Search

An emerging railway technology called smart railway promises to deliver higher transportation efficiency, enhanced comfort in services, and greater eco-friendliness. The smart railway is expected to integrate fifth-generation mobile communication (5G), Artificial Intelligence (AI), and other technologies, which poses new problems in the construction, operation and maintenance of railway wireless networks. Wireless Digital Twins (DTs), which have recently emerged as a new paradigm for the design of wireless networks, can address these problems and enable the whole lifecycle management of railway wireless networks. However, there are still many scientific issues and challenges for railway-oriented wireless DT. Relevant key technologies to solve these problems are introduced and described, including characterization of materials' physical-EM properties, autonomous reconstruction of Three-dimensional (3D) environment model, AI-empowered environmental cognition, Ray-Tracing (RT), model-based and AI-based RT acceleration, and generation of multi-spectra sensing data. Moreover, this paper presents our research results for each key technology and describes the wireless network planning and optimization system based on high-performance RT developed by our laboratory. This paper outlines the framework for realizing the wireless DT of smart railways, providing the direction for future research.

Abstract With the introduction of emerging technologies such as cloud computing, the railway communication network has the characteristics of complex structure and blurred boundaries, which leads to a series of security threats including information leakage and malicious access. Specifically, the third-party cloud services are difficult to be supervised, and network traffic is untrustworthy. To ensure system security, we propose a zero-trust security model in this paper. Then, we introduce blockchain and Merkle tree to build a distributed identity storage scheme for guaranteeing reliable, confidential and efficient data updates, and improving authentication efficiency. Furthermore, the proxy was introduced for two-way authentication with cloud servers, so that internal and external threats could be counteracted. Moreover, reputation assessment mechanism has been adopted to reduce the possibility of nodes accessing malicious cloud services. Performance analysis demonstrated that the proposed security model is able to enhance the security, efficiency and stability of the system, and consequently can guarantee the safety and reliability of railway transportation.

The fifth-generation (5G) wireless communication has experienced an upsurge of interest for empowering vertical industries, due to its high data volume, extremely low latency, high reliability, and significant improvement in user experience. Specifically, deploying 5G on high-speed railway (HSR) is critical for the promotion of smart travelling such that passengers can connect to the Internet and utilize the on-board time to continue their usual activities. However, there remains a series of challenges in practical implementation, such as the serious Doppler shift caused by the high mobility, the carriage penetration loss especially in the high-frequency bands, frequent handovers, and economic issues. To address these challenges, we propose three schemes in this article to improve the coverage of 5G networks on the train. In particular, we provide a comprehensive description of each scheme in terms of their network architecture and service establishment procedures. Specifically, the mobile edge computing (MEC) is used as the key technology to provide low-latency services for on-board passengers. Moreover, these three schemes are compared among themselves regarding the quality-of-service, the scalability of service, and the related industry development status. Finally, we discuss various potential research directions and open issues in terms of deploying 5G networks on HSR.

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 (Pd).

Firstly, the different characteristics of terahertz waves compared to lower frequency bands were summarized, including the influence of meteorological factors and rough surfaces of materials on THz waves.Next, it was proposed to use ray-tracing (RT) technology to calibrate the three-dimensional (3D) environment model and electromagnetic parameters of materials in the target scenarios through very limited channel measurement data.Then, the reversed parameters were utilized to perform in a similar but more general scenario, instead of a large amount of channel measurement data.Finally, the channel characteristics were analyzed by extraction of the channel key parameters, such as path loss, shadow fading, Rician K-factor, delay spread, angular spread, and doppler parameters in a mobile scenario.Representing two ends of 6G THz use cases from indoor scenario to outdoor scenario, case studies were made for wireless connections on a desktop and vehicular communications, respectively.As for the outdoor scenario, the influences of meteorological factors were additionally considered.The work is of great significance for the design and evaluation of the THz communication system.

Urban rail traffic is an important part of modern transportation infrastructure.As a new generation of mobile communication technology, 5G can provide high data rate and low latency wireless transmission, which helps to improve the efficiency and service quality of urban rail traffic system.Due to the complexity of urban rail traffic scenarios, accurate communication scenario classification, channel characterization and channel models are required to provide theoretical support for the design of urban rail traffic 5G communication systems.The classification of 5G urban rail traffic radio propagation scenarios to support channel measurements and modeling was proposed.Current status of urban rail traffic channel measurements and modeling was shown, and the current challenges were analyzed.The applications of artificial intelligence in channel feature extraction and channel modeling were discussed, and the 5G urban rail traffic channels by considering reconfigurable intelligent surface and unmanned aerial vehicle were analyzed.Finally, the research on 5G urban rail traffic channel modeling at millimeter wave frequency band was described.

As a new generation of application infrastructure and strategic emerging technologies, the internet of things (IoT) is an inevitable trend to be integrated into the rapid development of high-speed railways (HSR). This article