, due to the passive feature of the RIS, it is challenging to acquire the instantaneous channel state information for RIS-user links. This paper investigates the outage-constrained transmission design for RIS-assisted multi-user multiple-input-single-output (MISO) systems under interference channel based on channel distribution information. The transmission design problem is formulated to maximize the sum transmission rate under constraints of the tolerable outage probability of each user, the power budget of each transmitter and the phase shift coefficient of each reflecting element. To solve the computational intractable problem, a block successive upper bound minimization (BSUM)-based algorithm is proposed where the feasible set is separated w.r.t. variables into several blocks, and for each block, a computationally efficient surrogate subproblem is formulated and solved. Furthermore, the non-decreasing behavior and optimality performance of the proposed algorithms are theoretically analyzed. Numerical results show that the proposed algorithm is more computational efficient than traditional alternative optimization based algorithm, and the proposed the outage-constrained transmission design is able to suppress the average outage rate to a required level as well as maximizing the sum transmission rate.

Orthogonal time frequency space (OTFS) modulation is a promising technology for communications under high mobility in the sixth-generation (6G) communications system. To enable machine-type communications (MTC) with high mobility in 6G, researchers have considered designing multiple access (MA) technologies based on OTFS modulation. The reliability and connectivity of OTFS-MA are highly correlated with the characteristics of the channel spreading function. In the spectrum-limited MA system where the channel spreading function is not sparse and compact enough, the system device capacity of OTFS-MA schemes is limited. To this end, a grant-free MA scheme, named orthogonal time frequency code space modulation enabled multiple access (OTFCSMA) is proposed in this article. In general, orthogonal code domain resources are introduced into OTFCSMA to enhance device connectivity and transmission reliability of MA systems based on OTFS modulation. In detail, firstly, the characteristic of the realistic channel spreading function is described, especially the reduced sparsity and compactness of the channel in the spectrum-limited system. Secondly, the principles for designing OTFCSMA are described, including orthogonal spreading/despreading, data interleaving/deinterleaving, device identification, two-stage channel estimation, and data recovery strategies. Thirdly, the system device capacity and the system complexity of OTFCSMA are analyzed. Fourthly, a date-block-wise device connectivity scaling-up scheme for OTFCSMA is proposed, based on which the exponential system user capacity growth is realized. Finally, the performances of OTFCSMA on transmission reliability and device connectivity are demonstrated, and the gain brought by orthogonal spreading is analyzed.

This paper investigates the system model and the transmit beamforming design for the Cell-Free massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system. The impact of the uncertainty of the target locations on the propagation of wireless signals is considered during both uplink and downlink phases, and especially, the main statistics of the MIMO channel estimation error are theoretically derived in the closed-form fashion. A fundamental performance metric, termed communication-sensing (C-S) region, is defined for the considered system via three cases, i.e., the sensing-only case, the communication-only case and the ISAC case. The transmit beamforming design problems for the three cases are respectively carried out through different reformulations, e.g., the Lagrangian dual transform and the quadratic fractional transform, and some combinations of the block coordinate descent method and the successive convex approximation method. Numerical results present a 3-dimensional C-S region with a dynamic number of access points to illustrate the trade-off between communication and radar sensing. The advantage for radar sensing of the Cell-Free massive MIMO system is also studied via a comparison with the traditional cellular system. Finally, the efficacy of the proposed beamforming schemes is validated in comparison with zero-forcing and maximum ratio transmission schemes.

The emerging 6G network envisions integrated sensing and communication (ISAC) as a promising solution to meet growing demand for native perception ability. To optimize and evaluate ISAC systems and techniques, it is crucial to have an accurate and realistic wireless channel model. However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment. Moreover, sensing channels have not been well modeled in the existing standard-level channel models. Therefore, in order to better model ISAC channel, a cluster-based statistical channel model is proposed in this paper, which is based on measurements conducted at 28 GHz. In the proposed model, a new framework based on 3GPP standard is proposed, which includes communication clusters and sensing clusters. Clustering and tracking algorithms are used to extract and analyze ISAC channel characteristics. Furthermore, some special sensing cluster structures such as shared sensing cluster, newborn sensing cluster, etc., are defined to model correlation and difference between communication and sensing channels. Finally, accuracy of the proposed model is validated based on measurements and simulations.

In this paper, we study a robust beamforming design in a downlink reconfigurable intelligent surface (RIS)-aided multiple-input single-output (MISO) communication system with consideration of bounded channel uncertainty. The goal of the design is to minimize the transmit power by employing the symbol-level precoding (SLP) at the base station (BS), while satisfying the quality-of-service requirements of the primary users (PU) and secondary user (SU). Unlike most existing works where RIS is only a signal reflector, in this paper, RIS also operates as a transmitter and delivers the secondary information to SU by switching the reflecting beamformers. In single-PU scenario, the secondary information recovery (SIR) scheme is proposed and a power minimization problem is formulated. To tackle the non-convex problem, we decompose the problem into two sub-problems to alternately optimize the transmit and reflecting beamformers. Then, two algorithms namely the penalty-based algorithm and the monotone accelerated projected gradient-based algorithm are proposed to address the unit-modulus constraint on the reflecting beamformer. The problem is further extended to the multi-PU scenario and the extended SIR scheme is provided. Finally, the simulation results demonstrate the effectiveness of our proposed algorithms and exhibit the advantages of the SIR scheme.

This paper investigates the joint uplink (UL) and downlink (DL) robust transmission design for cell-free networks. The total data amount of the UL and DL transmissions is maximized in the presence of the statistical channel state information error by optimizing the UL digital combiners, UL transmission power, DL transmit beamforming vectors and UL/DL time allocation subject to the tolerable UL/DL outage probability constraints, the UL/DL minimum data amount requirements and the UL/DL power budgets. To tackle the variables coupling in outage probability constraints, the property of perspective function and the quadratic transform are applied. Then, the Bernstein-type inequality is used to derive the computationally tractable forms of the outage probability constraints. The considered problem is further decomposed into four subproblems and solved by the proposed alternating optimization (AO)-based algorithm. Numerical results show the proposed algorithm outperforms three existing AO-based baselines in terms of convergence speed and optimality performance. The impacts of the UL transmission power and tolerable outage probability on the UL/DL transmission and time allocation are revealed. Moreover, the effective APs for each user are defined and illustrated to show the coordination among the APs.

This paper investigates the age of information (AoI) and energy efficiency of wireless powered industrial Internet of Everything (IIoE) network, where multiple low-power IIoE devices (IIoEDs) are wirelessly charged by a hybrid access point (HAP) to transmit their sensing information to the control nodes. To enhance the system’s information timeliness with high energy efficiency, we define a novel performance metric, i.e., age-energy efficiency (AEE), which depicts the achievable AoI gain per unit energy consumption. Then, an optimization problem is formulated to maximize the system long-term AEE by jointly optimizing the IIoEDs scheduling and the HAP’s transmit power. Due to the non-convexity of the formulated problem and the intractable challenges with discrete binary variables, we first model the problem as a two-stage discrete-time Markov decision process (MDP) with carefully designed state spaces, action spaces, and reward functions. We then propose a deep reinforcement learning (DRL)-based approach to find the effective scheduling strategy and transmit power. To improve the accuracy of the learned policy, we design a dual-layer deep Q-network (DLDQN) algorithm with fast convergence. Simulation results show that our proposed DLDQN algorithm can improve the AEE by at least 25% when the number of IIoEDs exceeds 50 compared with benchmarks. Moreover, with the proposed DLDQN algorithm, the system long-term AEE can be improved with the increase of the number of IIoEDs.

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

This paper investigates the beamforming design in the Cell-Free massive multi-input multi-output (MIMO) integrated sensing and communication (ISAC) system, termed as the CF-ISAC system, in presence of the channel state information (CSI) estimation error. The beamforming design is formulated into a sensing beampattern matching mean square error minimization problem under the constraints of the power budgets of the access points (APs) and the ergodic rate requirements of the users. A computationally tractable lower bound of the ergodic rate over the imperfect CSI is derived based on the Jensen's Inequality, and then a successive convex approximation based algorithm is proposed to solve the considered problem. Numerical results illustrate the beampatterns for different direction of arrival estimations of the targets. The advantage of the CF-ISAC system for radar sensing is revealed based on the relative location between the AP and the target.

This paper investigates the energy-efficient beamforming design in a simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system, where the antenna selection scheme is adopted. An energy efficiency (EE) maximization problem is formulated by optimizing the transmit beamformers and the phase shift vectors subject to the power budget constraint of the base station (BS), the maximum transmit power constraint per antenna and the users' data rate requirements. An alternating optimization-based algorithm is proposed to tackle the coupled variables, and the quadratic transform is used to deal with the fractional formulations. Simulation results demonstrate that the antenna selection scheme can significantly improve the EE performance by suppressing the energy consumption due to massive antennas. With the assistance of the STAR-RIS, the EE performance is further enhanced.

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

The millimeter-wave (mmWave) train-to-ground (T2G) communications is an essential enabling technology for future intelligent railways, where the acquisition of beam alignment information is one of the most challenging and significant issues. Hence, in this paper, we investigate the optimal beamformers and performance bounds for the mmWave T2G beam alignment with the aid of train location information. We first propose a mmWave T2G system model, which can be used by arbitrary array geometry and identifies a clear relationship between the T2G scenario and the wireless channel. Then, based on the Cramer Rao bound (CRB), we provide two bounds characterizing the average and worst minimum mean square error (MMSE) of beam alignment with the bounded error model of train location. Next, two non-convex optimization problems are formulated aiming to find the transmitting beamformers that can minimize the bounds, which is solved optimally by relaxation and recovery techniques. Finally, numerical simulations are conducted to validate the proposed beamformers and bounds for the mmWave T2G beam alignment. In particular, the results show that the MSE performance of optimal beamformers converges to the bounds by applying the maximum likelihood estimator (MLE) in the high SNR regime.

This paper aims to enhance user fairness in unmanned aerial vehicles (UAVs) enabled cell-free wireless networks. We consider a scenario where multiple UAVs serve as access points for ground users (UEs). We jointly design the UAV deployment, power allocation, UAV-UE association, and pilot assignment to maximize the minimum downlink user rate, while taking account the impact of pilot contamination. The design is formulated as a mixed-integer non-convex optimization problem. To circumvent the problem non-convexity and facilitate the design of a computationally efficient suboptimal solution, a series of transformations and approximations are proposed based on the particle swarm optimization and successive convex approximation techniques. Simulation results are presented to demonstrate the effectiveness and advantages brought by the joint design for enhancing user fairness. Furthermore, our study provides new insights into the feasibility of using sparse association to reduce costs while maintaining performance, especially in scenarios with a large number of users.

With the rapid development of wireless communications, the industries and academia acknowledge that the millimeter-wave (mm-wave) frequency band is rich in spectral resources. This paper considers the mm-wave train-ground communication system with multiple mobile relays (MRs) in a high-speed rail (HSR) scenario. We use the deep reinforcement learning (DRL) method to solve the beam management problem to maximize the system throughput. First, the inter-beam interference in the MRs scene is modeled due to the effect of the inter-beam angle on the system performance. Second, the maximization problem of system throughput is constructed for the beam management at both the transmitter and receiver. And we proposed the dynamic joint beam management scheme for the base station and the MRs located on the train’s roof based on DRL. Finally, the performance evaluation shows that the proposed scheme displays low computational complexity in the online phase. Additionally, the system throughput performance is close to the ideal optimal beam tracking algorithm, which combines high performance and low complexity, proving the feasibility of using DRL theory for beam management in train-ground communication.

Reconfigurable intelligent surface (RIS) has received increasing attention due to its capability of extending cell coverage by reflecting signals toward receivers. This paper considers a RIS-assisted high-speed train (HST) communication system to improve coverage probability. We derive the closed-form expression of coverage probability. Moreover, we analyze impacts of some key system parameters, including transmission power, signal-to-noise ratio threshold, and horizontal distance between base station and RIS. Simulation results verify the efficiency of RIS-assisted HST communications in terms of coverage probability.

High-speed train (HST) communications are envisioned to provide diversified broadband services by integrating with 5G while the high mobility induces fast-fading channels and potentially degrades the system performance. To address this issue, we investigate an HST communication network empowered by intelligent reflecting surfaces (IRSs) with the multiple-input–multiple-output (MIMO) technology. Statistical channel state information (CSI) is exploited to mitigate the impact of the fast time-varying fading. The transceiver beamforming vectors and the IRS phase shift matrix are optimized to improve the system performance in terms of the outage probability and the ergodic capacity considering the channel uncertainty. First, we derive the analytical expression of the outage probability with a generalized Marcum$Q $-function. Then, we develop an alternating optimization algorithm to minimize the outage probability by capitalizing on the generalized eigenvalue–eigenvectors. Moreover, the ergodic capacity is deduced with statistical CSI and then optimized by analyzing the upper bound with Jensen’s approximation. Extensive simulations show that simulation results are consistent with the theoretical analysis, and the IRS-assisted system significantly outperforms the system without IRS in terms of the outage probability and the ergodic capacity.

The sixth generation (6G) wireless communication aims to enable ubiquitous intelligent connectivity in future space–air–ground–ocean-integrated networks, with extremely low latency and enhanced global coverage. However, the explosive growth in Internet of Things devices poses new challenges for smart devices to process the generated tremendous data with limited resources. In 6G networks, conventional mobile edge computing (MEC) systems encounter serious problems to satisfy the requirements of ubiquitous computing and intelligence, with extremely high mobility, resource limitation, and time variability. In this article, we propose the model of wireless computing power networks (WCPNs), by jointly unifying the computing resources from both end devices and MEC servers. Furthermore, we formulate the new problem of task transfer, to optimize the allocation of computation and communication resources in WCPN. The main objective of task transfer is to minimize the execution latency and energy consumption with respect to resource limitations and task requirements. To solve the formulated problem, we propose a multiagent deep reinforcement learning (DRL) algorithm to find the optimal task transfer and resource allocation strategies. The DRL agents collaborate with others to train a global strategy model through the proposed asynchronous federated aggregation scheme. Numerical results show that the proposed scheme can improve computation efficiency, speed up convergence rate, and enhance utility performance.

Recently, grant-free orthogonal time–frequency space-based tandem spreading multiple access (OTFS-TSMA) is proposed for machine-type communications (mMTCs) in smart railways environmental sensing. To achieve massive connections with scarce radio resources, OTFS-TSMA combines the advantages of OTFS and TSMA. It shows high connectivity and reliability under time–frequency-selective fading channels. Meanwhile, smart railways require over-horizon and all-weather environmental sensing based on mMTC, and the implementation of both would cost a lot in terrestrial networks. With the development of low-Earth orbit (LEO) satellites, enabling smart railways mMTC over LEO satellite is a potential diagram. However, in this scenario, due to the larger transmission delay and Doppler frequency shift, the time–frequency resource requirements of the OTFS modulation-based system increase significantly and are difficult to meet. To this end, OTFS-TSMA based on differential Doppler shift is proposed in this article. Specifically, in this article, the satellite-to-ground communication system model consisting of three sections is introduced, and the Doppler shift and differential Doppler shift characteristics of access points (APs) are investigated. Next, it is proven that designing OTFS-based multiple access schemes over the LEO satellite based on differential Doppler shift is not only resource-friendly but also has the advantages of service continuity and controllable multiuser interference. Then, the transceiver of differential-Doppler-shift-based OTFS-TSMA and its improved designs are proposed. Finally, the simulation results demonstrate that the proposed transceiver realizes high resource efficiency, collision resolution capability, and reliability for smart railways mMTC over the LEO satellite.

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

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

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

Orthogonal time frequency space (OTFS) modulation is an emerging technology to tackle time-frequency (TF) selective channel in high mobility scenarios. In OTFS, resource is multiplexed in the delay-Doppler (DD) domain. Based on the potential sparsity, separability, stability and compactness of the channel spreading function, OTFS is able to realize lower complexity of channel estimation, higher diversity and higher reliability compared with orthogonal frequency division multiplexing (OFDM). However, the channel spreading function for practical communication systems is rarely considered in the current OTFS-related literature. High-speed railway (HSR) is a typical high mobility scenario with trains travelling at over 200km/h, which has the potential to employ OTFS. To this end, the HSR channel spreading function is characterized and the performance of OTFS in HSR is evaluated based on the realistic channel measurement in this article. Firstly, the HSR channel in TF domain is measured based on the long term evolution railway (LTE-R) network. Then, the characteristics of the channel spreading function are analyzed. In particular, the impact of time domain channel fading on the spreading function is investigated. The characteristics of the measured spreading function are analyzed with the proposed metrics in railway viaduct and tunnel scenarios. Based on the above analysis, an algorithm for generating the channel spreading function is proposed. Feasibility of the proposed DD domain channel generation algorithm is verified through comparing metrics of which to those of the measured channel. By simulating the bit error rate (BER) and mean square channel estimation error performances of OTFS modulation in the practical band-limited systems, it is shown that the impacts of Doppler shift, delay, SFFT and time domain channel fading need be considered for the application of OTFS modulation, in contrast to the state-of-art DD domain channel generation scheme based on tap delay link (TDL) model. For example, compared to OTFS modulation under the ideal channel spreading function, OTFS modulation requires a signal gain greater than 5 dB under the practical channel spreading function affected by above factors, to achieve the same BER less than 10−2 under parameters defined in simulation.

In this paper, we consider the design of a wireless sensor network (WSN) that aims at monitoring the environment and collecting data periodically. In view of the limited energy and computational capability of the sensor nodes, a mobile edge computing (MEC) server is deployed in the WSN as a data processing unit. The goal of the design is to maintain the freshness of the data, which is characterized by the criterion of the age of information (AoI). Therefore, we analyze the long-term average AoI of the considered network. Then, the energy and time constraints for the WSN are modeled with consideration of transmission and computation. Next, a non-convex average AoI minimization problem is formulated subject to the energy and time constraints by jointly optimizing the sampling rate, computing scheduling, and transmit power. To tackle the challenging problem, the geometric programming and successive convex approximation (SCA) technique are applied to develop an algorithm with convergence guarantee. Moreover, to exhibit the benefits of the MEC server, a joint design is investigated for the WSN without the MEC server. Finally, the numerical results demonstrate the efficiency of our proposed SCA-based algorithm and show the impact of the sampling rate on the AoI performance.

In this paper, we consider a beampattern design problem for a dual-functional radar-communication (DFRC) system with the aid of the reconfigurable intelligent surface (RIS). The goal of the design is to meet the communication requirements of the users while the beampattern can match the ideal beampattern. We formulate the beampattern design as a non- convex optimization problem by jointly optimizing the transmit and passive beamformers. To solve the problem, three methods, namely the semidefinite relaxation (SDR)-based method, penalty- based method, and Riemannian conjugate gradient (RCG)-based method are proposed. The simulation results illustrate that: i) the radar performance can be significantly enhanced with the aid of RIS; ii) a tradeoff should be made between the communication performance and radar performance; iii) 4 control bits are sufficient to make the performance close to that of continuous phase shift.

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

The fast moving characteristics of high-speed trains pose a challenge to the beam alignment of high-speed railway millimeter wave communication systems. With powerful learning capabilities, machine learning-based methods can help improve the beam alignment performance, such as greatly reducing the delay. In this paper, a non-convex optimization problem is formulated aiming at maximizing the received power of downlink transmission, and deep reinforcement learning is used to assist beam alignment. Particularly, an adaptive beam alignment algorithm based on prioritized experience replay double deep Q-Learning is proposed to adjust beam direction dynamically. The algorithm observes the position of the train and the beam direction of the train roof mobile relay to guide the beam adjustment. To reduce the adjustment frequency and the requirements for train position accuracy, the service range of remote radio head is divided into multiple location bins. The beam direction adjustment is only executed when the train enters the next location bin. Simulation results verify that compared with other baseline schemes, the proposed algorithm can effectively improve the received power and reduce the beam alignment delay.

In this paper, we investigate a robust beamforming design in a millimeter wave (mmWave) communication network with consideration of the random blockages. The network with multiple remote radio units (RRUs) is taken into account, where the joint transmission coordinated multi-point (JT-CoMP) scheme is adopted to improve the spectrum efficiency. To further enhance the communication performance and maintain the reliability of the network, an active intelligent reflecting surface (IRS) panel is deployed. We formulate the robust beamforming design of the mmWave communication network as an optimization problem aiming at minimizing the total transmit power at the RRUs subject to the average signal-to-interference-plus-noise ratio (SINR) constraints and power constraint over the active IRS. To deal with the challenge caused by the variables coupling, an algorithm based on the alternating optimization (AO) and semidefinite programming (SDP) is proposed. Numerical results illustrate that: i) the transmit power of the network can be reduced by deploying both the passive and active IRSs; ii) the integration of the active IRS and CoMP scheme can obtain a significant performance gain compared to that of the conventional passive IRS and CoMP.

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

In this work, we investigate the transmission and offloading strategy in the nonorthogonal multiple access (NOMA)-enabled fog computing system for the Internet of Things (IoT). We aim to minimize the total energy consumption of the IoT system while satisfying the latency requirements. Due to the energy minimization problem is a mixed-integer nonlinear programming, we decompose the problem into two subproblems for different optimizing variables, i.e., fog node selection and resource allocation subproblems, and propose a multinode collaboration transmission and computation (MCTC) algorithm. Specifically, the fog node selection subproblem can be transformed into the assignment problem, which is constructed as a bipartite graph to obtain the node selection strategy. For the resource allocation subproblem, we propose an iterative algorithm to obtain the offloading workload, duration allocation, and computation resource. Simulation results are provided, which demonstrate that the proposed algorithm outperforms the other strategies by 56.88% at least.

This article studies the coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios, including smart farming and smart ranching, where the None-Line-of-Sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for the TS-based system than that for PS-based one.

Recently, unmanned aerial vehicle (UAV)-assisted ground targets localization is widely used in search and rescue (SAR) scenes. In this article, we propose a UAV search and localization strategy, which can search and locate unknown number of victims. The proposed strategy uses the UAV as a mobile anchor to measure time of arrival (TOA) and can effectively mitigate localization errors caused by non-line-of-sight (NLOS) propagation. Generally speaking, the strategy is divided into two parts: 1) trajectory planning and 2) localization. By selecting waypoints and planning a suitable search trajectory, UAV can obtain all the measurement information in deployment area and ensure that each target has at least three anchors for localization even in the NLOS propagation environment. In the localization stage, a new estimator is proposed based on maximum-likelihood estimation (MLE), which estimates average NLOS bias together with the coordinates of target and can be well solved by the particle swarm algorithm. The simulation results show that the proposed strategy outperforms for mitigating NLOS error compared with other methods, and can effectively ensure high localization accuracy in the NLOS propagation environment.

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectrum efficiency. The nature of sporadic activity in mMTC provides a solution to enhance spectrum efficiency by employing compressive sensing (CS) to perform multiuser detection (MUD). However, CS-MUD suffers from high computation complexity and fails to meet the strict latency requirement in some critical applications. To address this problem, in this paper, we propose a novel deep learning (DL) based framework for grant-free non-orthogonal multiple access (GF-NOMA), where we utilize the information distilled from the initial data recovery phase to further enhance channel estimation, which in turn improves data recovery performance. Besides, we design an interpretable and structured Model-driven Prior Information Aided Network (M-PIAN) and provide theoretical analysis that demonstrates the proposed M-PIAN can converge faster and support more users. Experiments show that the proposed method outperforms existing CS algorithms and DL methods in both computation complexity and reconstruction accuracy.

Recently, intelligent reflecting surface (IRS) has become a research focus for its capability of controlling the radio propagation environments. Compared to the conventional terrestrial IRS, aerial IRS (AIRS) exploiting unmanned aerial vehicle (UAV)/high-altitude platform (HAP) can provide better deployment flexibility. To this end, a three-dimensional (3D) one-cylinder model is first developed for AIRS-assisted multiple-input multiple-output (MIMO) narrowband channels. In order to change the wireless channel with AIRS and create a favorable propagation environment, we propose a novel method of designing the phase-shifts for the IRS elements. Based on the model, channel impulse response (CIR), space-time correlation function, and channel capacity are derived and thoroughly investigated. A key observation in this paper is that multipath and Doppler effects in radio propagation environments can be effectively mitigated via adjusting the phase-shifts of IRS. More specifically, for the special propagation environments in the absence of any scatterers, it is found that the effects of multipath fading can be completely eliminated by IRSs. While for the general propagation environments with multiple scatterers, a small number of IRS elements can also significantly reduce the Doppler spread and the deep fades of the channels. Based on the numerical investigation of channel correlations, it is shown that channel non-stationarity is not introduced into the time domain when the phase shift of IRS is linear related to the time. Moreover, the channel capacity can also be improved by the proposed methods. Finally, the model with non-ideal IRSs is considered and it is found that using non-ideal IRSs results in poor performances compared with using ideal IRSs. These conclusions will provide a fundamental support for developing intelligent and controllable propagation environments of the future sixth-generation (6G) wireless networks.

Video surveillance is gaining increasing popularity to assist in railway intrusion detection in recent years. However, efficient and accurate intrusion detection remains a challenging issue due to: (a) limited sample number: only small sample size (or portion) of intrusive video frames is available; (b) high inter-scene dissimilarity: various railway track area scenes are captured by cameras installed in different landforms; (c) high intra-scene similarity: the video frames captured by an individual camera share a same background. In this paper, an efficient few-shot learning solution is developed to address the above issues. In particular, an enhanced model-agnostic meta-learner is trained using both the original video frames and segmented masks of track area extracted from the video. Moreover, theoretical analysis and engineering solutions are provided to cope with the highly similar video frames in the meta-model training phase. The proposed method is tested on realistic railway video dataset. Numerical results show that the enhanced meta-learner successfully adapts unseen scene with only few newly collected video frame samples, and its intrusion detection accuracy outperforms that of the standard randomly initialised supervised learning.

To support the operational response for ships in distress and oil spills, a space-air-sea-ground integrated monitoring network-based forecasting system was constructed. A three-layered structure of the framework was designed, including sensing layer, network layer and application layer. The observations are collected by space-air-sea-ground sensors, followed with the transmission by the network layer. These observations are received and processed, and are then combined with the forecasting. The coverage, availability and reliability of various observations are discussed. The uncertainty in forecasting and the approach to improve its performance are studied. The system was applied to the uncontrolled ship and the following oil spill from Sanchi accident occurred in East China sea in 2018. The space-bore synthetic aperture radar (SAR) observations for ship and oil spill are verified by air-borne SAR observations and on-site observations. The accuracy of the forecasting trajectory is improved by integrating more observations. The uncertainty in the forecasting is accounted for by an ensemble of the available wind and current forcing, in order to improve the reliability of the conclusions. The monitoring and forecasting play a complementary role, which have improved the support to maritime transportation emergency response.

Fifth-Generation mobile networks for Railway (5G-R) is a promising train-ground communication solution to deal with the design challenges on ubiquitous connections and high reliability transmission for smart railways. Considering the linear coverage scenario along the railway lines, deep overlapping coverage can enhance the coverage reliability, but the capacity of cell edge users is plagued by severe inter-cell interference. In this paper, the fundamental performance of 5G-R systems with linear redundant coverage is investigated. We quantitatively analyze the impact of inter-cell interference on the capacity of cell edge users, in which the exponential effective signal to interference plus noise (SINR) mapping method is utilized to analyze SINR distribution of 5G-R users. Then, we investigate the coverage reliability considering the base station (BS) failure. Finally, taking cell edge user capacity as the optimization objective, we analyze the cell edge user capacity-coverage reliability tradeoff for 5G-R systems, and then provide useful insights into BS deployment for 5G-R systems through performance simulations and numerical results.

Unmanned aerial vehicles (UAVs) with huge-capacity batteries could be employed to wirelessly charge the ground sensor users (GSUs) and enhance the coverage of aerial wireless networks in outdoor Internet of Things (IoT). This paper investigates the information and energy coverage of UAV-enabled simultaneous wireless information and power transfer (SWIPT) networks. Both power splitting (PS) and time switching (TS) receiver architectures are considered. By using stochastic geometry approach, the general and explicit expressions of the information coverage probability (ICP), the energy coverage probability (ECP) and the joint information and energy coverage probability (JIECP) are derived under the nonlinear and linear energy harvesting (EH) models, respectively. Particularly, the Laplace transform and the probability generating functional (PGFL) are used to derive the ICP. And, Campbell’s theorem and the maximum function are applied to obtain the ECP and the JIECP, respectively. To achieve the optimal UAVs’ deployment density, the maximization optimization problems are formulated for the PS-based and TS-based systems, respectively. By using the series expansion of$Q(x)$($Q$-function) with large$x$, the closed-form approximating optimal solutions to the formulated problems are obtained. Monte Carlo simulations validate the correction of our obtained theoretical results, and numerical results show that the performance of the PS-based system is superior to that of the TS-based one. Moreover, when the energy requirement of GSUs or the transmit power of UAVs is relatively large, or when the information requirement of GSUs or the UAV deployment density is relatively small, compared with the nonlinear EH model, the analysis bias caused by traditional linear EH model is relatively large and in these cases, traditional linear EH model cannot be used to replace the nonlinear EH one for the system performance analysis or optimal system design.

For the ultra-reliable low-latency communication (URLLC), the existing joint design algorithms of channel training and data transmission are not applicable due to the stringent reliability requirement and limited blocklength. To address this issue, we develop a low-complexity joint design framework for MISO communication based on the finite blocklength code (FBC). Specifically, an approximate bound of the packet error probability (PEP) is first derived and validated by practical modulation and coding schemes. It reveals the inherent tension between reliability, latency, and information bit number. Then, we formulate the joint design into a nonconvex optimization problem with the objective to maximize the information bit number. By exploiting the monotonicity of the PEP approximate bound, we provide closed-form solutions of power and blocklength allocation. Thereby, we develop a low-complexity algorithm to support the URLLC services aiming at the information bit number maximization. Furthermore, we investigate the joint designs to optimize the reliability, latency, and total energy, respectively, to fully meet the diverse demands of URLLC services. Finally, numerical results are provided to validate the proposed joint designs. The results show the outage capacity-based design severely underestimates the required wireless resources.

This paper studies the robust beamforming design for simultaneous wireless information and power transfer (SWIPT)-enabled networks, where the rate-splitting (RS) scheme and the power-splitting (PS) energy harvesting (EH) receiver are adopted for secure information transfer and EH, respectively. In order to explore the worst-case energy efficiency (EE) performance limit of the system, an EE maximization problem is formulated with the elliptically bounded channel state information error model under the constraints of the quality of service (QoS) requirements of information decoding users, the EH requirements of EH users and the power budget at the transmitter. To tackle the formulated non-convex problem, a sequential minimal optimization-based algorithm is first proposed to construct a mapping table and the optimal PS ratios of the PS EH receiver are found by searching the table. Then, a dual-layer iterative algorithm is designed to obtain the maximal EE based on the Dinkelbach’s method in the inner loop and the successive convex approximation method in the outer loop. To accelerate the convergence of the outer loop, an efficient initialization algorithm is also designed. Simulation results show that the RS scheme contributes to the EE enhancement, and the PS EH receiver enlarges the rate-energy region restricted by the non-linear EH circuit. Moreover, traditional sum-rate maximization design and power minimization design may induce a notable worst-case EE performance loss at the high-power region and the low-QoS requirement region, respectively.

Massive internet of things (mIoT) could play an important role in the future smart high-speed railway (HSR), where grant-free multiple access technologies are required. Recently, tandem spreading multiple access (TSMA) has been raised for mIoT without mobility which achieves high connectivity and reliability. Meanwhile, orthogonal time frequency space (OTFS) modulation shows its potential to combat high mobility in point-to-point communication systems. To this end, in this article, we jointly design OTFS and TSMA, and propose OTFS-TSMA for HSR mIoT. The principle of OTFS-TSMA transceiver is described, where OTFS and TSMA are improved respectively. Especially, two-dimension cyclic shift of DD domain elements in OTFS is transformed into cyclic shift of Doppler elements, segments, symbols and chips by the proposed novel resource allocation and interleaving schemes. Data recovery approaches of the four categories of cyclic shift are given, thus massive user interference is mitigated. Simulation results illustrate that both high user connectivity and transmission reliability in HSR massive IoT can be achieved by OTFS-TSMA.

This paper investigates a joint beamforming design in a multiuser multiple-input single-output (MISO) communication network aided with an intelligent reflecting surface (IRS) panel. The symbol-level precoding (SLP) is adopted to enhance the system performance by exploiting the multiuser interference (MUI) with consideration of bounded channel uncertainty. The joint beamforming design is formulated into a nonconvex worst-case robust programming to minimize the transmit power subject to single-to-noise ratio (SNR) requirements. To address the challenges due to the constant modulus and the coupling of the beamformers, we first study the single-user case. Specifically, we propose and compare two algorithms based on the semidefinite relaxation (SDR) and alternating optimization (AO) methods, respectively. It turns out that the AO-based algorithm has much lower computational complexity but with almost the same power to the SDR-based algorithm. Then, we apply the AO technique to the multiuser case and thereby develop an algorithm based on the proximal gradient descent (PGD) method. The algorithm can be generalized to the case of finite-resolution IRS and the scenario with direct links from the transmitter to the users. Numerical results show that the SLP can significantly improve the system performance. Meanwhile, 3-bit phase shifters can achieve near-optimal power performance.

Towards the sixth generation (6G) wireless communications, multiple access is a potential technology to achieve thousand times of traffic capacity enhancement comparing with 5G. This paper investigates the feasible signal-to-interference-plus-noise-ratio (SINR) region for the multi-cell downlink and uplink non-orthogonal multiple access (NOMA) systems. Within a cell, the signals of different users are multiplexed on a channel with different power levels and successive interference cancellation is applied at the receivers to decode the signals. For the downlink, based on Perron-Frobenius Theory, we first derive a necessary and sufficient condition for an SINR vector to be feasible under a fixed decoding order. Then, a closed-form expression for the feasible SINR region is given by the union of the SINR regions under all possible decoding orders. Following a similar idea, the expression of the feasible SINR region for the uplink is also derived. Furthermore, the duality between the multi-cell downlink and uplink NOMA systems is explored. It is proved that the two systems have same feasible SINR region under dual channels. Finally, we propose an efficient algorithm to approximate the SINR region boundary. Simulation results are provided to validate the efficiency of the algorithm and compare the performance with orthogonal multiple access scheme.

For reliable and efficient communications of aerial platforms, such as unmanned aerial vehicles (UAVs), the cellular network is envisioned to provide connectivity for the aerial and ground user equipment (GUE) simultaneously, which brings challenges to the existing pattern of the base station (BS) tailored for ground-level services. Thus, we focus on the coverage probability analysis to investigate the coexistence of aerial and terrestrial users, by employing realistic antenna and channel models reported in the 3rd Generation Partnership Project (3GPP). The homogeneous Poisson point process (PPP) is used to describe the BS distribution, and the BS antenna is adjustable in the down-tilted angle and the number of the antenna array. Meantime, omnidirectional antennas are used for cellular users. We first derive the approximation of coverage probability and then conduct numerous simulations to evaluate the impacts of antenna numbers, down-tilted angles, carrier frequencies, and user heights. One of the essential findings indicates that the coverage probabilities of high-altitude users become less sensitive to the down-tilted angle. Moreover, we found that the aerial user equipment (AUE) in a certain range of heights can achieve the same or better coverage probability than that of GUE, which provides an insight into the effective deployment of cellular-connected aerial communications.

Different from the traditional terrestrial intelligent reflecting surface (IRS), aerial IRS (AIRS) can provide some unique advantages, such as flexible deployment and wider-view signal reflection. In this paper, a three-dimensional (3D) single cylinder simulation channel model is proposed for AIRS-aided multiple-input multiple-output (MIMO) communication systems, where the considered propagation scenario consists of a fixed base station (BS) and a mobile station (MS). Based on the model, the channel impulse response (CIR), spreading function, and channel capacity are derived. Then, some heuristic algorithms are proposed to obtain the phase shifts of the IRS elements. It is found that multipath fading and Doppler effects stemming from the movement of MS can be effectively mitigated via adjusting the tunable phase shifts of the IRS elements. Moreover, the channel capacity of the system could also be improved by the proposed schemes. These findings can be used to lay a foundation for developing intelligent and controllable propagation environments.

This paper considers an intelligent reconfigurable surface (IRS) assisted high-speed train communication system to address blockage issues, thus enhancing the spectrum efficiency and reducing the outage probability. However, instantaneous channel state information (CSI) at the base station is hard to be acquired in high-speed railway communication scenarios. To deal with the channel uncertainty, the outage performance is investigated by exploiting the statistical CSI in the downlink multiple-input multiple-output system, and we derive a closed-form expression of the outage probability. Moreover, an outage probability minimization problem is formulated to facilitate the IRS phase shift design and transceiver beam-forming design at the BS and the mobile relay on the train, subject to the transmit power constraint. Simulation results validate the efficacy of the proposed algorithm in terms of outage probability and effective rate, which is in close agreement with the theoretical analysis. Besides, the impacts of critical system parameters including the transmit power and the signal-to-noise ratio (SNR) threshold on the system performance are presented.

Wireless Internet-of-Things (IoT) networks empowered by blockchain have became a promising architecture to establish trust and consensus mechanisms in a distributed manner. However, the computational complexity and limited on-board energy of wireless devices impose great challenges on applying blockchain into IoT networks. To address this issue, this work introduces multiple mobile edge computing (MEC) to provide sufficient computational power for miners (i.e., IoT devices). As such, the computation-intensive tasks of the miners can be either computed locally or offloaded to some certain MEC servers to fully exploit the computation resources. Moreover, to decrease the energy consumption of the IoT networks, an optimization problem is formulated to maximize the energy efficiency of IoT devices by jointly optimizing the computation mode selection and power allocation. Since the formulated problem is generally intractable with mixed-integer variables, an Fmincon-based algorithm is proposed, which guarantees a globally optimal solution. To further reduce the computational complexity of the proposed optimal method, a Deep Neural Network (DNN)-based deep learning method is applied to facilitate the computation of the proposed algorithm. Finally, numerical results demonstrate the advantages of the proposed network architecture and the algorithm in terms of both the energy and computational efficiency.

Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability of improving coverage and communication performance. In this paper, a geometric RIS-assisted multiple-input multiple-output channel model is proposed for fixed-to-mobile (F2M) communications based on a three-dimensional cylinder model. The receiver is regarded as in motion, and the time-varying angles and propagation distances are derived to describe non-stationary channels. Based on the proposed channel model, the effect of RIS on spacetime correlation function is investigated. The results can be used to evaluate and optimize the performance of RIS-assisted F2M communication systems.

This article investigates a multifog server (FS)-assisted nonorthogonal multiple access (NOMA)-based wireless powered network, where an energy-limited wireless device (WD) first harvests energy from a power transmitter (PT) and multiple helping FSs and then uses the harvested energy to partially offload its computing task to the FSs with NOMA for computing. To explore the WD's performance limit in terms of achievable computation rate, an optimization problem is formulated by jointly optimizing the time assignment, the power allocation, and the computation frequency under multiple system constraints. Since the problem is nonconvex with no known solution, an efficient solution approach is designed to achieve the ε-optimal solution, in which the transmit power vector and the computation frequency are jointly optimized with fixed-time assignment, and then, a golden section search (GSS)-based algorithm is designed to find the optimal time assignment. For the case when the FS is with sufficiently strong computation capability, some semiclosed-form results are derived. Numerous results show that our proposed design achieves much higher computation rate than benchmark schemes. Moreover, with the increment of the helping FSs, the achievable computation rate increases while the increasing rate is decreased. Besides, by employing NOMA, the WD's computation rate is also improved compared with orthogonal multiple access (OMA)-based scheme. Additionally, in such a system, with nonlinear energy harvesting (EH) model adopted, the more the helping FSs are deployed, the more the performance loss caused by the traditional linear EH model can be reduced.

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.

Unmanned aerial vehicle (UAV) communications are expected to play a major role in future space-air-ground integrated networks (SAGINs). In this paper, a geometric three-dimensional (3D) non-stationary channel model operating at millimeter-wave (mmWave) band is proposed for wideband UAV multiple-input multiple-output (MIMO) communications based on a multiple-layer cylinder reference model, where both stationary and moving clusters around transmitter (Tx) and receiver (Rx) are considered. Unlike the existing UAV-based GBSMs, the proposed model considers both local and far clusters in the propagation environments. On this basis, a continuous-time Markov model with two states is used to model the dynamic properties of clusters (i.e., clusters appear/disappear with time), and the closed-form expressions of the survival probabilities of clusters are derived. Furthermore, we derive and investigate some significant statistical properties, including space-time-frequency correlation function, quasi-stationary interval, and Doppler power spectrum. Numerical results show that the local mobile cluster (LMC) component leads to higher time correlation compared with the local stationary cluster (LSC) component. In addition, it is found that the LMC component leads to larger quasi-stationary interval compared with the LSC component. Finally, it is found that the motion of transceivers and clusters, and the changes of carrier frequency introduce significant fluctuations in Doppler power spectrum. These observations and conclusions can be considered as a guidance for mmWave UAV MIMO system design.

This paper investigates a multi-node wireless powered communication network (WPCN), where a hybrid access point (HAP) first charges an Internet of Thing (IoT) device wirelessly with the assistance of multiple selfish wireless nodes (WNs), and then the IoT device uses the harvested energy to transmit real-time status updates to the HAP. Two incentive schemes, i.e., the energy-incentive scheme and the price-incentive scheme, are designed to overcome the selfishness of the WNs and enhance the per-packet AoI performance. For the energy-incentive scheme, an AoI-energy utility function is defined and an optimization problem is formulated to maximize the AoI-energy utility value of the HAP-IoT device pair. By using equality constraint elimination and Lagrangian method, the problem is solved and some closed-form solutions are derived to obtain the optimal solution. For the price-incentive scheme, an AoI-price utility function is defined and a Stackelberg game is established to maximize the utility of the HAP-IoT device pair. By using function transformation and Lagrange method, some semi-closed-form solutions are derived to maximize their own profits of the HAP and WNs in a distributed way. Numerical results show that our proposed two incentive mechanisms are able to achieve higher network utility values than the benchmark scheme. The more the number of WNs, the lower the AoI of each status update packet and the higher utility value of the HAP. It also shows that by positioning WNs closer to the IoT device, the better per-packet AoI performance can be achieved by both incentive mechanisms. Additionally, for the energy-incentive mechanism, its achieved AoI gain and energy gain decrease with the increment of the distance between the HAP and the IoT device. But for the price-incentive mechanism, the opposite phenomenon is observed.

Orthogonal time frequency space (OTFS) modulation has been widely considered for high-mobility scenarios. Satellite-to-ground communications have recently received much attention as a typical high-mobility scenario and face great challenges due to the high Doppler shift. To enable reliable communications and high spectral efficiency in satellite mobile communications, we evaluate OTFS modulation performance for geostationary Earth orbit and low Earth orbit satellite-to-ground channels at sub-6-GHz and millimeter-wave bands in both line-of-sight and non-line-of-sight cases. The minimum mean squared error with successive detection (MMSE-SD) is used to improve the bit error rate performance. The adaptability of OTFS and the signal detection technologies in satellite-to-ground channels are analyzed. Simulation results confirm the feasibility of applying OTFS modulation to satellite-to-ground communications with high mobility. Because full diversity in the delay-Doppler domain can be explored, different terminal movement velocities do not have a significant impact on the performance of OTFS modulation, and OTFS modulation can achieve better performance compared with classical orthogonal frequency division multiplexing in satellite-to-ground channels. It is found that MMSE-SD can improve the performance of OTFS modulation compared with an MMSE equalizer.

Scenario identification plays an important role in improving communication system performance. Considering that the scenarios of vehicle communications are dynamic due to movements of vehicles, and there are obvious differences in channel characteristics, vehicle speeds, traffic densities between various scenarios, the requirement for real-time scenario identification of vehicular communications is increasingly urgent. Vehicular communication systems can select appropriate channel models and transmission mode by correctly identifying the current scenarios to maintain an effective and reliable operating state. This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. Channel characteristics extracted from channel measurements in different scenarios form the datasets used to training, then a back-propagation neural network (BPNN) is trained, and a scenario identification model is obtained. Furthermore, the model configuration scheme is explored and presented which can make the proposed identification model achieves optimal performance. Subsequently, identification accuracy is verified by using validation data of the corresponding scenarios. The results show that the identification accuracies are all above 98 % in four typical scenarios of urban areas, highways, tunnels, and vehicle obstructions, which indicates that the model proposed in this paper shows good performance in scenario identification for intelligent vehicular communications.

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance.

The integration of unmanned aerial vehicles (UAVs) and millimeter wave (mmWave) technology can provide high data rate for the fifth generation (5G) and beyond wireless networks. In this paper, a non-stationary geometric mmWave multiple-input multiple-output (MIMO) channel model is proposed for air-to-air (A2A) communications based on a three-dimensional (3D) cylinder model. To describe the real A2A propagation environments, a two-state continuous-time Markov process is introduced to model the dynamic properties (appearance and disappearance) of scatterers. The movements of the transmitter and receiver result in time-varying angles and propagation distances that make the model more general. Based on the proposed model, the space-time-frequency correlation functions are derived using the survival probabilities of scatterers. The effects of some important model parameters on channel correlation and non-stationarity are investigated. The observations and conclusions can be used to evaluate and optimize the performance of mmWave UAV A2A communication systems.

This work considers opportunistic mobile computation offloading between a requestor and a helper. The requestor device may offload some of its computation-intensive tasks to the helper device. The availability of the helper, however, is random. The objective of this work is to find the optimum offloading decisions for the requestor to minimize its energy consumption, subject to a mean delay constraint of the tasks. The problem is formulated as a constrained Markov decision process by taking into consideration the random task arrivals, availability of the helper, and time-varying channel conditions. Optimal offline solution is first obtained through linear programming. An online algorithm is then designed to learn the optimum offloading policy by introducing post-decision states into the problem. Simulation results demonstrate that the proposed online algorithm achieves close-to-optimum performance with much lower complexity.

As the sophisticated applications with latency constrained are difficult to operate on mobile devices with lower computing capability, fog-computing based radio access networks (F-RANs) have become a research hotspot as a revolutionary network architecture. It can offload a proportion of user's input tasks and provide scalable computation services at the fog-computing access points (F-APs), which reduce the task processing time and extend the battery life of local devices. In addition, the incorporation of millimeter-wave (mm-wave) band further improves the network performance with the uploading rate greatly increased. In this paper, we consider the multi-user uplink scenario, and formulate the problem of minimizing the total energy consumption of all users within the required latency. On the one hand, the optimization of user association joint with sub-channel allocation is studied to determine the connection status between users and edge F-AP nodes. It is modeled as a two-sided matching game representing the resource competition among users. As a result, the sub-optimal solution is obtained at lower complexity. On the other hand, the optimization of computation offloading for all users is also performed. Local devices can adjust CPU computing speed using dynamic voltage scaling (DVS) technology, and the offloading ratio is solved by convex programming. Both yield closed-form solutions. To this end, performance evaluation under multiple system parameters demonstrates the lower energy consumption and higher effectiveness of our proposed scheme in comparison with the baseline schemes.

Unmanned aerial vehicles (UAVs) have aroused great attention for future wireless communications since the drone can be used as an aerial base station and also as a cellular user to perform on-demand tasks. Because the reliable and robust connection between UAV and ground station (GS) plays an essential role in the UAV-based air-to-ground (AG) communications, the accurate radio channel model is needed. In this paper, we conducted AG wideband radio channel measurements in a hilly environment at 6.5 GHz with a bandwidth of 500 MHz, which is one of the high bands for ultra-wideband (UWB) communications in IEEE standards. The large-scale characteristics, including path loss and shadow fading, are represented by the close-in (CI) model. The small-scale characteristics that illustrate the fading and the multipath effect are analyzed with three critical parameters, including the small-scale fading, Rician K-factor and root-mean-square (RMS) delay spread. Afterward, the tapped delay line (TDL) model is built according to the detected multipath in the power delay profile (PDP), and the channel impulse response (CIR) is discussed for wideband channel modeling. The results can be used for the design and analysis of the AG systems.

This paper investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

The B5G is expected to support multiple massive machine-type communication (mMTC) services. However, limited resources impede the access of a large number of machine-type devices, and existing access frameworks are not efficient for transmitting small data packets. In this paper, we propose a compressive sensing (CS) approach for contention based random access scheme to support massive connections (≥ 106devices) within a certain time-frequency resource. Different from the four-step contention based access scheme in LTE, we adopt a one-step access scheme to save the energy and spectrum resources. It breaks the bottleneck of existing CS multiuser detection methods which have poor scalability (e.g., assuming ≤ 104devices in relevant literatures) due to the high computational complexity of CS. The improved performance of the newly proposed method is observed in our experimental results.

In the real propagation environments, multipath components (MPCs) in wireless channel usually exist as clusters. Cluster based structure of MPCs has been widely used in wireless channel modeling. In this paper, a novel MPC clustering algorithm is proposed based on spectral clustering. Considering that MPCs having strong power should usually be grouped into different clusters, the algorithm introduces a power-weighted processing to identify the similarity of each MPC. The process of weighting the power is conducted by generating similarity matrix using the full link method of Gaussian kernel function in the traditional spectral clustering algorithm. In order to achieve high clustering accuracy, the dimensionality reduction method of Laplacian Eigenmap is decomposed by using the normalized cut method, the obtained eigenvectors are re-clustered to cut the generated similarity matrix for MPC clustering. In the simulation results, it is found that this algorithm can well separate MPCs with high powers into different clusters and achieves better clustering performance compared with KPowerMeans, Kmeans, and the traditional spectral clustering algorithms.

Unmanned aerial vehicle (UAV) communications are envisioned to support numerous applications in the fifth and sixth generations wireless networks. In this paper, a three-dimensional (3D) non-stationary semi-spherical geometrical model is proposed for narrowband multi-input multi-output (MIMO) UAV channels. The Gauss-Markov mobility model is used to characterize the UAV rotation for the first time, which results in time-varying elevation and orientation angles of the antenna array and further leads to channel non-stationarity. Based on the proposed model, the space-time correlation function is derived and investigated in terms of the UAV movements (including pitch, roll, and heave). These observations and conclusions can be used as a reference for the system design and performance analysis of UAV-MIMO communication systems.

Vehicle obstruction is a special scenario in vehicle-to-vehicle (V2V) communications. In this case, channel characteristics including path loss and spatial distributions are obviously different from other typical vehicular communication scenarios. However, the vehicle obstruction scenario is difficult to be identified by global navigation satellite systems (GNSS) or radars, so it is difficult for V2V communication systems to respond to sudden changes in channel characteristics due to vehicle obstructions. Therefore, by correctly identifying the vehicle obstruction scenarios, V2V communication systems can select appropriate propagation channel models to maintain an effective and reliable operating state. For this reason, this paper presents a machine-learning-based vehicle obstruction scenario identification approach for V2V communications. Channel characteristics extracted from measurements form the datasets used to training, then the back-propagation neural network (BPNN) is trained and a scenario identification model is obtained. Subsequently, identification accuracy is verified by using validation data. The results show that the identification accuracy for vehicle obstruction scenarios is more than 97%, which indicates that the approach proposed in this paper shows good performance in vehicle obstruction scenario identification in V2V communications.

This paper investigates the energy-aware dynamic resource allocation and computation offloading in high-speed railway networks with dynamic time division duplex (D-TDD), where all tasks of passengers in the high-mobility train are aggregated to be a "big task", and the vehicle server (VS) deployed on the train completes the "big task" by two processing modes, i.e., local computing and vehicular edge offloading. For such a system, an optimization problem is formulated to minimize the energy consumption by jointly optimizing the size of task block, the offloading ratio, the transmit power and the central processing unit (CPU) speed of VS with multiple system constraints. As the problem is non-convex with unknown solution, a bi-level optimization method is proposed to solve it. Via one-dimensional searching and Lagrangian multiplier method, the global optimal solution is obtained with high computational complexity. Then, a suboptimal approach is proposed with low computational complexity. Numerical results show that the performance of our proposed scheme significantly outperforms some other benchmark schemes, and a great performance gain is obtained via employing D-TDD. Besides, the effect of Doppler shift on the system performance is also discussed.

Powered by the Internet of Things (IoT), the cellular, vehicular, and other emerging networks, such as space-air-ground integrated networks, are expected to comprehensively evolve into the new era of the Internet of Everything (IoE) in which the propagation links between the IoT devices or sensors become diversified. Thus, a general propagation channel model is required. The line-of-sight (LOS) probability plays an essential role in the channel modeling, especially for built-up environments where the blockages from buildings are unfavorable to the signal transmission. The 4-D LOS probability model with considering the 3-D environment and frequency is comprehensively investigated in this article. Using the geometry-based stochastic method, the LOS probability is derived for arbitrary sizes, heights, and orientations of buildings in finely defined urban scenarios. The major contribution is the universality, simplicity, and good extension of the proposed model with comprehensive considerations of the influence of frequency, the type of urban, and the height of transceiver. The simulation results show that the propagation with higher frequency has a higher LOS probability. Moreover, the size, height, and density of building in urban environments are closely related to the LOS probability. The good agreements of the comparisons with the ray-tracing (RT) model and standard models show the accuracy of our proposed model. These results will be useful in the modeling of various channels and the design of IoT wireless communications systems.

Railway communications has attracted significant attention from both academia and industries due to the booming development of railways, especially high-speed railways (HSRs). To be in line with the vision of future smart rail communications, the rail transport industry needs to develop innovative communication network architectures and key technologies that ensure high-quality transmissions for both passengers and railway operations and control systems. Under high mobility and with safety, eco-friendliness, comfort, transparency, predictability, and reliability. Fifth-generation (5G) technologies could be a promising solution to dealing with the design challenges on high reliability and high throughput for HSR communications. Based on our in-depth analysis of smart rail traffic services and communication scenarios, we propose a network slicing architecture for a 5G-based HSR system. With a ray tracing-based analysis of radio wave propagation characteristics and channel models for millimeter wave (mmWave) bands in railway scenarios, we draw important conclusions with regard to appropriate operating frequency bands for HSRs. mymargin Specifically, we have identified significant 5G-based key technologies for HSRs, such as spatial modulation, fast channel estimation, cell-free massive multiple-input-multiple-output (MIMO), mmWave, efficient beamforming, wireless backhaul, ultrareliable low latency communications, and enhanced handover strategies. Based on these technologies, we have developed a complete framework of 5G technologies for smart railways and pointed out exciting future research directions.

Discriminating between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, orLOS identification, is important for a variety of purposes in wireless systems, including localization and channel modeling. LOS identification is especially challenging in vehicle-to-vehicle (V2V) networks since a variety of physical effects that occur at different spatial/temporal scales can affect the presence of LOS. This paper investigates machine learning techniques for LOS identification in V2V networks using an extensive set of measurement data and then develops robust and efficient identification solutions. Our approach exploits several static and time-varying features of the channel impulse response (CIR), which are shown to be effective. Specifically, we develop a fast identification solution that can be trained by using the power angular spectrum. Moreover, based on the measurement data, we also compare three different machine learning methods, i.e., support vector machine, random forest, and artificial neural network, in terms of their ability to train and generate the classifier. The results of our experiments conducted under various V2V environments, which were then validated using$K$-fold cross-validation, show that our techniques can distinguish the LOS/NLOS conditions with an error rate as low as 1%. In addition, we investigate the impact of different training and validating strategies on the identification accuracy.

This paper investigates the wireless-powered hierarchical fog-cloud computing networks, where multiple energy-constrained users harvest energy from a hybrid access point (HAP) firstly and then use their harvested energy to offload their computation tasks to fog/cloud servers via the HAP or compute their tasks locally. To pursue multi-user fairness, an optimization problem is formulated to maximize the minimal energy balance among all users by jointly optimizing time assignments, computation central processing unit (CPU) frequencies, and the computing mode selection. Since the problem is mixed-integer combinatorial non-convex, which is intractable, a generalized Benders decomposition (GBD)-based method is proposed, which guarantees the globally optimal solution. To release the high computational complexity of the proposed GBD-based method, a penalized successive convex approximation (P-SCA)-based algorithm is designed as an alternative to obtain a suboptimal solution with low computational complexity. Numerical results show that among different optimizable factors in the system, computing mode selection is the dominant one on affecting the system performance. Moreover, for each user, local computing is a better choice, if it is with relatively poor channel gain and small local computing delay. Otherwise, fog/cloud computing may be a better choice. Additionally, for the users with relatively high channel gains, if their local computing delays are less than those selecting fog computing, cloud computing should be a better choice.

This paper studies the secrecy energy efficiency (SEE) for MISO power-splitting (PS) SWIPT networks in the presence of multiple passive eavesdroppers (Eves), where the non-linear energy harvesting (EH) model and the dual-layer PS receiver architecture are employed. With only channel distribution information (CDI) of Eves known and the artificial noise (AN) embedded into the transmit signals at the transmitter, a SEE maximization problem is formulated under constraints of the minimal rate and EH requirements of legitimate receivers and the power budget at the transmitter. To tackle the difficulty caused by the fractional objective function and the probability constraints in solving the considered problem, the second-layer PS ratios are firstly optimized by bisection and sum-of-ratios maximization methods, and then the transmit beamforming vectors, the AN covariance matrix and the first-layer PS ratios are jointly optimized by using successive convex approximation (SCA) and Dinkelbach's methods. The proposed solution approach is theoretically proved to converge to a stationary point of the SDR form of the considered problem, which is further shown to be the optimal one. Numerical results show that our proposed design achieves the highest SEE over traditional power minimization and secrecy rate maximization designs. Moreover, when the rate requirement is larger than a threshold or the available power is less than a threshold, traditional power minimization design or secrecy rate maximization design is able to achieve a similar SEE to our proposed design. Besides, the dual-layer PS receiver architecture is able to improve the EH efficiency and system SEE.

A reliable vehicle-to-vehicle (V2V) channel model is necessary for intelligent transportation systems (ITSs) design. Due to the high mobility of vehicles and the low heights of antennas, the line-of-sight (LOS) propagation paths in V2V communications are more likely to be obstructed by large vehicles such as buses. Therefore, it is worthwhile to conduct in-depth investigations on obstructed line-of sight (OLOS) propagation channels caused by vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions at 5.9 GHz band are conducted. Based on the measured data, it can be found that the obstructions of large vehicles not only cause additional attenuation, but also significantly affect the angular distribution of multipath components (MPCs). In addition, a cluster-based dynamic V2V channel model is proposed for OLOS scenarios. In the proposed model, the influences of vehicle obstructions on path loss, delay and angle dispersion are intuitively embodied as changes in the statistical distribution of MPCs clusters. Finally, the rationality and accuracy of the proposed model is validated by comparing the measured and simulated channels. The results in the paper are useful for enriching the understanding of V2V channels and provide supports for vehicular communication systems design and performance evaluation.

This paper studies the robust energy efficient beamforming design for MISO systems where only channel distribution information (CDI) is assumed to be available at the transmitter. To capture the general relationship between the data transmission and the energy consumption, the dynamic energy consumption model (DECN) is adopted. An optimization problem is formulated to maximize the system energy efficiency under the constraints of rate outage probability and total available power. The problem is difficult to tackle due to the fractional objective function and the information outage constraints. To solve it, the semidefinite relaxation (SDR) is applied at first and then, a solution approach based on the successive convex approximation (SCA) and the Dinklebach's methods is presented. It is proved that our proposed solution approach is able to converge to a stationary point of the formulated optimization problem. Numerical results demonstrate that DECN has a great impact on system EE. It is observed that there is a saturation point on the system EE in term of available power and the required power corresponding to the saturation point of EE highly depends on circuit power. Particularly, higher circuit power leads to a larger required power but a smaller maximal system EE.

This paper investigates the optimal design of wireless- powered hierarchical fog-cloud computing networks, where energy-constrained users first harvest energy from a hybrid access point (HAP) and then offload their computation tasks to fog/cloud servers via the HAP or compute the tasks locally by us- ing the harvested energy. An optimization problem is formulated to maximize the minimal energy balance among multiple users by jointly optimizing offloading decisions, communication and computation resource allocations in the system, where computational capacity, processing delay, energy harvesting (EH) and energy consumption constraints are considered. To efficiently solve such a mixed-integer combinatorial non-convex problem, a penalized successive convex approximation (P-SCA)- based algorithm is designed, which is able to converge to a suboptimal solution with the polynomial time computational complexity. Numerical results show that compared to communication and computation resource allocation, the offloading decision is the dominant factor on affecting the system performance. It is also found that local computing is a better choice for users with relatively poor channel gains while fog/cloud computing is a better choice for users with relatively good channel gains. Specifically, cloud computing is preferred if the cloud computational capacity is strong enough and the wired-link data rate is high enough; Otherwise, fog computing is preferred. Besides, more users are served, less max-min energy balance can be obtained.

This paper investigates the efficient cooperation in wireless-powered communication networks, where an access point (AP) and multiple helpers first together charge up a sensor via radio-frequency (RF)-based wireless power transfer (WPT), and then the sensor uses the harvested energy to transmit real-time status updates to the AP. Due to the selfishness of the helpers, payment is provided as an incentive to them by the sensor-AP communication pair. For such a system, a Stackelberg game approach is designed to establish efficient cooperation between the helpers and the sensor-AP communication pair. An Age of Information (AoI)-based utility and a profit-based utility are defined for the sensor-AP pair and the helpers, respectively. Optimization problems are formulated to maximize their utilities. An explicit expression of the optimal transmit power of the helper is derived, with which a Dinkelbach's Programming (DP)-based algorithm is designed to jointly find the optimal payment price and transmit power at the AP, and at the same time, the Stackelberg equilibrium (SE) is achieved. Moreover, a closed-form expression of the minimum AoI of the system is also presented. Numerical results show that the more helpers there exist, the higher payment price the AP should fix, and meanwhile, the lower AoI of the sensor-AP communication pair can be achieved. Besides, it is shown that by increasing the transmit power at the AP, the optimal average AoI could not be reduced evidently.

This paper presents a novel channel estimation scheme for massive multiple input multiple output (MIMO) millimeter wave (mmWave) communication system with massive uniform linear array (ULA) at base station (BS) and hybrid architecture. Through practical channel modeling, each channel path is composed of angle information and channel gain information that can be estimated separately. We first propose a general iterative index detection-based channel estimation algorithm (IDCEA) that can obtain both direction of arrival (DOA) and channel gain of each channel path. We then design an enhanced hybrid precoding scheme from the angle domain viewpoint to reduce the inter-beam interferences. Simulation results show that the proposed channel estimation can be better than traditional methods. Finally, numerical examples are provided to corroborate the proposed studies.

Cell-free massive multiple-input multiple-output (MIMO) is a promising network architecture for future wireless systems. This paper investigates the uplink performance of cell-free massive MIMO systems employing the least-square (LS) estimator over spatially correlated fading channels. We first derive a generalized closed-form expression of the spectral efficiency as a function of the number of access point (AP) antennas and the spatial correlation matrices. We use this result to analyze the impact that the fronthaul, number of users and number of APs have on the energy efficiency. Compared to traditional co-located massive MIMO using maximum ratio combining (MRC), our analysis shows that the large performance gain of cell-free massive MIMO with low-complexity linear LS estimators.

This paper investigates the information-energy (I-E) region for simultaneous wireless information and power transfer (SWIPT) system in mobility scenarios, where a moving transmitter transmits information and energy to a power splitting (PS)-based receiver. An optimization problem is formulated to explore the system I-E region under the nonlinear energy harvesting (EH) model by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver. Since the problem is nonconvex, a successive convex approximate-based (SCA-based) algorithm is proposed, which is able to find the sub-optimal solution with low complexity. For comparison, the I-E region of the system under the linear model is also studied, where some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the linear EH model, the nonlinear EH model yields a smaller I-E region due to the limitations of EH circuit features. Nevertheless, using the nonlinear EH model avoids the false achievable I-E region for practical mobile SWIPT systems. Besides, it shows that the higher moving speed yields the smaller I-E region. Moreover, with the increment of the required information amount, the harvested energy bias caused by the linear EH model decreases, but the bias ratio increases.

Mobile edge computing enables mobile devices to offload their computation intensive applications to servers deployed at the network edge for improving energy efficiency. Multiple mobile users may compete for the wireless network resources when uploading their applications, and coordinating the subcarrier resource allocations among the users is important to optimize the system level energy performance. Partitioning an application further provides the flexibility that allows the mobile device to offload the optimum portion of the application in order to take the best advantage of the available radio resources in computation offloading. In this paper, we study joint partitioning decisions and subcarrier assignments in a multi-cell networks. The objective is to minimize the total energy consumption of the mobile users while satisfying the hard completion time requirements of the applications. The problem is formulated as a mixed-integer nonlinear programming, which is then decomposed into two coupled sub-problems, one linear programming for the offloading decisions under latency constraint of each application, and another matching game with externalities for subcarrier assignments. A joint subcarrier allocation and offloading decision algorithm is proposed, in which matching theory is used to design the user-subcarrier assignments, based on which the optimal offloading ratio is derived for each user. Simulation results demonstrate that the proposed algorithm can greatly reduce the average energy consumption of the mobile users, compared to several other subcarrier assignment and offloading schemes.

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with the increment of time between two received updates. Due to EH, a waiting time is required at the sensor before transmitting the status update. An optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. A two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and the inner loop presents a semi-closed-form expression of the optimal waiting time policy based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results show that our proposed optimal transmission policy outperforms the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are in slight load. It also shows that the system U-AoI first decreases and then keeps unchanged with the increments of EH circuit's saturation level.

Unmanned aerial vehicles (UAVs) communications are considered as a promising technology in various areas. In this paper, a three-dimensional (3D) non- stationary geometry-based stochastic model (GBSM) is proposed for UAV air-to- air (A2A) communication environments. The proposed GBSM considers not only both the ground surface and roadside reflections, but also the arbitrary trajectories of both UAV terminals. Based on the proposed model, some important statistical properties such as time- variant time-frequency correlation function and the Doppler power spectrum are derived and analyzed. Finally, numerical results show that a variation of the velocity and moving direction of the UAV has major impacts on the statistical properties of the radio channels, which indicates its usefulness for the performance analysis of UAV communication systems under non- stationary conditions.

For radio propagation, the wireless channel generally changes more significantly in the non-line-of-sight (NLOS) scenario compared to the line-of-sight (LOS) scenario. For vehicle-to-vehicle (V2V) communications, the most typical NLOS scene is large vehicle obstructions. Therefore, in order to understand the radio propagation mechanism and channel characteristics in V2V communication, it is necessary to carry out indepth investigations on V2V channel under the condition of large vehicle obstructions. In this paper, actual V2V channel measurements with large vehicle obstructions in the urban environment at 5.9 GHz are carried out. Based on the measured data, extraction and analysis of channel parameters are performed. Specifically, this paper focuses on the directional analysis of V2V channel in the case of large vehicle obstructions. It can be find that the occlusion of large vehicles not only causes additional attenuation of signal energy, but also significantly affects the angular distribution of multipath components in three- dimensional space, and brings larger angular dispersion. These results would be useful for establishing a more accurate channel model and serveing the design of V2V communication system.

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

This paper investigates the simultaneous wireless information and power transfer (SWIPT) networks with coexisting power-splitting users (PSUs) and time-switching users (TSUs) under the nonlinear energy harvesting (EH) model, where a multiantenna hybrid access point (H-AP) transmits information and power to multiple PSUs and TSUs. For such a network, an optimization problem is formulated to minimize the required transmit power at the H-AP subject to users' information rate and harvested energy constrains by jointly optimizing the H-AP's transmit beamforming vectors, PSUs' power splitting (PS) ratios, and TSUs' time switching (TS) factors. Due to the interferences among PSUs and TSUs, and the nonlinear EH model, the problem is nonconvex and has no known solution method. Thus, a two-layer algorithm is first presented based on semidefinite relaxation (SDR) and it is theoretically proved that the global optimum is achieved. However, since 1-D search is adopted by the two-layer algorithm, which may be too computationally exhaustive, a successive convex approximate-based (SCA-based) algorithm is then proposed as an alternative, which is able to find the near-optimal solution with low complexity by using the first-order approximation. The numerical results show that with the same EH requirements, TSUs are more likely to enter into the saturation region compared with PSUs, but their EH efficiency is higher than that of PSUs. It is also shown that the minimal required transmit power under the nonlinear EH model is much lower than that under the linear one. Although after the saturation point of the nonlinear EH model, the linear one yields a lower required transmit power, it is a fake result, because the linear EH model mismatches the nonlinearity of practical EH circuits.

Advances in Internet of Things (IoT) bring massive intelligent applications, many of which are computation intensive and time sensitive. With limited resources of IoT devices, mobile computation offloading can be exploited to offload part of the applications to nearby devices that have more powerful computing resources, thereby speeding up the applications and reducing the energy consumption. In this paper, we consider application partitioning and collaborative computation offloading in IoT networks, in order to meet the completion deadline of the applications while minimizing the overall energy consumption. The problem is formulated as a binary integer linear programming problem, which is transformed into a weighted bipartite matching problem and then solved by the centralized Kuhn-Munkres algorithm. To fit the large-scale IoT scenarios, three distributed algorithms are then introduced from different perspectives. The first one is referred to as the noncooperative matching (NCM) algorithm, where each node makes offloading decision based on its own interest in minimizing energy consumption. Afterward, an asynchronous greedy matching (AGM) algorithm is developed by considering the mutual interest of the requestor and collaborator pairs in terms of their energy consumptions. Finally, a maximum differential energy matching (MDEM) algorithm is devised by relaxing the network stability requirement, which can further benefit the energy efficiency for all network nodes. Theoretical analysis and simulation results demonstrate that both the NCM and AGM algorithms guarantee the network stability and improve the energy saving compared with entirely local execution, while the MDEM algorithm can further achieve near-optimal energy consumption at the expense of higher implementation overheads.

This paper investigates a fog computing-assisted multiuser simultaneous wireless information and power transfer network, where multiple sensors with power splitting (PS) receiver architectures receive information and harvest energy from a hybrid access point (HAP), and then process the received data by using local computing mode or fog offloading mode. For such a system, an optimization problem is formulated to minimize the sensors' required energy while guaranteeing their required information transmissions and processing rates by jointly optimizing the multiuser scheduling, the time assignment, the sensors' transmit powers, and the PS ratios. Since, the problem is a mixed integer programming problem and cannot be solved with existing solution methods, we solve it by applying problem decomposition, variable substitutions, and theoretical analysis. For a scheduled sensor, the closed-form and semi-closed-form solutions to achieve its minimal required energy are derived, and then an efficient multiuser scheduling scheme is presented, which can achieve the suboptimal user scheduling with low computational complexity. Numerical results demonstrate our obtained theoretical results, which show that for each sensor, when it is located close to the HAP or the fog server, the fog offloading mode is the better choice; otherwise, the local computing mode should be selected. The system performances in a frame-by-frame manner are also simulated, which show that using the energy stored in the batteries and that harvested from the signals transmitted by previous scheduled sensors can further decrease the total required energy of the sensors.

This paper considers an MISO simultaneous wireless information and power transfer (SWIPT) system, where one transmitter serves multiple authorized receivers in the presence of several potential eavesdroppers (idle receivers). To prevent the information interception by eavesdroppers, artificial noise (AN) is embedded into the transmit signals. The non-linear energy harvesting (EH) model is adopted and a novel power-splitting (PS) EH receiver architecture is proposed. Stochastic uncertainty channel model (SUM) is considered for the idle receivers due to outdated channel feedback. A global energy efficiency (GEE) maximization problem is formulated by jointly optimizing the transmit beamforming vectors, the AN covariance matrix, and the PS ratios, under the minimal rate and secure transmission constraints of authorized receivers, the EH requirement constraints of idle receivers, and the total available power constraint at the transmitter. Since the problem is non-convex with no known solution, it is solved based on the following solution framework. Firstly, the PS ratios are optimized by using the bisection method and successive convex approximation (SCA), and then, the transmit beamforming vectors and the AN covariance matrix are jointly optimized by using a Dinkelbach's Algorithm based method, where SCA is applied to solve its inner subproblem. It is theoretically proved that by involving AN, the system GEE can be improved. Numerous results show that system GEE first increases and then keeps unchanged with the increment of the total available power, but it first keeps unchanged and then decreases with the increment of the minimal rate requirement. It is also observed that compared with traditional EH receiver architecture and linear EH model, our proposed PS EH receiver architecture is able to achieve higher GEE and avoid false output power at idle receivers.

In recent years, unmanned aerial vehicle (UAV) communication technology has played an important role in both military and civilian applications. However, with the rapid development of military equipment, the execution efficiency of single UAVs is often limited, for which complex combat missions cannot be completed well. Therefore, UAV swarm has become an important research trend in the field of UAVs. In this paper, we consider the problem of channel estimation and self-positioning for the UAV swarm, where multiple small UAVs are displaced by arbitrarily unknown displacements due to the dynamic moving. To explore the physical characteristics of UAV swarm, the parameters of the channel are decomposed into the direction of arrival (DOA) information, the relative position information, and the channel gain information. Utilizing the rank reduction (RARE) estimator, DOAs of the different target users can be estimated efficiently, regardless of the position of the UAVs. After obtaining the DOA information, we estimate the channel gain information using small amount of training resources, which significantly reduces the training overhead and the feedback cost. Moreover, the unknown displacements among UAVs can be self-recovered from the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop both the sphere decoding (SD) and the least square (LS) based methods. The deterministic Cramér-Rao bound (CRB) of the self-positioning estimation is derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

Multi-link transmission is one of the promising communication techniques capable of improving system capacity and cell-edge user spectral efficiency in railway communication networks. The performance of multi-link transmission heavily depends on the propagation characteristics of radio channels, especially the correlation property between multiple radio channels. Thus, it is important to characterize the multi-link channel in railway communication networks. In this paper, extensive wideband measurements in a viaduct railway environment at 460 MHz with two base stations and one mobile station are performed. Large-scale parameters (LSPs), including large-scale fading, Ricean K-factor, delay spread, and angle spread, are extracted from the measurement data. Based on the measurements, auto-correlation and cross-correlation properties of each LSP are investigated. A Markov-based multi-link tapped-delayline model for railway communications is established, where the Markov chains are introduced to model the birth and death state of multipath components in multi-link scenarios. Using the relationship between the correlation coefficients of complex random variables (RVs), the amplitude and the phase of taps with different delays are modeled as correlated RVs. The proposed channel model is implemented and validated with measurements.

Characterization and modeling of massive multiple-input multiple-output (MIMO) channel has been one of the research hotspots in the field of wireless communications. One important feature of the massive MIMO channel is the spatial non-stationarity. To statistically model the spatial non-stationary massive MIMO channels, a cluster-based channel model is proposed in this paper. The model incorporates both inter- and intra-cluster properties and the cluster evolution over the large-scale array. A hybrid data processing scheme is applied to extract the multipath components (MPCs) and clustering the MPCs over a large-scale antenna array. The global angular spread, cluster angular spread, and cluster delay spread are modeled with log-normal distributions. Observed cluster length and MPC length within clusters, which are introduced to describe the cluster existence over the array and MPCs existence within the cluster, respectively, are statistically modeled with the exponential distributions. Moreover, both the cluster and MPC arrival intervals, which are used, respectively, to describe the cluster occurrence position on the array and MPC occurrence position within the cluster, can be statistically modeled with the uniform distributions. Finally, the model implementation is validated by comparing with the different channel performance metrics between measurements and simulations.

With the rapid development of wireless communications, the understanding of three-dimensional (3D) propagation channels becomes essential for design and testing of some new wireless technologies, e.g., massive multiple-input multiple-output (MIMO) and full dimensional beamforming. To not only fully exploit the 3D multiplexing but also circumvent the size limitation of base station (BS), antenna elements in massive MIMO are usually arranged both horizontally and vertically. Based on an elaborate channel measurement campaign conducted at 11 GHz in a lobby environment, a 3D extended cluster-based channel model is proposed in this paper for massive multiple-input single-output (MISO) multi-user communications. In the model, the channel characteristics in both azimuth and elevation dimensions, and the visibility regions which are parametrized by observed cluster lengths across the large-scale array in both horizontal and vertical directions, are taken into consideration. Moreover, the spherical wavefront phenomenon observed from the measurements is also incorporated in the model. Model parametrization, implementation, and validation are presented in detail. Validations show that the proposed model can accurately reflect the realistic channel, and the spatial non-stationarity and the spherical wavefront should be carefully considered in the channel models for large-scale array communications.

In this paper, we consider the problem of self- positioning for the unmanned aerial vehicle (UAV) swarm, where multiple small UAVs are arranged by unknown displacement due to the dynamic moving. These multiple small UAVs also formulate a virtual massive antenna array that can estimate the direction of arrivals (DOAs) of target users efficiently, regardless of the relative position of the UAVs. After obtaining the DOA information, the unknown displacements among UAVs can also be self-recovered, automagically realizing the important functionality of self-positioning for UAV swarm. The self-positioning problem falls into the category of the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop a novel self-positioning algorithm based on least square (LS) method. Moreover, the deterministic Cramer-Rao bound (CRB) of the self-positioning estimation is derived in closed- form. Finally, numerical examples are provided to corroborate the proposed studies.

By allowing mobile device to offload all or part of a latency-constrained computational task to a base station at the edge of a network, mobile edge computing (MEC) is a promising technique to help the mobile device save its energy consumption. In this paper, we consider the scenario with one mobile device and multiple edge base stations, which is usual in practice. Nonorthogonal multiple access (NOMA) technique is implemented. An optimization problem is formulated, in which the transmission power to every edge base station and the amount of data to be offloaded for computation is optimized to minimize the total energy consumption of the mobile device. However, the formulated optimization problem is non-convex. To get the global optimal solution, we decompose the formulated optimization problem into two levels. In the lower level, a convex optimization problem is required to be solved. By finding out some special property of the lower level optimization problem, the upper level optimization problem can be formulated as a monotonic optimization problem, whose global optimal solution is achievable. Numerical results verify the effectiveness of our proposed method.

As the advent of cognitive radios, blind encoder identification has attracted increasingly attentions since it plays an important role in blind signal processing. The existing works mainly focus on additive white Gaussian noise (AWGN) channel, while the blind identification in multipath scenarios has not been sufficiently investigated. In this paper, we consider the blind low-density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel. Then, a likelihood-based classifier is proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters, including multipath fading channel and modulated symbols. Then, we adopt an average log-likelihood ratio (LLR) estimator to classify the unknown encoder. Numerical results show that the proposed algorithm provides promising identification performance in multipath channels, especially in the low signal- to-noise ratio region.

This paper investigates a simultaneous wireless information and power transfer (SWIPT)-enabled non- orthogonal multiple access (NOMA) network with full- duplex (FD) relaying, where a multi-antenna source transmits information to two users. The nearby user is with multiple antennas, which receives its own information and harvests energy from the signals transmitted by the source and also help forward information to the far-end user. For such a system, an optimization problem is formulated to minimize the required transmit power by jointly optimizing beamforming vectors and power splitting (PS) ratio under the energy harvesting and users' data rate constraints of both users. As the problem is non- convex with unknown solution, a bilevel- optimization method is proposed to solve it via semidefinite relaxation (SDR) and the global optimal solution is achieved with perfect self- interference cancellation. However, since self- interference may not be cancelled perfectly in practice, a successive convex approximation (SCA) based algorithm with low complexity is proposed to obtain a near optimal solution. Numerical results show that integrating NOMA, FD relaying and SWIPT in a single communication system is able to greatly reduce the required transmit power. Besides, the effects of the parameters including the data rate threshold and the energy storage amounts, on the system performance are also discussed.

To cope with an enormous number of concurring uplink connections in the future network, a novel non-orthogonal multiple access scheme is proposed in this paper, where a composite channel is formed by allowing different users to share the same pilot. The composite channel estimate is then used to equalize the superposed signal from multiple users. With the help of channel hardening feature of massive MIMO, the superposed signal is transformed into a power-domain NOMA form, from which data of different users can be restored successively with interference cancellation. The optimal decoding order as well as power selection scheme is first derived with global channel information. Then distributed power selection schemes are evaluated to reveal the overloading performance of the proposed scheme when other concurring user's channel information is not available. Simulation results show that the proposed scheme can achieve satisfactory overloading performance. Among the distributed power selection methods, the path loss partial compensation based one approaches the optimal performance, making it a promising solution in reality.

This paper investigates the transceiver design in a wireless multiple-input multiple-output (MIMO) switching network in which multiple users exchange messages via a multi-antenna relay. Previous work assumed that perfect channel state information (CSI) is known at the relay, which is intractable in practice. Regarding different types of CSI imperfection of uplink and downlink transmission, a statistical and a norm- bounded uncertainty model are adopted to characterize the imperfect CSI of uplink and downlink respectively for the transceiver design. An optimization problem is formulated by minimizing the worst-case mean square error (MSE) with respect to channel uncertainty in the constraint of the maximum transmit power of the relay. Since the problem is non-convex and difficult to solve, we divide the original problem into two subproblems in which the channel uncertainty of uplink and downlink are treated individually. For the uplink subproblem, we propose an iterative approach to determine a closed- form solution of the robust transceiver. In addition, a Sylvester equation is formulated which closed-form solution is provided explicitly for the downlink channel uncertainty subproblem. An overall iterative algorithm is proposed by combining the two algorithms for the subproblems, which can solve the original problem efficiently in a low complexity. Simulation results show that the proposed iterative algorithm reduces the sum MSE significantly in the channel uncertain scenarios.

Mobile computation offloading enables resources-constrained mobile devices to offload their computation intensive tasks to other available computing resources for local energy savings. In this paper, we study the offloading decision and task scheduling issues when multiple serving vehicles (SVs) can be utilized in vehicular network. The overall energy consumption of users with Dynamic Voltage Scaling (DVS) technology is minimized subject to the delay constraint of each task. For the ideal cases, the assignment and scheduling in an offline style are firstly formulated as a mixed-integer nonlinear programming (MINLP) problem, and the optimal solution is derived based on dynamic programming. After that, two online strategies, Energy Consumption Minimization (ECM) based low-complexity assignment, and Resource Reservation (RR) assignment are also proposed. Simulation results demonstrate the improvements in energy saving when the proposed strategies incorporated with the DVS technology are adopted.

In this paper, a non-time-slotted cognitive radio network (CRN) is considered, where the primary user (PU) can change its state between transmission and mute at any time within one secondary user (SU) frame. In such a case, the traditional half-duplex sensing, which divides the secondary transmission process into the sensing and transmission stages, has a problem that fails to detect the PU transition during the transmission stage. To overcome this problem, the full-duplex sensing is adopted, which allows the SU to sense the spectrum and transmit signals simultaneously. The SU throughput per frame is evaluated to determine the optimal duration of the SU frame. Note that the throughput scales with the duration of the SU frame in the absence of the PU. However, the PU may start to transmit at any time within the SU frame. Hence, we formulate a throughput optimization problem with respect to the duration of the SU frame, under the constraint that the interference induced by the SU does not exceed the interference temperature of the PU, which is nonconvex. We further indicate that the optimal solution can be achieved when considering some practical constraints.

With the increasing quality of service (QoS) demands for indoor multimedia applications, millimeter wave (mmWave) communications are emerging as a promising candidate for the wireless personal area networks (WPANs). On the one hand, it has the advantage of providing several-Gbps transmission rate. However, due to the unique characteristics in 60-GHz frequency band, such as high propagation loss, beamforming is fully exploited for mmWave links to achieve directional transmission and reception. In this paper, we propose a novel QoS aware scheduling algorithm for concurrent transmission in mmWave WPANs based on coalition game. First, we formulate the problem of concurrent transmission scheduling into a non-convex integer programming problem. Then, we propose a coalition game based algorithm to maximize the number of flows satisfying the corresponding QoS requirements, while improving the network resource utilization effectively. Besides, our proposed algorithm converges to a Nash-stable equilibrium with greatly reduced complexity. Through extensive simulations under various system parameters, we demonstrate our scheme achieves better network performance in terms of the throughput and the number of flows scheduled successfully, compared with existed protocols.

Massive multiple-input multiple-output (MIMO) has been selected as one of the key technologies of the fifth generation mobile communication system (5G). It can provide high spectral and power efficiency, thus it is suitable to be deployed in different hotspot scenarios. In this paper, a channel measurement campaign using a wideband channel sounder and a 256-element virtual uniform rectangular array (URA) for massive MIMO communications is presented. The measurements were respectively conducted at 6 GHz and 11 GHz, and the subway station environment was considered. The typical channel parameters, root mean square (RMS) delay spread and coherence bandwidth, are analyzed based on the measurements. Moreover, the channel characteristics in angular domain are obtained by applying the space-alternating generalized expectation-maximization algorithm (SAGE). The SAGE estimates are validated by relating them to the physical environment. The overall elevation angle distribution of multipaths is found to be fitted well with Laplace distributions. The global angular spread, including the azimuth spread of departure (ASD) and elevation spread of departure (ESD), are both fitted well with Lognormal distributions. The results in this paper can be fed into the new channel simulator for massive MIMO, and are useful for the design and application of the practical massive MIMO system in the future.

Massive multiple-input multiple-output (MIMO) is one of the key technologies of the fifth generation mobile communication system (5G). For development of massive MIMO systems, the directional properties of their wireless channel are of great importance. In this paper, directional analysis is presented based on a channel measurement campaign for massive MIMO communications in a theater environment. The measurements were conducted at 11 GHz with a bandwidth of 200 MHz. A 256-element virtual uniform rectangular array (URA) was used during the measurements, and the channel characteristics in angular domain are determined by applying the space-alternating generalized expectation-maximization algorithm (SAGE). According to the SAGE estimates, channel nonstationarity over the large-scale array is discussed. The dominant scatterers are identified, by directly relating the SAGE estimates to the physical objects in the measurement environment. Moreover, direction spread over the large-scale array at the Tx side is estimated, and channel performance on beamforming is evaluated. Corresponding analysis is given in detail. These results are useful for the design and application of the practical massive MIMO system in the future.

In this paper, a wireless wideband downlink transmission through a multipath and highly time-varying channel is considered for high-speed railway. In this scenario, non-Gaussian noise is generally involved at the receiver side which may lead to communication outage. In previous studies, the optimal receiver algorithms we designed are only applicable for Gaussian noise and time-invariant channels. But for impulsive noise and time-varying multipath channels, new algorithms are needed to be proposed. In order to resolve the issue, a synthesis scheme is proposed in this paper. First, we adopt impulsive noise detection algorithm to discriminate impulsive noise from the received signal. Then the discriminated noise can be suppressed by the blanking algorithm. After that, the refined signal goes through a well-designed beamforming network, which transforms the received signal with varying frequency offsets into an angle domain. Thus, the frequency offset in each angle is near-constant which can be accurately estimated. The simulation results indicate that the proposed scheme significantly improves the reliable performance of the high-speed railway communication systems.

This paper investigates the artificial noise (AN)-aided multi-cell coordinated beamforming (MCBF) for secure simultaneous wireless information and power transfer in both centralized and distributed manners. The proposed transmit design is formulated into a power-minimization problem to guarantee the authorized users' information and energy harvesting (EH) requirements while avoiding the information interception by unauthorized users. Power splitting receiver architecture and the non-linear EH model are employed. Both perfect and imperfect channel state information (CSI) cases are considered. For the perfect CSI case, the non-robust design is presented by applying semi-definition relaxation (SDR). When no user harvests energy, the global optimum is guaranteed, and when some users harvest energy, approximate global optimum is achieved. For the imperfect CSI case, the worst-case robust design under the deterministic uncertainty channel model is studied, where a solving approach based on SDR and S-procedure is proposed, and the statistically robust design under the stochastic uncertainty channel model is also studied, where an upper bound to the global optimum is obtained by using SDR and Bernstein-type inequality. We further propose a distributed AN-aided MCBF design framework by using an alternating direction method of multipliers for the non-robust, worst-case robust, and statistically robust designs, with which each BS is able to optimize its own transmit design with the local CSI. Simulation results demonstrate our theoretical analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It also shows that employing the non-linear EH model is able to avoid false output power and save power consumption at the BSs.

To keep pace with the rapid growth of mobile traffic demands, dense deployment of small cells in millimeter wave (mmWave) bands has become a promising candidate for next-generation wireless communication systems. With a greatly increased data rate from huge bandwidth of mmWave communications, energy consumption should be mitigated for higher energy efficiency. Due to content popularity, many content-based mobile applications can be supported by the multicast service. mmWave communications exploit directional antennas to overcome high path loss, and concurrent transmissions can be enabled for better multicast service. On the other hand, device-to-device (D2D) communications in physical proximity should be exploited to improve multicast performance. In this paper, we propose an energy-efficient multicast scheduling scheme, referred to as EMS, which utilizes both D2D communications and concurrent transmissions to achieve high energy efficiency. In EMS, a D2D path planning algorithm establishes multi-hop D2D transmission paths, and a concurrent scheduling algorithm allocates the links on the D2D paths into different pairings. Then, the transmission power of links is adjusted by the power control algorithm. Furthermore, we theoretically analyze the roles of D2D communications and concurrent transmissions in reducing energy consumption. Extensive simulations under various system parameters demonstrate the superior performance of EMS in terms of energy consumption compared with the state-of-the-art schemes. Furthermore, we also investigate the choice of the interference threshold to optimize network performance.

In industrial internet of things (IIoT), massive machine-type communications (mMTC) system is introduced to provide communication services for large-scale industrial devices. To achieve massive access with scarce radio resources in mMTC, multiple access protocol has to be reconsidered to cope with the resulting collision problem. Currently, various novel multiple access schemes have been proposed. Among them, coded tandem spreading multiple access (CTSMA) is an emerging physical (PHY) layer multiple access scheme to resolve the collision in mMTC. In this paper, a multislot design scheme of CTSMA is proposed to further promote the collision resolution capability. In this scheme, CTSMA is combined with the MAC layer scheme coded slotted ALOHA (CSA). The analysis shows that the multislot design can effectively take advantage of CTSMA and CSA to enhance the mMTC system performance for the short uplink contention period, which is suitable for the IIoT applications.

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

This paper proposes a novel direction-of-arrival (DOA)-aided channel estimation for a hybrid millimeter-wave (mm-wave) massive multiple-input multiple-output system with a uniform planar array at the base station. To explore the physical characteristics of the antenna array in mm-wave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the 2-D discrete Fourier transform and enhance the estimation accuracy via the angle rotation technique. We then estimate the channel gain information using a small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, to examine the estimation performance, we derive the theoretical bounds of the mean squared errors (MSEs) and the Cramér-Rao lower bounds (CRLBs) of the joint DOA and channel gain estimation. The simulation results show that the performances of the proposed methods are close to the theoretical MSEs' analysis. Furthermore, the theoretical MSEs are also close to the corresponding CRLBs.

Non-stationary mobile-to-mobile (M2M) channel modeling has gained strong momentum as it is vital for developing M2M communications technology. Traditional geometry-based channel models (GSCMs) for M2M communications usually assume fixed velocity and moving direction, which differs from the realistic M2M scenarios and also makes it difficult to incorporate non-stationarity of channel into the regular-shaped GSCMs. In this paper, a mobility model-based method is proposed to incorporate non-stationarity into M2M channel modeling by introducing dynamic velocities and trajectories. A revised Gauss-Markov mobility model is first presented together with the cluster-based two-ring M2M reference model. The mobility model uses tuning parameters to adjust the degree of mobility randomness and covers different M2M mobility trajectories. Then, a closed-form time-variant time-frequency correlation function and the Doppler power spectrum are derived from the model. Based on the numerical analysis, it is found that for a regular-shaped GSCM with a fixed M2M scattering environment, the motion does not introduce non-stationarity, however, the dynamic motion (i.e., the changes of velocity and moving direction) leads to non-stationarity, which is reflected by the time-variant time correlation function and Doppler spectrum. Different propagation modes, cluster number, and intra-cluster nonisotropic scattering also have major impacts on channel non-stationarity. Moreover, the randomness of the mobility model is found to significantly increase the degree of channel non-stationarity. These conclusions are useful for M2M non-stationary channel simulation and communication system evaluation.

This paper investigates the secure transmit design for simultaneous wireless information and power transfer system under the non-linear energy harvesting (EH) model, where a transmitter sends confidential information and transfers energy to multiple information receivers (IRs) and EH receivers (ERs) with the existence of multiple eavesdroppers (Eves). To prevent confidential information leakage, multiple artificial redundant signals (MARSs) are embedded in the transmit signals. The goal is to minimize the total transmit power by jointly optimizing transmit beamforming vectors and the covariance matrixes of MARSs, such that the minimal information rate and EH requirements at IRs and ERs are guaranteed while making the received signal-to-Interference ratio at ERs and Eves lower than their information decoding thresholds. Both the non-robust and the robust designs are studied. For the non-robust design, the optimal solution is derived. For the robust design, an approximate optimal solution is obtained by using Gaussian randomization procedure. Simulation results show that compared with traditional non-MARS-aided beamforming design, our proposed design is superior in terms of the total required transmit power. It also shows that employing the non-linear EH model can avoid false output power at the ERs and/or save power at the transmitter.

In this paper, a novel joint design of beamforming and power allocation is proposed for a multi-cell multiuser multiple-input multiple-output non-orthogonal multiple access network. In this network, base stations adopt coordinated multipoint for downlink transmission. We study a new scenario where the users are divided into two groups according to their quality-of-service requirements, rather than their channel qualities as investigated in the literature. Our proposed joint design aims to maximize the sum rate of the users in one group with the best effort while guaranteeing the minimum required target rates of the users in the other group. The joint design is formulated as a non-convex NP-hard problem. To make the problem tractable, a series of transformations is adopted to simplify the design problem. Then, an iterative suboptimal resource allocation algorithm based on successive convex approximation is proposed. In each iteration, a rank-constrained optimization problem is solved optimally via semidefinite program relaxation. Numerical results reveal that the proposed scheme offers significant sum-rate gains compared to the existing schemes and converges fast to a suboptimal solution.

This paper introduces downlink non-orthogonal multiple access (NOMA) into short-packet communications. NOMA has great potential to improve fairness and spectral efficiency with respect to orthogonal multiple access (OMA) for low-latency downlink transmission, thus making it attractive for the emerging Internet of Things. We consider a two-user downlink NOMA system with finite blocklength constraints, in which the transmission rates and power allocation are optimized. To this end, we investigate the trade-off among the transmission rate, decoding error probability, and the transmission latency measured in blocklength. Then, a 1-D search algorithm is proposed to resolve the challenges mainly due to the achievable rate affected by the finite blocklength and the unguaranteed successive interference cancellation. We also analyze the performance of OMA as a benchmark to fully demonstrate the benefit of NOMA. Our simulation results show that NOMA significantly outperforms OMA in terms of achieving a higher effective throughput subject to the same finite blocklength constraint, or incurring a lower latency to achieve the same effective throughput target. Interestingly, we further find that with the finite blocklength, the advantage of NOMA relative to OMA is more prominent when the effective throughput targets at the two users become more comparable.

This paper proposes a novel direction of arrival (DOA) estimation for hybrid millimeter wave (mmWave) massive MIMO systems with the uniform planar array (UPA) at base station (BS). To explore the physical characteristics of antenna array in mmWave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the two dimension discrete Fourier transform (2D-DFT) efficiently, and then the estimation accuracy can be further enhanced via the angle rotation technique. To examine the estimation performance, we derive the theoretical bounds of the mean squared error (MSE) performance of the DOA estimation in high signal-to-noise ratio (SNR) region. Simulation results are provided to corroborate the proposed studies, and show that the proposed DOA estimation method is close to the theoretical MSE performance.

This paper investigates the joint optimal beamforming and power-splitting receiver architecture design for simultaneous information and power transfer (SWIPT) under non-linear energy harvesting (EH) circuit environment, where hybrid access point (H-AP) equipped with multiple antennas simultaneously transmits information and power to multiple single-antenna users. For such a system, in order to achieve green communication design, we formulate an optimization problem to minimize the total transmit power of H-AP subjecting to the required signal-to-interference-plus- noise ratio (SINR) and the harvested power constrains at each user under non-linear EH model. Since the problem is non-convex, the relaxed semidefinite program (SDP) is used to solve it, and it is proved that the semidefinite relaxation (SDR) process is tight and our optimal solution achieves the global optimum. Numerical results show that a considerable gain could be achieved if the SWIPT beamforming vector and the power splitting ratios are jointly designed under the non-linear EH model compared with traditional linear EH model, since the non-linear EH model captures and matches the real EH circuits' non-linear features. Moreover, the feasible region of traditional linear EH model in the non-linear EH circuit environment is also characterized.

This paper investigates the power minimization problem for a simultaneous wireless information and power transfer (SWIPT) system in MISO wiretap networks, where one multiple-antenna transmitter intends to transmit required amount of information and energy to its legitimate receiver while restrict the information leakage to an eavesdropper (Eve). The nonlinear EH model is employed for SWIPT. Two uncertainty MISO channel models are considered for the legitimate receiver, i.e. the deterministic uncertainty model (DUM) and the stochastic uncertainty model (SUM), and the Eve is assumed not to feed back its channel to the transmitter. For the DUM, the worst-case design with global optimum is solved by our proposed method based on semidefinite relaxation (SDR) and S-procedure. For the SUM, the statistically robust design with a tight upper bound to global optimum is obtained by our proposed method based on SDR and Bernstein-type inequality. Numerous simulation results demonstrate the validity and efficiency of our proposed robust transmit design methods. Compared with the traditional linear EH model, employing the nonlinear EH model can avoid false output power at the legitimate receiver or save power consumption at the transmitter as the real circuits are working in the nonlinear output field rather than the linear one.

With the thriving of internet of things (IoT) industry, the importance of massive machine type communication (mMTC) is increasingly significant. Due to massive connections in mMTC, grant-free random access is preferred for the sake of saving the control signaling overheads. Currently, various techniques has been proposed to address the challenges in the grant-free random access system. Among them, tandem spreading is a novel spreading mechanism to solve the collision problem. In this paper, a generalized version of tandem spreading is introduced as the coded tandem spreading multiple access (CTSMA). Compared to the previous work, CTSMA enables multiple redundancy segments to support more simultaneous transmissions. Moreover, a flexible tandem spreading codebook design is introduced. Simulation results show the advantage of CTSMA on other related schemes and indicate the factors to influence the CTSMA performance under different conditions.

This paper proposes a practical channel estimation scheme for downlink 60GHz indoor systems with the massive uniform rectangular array (URA) at base station (BS). Through array signal processing theory, the parameter of each channel path can be decomposed into the angular information and the channel gain information that can be estimated separately. We first prove that the two dimensional Discrete Fourier transform (2D-DFT) with phase rotation operation can be applied to efficiently estimate the angular information. Then, the channel gain information could be easily obtained with small amount of training resources in a linear manner. Interestingly, since uplink and the downlink angular information is reciprocal, the proposed method applicable for both TDD and FDD systems. Simulation results are provided to corroborate the proposed studies.

This paper investigates how to achieve secure information transmission in multi-cell multi-user networks, where artificial noise (AN) aided multi-cell coordinated beamforming (MCBF) is designed to guarantee the authorized users' QoS requirements while avoiding the information being intercepted by unauthorized users. To realize the green communication target, we formulate an optimization problem to minimize the total transmit power by jointly optimizing the beamforming and AN vectors at all BSs. Since the problem is nonconvex and not easy to be solved by using existing solution methods, we then solve it by applying semi-definition relaxation (SDR) and prove that our proposed method can guarantee the global optimal solution under full channel state information (CSI). Moreover, we further design a distributed AN-aided MCBF for the system by using alternating direction method of multipliers (ADMM), with which each BS can calculate the beamforming and AN vectors with its local CSI. Simulation results demonstrate our analysis, which show that our proposed distributed algorithm converges to the optimal results obtained by the centralized one. It is also observed that for the same secure transmission requirement, the total power consumed by our proposed AN-aided MCBF decreases with the increment of transmit antennas, where less part of the power is used by AN and more part is used for information beamforming.

With the advent of cognitive radio (CR) and dynamic spectrum access techniques, where multiple signals may coexist within the same frequency band, multiuser modulation classification problem becomes a vital issue, which has not been sufficiently investigated. In this paper, we consider a cooperative multiuser modulation classification problem, in the presence of unknown multipath channels. A likelihood-based (LB) classifier using the expectation-maximization (EM) algorithm is proposed, which enables to find the maximum likelihood estimates (MLEs) iteratively. Numerical results show that the proposed algorithm achieves significant improvement on the classification performance with a small number of samples when compared to the conventional methods, which demonstrates its reliability and efficiency of identifying modulations of multiple users under the multipath scenarios.

In this paper, we consider a point-to-point single antenna communication system where the receiver decodes information and harvests energy at the same time. Two practical schemes, namely power splitting scheme and time switching scheme, are investigated, in which the receiver splits or switches signals for information decoding and energy harvesting. We derive the characteristics of the formulated problem which maximizes outage received power under the outage capacity probability and outage received power probability constraints. Moreover, the numerical results prove the correctness of the characteristics of the optimization problem. In particular, it illustrates that the PS scheme outperforms the TS scheme.

In this paper, we consider a wireless communication pair powered with radio frequency (RF) signal, in which the transmitter first harvest energy from an energy access point and then transmit information to the destination. Optimization problems aiming at maximizing throughput are investigated with a consideration of two types of power constraints at the transmitter: i) short-term power constraint, which corresponds to the case that no battery capacity is assumed at the transmitter and ii) long-term power constraint, which corresponds to the case that battery capacity at the transmitter is large enough. The formulated optimization problems are non-convex but are transformed to be convex ones in the first step. For the optimization problem with short-term power constraint, a fast algorithm with a combination of bisection search and closed-form solution is proposed for the first time. For the optimization problem with long-term power constraint, an online calculation method is designed for the first time. Numerical results prove the effectiveness of the proposed algorithms.

Millimeter wave MIMO systems have been proposed to achieve higher spectral efficiency via the hybrid beamforming structure, which consists of analog beamforming and digital beamforming. In analog beamforming, each antenna subarray generates a codebook based directional beam, which determines the equivalent MIMO channel. Then digital beamforming can be applied to fully exploit the spatial multiplexing gain of MIMO channels. In this paper, we propose a low complexity analog beam selection scheme to achieve near-optimal spectral efficiency. The core of our scheme is the beam selection criterion, which is derived from capacity analysis. Through simulations under different channel conditions, the near-optimal performance of our scheme is demonstrated, and our scheme is able to achieve good enough performance with a small number of candidate beam combinations.

Mobile computation offloading (MCO) is an emerging technology to offload the resource-intensive computations from smart mobile devices (SMDs) to nearby resource-rich devices (i.e., cloudlets) via wireless access. However, the link duration between a SMD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by a SMD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a semi-Markov decision process (SMDP)-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the SMDs in vehicles, and an application generated by a SMD includes a series of tasks that have dependency among each other. In this paper, we adopt a semi-Markov decision process (SMDP) framework to formulate the bus-based cooperation computing problem as a delay-constrained shortest path problem on a state transition graph. The value iteration algorithm (VIA) is used to find the efficient solution to the bus-based cooperation computing problem. Experimental results show that the proposed SMDP-based cloudlet cooperation strategy can improve the performance of computation on the SMD in the cost (i.e., energy consumption and application completion time) and the offloading rate.

Channel measurements of large-scale multiuser multiple-input multiple-output (MU-MIMO) radio propagation channels are presented. In the setup, three users with patch antennas communicate simultaneously with a base station (BS) equipped with a large antenna array in an indoor environment. Both a uniform linear array (ULA) and a uniform planar array (UPA) are used, and their relative ability to separate MU-MIMO signals is examined. At the mobile station (MS) side, the effect of inter-user spacing (i.e., the spacing between different users) is investigated. This evaluation is done by means of the correlation matrix distance metric (between each pair of users) and the singular value spread of the system. Our investigation shows that the users can be spatially separated in a large antenna array system in line-of-sight propagation conditions even when they are located close to each other. Furthermore, the users tend to be more separable when a ULA is adopted, compared to using a UPA. Finally, we also confirm that larger user separation distance results in increased channel orthogonality by measurements.

This paper investigates joint maximum likelihood (ML) Doppler shift and channel estimation problem for high speed railway (HSR) wireless communication systems with applying massive uniform linear array (ULA). For characterizing the performance of the considered scenarios, we provide a tractable framework for analyzing the performance of on Doppler shift estimation. Furthermore, the analytical expressions of ML channel estimation are derived, under the joint consideration of Doppler shift. Both analysis match their corresponding Cramer-Rao Bounds (CRBs) well. Finally, simulation results are provided to corroborate our proposed studies.

In this paper, we present a measurement campaign of indoor massive MIMO channels by using a wideband channel sounder and a 64-element virtual linear array. The measurements are conducted at 6 GHz, with a bandwidth of 200 MHz. Both the light-of-sight (LOS) and obstructed-line-of-sight (OLOS) scenarios are considered in measurements. The Frequency Domain Space-Alternating Generalized Expectation maximization algorithm (FD-SAGE) is used to determine the channel characteristics in angular domain. In order to validate the obtained FD-SAGE estimates, the power azimuth spectrum (PAS) has been calculated using the Bartlett Beamformer (BBF). The non-stationarity of radio channels, which is reflected by power of the estimated MPCs, azimuth of departure (AOD), and azimuth of arrival (AOA), is discussed. The direction spread is estimated, which reflects spatial dispersion of wireless channel. It is also found that the diffuse scattering components at 6 GHz in OLOS scenario is richer than that in LOS scenario, and the magnitude of the diffuse scattering components are comparable with the specular components. It is suggested that the non-stationarity of the diffuse scattering components should not be neglected in the indoor massive MIMO channel modeling at the frequency bands below 6 GHz.

Computation offloading to nearby devices offers resources-constrained mobile devices the opportunity to reduce local computation load and energy consumption when supporting applications with intensive computation load. In this paper, we consider joint job partitioning and collaborative computation offloading in a multi-user wireless network, aiming at minimizing the energy consumption of the overall network under the completion time constraint of each application. The problem is formulated as a binary integer linear programming problem and then transformed into a weighted bipartite graph matching problem. The Kuhn-Munkres algorithm is firstly adopted to obtain the optimal partition and scheduling. Three distributed scheduling algorithms are then introduced. The first one is referred to as stable matching, where each mobile node chooses matching collaborator or requester based on its own interests. Next an asynchronous greedy algorithm is proposed, where every requester-collaborator pair cooperatively reduces their total energy consumption. Finally, based on the greedy algorithm, a modified version, referred to as Maximum Differential Energy Matching (MDEM) algorithm is devised, which minimizes the overall network energy cost by relaxing the stability criterion in order to benefit energy consumption of all nodes. Simulation results show that the MDEM algorithm achieves near-optimal performance in terms of total energy consumption for the network.

The fifth generation (5G) wireless network faces unprecedented massive concurrent random access requests in uplink (UL) contention-based (CB) transmission. In this paper, a non-orthogonal coded access (NOCA) UL transmission in combination with parallel interference cancellation (PIC) based reception scheme at base station is investigated. Non-orthonormal code generated by Zadoff-Chu (ZC) sequences is used to spread the data bits over orthogonal frequency division multiple (OFDM) symbols. It is shown through both link level and system level simulations that in dense urban scenario NOCA-PIC scheme is capable of working in highly overloaded system conditions. It can support more than 4 times concurrent random access number compared with OFDM based CB transmission.

Mobile computation offloading is an emerging technology to migrate resource-intensive computations from resource-limited mobile devices (MDs) to resource-rich devices (such as a cloud server) via wireless access. Accessing to remote cloud server usually introduces a long delay to first deliver parameters to the server and then retrieve the results back. For applications that are time sensitive, offloading to nearby cloudlets is preferred. However, the link duration between an MD and a single cloudlet can be very limited in a vehicular network. As a result, offloading actions taken by an MD may fail due to link breakage caused by mobility. Meanwhile, some vehicles, such as buses, always follow relatively fixed routes, and their locations can be predicted much easier than other vehicles. By taking advantage of this fact, we propose a bus-based cloudlet cooperation strategy, where the bus-based cloudlets act as computation service providers for the MDs in vehicles, and an application generated by an MD includes a series of tasks that have dependency among each other. The proposed bus-based cloudlet cooperation strategy (BCCS) finds the optimal set of tasks to be offloaded to each cloudlet. Experimental results show that the proposed BCCS strategy can reduce both the energy consumption of the MDs and completion time of the applications.

In the real-world environments, multipath components (MPCs) of wireless channels are generally distributed as groups, i.e., clusters. Modeling the clustered MPCs is important and necessary for channel modeling and an automatic clustering algorithm is thus required. This paper proposes a novel Kernel-power-density (KPD) based algorithm for MPC clustering. It uses the Kernel density to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. The proposed algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. Simulations validate the KPD algorithm and almost no performance degradation is found even with a large number of clusters and large cluster angular spread. The KPD algorithm enables applications with no prior knowledge about the clusters such as number and initial locations. It can be used for the cluster based channel modeling for 4G#x002F;5G communications.

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

This paper proposes a practical channel estimation for 60-GHz indoor systems with the massive uniform rectangular array at base station. Through antenna array theory, the parameters of each channel path can be decomposed into the angular information and the channel gain information. We first prove that the true direction of arrivals of each uplink path can be extracted via an efficient array signal processing method. Then, the channel gain information could be obtained linearly with small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, the proposed scheme unifies the uplink/downlink channel estimations for both the time duplex division and frequency duplex division systems, making itself particularly suitable for protocol design. Compared with the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the 2-D fast Fourier transform. Meanwhile, the number of user terminals simultaneously served can be increased from a sophisticatedly designed angle division multiple access scheme. Simulation results are provided to corroborate the proposed studies.

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

Massive machine-type communication (MMTC) is the key technology to meet the flourishing Internet of things industry. One prominent characteristic of MMTC is massive connections which brings a critical challenge of multiple access interference (MAI) on channel estimation, user identification, and data detection. Currently, various techniques have been proposed to mitigate the MAI, but those MAI alleviations are limited. Therefore, this paper proposes a novel technique called tandem spreading network-coded division multiple access (TSNDMA) to effectively cope with the MAI problem. In this technique, first a novel channel-reciprocity-based precompensation scheme is proposed for channel estimation. Then, a tandem spreading mechanism and a physical-layer network coding-based redundancy design are introduced for user identification and data detection with corresponding algorithms at the receiver. Simulation results show that TSNDMA can effectively mitigate the MAI to achieve a favorable system performance.

Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multipleinput-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for clusterbased channel modeling.

In this paper, we investigate the cooperative modulation classification problem under multipath scenarios with blind channel information. Multipath channels cause severe degradation on the modulation classification performance, which has not yet been thoroughly solved in the existing literature. To address this issue, a likelihood-based classifier using the expectation-maximization algorithm is proposed, which is capable of finding the maximum likelihood estimates of unknown parameters in a tractable way. Furthermore, to evaluate the upper bound performance of the proposed algorithm, the Cramér-Rao lower bounds of the joint estimates of unknown parameters are derived. Extensive simulations show that the classification performance of the proposed algorithm with good initialization scheme is close to the performance upper bound in the high signal-to-noise ratio region. The results also demonstrate that the proposed algorithm provides significant performance improvement in the multipath channels compared with conventional approaches.

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

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

This paper investigates the energy efficient resource allocation for OFDM relay networks, where K users receive information via L helping relays. For such a system, an optimization problem is formulated to maximize the system energy efficiency (EE) by jointly optimizing the relay selection, subcarriers assignment and power allocation under the proportional rate constraints and available power constraint. Since this problem is non-convex with integer variables, which is nontrivial to be solved by using known methods, we design an efficient low- complexity algorithm to solve it. Simulation results show that by using our proposed resource allocation scheme, the approximate optimal results can be achieved. It is also shown that the circuit power (including a rate-dependent part and a constant part) in the consumed power has a great impact on limiting the EE resource allocation to obtain a high spectral efficiency. Besides, the effects of the relay selection, subcarrier assignment and power allocation on the system performance are also discussed via simulations.

The paper presents a new channel estimation scheme for downlink 60GHz indoor environment with Massive Multiple Input Multiple Output (Massive MIMO) systems by exploiting the physical characteristics of the uniform planar array (UPA) and 2-dimensional discrete Fourier Transform (2D-DFT) operation. The proposed channel estimation algorithm can be carried out with very small number of training resources, which significantly reduces the training overhead and the feedback cost. Meanwhile, the number of user terminals (UTs) served simultaneously can be increased by exploiting the spatial pattern of different UTs. Compared to the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the fast Fourier transform (FFT). Finally, simulation results are provided to corroborate the proposed studies.

Due to the extra loss resulting from tunnel curve, most of the theoretical models cannot be directly used for propagation inside curved tunnels. In this paper,extensive simulations are made for propagation at different frequencies in different types of curved tunnels, by using a ray-tracing simulator that is validated by numerous measurements. From the simulated received power, the extra losses of tunnel curve in various cases are extracted, analyzed, and stochastically modeled by normal distributions. Thus, with the parameters presented in this paper, the total propagation loss inside curved tunnels can be predicted by adding the stochastically generated extra loss to the propagation loss in the straight tunnel. This provides a fairly simple and effective way to extend the theoretical propagation models to fulfill network planning and system design for communication systems in real curved tunnels.

In this paper, we propose a novel channel impulse response (CIR) clustering algorithm using a sparsity-based method, which exploits the feature of CIR that power of multipath component (MPC) is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved by using reweighted ℓ1minimization. Then a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. The proposed algorithm incorporates the physical behaviors of MPCs into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. The results in this paper can be used to parameterize the CIR model of radio channels.

It has been widely recognized that relaying is an important method for increasing the reliability and spectral efficiency of communications systems, and it is thus helpful for improving the performance of vehicle-to-vehicle (V2V) communication systems. However, designing and evaluating V2V relay networks require understanding the effect of shadowing, as this critically impacts the performance of the relay system. Even though the theoretic performances of various relaying schemes have been well investigated, there is a lack of empirical test that incorporates realistic shadowing effects. In this paper, we analyze the performance of relaying transmission in V2V scenarios based on measurements in scenarios where shadowing occurs through large vehicles such as buses. We investigate several potential locations for the relay nodes, and the measurements are performed with two static transmitters (TX) and one dynamic receiver (RX). Outage probabilities of several relaying schemes such as multi-hop decode-and- forward, multi-hop amplify-and-forward, and diversity-amplify-and-forward are estimated and discussed based on the measured instantaneous end- to-end signal-to-noise ratio (SNR). It is found that: (i) shadowing effect caused by the bus between V2V line-of-sight (LOS) links increases the outage probability for the non-LOS (NLOS) direct transmission; (ii) using relay node on the bus roof can significantly improve transmission, however, a strong shadowing effect may reduces the acceptable communication distance of relaying scheme; and (iii) the diversity-amplify-and- forward relaying scheme generally has the best performance. Our results can be used to design a relay system for V2V communications.

Mobile Hotspot Network (MHN) is designed as a new communication system for high speed mobile group vehicles and aiming at providing multi-Giga bps data rate by using millimeter wave (mmWave). According to the MHN setup, the channel at 30 GHz band is simulated by a ray-tracing tool in a typical straight subway tunnel. In order to assess the system physical layer performance, system simulations based on entire channel characteristics provide an profound significance. the channel parameters such as path loss, power delay profile, decorrelation distance, Rician K-factor, Doppler characteristics, etc., are extracted and analyzed for verifying system feasibility and can serve as a guide for physical layer design.

Massive MIMO has been chosen as one of the candidate technologies of the fifth-generation mobile communication system (5G), and channel modeling of massive MIMO is of great importance. The most direct and effective approach to investigate the propagation characteristics of massive MIMO channels is channel measurements. However, there are only few measurements of massive MIMO channels, and there still lacks deep investigations of massive MIMO channel characteristics. In this paper, we present a measurement campaign of indoor massive MIMO channels, by using a linear large-scale array with 64 elements. The measurements are conducted at 2 GHz, 4 GHz, and 6 GHz, respectively, with a bandwidth of 200 MHz. Both LOS and NLOS propagation scenarios are considered in the measurements. The basic channel parameters are extracted, including path loss, delay spread, and coherence bandwidth. The non-stationarity of radio channels, which is reflected by the variations of delay spread and coherence bandwidth over different array locations, is discussed. The impact of carrier frequency on the above channel parameters is further discussed. The results would be useful for the design of massive MIMO system in the indoor environments.

Deploying multiple antennas on high speed trains is an effective way to enhance the information transmission performance for high speed railway (HSR) wireless communication systems. However, how to efficiently deploy N (N ≥ 2) antennas on a train has not been studied yet. In this paper, we investigate efficient antenna deployment strategies for HSR communication systems where two multi-antenna deployment strategies, i.e., the equidistant strategy and the fixed-interval strategy, are considered. To evaluate the system performance, mobile service amount and outage time ratio are introduced. Theoretical analysis and numerical results show that, when the length of the train is not very large, for N = 2 case, by increasing the distance of neighboring antennas in a reasonable region, the system performance can be enhanced, and for N> 2 case the two strategies have much difference performance behavior in terms of instantaneous channel capacity, and the fixed-interval strategy may achieve much better performance than the equidistant one in terms of service amount and outage time ratio when the antenna number is much large.

To attain seamless handover and reduce the handover failure probability for high-speed railway (HSR) systems, this paper proposed a remote antenna unit (RAU) selection assisted handover scheme based on two HST antennas and distributed antenna system (DAS) cell architecture. The RAU selection is adopted to provide high quality received signals for trains in DAS cells and the two HST antennas are employed on trains to realize seamless handover. Moreover, to efficiently evaluate the system performance, a new metric termed as handover occurrence probability is define for describing the relation between handover occurrence position and handover failure probability. We derive the expressions of the received signal strength, the handover trigger probability, the handover occurrence probability, the handover failure probability and the communication interruption probability of our proposed method. Numerical experimental results are provided to compare our proposed scheme with traditional handover scheme and some existing ones. It is shown that, our proposed scheme is able to achieve the lowest handover failure probability and communication interruption probability among all schemes.

The local average power estimation is needed by communications system for use in coverage assessment, power control, and handoff. Aiming at satisfying the broadband communications and higher safety requirements of next generation communications system, this paper proposes novel criteria of local mean power estimation, including the statistical averaging length, sample number, and sample interval. The multi-path fading is Nakagami-m distributed and the basic procedure is similar to Lee Criteria. The performance of the estimation algorithm is compared with the Lee method which is based on Rayleigh distribution. When it is LOS propagation, which covers the most cases in railway scenario, the measured length necessary to obtain the local average power is determined to be in the range of 10 to 25 wavelengths. The sufficient number of samples depends on one parameter of Nakagami-m distribution and varies from 1 to 14. It is based on the 95 percent and 99 percent confidence interval and less than 1 dB error in estimating. The sample interval increases greatly in comparison with Lee criteria, which can reduce the measurement overhead while remaining the high estimating accuracy.

In this paper, we investigate the delay-aware dynamic resource management problem for multi- service transmission in high-speed railway wireless communications, with a focus on resource allocation among the services and power control along the time. By taking account of average delay requirements and power constraints, the considered problem is formulated into a stochastic optimization problem, rather than pursuing the traditional convex optimization means. Inspired by Lyapunov optimization theory, the intractable stochastic optimization problem is transformed into a tractable deterministic optimization problem, which is a mixed-integer resource management problem. By exploiting the specific problem structure, the mixed-integer resource management problem is equivalently transformed into a single variable problem, which can be effectively solved by the golden section search method with guaranteed global optimality. Finally, we propose a dynamic resource management algorithm to solve the original stochastic optimization problem. Simulation results show the advantage of the proposed dynamic algorithm and reveal that there exists a fundamental tradeoff between delay requirements and power consumption.

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

Ambient backscatter, a new communication technology, can permit battery-free devices to communicate with other devices through reflecting the ambient radio frequency signals. One challenge for ambient backscatter communication system is to recover the backscattered information bits hidden in the received signals. Existing solutions are mainly based on energy detector and thus provide poor performance at low signal noise ratio (SNR). To solve this problem, a detection algorithm based on statistical covariances is suggested in this paper. Specifically, we calculate the distributions of two covariance-based statistics, design the detection rule, and then derive the closed- form expressions for detection probability and bit error rate (BER). It is found that our proposed algorithm outperforms the energy detector at low SNR regions. Finally, the simulation results are provided to corroborate our theoretical studies.

Existing studies on cognitive two-way relay networks assume that the primary users (PUs) transmit data with two power levels. In practice, it is possible that the PUs may adopt multiple power levels to maximize their throughputs or to compensate for wireless link fading. In such a scenario, there exist two key problems: one is the detection accuracy for the secondary users (SUs), and the other is the throughput tradeoff between PUs and SUs. To address the detection accuracy challenge, we propose a cooperative soft-combination-based energy detector and demonstrate its benefit over other hard-combination-based detectors. To solve the throughput tradeoff problem, we derive the throughput expressions for both PUs and SUs and analyze the impact of our detection accuracy on the throughputs. It is shown that the SU's throughput varies with the number of the PU's transmit power levels and achieves a maximum value when the number is small. We also find that increasing the detection capability of the SU reduces its own throughput, while increases the PU's throughput.

Along with emerging increasing multimedia service need from the big amount of users in the train, wireless cellular railway network has big energy saving potential with its own specific characteristics, which is widely deployed over the world. Enhanced Multimedia Broadcast Multicast Service (E-MBMS) is a prevalent solution for high speed data handling. In this paper, to achieve an energy efficient solution for wireless cellular railway network, we proposed an optimal power control solution to adjust eNodeB’s transmission power adaptively based on actual Signal to Interference plus Noise Ratio (SINR) for Multimedia Broadcast multicast service Single Frequency Network (MBSFN) at User Equipment (UE) side when provide E-MBMS. We established the system model and proposed the optimization algorithms to calculate the E-MBMS transmit power. We also designed the geometry group scheme to significantly reduce the calculation effort. And proposed the Mobility offset parameter to cope with high speed mobility of the train and variation of signal level. Finally, Simulation results showed that this solution can be a good way for high network energy efficiency and can dynamically adjust the output power for the multicast service in the wireless cellular railway communication network.

In this paper, we investigate the specific emitter identification (SEI) problem, which distinguishes different emitters using features generated by the nonlinearity of the power amplifiers of emitters. SEI is performed by measuring the features representing the individual specifications of emitters and making a decision based on their differences. In this paper, the SEI problem is considered in both single-hop and relaying scenarios, and three algorithms based on the Hilbert spectrum are proposed. The first employs the entropy and the first- and second-order moments as identification features, which describe the uniformity of the Hilbert spectrum. The second uses the correlation coefficient as an identification feature, by evaluating the similarity between different Hilbert spectra. The third exploits Fisher's discriminant ratio to obtain the identification features by selecting the Hilbert spectrum elements with strong class separability. When compared with the existing literature, we further consider the identification problem in a relaying scenario, in which the fingerprint of different emitters is contaminated by the relay's fingerprints. Moreover, we explore the identification performance under various channel conditions, such as additive white Gaussian noise, non-Gaussian noise, and fading. Extensive simulation experiments are performed to evaluate the identification performance of the proposed algorithms, and results show their effectiveness in both single-hop and relaying scenarios, as well as under different channel conditions.

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

The communication delay of train control services has a great impact on the track utilization and speed profile of high-speed trains. This paper undertakes stochastic delay analysis of train control services over a high-speed railway fading channel using stochastic network calculus. The mobility model of high-speed railway communications system is formulated as a semi-Markov process. Accordingly, the instantaneous data rate of the wireless channel is characterized by a semi-Markov modulated process, which takes into account the channel variations due to both large- and small-scale fading effects. The stochastic service curve of high-speed railway communications system is derived based on the semi-Markov modulated process. Based on the analytical approach of stochastic network calculus, the stochastic upper delay bounds of train control services are derived with both the moment generating function method and the complementary cumulative distribution function method. The analytical results of the two methods are compared and validated by simulation.

For industrial wireless sensor networks, it is essential to reliably sense and deliver the environmental data on time to avoid system malfunction. While energy harvesting is a promising technique to extend the lifetime of sensor nodes, it also brings new challenges for system reliability due to the stochastic nature of the harvested energy. In this paper, we investigate the optimal energy management policy to minimize the weighted packet loss rate under the delay constraint, where the packet loss rate considers the lost packets, both during the sensing and delivering processes. We show that the above-mentioned energy management problem can be modeled as an infinite horizon average reward constraint Markov decision problem. In order to address the well-known curse of dimensionality problem and facilitate distributed implementation, we use the linear value approximation technique. Moreover, we apply stochastic online learning with a post-decision state to deal with the lack of the knowledge of the underlying stochastic processes. A distributed energy allocation algorithm with a water-filling structure and a scheduling algorithm by an auction mechanism are obtained. Experimental results show that the proposed algorithm achieves nearly the same performance as the optimal offline value iteration algorithm while requiring much less computation complexity and signaling overhead, and outperforms various existing baseline algorithms.

Large-scale antenna (LSA) has gained a lot of attention recently since it can significantly improve the performance of wireless systems. Similar to multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM, LSA can be also combined with OFDM to deal with frequency selectivity in wireless channels. However, such combination suffers from substantially increased complexity proportional to the number of antennas in LSA systems. For the conventional implementation of LSA-OFDM, the number of inverse fast Fourier transforms (IFFTs) increases with the antenna number since each antenna requires an IFFT for OFDM modulation. Furthermore, zero-forcing (ZF) precoding is required in LSA systems to support more users, and the required matrix inversion leads to a huge computational burden. In this paper, we propose a low-complexity recursive convolutional precoding to address the issues above. The traditional ZF precoding can be implemented through the recursive convolutional precoding in the time domain so that only one IFFT is required for each user and the matrix inversion can be also avoided. Simulation results show that the proposed approach can achieve the same performance as that of ZF but with much lower complexity.

Effective inter-vehicle communication is fundamental to a decentralized traffic information system based on Vehicular Ad Hoc Networks (VANETs). To reflect the uncertainty of the information propagation, most of the existing work was conducted by assuming the inter-vehicle distance follows some specific probability models, e.g., the lognormal or exponential distribution, while reducing the analysis complexity. Aimed at providing more generic results, a recursive modeling framework is proposed for VANETs in this paper when the vehicle spacing can be captured by a general i.i.d. distribution. With the framework, the analytical expressions for a series of commonly discussed metrics are derived respectively, including the mean, variance, probability distribution of the propagation distance, and expectation for the number of vehicles included in a propagation process, when the transmission failures are mainly caused by MAC contentions. Moreover, a discussion is also made for demonstrating the efficiency of the recursive analysis method when the impact of channel fading is also considered. All the analytical results are verified by extensive simulations. We believe that this work is able to potentially reveal a more insightful understanding of information propagation in VANETs by allowing to evaluate the effect of any vehicle headway distributions.

A novel approach for specific emitter identification using Hilbert spectrum is proposed for both single-hop and relaying scenarios. In particular, two features, i.e., the energy entropy and color moments, are extracted from the Hilbert spectrum of the signal of interest as identification features. The spectrum is obtained through the Hilbert-Huang transform, which is a powerful tool for the analysis of non-linear and nonstationary signals by decomposing them into a set of intrinsic mode functions. The identification task is solved by applying the support vector machine. We further extend the identification problem to a relaying scenario, in which the fingerprint of different emitters may be contaminated by the relay's fingerprints. To the best of our knowledge, this case has not been investigated so far in the literature. At last, simulation results validate that the proposed approach can effectively cope with the specific emitter identification problems in both single-hop and relaying scenarios.

The user behavior is one of the key factors to impact the communication performance of vehicular ad hoc networks. Generally, more active vehicles can enhance the connectivity performance. Nevertheless, an increasing number of active vehicles may bring about significant interference and thus negatively affect the connectivity. In this paper, a highway model is presented to study the effects of active vehicle ratio on connectivity performance in the presence of co-channel interference. A closed form expression of the inter-vehicle connectivity probability is also derived to evaluate the impacts of active ratio, traffic flow, and fading factors under Nakagami fading channel. Analytical results of inter-vehicle connectivity probability are shown to be consistent with the simulation results, and can be applied to estimate connectivity performance with interference in the network design for highway vehicular scenarios.

To statistically model time-variant vehicle-to-vehicle (V2V) channels, the dynamic multi-path components (MPCs) are characterized based on suburban measurements conducted at 5.3 GHz. The correlation matrix distance (CMD) is used to determine the size of local wide-sense stationary (WSS) region. Within each WSS time window, MPCs are extracted using wideband spatial spectrum of Bartlett beamformer. A MPC distance (MCD)-based tracking algorithm is used to identify the "birth" and "death" of MPCs over different WSS regions, and the lifetime of MPC is modeled with a truncated Gaussian distribution. Distributions of number of MPCs and their positions are statistically modeled. The MPC characterization considers both angular and delay domain properties as well as the dynamic evolution of MPCs over different WSS regions. The results shows insight into the dynamic behaviors of MPCs in V2V environments, and is useful for the scatterer modeling in the geometry-based stochastic channel modeling.

In this paper, the resource allocation problem is investigated for Device-to-Device (D2D) communications underlaying cellular networks with bursty traffic arrival. We formulate an infinite horizon average reward constraint Markov decision process (CMDP) that aims at minimizing the average delay under the dropping propability constraint. Then, we present a reduced-state Bellman's equation with linear value function approximation to deal with the curse of dimensionality problem in solving the CMDP. A distributed resource allocation algorithm is derived with low computation complexity and signaling overhead, which consists of a subchannel bidding mechanism to obtain the optimal control action, and a distributed online stochastic learning algorithm to estimate the value function and the optimal Lagrangian Multipliers (LMs). Simulation results show that the performance of our proposed algorithm is very close to that achieved by the offline value iteration algorithm, and is better than various baselines algorithms.

Most of the existing channel models are based on the assumption of wide sense stationary uncorrelated scattering (WSSUS) properties, which are not suitable for time-varying vehicle-to-vehicle (V2V) channels. A method for generating correlated taps in stochastic V2V channel model is proposed in this paper, which represents the non-WSSUS properties adequately. With the method for generating correlated taps, the proposed tapped delay line channel model is correlated both in the amplitude part and the phase part. In the model, the amplitude statistics follows the Weibull distribution, and the phase statistics follows the linear function of uniform distribution, which are more accurate to represent the fading properties of V2V channel. Furthermore, simulation results show that the proposed model can generate correlated V2V channel with arbitrary amplitude and phase correlation coefficients accurately.

Wireless communication link quality can be determined by the transmission protocol design, the interference level, the channel fading properties, and the mobility characteristics etc. As one of the most essential features for vehicular networks, high mobility brings in intermittent connectivity and relative short link lifetime. Therefore, an analytical model on link lifetime can be of great help for an efficient and effective vehicular communication protocol design. In this paper, the impact of mobility on link lifetime in the highway environment is modeled and analyzed by utilizing the discrete-time Markov chain (DTMC). Specifically, two link lifetime theoretical models, the first-order Markov model and the second-order Markov model, are studied and analyzed for effectively predicting the proprieties of a vehicular communication link. It is shown that our proposed models, especially the second-order Markov model, can provide more accuracy in performance prediction. Additionally, extensive simulations are carried out to verify our analytical results on link lifetime.

With the rapid development of high-speed railway (HSR) system, there is an increasing demand on providing high throughput and continuous multimedia (CM) services for HSR passengers. In this paper, we investigate the downlink resource allocation problem for on-demand CM services in HSR OFDMA systems with a cellular/infostation integrated network architecture. The resource allocation problem is formulated as a two-stage optimization programming, which aims at maximizing the total reward of delivered services then minimizing the weighted total number of cumulative discontinuity packets over the trip of the train. An equivalent one-stage programming is proposed to resolve the difficulty of multi-stage optimization. The resultant mixed integer programming (MIP) is NP-hard in general, we thus reformulate it as a sparse ℓ0-minimization problem and then relax it to a linear programming (LP). Furthermore, a reweighted ℓ1-minimization technique is applied to improve the system performance. Simulation results are provided to validate the proposed algorithms.

Wave propagation characteristics in curved tunnels are of importance for designing reliable communications in subway systems. This paper presents the extensive propagation measurements conducted in two typical types of subway tunnels—traditional arched “Type I” tunnel and modern arched “Type II” tunnel—with 300- and 500-m radii of curvature with different configurations—horizontal and vertical polarizations at 920, 2400, and 5705 MHz, respectively. Based on the measurements, statistical metrics of propagation loss and shadow fading (path-loss exponent, shadow fading distribution, autocorrelation, and crosscorrelation) in all the measurement cases are extracted. Then, the large-scale fading characteristics in the curved subway tunnels are compared with the cases of road and railway tunnels, the other main rail traffic scenarios, and some “typical” scenarios to give a comprehensive insight into the propagation in various scenarios where the intelligent transportation systems are deployed. Moreover, for each of the large-scale fading parameters, extensive analysis and discussions are made to reflect the physical laws behind the observations. The quantitative results and findings are useful to realize intelligent transportation systems in the subway system.

The high-speed railway (HSR) propagation channel has a significant impact on the design and performance analysis of wireless railway control systems. This paper derives a stochastic model for the HSR wireless channel at 930 MHz. The model is based on a large number of measurements in 100 cells using a practically deployed and operative communication system. We use the Akaike information criterion to select the distribution of the parameter distributions, including the variations from cell to cell. The model incorporates the impact of directional base station (BS) antennas, includes several previously investigated HSR deployment scenarios as special cases, and is parameterized for practical HSR cell sizes, which can be several kilometers. The proposed model provides a consistent prediction of the propagation in HSR environments and allows a straightforward and time-saving implementation for simulation.

High-speed railways (HSRs) have been widely introduced to meet the increasing demand for passenger rail travel. While it provides more and more conveniences to people, the huge cost of the HSR has laid big burden on the government finance. Reducing the cost of HSR has been necessary and urgent. Optimizing arrangement of base stations (BS) by improving prediction of the communication link is one of the most effective methods, which could reduce the number of BSs to a reasonable number. However, it requires a carefully developed propagation model, which has been largely neglected before in the research on the HSR. In this paper, we propose a standardized path loss/shadow fading model for HSR channels based on an extensive measurement campaign in 4594 HSR cells. The measurements are conducted using a practically deployed and operative GSM-Railway (GSM-R) system to reflect the real conditions of the HSR channels. The proposed model is validated by the measurements conducted in a different operative HSR line. Finally, a heuristic method to design the BS separation distance is proposed, and it is found that using an improved propagation model can theoretically save around 2/5 cost of the BSs.

Vehicle-to-Vehicle (V2V) communication is an en-abler for improved traffic safety and congestion control. As for any wireless system the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel in which a bus acts as a shadowing object between two passenger cars. The measurement setup is based on a WARP FPGA software radio as transmitter, and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the large-scale path loss, shadowing, and small-scale fading. The main effect of the bus is that it is acting as an obstruction creating an additional 15–20 dB attenuation. A Nakagami distribution is found to describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance-dependency of the path loss is analyzed, and a stochastic model is developed to reflect the impact.

Vehicle tracking information is necessary to enable safety communication systems and intelligent transportation systems. Compression technologies with high efficiency and low complexity provide a promising approach to address the transmission and computing problems in vehicle tracking applications. Especially, vehicular trajectory with sparse errors that happened in the measurement sensing process poses a great challenge on traditional compression algorithms. In this paper, we analyze and design a compressive sensing (CS) based erroneous trajectory compression and recovery algorithm for vehicle tracking scenario. Moreover, some theoretical bounds for the proposed recovery optimization problem are analyzed and proved. The CS-based method proposed in this paper could not only achieve a fairly high compression rate and recovery accuracy, but fit the bandwidth mismatch between the road side unit (RSU) and on board unit (OBU). In another aspect, the Kalman filtering (KF) technology is applied for further optimizing the system performance, e.g. mean square error (MSE). Extensive simulations with real vehicular trajectories are carried out, which shows that CS-based compression algorithm achieves relatively high compression performance compared to some state-of-the-art trajectory compression algorithms.

In the Time Division Multiple Access (TDMA) Ad Hoc network system, time slot allocation is one of the most important factors affecting the performance of the network. A good time slot allocation algorithm can effectively reduce the average delay and delay jitter of the network, thus improve the network efficiency. This paper proposes an uniform time slot allocation scheme for TDMA-based Ad Hoc network based on genetic algorithms. The scheme aims at searching out the optimal one in the all feasible solutions for the time slot allocation by the genetic algorithms. The simulation results show that the proposed algorithm is correct and effective.

The performance analysis of high-speed railway (HSR) network has attracted broad attention in recent years. The characteristics of HSR scenario, compared with traditional public network scenario, mainly lies in the special traffic between trains and stations, i.e., train control traffic, video monitoring traffic and passenger traffic in general. Since the performance analysis of traffic in HSR, especially train control traffic and video monitoring traffic, can better guarantee the safety and improvement of railway, thus this paper researches and analyzes the performance bounds of HSR railway uplink traffic. The work is carried out based on a group of actual measurement data gathered from Chinese Train Control System (CTCS) and an advanced performance analysis tool stochastic network calculus (SNC). Finally, by means of the measurement data in the uplink direction, the stochastic backlog and delay bounds of train control traffic and video monitoring traffic under different violation probabilities and bit rates are derived and compared.

In this paper, we study the cognitive radio networks where the primary users transmit data with multiple power levels. A cooperative sensing algorithm with soft combination based on energy detection is proposed to improve the detection accuracy. We compare the detection performance between our proposed cooperative soft combination and existing hard combination, such as averaging combination, maximum combination and minimum combination. It is found that the suggested algorithm outperforms other existing hard combination schemes. It is also shown that the increasing number of the cooperative users can result in better detection performance at low signal-to-noise ratio (SNR), while will obtain limited contribution for detection in high SNR. In the end, we validate our work by various simulation results.

The heterogeneous vehicular network (HVN) is a novel kind of structure for intelligent transportation systems. It can help to improve the quality of wireless communication and support entertainment service during the trip. HVN is composed of two types of links, vehicle-to-infrastructure (V2I) links and vehicle-to-vehicle (V2V) links. In this paper, we focus on the problem of resource scheduling for HVN. The goal is to investigate how to select the relaying mobile vehicles and how to choose the link to transmit between V2V and V2I to achieve the best system performance. To explore the maximum total information transmission rate with the consideration of fairness among the mobile vehicles, we formulate an optimization problem to maximize the weighted sum-rate for the system. As the problem is difficult to solve, we thus design a new efficient scheduling algorithm on the basis of bipartite graph. Simulation results demonstrated that our proposed algorithm can achieve the optimal solution approximately with low complexity.

By analyzing the characteristics of energy consumption in wireless sensor networks, this study finds out that the pivotal aspect in controlling energy consumption is the media access control protocol, and introduces the media access control protocol of typical wireless sensor network in various categories, meanwhile, abstractly divides the context of use into three conditions. Based on the OPNET, this paper simulates different scenarios in terms of the energy consumption and delay performance under the varieties of protocol, generating the result that none of them could provide the optimal performance in the different scenarios. Therefore, for the sake of adapting into complex application scenarios, it is necessary to develop the media access control protocol with further improvements, in terms of the general applicability or business mode with adaptive ability.

In this paper we present a media access control protocol in MANETs (Mobile Ad-hoc Networks) named DA-STDMA (Directional Antenna Spatial Time Division Multiple Access), which is designed specifically to operate in a multi-hop ad hoc network where the physical layer of the network employs directional antennas. A slot allocation algorithm is proposed to determining the assignment of capacities for the links of the network. The performance of this algorithm and the DA-STDMA protocol are evaluated via simulation studies. The results show that DA-STDMA has a higher reusability of timeslots, which greatly optimizes network resource, saves network overhead, and transmits more data in a specific period of time.

Wireless sensor networks (WSNs) are formed by a large number of arbitrarily or randomly deployed sensing nodes, monitoring and measuring physical parameters extracted from the environment. The correlation of shadow fading caused by the similar propagation environments in wireless channel poses significant research challenges in system design. This paper proposes a general shadowing correlation model as a four-slope piecewise function of distance as well as angle according to different network topological structure and geographical deployment. It can be applied for the generation of channel model contains any pair of correlated communication links in WSNs environment.

This article proposes a framework of handover decision in LTE networks under high-speed mobility scenario,which is expected to support the subject's mobility at a speed of up to 500 km/h. In order to improve the handover performance in high-speed railway, a Location-Based Handover Algorithm (LBHA) is researched in this paper. The basic idea is that the vehicle speed sensor (VSS) calculates the train's distance to eNodeB (evolved Node B) by sensing its real-time velocity, and then reports the distance information to the source eNodeB periodically. Based on the location information from the train, the source eNodeB will estimate the train's relative distance to eNodeB at next reporting time, which will be further used to make handover decision in time. For better handover performance, the mobile station's location information as well as the receive signal strength has been collected to aid the handover decision. Finally, some numerical results are shown to demonstrate the effectiveness of the proposed handover decision algorithm. Compared with the traditional handover algorithm, LBHA adds 15% probability of successful handover to the traditional one, and reduces 10% probability of unnecessary handover. Furthermore, this algorithm is easy to be carried out.

Vehicle-to-Vehicle (V2V) communication is widely used to improve traffic safety. However, the V2V link is easily blocked due to the low antenna height. The vehicular obstructions also lead to strong shadowing effect, which has been ignored in the V2V system performance evaluation. By incorporating channel state transition with a measurement-based dual-slope path loss model, a light-of-sight (LOS) blocked model is presented for the V2V highway scenarios. The joint effects of radio environment and traffic flow on link connectivity are investigated to represent the obstruction probability and inter-vehicle connectivity probability. All the analytical results are validated by extensive simulations under a measurement-based propagation model. The results can be used for the system design and development of a channel simulator for V2V communications.

High-speed railway (HSR) has been widely introduced to meet the increasing demand for passenger rail travel. While it provides more and more conveniences to the people, the huge cost of the HSR has laid big burden on the government finance. Reducing the cost of HSR has been necessary and urgent. Optimizing the arrangement of the base stations (BS) by improving the prediction of the communication link is one of the most effective methods, which could reduce the number of the BSs to a reasonable number. However, it requires a carefully developed propagation model, which has been largely neglected before in the research on the HSR. In this paper, we propose a standardized path loss model for HSR channels based on an extensive measurement campaign in 4594 HSR cells of the "Zhengzhou-Xian" HSR line. The measurements are conducted using a practically deployed and operative GSM-Railway (GSM-R) system to reflect the real conditions of the HSR channels. The proposed model is validated by the measurements conducted in another operative railway - "Beijing-Shanghai" HSR line. The results are helpful for the HSR communications system designers to gain a better tool in the system planning, and propagation researchers to assess where the most pressing needs in the modeling of HSR channels lie.

Green communication is ecological, economic and essential but seldom discussed in railway cellular network because safety is always the focus and has the highest priority. By analyzing the train scheduling and railway communication characteristics, we find that railway cellular network, especially for radio base station, has a big potentiality for energy saving to reduce carbon footprint without interrupting the safety rules. In this paper, we propose a simple train detection strategy by HANDOVER procedure under 3GPP specification. To implement this strategy under railway system model, we define cells' state along the railway. Then the cells are allocated to different orthogonal groups, which form a series of clusters identified by Finite states. A flexible and effective solution is designed based on mealy-type finite state machine to transit every cell between energy saving mode and full running mode. Our evaluation results show that a significant ratio, approximately up to 33% energy can be saved, by employing the configuration data for an actual high- speed railway line in China.

One open problem for wireless communication in the high-speed railway environment is channel capacity. In this paper, we study the problem of ergodic capacity for the time-selective and frequency-selective wireless channels in the high-speed railway environment. Firstly, we build up the channel model for the wireless communication between mobile users on the high-speed train and the base station along the railway via the train antennas. Next both the upper bound and the lower bound for the ergodic channel capacity are derived on the basis of this model. Furthermore, the closed-form expression for the ergodic channel capacity is obtained at high SNR. Finally, simulation results are provided to corroborate our proposed studies.

In this paper, we introduce a three-phase transmission scheme for cloud radio networks against eavesdropper. We study the security and reliability performance of this scheme in the form of intercept probability and outage probability, respectively. Channel estimation error is considered in our study instead of assumption of perfect channel state information. It is found that the security-reliability performance improves as the number of relay increases. Moreover, the length of training symbol considerably influences the performance. Simulation results are provided to corroborate our studies.

In this work, a two-phase optimal-relay transmission scheme for relay networks against eavesdropper is proposed. The security and the reliability performance of this scheme are evaluated in the form of the intercept probability and the outage probability, respectively. In addition, the closed-form expression of security-reliability tradeoff (SRT) is obtained to show the compromise of the security and reliability performance of the system. Specifically, instead of common assumptions of perfect global channel state information in most literature, the channel estimation error and its effects on performance analysis are considered in this paper. It is shown that the security (or reliability) can be enhanced by degrading the reliability (or security) requirement, and the performance can be improved by increasing the number of relays. Finally, simulation results are provided to corroborate our proposed studies.

With the fast development of high-speed railways, the need for wireless communication to satisfy secure demand of train and application demand of passengers has become evident.As a part of large- scale propagation effects, shadow fading is of great importance to design hand-over algorithm, and to evaluate macro diversity gain in wireless communications. This paper offers an investigation on the cross-correlation property of shadow fading in high-speed railway (HSR) network, based on the empirical data measured along Xingyang to Luoyang HSR line of China. The measurements indicate that the crosscorrelation coefficient of shadow fading is correlated to the separation distance between two base stations. Several models, to describe the relation between the cross-correlation coefficient and the separation distance, are proposed and compared with each other in this paper, and a decaying exponential model is finally suggested to characterize the cross-correlation property in the HSR environments.

In this study, the authors form a wireless channel model for orthogonal frequency‐division multiplexing (OFDM) systems with stochastic high‐level Petri net (SHLPN) formalism in order to simplify the cross‐layer performance analysis of modern wireless systems. Compared with existing finite state Markov channel model whose state space grows exponentially with the number of OFDM subchannels, the author's proposed SHLPN model uses state aggregation technique to deal with this problem. Closed‐form expressions to calculate the transition probabilities among the compound markings of the SHLPN model are provided. When applied to derive the performance measures for OFDM system in terms of the average throughput, average delay and packet dropping probability, the SHLPN model can accurately capture the correlated time‐varying nature of wireless channels. Simulation is performed to show that the numerical results offered by the proposed model are more accurate compared with other simplified channel models for avoiding state space complexity.

High-speed railway (HSR) brings convenience to peoples' lives and is generally considered as one of the most sustainable developments for ground transportation. One of the important parts of HSR construction is the signaling system, which is also called the “operation control system,” where wireless communications play a key role in the transmission of train control data. We discuss in detail the main differences in scientific research for wireless communications between the HSR operation scenarios and the conventional public land mobile scenarios. The latest research progress in wireless channel modeling in viaducts, cuttings, and tunnels scenarios are discussed. The characteristics of nonstationary channel and the line-of-sight (LOS) sparse and LOS multiple-input-multiple-output channels, which are the typical channels in HSR scenarios, are analyzed. Some novel concepts such as composite transportation and key challenging techniques such as train-to-train communication, vacuum maglev train techniques, the security for HSR, and the fifth-generation wireless communications related techniques for future HSR development for safer, more comfortable, and more secure HSR operation are also discussed.

Vehicle-to-vehicle (V2V) communication is an enabler for improved traffic safety and congestion control. As for any wireless system, the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel, in which a bus acts either as a shadowing object or as a relay between two passenger cars. The measurement setup is based on a Wireless Open-Access Research Platform (WARP) Field-Programmable Gate Array (FPGA) software radio as transmitter and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the path loss, shadowing, small-scale fading, delay spread, and cross correlation. The main effect of the bus is that it is acting as an obstruction creating an additional 15- to 20-dB attenuation and an increase in the root-mean-square delay spread by roughly 100 ns. A Nakagami distribution is found to well describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance dependence of the path loss is analyzed and a stochastic model is developed.

In this paper, we study the performance of mode selections in device-to-device (D2D) communications in terms of end-to-end average throughput, average delay, and dropping probability, considering dynamic data arrival with non-saturated buffers. We first introduce a general framework that includes three canonical routing modes, namely D2D mode, cellular mode, and hybrid mode, which can be combined with different resource allocation restrictions to represent the semi-static and dynamic selections of the three resource sharing modes. A queuing model is developed when the routing mode for every D2D connection is chosen, and an exact numerical analysis and an approximate decomposition and iteration approach are proposed. The performance measures are obtained from the decomposition approach and validated by means of simulation. We further introduce a mode selection scheme that adaptively chooses to semi-statically or dynamically select the resource sharing modes according to the estimated performance measures.

This paper analyzes the outage probability of space-time network coding (STNC) with amplify-and-forward (AF) relays in a cooperative relaying system, where multiple sources transmit their information to a common destination with the help of multiple AF relays in time-division multiple-access (TDMA) mode. We derive an approximate closed-form expression of the outage probability for STNC with an arbitrary number of AF relays for independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels. Numerical results validated our analysis. Moreover, with the developed result, we also discuss the impact of the transmit signal-to-noise ratio (SNR), the outage threshold, the number of relays and the nonorthogonal codes on the system performance.

This paper studies the queuing performance of direct communications between user equipments in cellular networks. Both the fast fading effects of the wireless channel and the dynamic variation of interference from a link associated with its backlogged state are considered in the service process description, which complicates the queuing model and results in the size of the underlying Markov process growing exponentially with the link number. We use the model decomposition and iteration approach in Stochastic Petri Nets (SPN) to deal with the coupling between the service rates of the different links and thus reduce the state space of the Markov process. Simulations are performed to verify the accuracy of the analytical results.

In a realistic high-speed railway environment, the track, terrain, vegetation, cuttings, barriers, pylons, buildings, and crossing bridges are the main sources of reflection, diffraction, and scattering. Moreover, the radiation pattern and the polarization of the transmitting and receiving antennas considerably influence the propagation. This paper presents a deterministic modeling approach covering all the effects in a realistic highspeed railway environment for the first time. The antenna influence and the mechanisms of transmission, scattering, and reflection are evaluated by developing a 3D ray-optical tool. The diffraction loss is obtained by the multi-edge diffraction models using raster databases. This approach compensates the limitation of the existent empirical and stochastic models used for the high-speed railway, and promotes the deterministic modeling towards to the realistic environment. Therefore, it allows a detailed and realistic evaluation and verification of the train control communications systems.

The high-speed railway propagation channel has significant effect on the design and performance analysis of wireless railway control systems. An important feature of the high-speed railway communications is the usage of directional transmitting antennas, due to which the receiver may experience strong attenuation of the line-of-sight (LOS) path under the base station (BS). This leads to a drop, and strong variations, of the signal strength under the BS. While the physical origin of the signal variations is different from conventional shadowing, it can be described by similar statistical methods. However, the effect has been largely neglected in the literature. In this paper we first define the region of the bottom of the BS, and then present a simple shadow fading model based on the measurements performed in high-speed railways at 930 MHz. It is found that the bottom area of the BS has a range of 400 m – 800 m; the standard deviation of the shadowing also follows a Gaussian distribution; the double exponential model fits the autocovariance of the shadow fading very well. We find that the directivity of the transmitting antenna leads to a higher standard deviation of shadowing and a smaller decorrelation distance under the BS compared to the region away from the BS.

The link duration property of Vehicular Ad Hoc Networks (VANETs), which is influenced by vehicle mobility and signal propagation environment, is studied in this paper. For our investigation, the inter-vehicle distance is partitioned into several non-overlapping segments as different transition states. After that, a modified Markov model is proposed, and the distance transition probability matrix is also derived to theoretically describe the effects of dynamically changed inter-vehicle distance under the pathloss transmission model. For the more complex fading channel model, we defined the virtual transmission range and considered the possible situations for breaking link connection, based on which an revised Markov model is also proposed to describe the joint effects of random movements and fading channel fluctuations. Finally, the numerical results obtained from simulation and our analytical model are compared to verify the accuracy of our work.

In this paper, the energy efficiency performance of device-to-device (D2D) communications in orthogonal frequency division multiplexing cellular networks is studied. Different from previous work that was based on the static interference model, we assume a dynamic number of competing flows, such as continuous transfers of file transport protocol or web browsing sessions. The complex, dynamic interaction of the amount of backlogged traffic at the D2D and cellular links introduced by the strong impact of interference between them is captured by the coupled-processors server in the formulated model. The energy efficiency and spectral efficiency of the different resource-sharing strategies are analyzed using the semidefinite optimization approach. It is shown that the simulation results match well with the analytical bounds under different traffic loads and topologies.

This paper reports (i) a set of measurements of the wireless propagation channel at 930 MHz, conducted along the "Zhengzhou-Xian" high-speed railway of China in various railway viaduct scenarios, and (ii) an analysis and modeling of the small-scale and large-scale channel parameters based on those measurements. The environment can be categorized into four cases, covering viaducts with different heights and in different suburban environments. Small values of fade depth, level crossing rates, and average fade duration are observed. Akaike's Information Criterion (AIC)-based evaluation indicates that the Ricean distribution is the best to describe small-scale amplitude fading. An analysis of the envelope autocovariance function shows that the coherence distance is less than 10 cm. The Ricean K-factor is modeled as a piecewise-linear function of distance. Moreover, a breakpoint path loss model is developed and shadow fading is investigated using the same break point as for the distance-dependent K-factor model. The Suzuki distribution is found to offer a good fit for the composite multipath/shadowing channels. We find that the viaduct height H, together with the number of surrounding scatterers, significantly affects the small- and large-scale channel parameters. These results are applicable to both normal-speed and high-speed railways, and will be useful in the modeling of railway viaduct channels and the design of railway wireless communication systems.

In this paper, we study the performance of Device-to-Device (D2D) communications with dynamic interference. In specific, we analyze the performance of frequency reuse among D2D links with dynamic data arrival setting. We first consider the arrival and departure processes of packets in a non-saturated buffer, which result in varying interference on a link based on the change of its backlogged state. The packet-level system behavior is then represented by a coupled processor queuing model, where the service rate varies with time due to both the fast fading and the dynamic interference effects. In order to analyze the queuing model, we formulate it as a Discrete Time Markov Chain (DTMC) and compute its steady-state distribution. Since the state space of the DTMC grows exponentially with the number of D2D links, we use the model decomposition and some iteration techniques in Stochastic Petri Nets (SPNs) to derive its approximate steady state solution, which is used to obtain the approximate performance metrics of the D2D communications in terms of average queue length, mean throughput, average packet delay and packet dropping probability of each link. Simulations are performed to verify the analytical results under different traffic loads and interference conditions.

Supporting mesh clients roaming without negative impact on the services with high priority is an important challenge in the design and deployment of a wireless mesh network which has emerged as a promising last mile broadband wireless access technology. In this paper, to improve the network performance, a handoff management scheme is proposed for wireless mesh networks. When the mesh client (MC) roams to the overlapping region between its associated mesh router (MR) and a new MR, these two MR transmit the information MC required simultaneously to decrease the error probability. On the other hand, when MC needs to handoff, it selects an optimal MR using priority and bandwidth requirements as metrics to ensure the quality of service of the on-going traffic. Simulation results show that the error probability and the total blocked traffic are decreased.

This paper presents analysis and spatial channel modeling based on a series of 930 MHz measurements conducted along the “Zhengzhou-Xi'an” high speed rail (HSR) of China. Raw data are collected in five cuttings in rural and suburban environments subject to the consideration of the geometry of cuttings, including crown width and bottom width. A comparison among different distributions of the small-scale-fading is made by using a root mean squared errors (RMSE) goodness-of-fit test, which shows that the Ricean distribution offers the best fit. Moreover, it is found that the rich reflection and scattering components in the cutting scenario reduce the effect of the dominant component of the received signal, which results in a Rayleigh distribution, validating a good fit in this line-of-sight (LOS) scenario. Due to the rich multipath components and the directional transmitting antennas in HSR, the Ricean K-factor is modeled as a piecewise function of distance associated with the median K-factor and a standard deviation. Our analysis shows that a “wide” cutting scenario, such as with shallower slopes and wide crown and bottom widths, can reduce the impact of channel fading.

How to provide reliable, cost-effective wireless services for high-speed railway (HSR) users attracts increasing attention due to the fast deployment of HSRs worldwide. A key issue is to develop reasonably accurate and mathematically tractable models for HSR wireless communication channels. Finite-state Markov chains (FSMCs) have been extensively investigated to describe wireless channels. However, different from traditional wireless communication channels, HSR communication channels have the unique features such as very high speed, deterministic mobility pattern and frequent handoff events, which are not described by the existing FSMC models. In this paper, based on the Winner II physical layer channel model parameters, we propose a novel FSMC channel model for HSR communication systems, considering the path loss, fast fading and shadowing with high mobility. Extensive simulation results are given, which validate the accuracy of the proposed FSMC channel model. The model is not only ready for performance analysis, protocol design and optimization for HSR communication systems, but also provides an effective tool for faster HSR communication network simulation.

The non-saturated performance of the IEEE 802.11 broadcast scheme in the two-dimensional (2-D) mobile ad hoc networks is studied in this paper. A simplified yet reasonable system model is built to derive the closed-form expressions of average packet reception probability, non-saturated throughput, and average queuing delay. Compared with the existing work in the literature, the proposed system model takes into account the effect of arbitrary transmission queue length. Moreover, our analysis considers both the impact of concurrent transmissions caused by identical backoff interval selection and that of hidden nodes caused by limited sensing range on an observed transmitter. All the analysis results are validated by extensive simulations.

This paper presents a set of 930 MHz measurements conducted along the “Zhengzhou-Xi'an” high-speed rail of China, to characterize short-term fading behavior of the rail viaduct scenario. Three measurement cases covering viaducts with different heights are reported. The analysis results include fade depth (FD), Ricean distribution fit and K-factor modeling, level crossing rates (LCR), and average fade duration (AFD). A small value of fade depth, around 15 dB, is observed. The Ricean distribution offers good fit in this line-of-sight (LOS) propagation scenario, and the K-factor estimated using moment-based method is modeled as a piecewise function, whose break point equals to the reference distance. It is found that the viaduct height H greatly affects the severity of fading and the feature parameters. The results are applicable to the design of high-speed rail communication systems and the modeling of the rail viaduct fading channels.

A novel multichannel multiple access (MMA) protocol is proposed for vehicular ad hoc networks in this paper. For increasing channel efficiency, two control channels are utilized in MMA to finish request-to-send/clear-to-send (RTS/CTS) interactions and acknowledgments (ACKs) separately. When a collision happens in the control channel, the waiting period before the next re-transmission is adjusted by a relative mobility based back-off scheme. Benefiting from the fully distributed channel selection scheme, which is also included in MMA, the data transmissions carried in the chosen service channels can be successfully accomplished without collision, and the influence of exposed terminals can also be effectively alleviated. Simulation results show that MMA outperforms the classic single channel and multichannel MAC protocols in average channel utilization, packet delay, and packet drop rate.

We consider a wireless relay network in which multiple single-antenna users communicate with each other through a multi-antenna relay. With our earlier proposed framework of zero-forcing relaying, the inter-user interferences are canceled. In this paper, we investigate power allocation schemes for both regenerative and non-regenerative relays to improve the throughput performance. Considering different traffic demand of each user, a queue-aware power allocation (QPA) scheme is proposed to stabilize the queue of each user by maximizing a “sum demand” metric. The demand metric of each user is proportional to both its channel throughput and its queue length. The fairness in the sense of transmission delay or packet loss ratio is thus achieved as queue length is associated with the two performances. The power allocation problem schemes use Lagrangian method and bisection search. With the QPA scheme, the system throughput performance is improved while keeping all queues relatively stable. The simulation results indicate that the QPA scheme strikes a balance among the fairness of all the users and improve the system throughput.

The saturation performance of the IEEE 802.11 broadcast scheme in two dimensional (2-D) mobile ad hoc networks is studied in this paper. A simplified yet reasonable system model is built to derive the closed-form expressions of two broadcast reliability metrics, Distance-based Packet Reception Probability and Average Packet Reception Probability. Compared with the existing work in the literature, the proposed system model accounts for more general 2-D network scenarios, and considers both the impact of concurrent transmissions caused by identical backoff intervals and that of hidden nodes caused by a limited sensing range on a tagged reception. All the analysis results are validated by extensive simulations.

In this paper, we introduce an algorithm of location assistant direction of departure (DOD) based beamforming scheme which is suitable for wireless communication in highspeed railway (HSR). The beamforming weights are calculated according to the locations of mobile station (MS) and base station (BS) together with the topology of multiple transmit antennas. The purpose of the algorithm is to make phase adjustment at the transmitter such that the output signal-to-noise ratio (SNR) at the receiver can be improved. The performance of beamforming with ideal DOD and estimated DOD which has deviation caused by location errors are both evaluated. Results show that beamforming with ideal DOD greatly outperforms all other transmission modes, i.e., transmit diversity and single-input single-output (SISO), while the inevitable DOD estimation error degrades the overall performance.

This paper addresses the power allocation (PA) and relay location problems in a dual-hop decode-and-forward relaying system over Weibull fading channels. The optimization problems such as optimal relay location with fixed power allocation, adaptive PA with fixed relay location and joint optimization of the PA and relay location, are investigated in order to maximize the ergodic capacity under identical Weibull fading. The above problems are also discussed to minimize the outage probability over non-identical Weibull fading channels. Numerical results show that the adaptive algorithms outperform fixed allocation schemes.

Two transmission schemes exist in wireless communication system for high-speed railway: the direct transmission and relay-based transmission. The main purpose of this paper is to evaluate the performance of these two transmission schemes. Specifically, the closed-form expressions of ergodic capacity for each transmission scheme are derived. By comparing these obtained capacity expressions, we find that the relay-based transmission has better performance in the low and intermediate SNR, while the direct transmission becomes a better choice at high SNR. It is also shown that the signal amplitude loss resulted from signal traversing through the carriage is a key parameter in determining the ergodic capacity for direct transmission scheme. Finally, the simulation results demonstrate an exact match with our theoretical findings.

This paper evaluates the conditional connectivity performance for vehicular ad hoc networks. Conditional connectivity is the probability that a communication pair can stay connected during a whole data transmission conditioned on the connection is successfully built up when the transmission is initialized. To analyze the conditional connectivity, a simplified but reasonable unidirectional highway model is proposed in this paper. By combining the probability density function of inter-vehicle initial distance and the distribution of vehicles' relative speed, which are both derived in this paper, the closed-form of conditional connectivity is obtained. All the analysis results are validated by extensive simulations.

We consider the multi-way relaying (MWR) network, where each user communicate with the other users through a relay. The single-antenna users transmit to the multi-antenna relay simultaneously. Then the relay randomly precodes the received or estimated signal, and broadcasts it back to all the users. Focusing on a specific user, we set up the equivalent space-time channel from the other users to it. Afterwards, for both amplify-and-forward and estimate-and-forward MWR, we design the ordered minimum mean square error (MMSE) receivers with successive interference cancellation (SIC) at users. In particular, three types of metrics determining the order of SIC are derived by approximating the interference-plus-noise term as Gaussian. Simulation results on error performance evidence that our proposed receivers are able to harvest the inherent temporal diversity gains within the MWR networks. Moreover, the proposed receivers perform even well in the overloaded scenarios.

Consider a wireless network, in which multiple users exchange data with each other via a multi-antenna relay. The users transmit to the relay simultaneously. Then the relay regenerates the transmitted signal and precodes it before broadcasting to the users. In particular, we propose a minimum mean square error (MMSE) based precoder, which takes the relay detection errors into account. We indicate that this precoder can improve the bit error performance when uplink channel is in a bad condition. Afterwards, we deploy the ordered MMSE successive interference cancellation (MMSE-SIC) receiver, which exploits temporal diversity gains over the multiple downlink slots. Simulation results show that the ordered MMSE-SIC receiver can achieve sufficient diversity gains over traditional MMSE receiver.

The propagation situations inside tunnels vary with the distance between transmitter and receiver, as well as the relative size of the users. This paper presents a novel hybrid propagation model covering three propagation mechanisms with two types of users. In the first case, the users are relatively small in size compared with the tunnel. The entire process is interpreted by combining the free space propagation model and the limited multi-mode propagation model. In the second case, for relatively large users , the free space propagation can be replaced by the near shadowing effect to some extent or totally. Hence, the whole procedure is described by coupling the statistical model in the near shadowing zone, the free space model, and the limited multi-mode model. Two groups of measurements are employed to validate the model. The results show good agreement and, therefore, the model supports an effective way to predict the propagation situation inside tunnels for different types of users.

This paper proposes a distance-dependent Ricean K-factor model for a line-of-sight (LOS) viaduct scenario in the high-speed rail (HSR) of China. Extensive narrowband measurements conducted at 930 MHz are utilized. The propagation environment can be categorized into two cases: moderate suburban and dense suburban. The estimated K-factors are modeled as a piecewise-linear function of distance. The statistical fluctuations of K-factors are well considered by introducing the standard deviation to the expression. A detailed comparison between the piecewise-linear K-factor model and that of other literature validates the proposed model. Our results will be useful in the modeling of HSR viaduct channels and the performance analysis such as channel capacity and throughput for HSR wireless communication systems.

In this paper, we propose two novel schemes, which are based on our proposed novel fractional order memory polynomial pre-distorter, to conduct the digital base-band power amplifier pre-distortion with the improvement of power amplifier efficiency. For the first scheme, fractional order terms are included in the conventional memory polynomial containing the odd and even order polynomial terms. For the second scheme, it is to replace even order polynomial terms with fractional order polynomial terms to improve the linear performance of power amplifiers. The theoretical expressions for two schemes are derived. Both simulation results and numerical analysis show that more 11.0dB and 8.5dB out-of-band suppression gain could be obtained employing the proposed two schemes, respectively, which are compared with the conventional pre-distortion methods based on Wiener-Hammerstein and memory polynomial power amplifier models. Moreover, corresponding FPGA realization shows that the proposed two schemes are much cost-effective in terms of hardware resources and complexity of the implementation is low.

Nowadays, the two most popular wireless networks, the third generation (3G) cellular networks and wireless local area networks (WLANs) are being deployed widely. Network selection becomes an essential issue when network operators integrate multiple access networks to enhance service provisioning. There have been many researches on various network selection schemes. In this paper, we propose a novel modelling approach based on Petri net to effectively analyze the performance of a randomized network selection scheme, which is actually a generalized extension of simple network selection algorithms such as the WLAN-first scheme. The Petri net-based modelling approach can characterize the complex user mobility and network selection procedure in a straightforward manner. To further address a large state space, we also develop a decomposition approach for the Petri net model to efficiently evaluate the system performance. Numerical results are provided to demonstrate the effectiveness and low complexity of our proposed approaches.

The impact of antenna array geometry on MIMO (Multiple-input Multiple-output) system in high speed railway scenario is investigated in this paper. The capacity of different antenna arrays and the effect of ULA (uniform linear array) azimuthal orientation on capacity are studied with a double-directional channel model including antenna effects and Doppler shift.

Precise location information is important in sensor networks to support a great many functions like location aided routing, tracking and other specific applications. This paper proposes a Cluster based Iterative GPS-Free Localization(CIGL) algorithm for wireless sensor network, which only depends upon the distances between the sensor nodes and their neighbors obtained by measurements like TOA. The proposed algorithm firstly selects a subset of nodes to establish Local Coordinate Systems (LCSs) on the basis of clustering results. By rotation and reflection transformation, all the LCSs converge to form the global coordinate system and complete the nodes localization in succession. The located nodes are nominated as new beacons to re-locate the remaining unknown nodes, namely expand localization coverage by iteration. Comparing with the previous approach, simulation results indicate that the proposed algorithm achieves considerable performance in terms of localization accuracy and coverage, especially when the communication range is small and the network deployment is sparse.

There is no unanimous consensus yet on the propagation mechanism before the break point inside tunnels. Some deem that the propagation mechanism follows the free space model, others argue that it should be described by the multimode waveguide model. Firstly, this paper analyzes the propagation loss in two mechanisms. Then, by conjunctively using the propagation theory and the three-dimensional solid geometry, a generic analytical model for the boundary between the free space mechanism and the multi-mode waveguide mechanism inside tunnels has been presented. Three measurement campaigns validate the model in different tunnels at different frequencies. Furthermore, the condition of the validity of the free space model used in tunnel environment has been discussed in some specific situations. Finally, through mathematical derivation, the seemingly conflicting viewpoints on the free space mechanism and the multi-mode waveguide mechanism have been unified in some specific situations by the presented generic model. The results in this paper can be helpful to gain deeper insight and better understanding of the propagation mechanism inside tunnels.

The delay caused by the reflected ray in broadband communication has a great influence on the communications in subway tunnel. This paper presents measurements taken in subway tunnels at 2.4 GHz, with 5 MHz bandwidth. According to propagation characteristics of tunnel, the measurements were carried out with a frequency domain channel sounding technique, in three typical scenarios: line of sight (LOS), Non-line-of-sight (NLOS) and far line of sight (FLOS), which lead to different delay distributions. Firstly IFFT was chosen to get channel impulse response (CIR) h(t) from measured three-dimensional transfer functions. Power delay profile (PDP) was investigated to give an overview of broadband channel model. Thereafter, a long delay caused by the obturation of tunnel is observed and investigated in all the scenarios. The measurements show that the reflection can be greatly remained by the tunnel, which leads to long delay cluster where the reflection, but direct ray, makes the main contribution for radio wave propagation. Four important parameters: distribution of whole PDP power, first peak arriving time, reflection cluster duration and PDP power distribution of reflection cluster were studied to give a detailed description of long delay characteristic in tunnel. This can be used to ensure high capacity communication in tunnels.

During almost fifty years, fade duration distribution (FDD) of random signals has been an open research topic. However, for Weibull fading channels, closed form solutions are still unknown. Minimum duration outage (MDO) definition is decisive to complement the traditional outage concept and it has been reported, up to now, only for Log-normal shadowing, Rayleigh fading and Nakagami fading. In this paper, FDD and MDO in Weibull channels are studied. By utilizing the relation between Weibull distribution and Rayleigh distribution, analytical results are derived for FDD as well as the performance measures under MDO principle such as frequency of outage, average duration of outage, and probability of outage. Numerical results are presented to illustrate the analytical results.

Kolmogorov-Smirnov (K-S) test-a non-parametric method to measure the goodness of fit, is applied for automatic modulation classification (AMC) in this paper. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs of the signal under each candidate modulation format. The K-S-based modulation classifier is first developed for AWGN channel, then it is applied to OFDM-SDMA systems to cancel multiuser interference. Regarding the complexity issue of K-S modulation classification, we propose a low-complexity method based on the robustness of the K-S classifier. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifier offers superior classification performance and requires less number of signal samples (thus is fast).

A novel cluster-head selection and update algorithm "Type-based Cluster-forming Algorithm (TCA)" is proposed, which outperforms both the lowest node ID (LID) and the Weighted Clustering Algorithm (WCA) in the ad hoc network scenario considered. The system's performance is investigated in a scenario, when the 50 communicating nodes belong to three different groups, for example, a group of rescue workers, fire-fighters and paramedics. It is demonstrated that the carefully designed protocol is capable of outperforming the above-mentioned benchmarkers both in terms of a reduced number of cluster-head updates and cluster-change events. Hence its quality-of-service may be deemed higher.

This paper focuses on three vital aspects in propagation characteristics inside tunnels: break point; near zone and far zone in a practical environment. Firstly, all the distinguishing features of radio channel in the areas before and after break point at different angles such as modal theory, ray-tracing theory, statistics and propagation model have been summarized. By collectively analyzing the research perspectives above, a panoramic view on the propagation characteristics of break point; far zone and near zone has been proposed. Based on a measurement carried out in Madrid operational subway environment at 2.4 GHz, some new radio channel characters of the break point; the regions before and after break point at higher frequency have been discussed and analyzed.

This paper presents the results of path loss measurements in "Zhengzhou-Xi'an" high-speed railway environment at 930 MHz band. A transmitter directional antenna height of 20~30 meters above the rail surface and a receiver omni-directional antenna height of 3.5 meters were used on the high-speed viaducts height of 10~30 meters above the ground. An automatic acquisition system was utilized in the measurements. The model makes distinctions among different terrain. The results of measurements provide practical values for path loss exponent and standard deviation of shadowing affected by the viaduct factor in suburban, open area, mountain area and urban propagation regions where the high-speed trains travel. Based on the measurement data, the empirical path loss model was developed, which could be used for predicting the path loss for the future railway communication systems, and provide the facilities for network optimization.

External interference is the pivotal issue that degrades the performance of wireless systems significantly as more and more wireless networks coexist with the limited frequency bands but lack of investigations. As a representation, the novel technique cognitive radio networks confront the similar interference problem extensively. In this paper, general statistical analyses of absolute (interference only) and relative (the ratio of signal to interference, SIR) external interference in both omnidirectional coverage and linear coverage networks are presented respectively. The scenario considered is a general condition in cognitive radio networks or several wireless networks coexist in the same area with mutual external interference that the interferers share the same frequency bands with the signal in Nakagami-m and shadowing fading channels. Assumed that the interferers outside the service networks obey Poisson distribution in space field to model the random architectures and parameters of external interferers for lacking of knowledge at the service base station receivers. Finally, extensive understandings of the nature of external interference are provided by the simulations and conclusions.

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. Recently time division duplex based wireless mesh networks (TDD-based WMNs) are emerging as a crucial technology for wireless multihop networks and broadband wireless access (BWA). Supporting real-time traffic in TDD-based WMNs is difficult and one of the main challenges is to provide strict end-to-end time delay guarantee from source node to destination node. In this paper, the cross-layer framework is proposed considering routing schemes, queueing model and resource scheduling schemes. Furthermore, an end-to-end realtime traffic scheduling algorithm is proposed. In the algorithm, a number of control slots are reserved for transmitting real-time routing messages to guarantee delay requirements while data reservation is fulfilled during the process of routing path discovery. Numerical results show that the proposed scheduling algorithm achieves low time delay and can satisfy real-time delay requirements.

In this paper, we focus on three different polynomial formats used in power amplifiers (PAs) pre-distortions. The nonlinearity of AM/AM and AM/PM introduced by power amplifiers are revealed by the transfer characteristics of PAs. We pick out the most effective polynomial format in evaluation of relative MSE (RMSE), the convergence speeds and the power spectral density (PSD). Such effective polynomial format is of great importance in PAs pre-distortion based on polynomial methods, in which the pre-distortion can be made more stable. The above mentioned aspects are all demonstrated through computer simulations and corresponding analysis.

The performance of multihop links is studied in this contribution by both analysis and simulations, when communicating over Generalized-K (KG) fading channels. The performance metrics considered include symbol error rate (SER), outage probability, level crossing rate (LCR) and average outage duration (AOD). First, the expressions for both the SER and outage probability are derived by approximating the probability density function (PDF) of the end-to-end signal-to-noise ratio (SNR) using an equivalent end-to-end PDF. We show that this equivalent end-to-end PDF is accurate for analyzing the outage probability. Then, the second-order statistics of LCR and AOD of multihop links are analyzed. Finally, the performance of multihop links is investigated either by simulations or by evaluation of the expressions derived. Our performance results show that the analytical expressions obtained can be well justified by the simulation results. The studies show that the KGchannel model as well as the expressions derived in this paper are highly efficient for predicting the various types of performance metrics and statistics for design of multihop communication links.

Railway transportation is regarded as the safest and most economical way of transportation on land. However, the delay of train makes it inconvenient for both railway work crews and passengers. As the high-speed railways which can reach a speed of over 350 km/h are booming up, train delay is becoming more and more important. On this concern, this article gives out a definition of expected punctual time region for trains, and then analyzes the main causes of train delays. Among those reasons causing delays of the train, it is important to draw out the weight of quality-of-service factors in Chinese Train Control System, and calculate the Mean Time between Disturbances on different railway classes for further evaluation.

Transmission interference is a key performance indicator of railway digital mobile communication system(RDMCS), whose performance will become worse in high-speed railway. The factors that influence the transmission interference of RDMCS are analyzed. Based on the relationship between speed and transmission interference, the scheme based on distributed antennas system (DAS) is proposed to improve transmission interference of RDMCS. The proposed scheme can reduce the collision probability between data transmission and handover with the improved performance of transmission interference. Effectiveness of the proposed scheme is verified by simulations and theoretical analysis.

In this paper, outage probability caused by multiple interferers in Nakagami-m fading channels is studied. A novel method is proposed to derive the exact and closed form expressions of outage probability in the presence of multiple Nakagami independent cochannel interference. Unlike some previous conclusions, the method proposed in this paper is not only usable with various integer Nakagami fading parameters or average powers but also presents exact derivation of outage probability. To circumvent the difficulties, proper iteration functions are adopted by studying and integrating the definitions without numerical integration and residue calculation. The method generally deals with the cases that with or without minimum level constraint at the receiver for satisfactory reception. Finally, the exact expressions are compared with previous proposed approximated expressions and provide the understanding of the nature of interference.

This paper proposes an Energy Efficient Clustering Scheme for mobile ad hoc networks. In the initial clustering stage, a node's residual energy, nearby topology, relative location and relative mobility are used for determining whether the node is suitable for being a cluster head. In the cluster maintaining stage, a strategy called Distance Estimation Broadcasting is designed to help a cluster member to estimate the distance between itself and its cluster head. Thus, the cluster members can use less energy to accomplish the data transmission. Moreover, a dynamic calculated Off-Duty Threshold is proposed to trigger the re-clustering operation when needed. Simulation results show that the proposed clustering scheme performs better than the previous Weighted Clustering Algorithm and Distributed Weighted Clustering Algorithm.

CBTC (Communication Based Train Control) will be based on mobile communication, which the key problem is how to support reliability of control data transmission to improve track utilization and enhance train safety. This paper proposed a kind of real-time transmission model for high speed train end-to-end control data operation, which based on circuit switched data service by traditional wireless network access to transmit simultaneous instructions between the train operation control system and locomotive. In order to support this end-to-end real-time data application, we have studied a circuit domain latency model and improved a Simple Tunnel Protocol technology to accelerate data service which is easy to be implemented in the network. We have analyzed the performance under different service pressure by using MMPP (Markov Modulation Poisson Process) to describe service traffic pattern and employed a priority scheduling algorithm to estimate approximate service latency. The system simulation showed that data transmission latency should be reduced in busy time by the way of service priority scheduling. Although it might introduce extra traffic expenses and require reservation certain protection circuit, the average standby time probability could be cut down significantly.

With the rapid development of high-speed railways all over the world, it is very promising to deliver public broadband access to passengers aboard high-speed trains. However, it has been a challenge to support high speed (e.g. above 350 km/h) mobile users with broadband communications, because of rapidly fading radio channels and Doppler frequency shift. This paper presents a review of network architectures providing broadband access to high-speed trains. A novel scheme referred as multimode multi-band and multi-system-based access architecture is proposed in this paper. It consists of three parts: (1) train network part that relays data to large number of users in carriages without changing their own terminals, (2) ground network part that deals with traffic from different operators' networks, and (3) ground-to-train network part that connects the train network and ground network. Moreover, this architecture could support high mobility with broad bandwidth, seamless handover, scalable and cost-effective deployment.

This paper addresses the clustering problem for highly mobile ad hoc networks. In the proposed scheme, Doppler shifts associated with received signal are used to estimate the relative speed between cluster head and cluster members. With the estimated speed, a node can predict its stay time in every nearby cluster. In the initial clustering stage, a node joins a cluster that can provide it with the longest stay time in order to reduce the number of re-affiliations. In the cluster maintaining stage, strategies are designed to help node cope with connection loss caused by channel fading and node mobility. Simulation results show that the proposed clustering scheme can reduce the number of re- affiliations and the average disconnection time compared with previous schemes.

Cooperative diversity has the ability of achieving multiplexing and diversity gains by taking advantage of the features and potentials of spatial distribution. Based on the asymptotic outage probability, we define the Cooperative Gain (CG) to evaluate the benefits of cooperation over direct transmission under high Signal-to-Noise Ratio (SNR). With the presence of Rayleigh fading and path loss, our analytical results illustrate the appropriate Cooperative Region (CR), in which the source prefers to coordinately work with the relay rather than transmit directly. Finally, we provide visible CRs and numerical results on the exact outage performance by Monte Carlo simulations.

In this paper, the closed expressions for statistics, such as outage probability (OP), level crossing rate (LCR) and average fade duration (AFD) of multiple interferers in Nakagami-m fading channels in cognitive networks are presented. Besides, the interference-tolerant scenario is considered that the spatially Poisson distributed interfering secondary (unlicensed) terminals share the same frequency bands with the primary devices and are not allowed to transmit within the interference region of the primary (licensed) users. Numerical results show the effects of channel characteristics, velocity and interference region radius on the statistics. And Nakagami parameter has less effect than velocity on LCR but affects AFD more than velocity.

In this paper, we investigate MIMO systems with transmit antenna selection over an intra-class correlated Rayleigh fading channel. In our study, one single transmit antenna with an aim to maximizing the total received signal-to-noise ratio (SNR) is selected for transmission, while it is assumed that the receive side use all the available antennas. Using binary-phase-shift-keying (BPSK) modulation as an illustration, we derive the exact bit error rates (BERs) of two diversity schemes, namely transmit antenna selection and full complexity schemes. Moreover, we compare the asymptotic performance of the two diversity schemes analytically. The effect of correlation among the sub-channels on the SNR degradation is also determined in terms of the correlation coefficient and the number of transmit/receive antennas. Finally, results show that the transmit antenna selection scheme can achieve better performance than the full complexity scheme at the expense of a small amount of feedback information from the receiver.

In this paper, the capacity of multiple-input multiple-output (MIMO) systems over a Rician fading channel is evaluated. We show that the capacity of MIMO Rician channels can be well approximated by that of correlated MIMO Rayleigh channels. We take the Rician channel with rank-1 mean matrices as an example, the asymptotic capacities at low and high signal- to-noise ratio (SNR) regions are analyzed. Finally, the asymptotic capacity loss of Rician channels relative to Rayleigh channels is derived.

An improved segmented polynomial-based pre-distortion method and a novel memory polynomial-based pre-distortion method with two-dimensional indexing are proposed in digital baseband power amplifier pre-distortion systems. Through computer simulations and corresponding analysis, the feasibility and reliability of the proposed methods are verified with three different polynomial formats in the evaluation of relative mean square error, the convergence speed, the random access memory storage units and the power spectral density.

This paper presents the delay spread characteristics based on the measurements of the wideband multiple-input multiple-output radio channels in outdoor environments. Measurement data were collected by a channel testbed which consists of four transmitters and eight receivers, and operates at a carrier frequency of 1.8 GHz with a bandwidth of 2.5 MHz. The four transmitting antennas and eight receiving antennas can work simultaneously. The experiments were originally conducted within the J. J. Pickle Research Campus, University of Texas at Austin. Based on the measurement data in the non-line-of-sight environments, the channel features such as multipath intensity profiles, root-mean-square delay spreads and their distributions were examined. It was found that the empirical probability density function of the rms delay spread in the non-line-of-sight MIMO channels roughly follows Gaussian distribution.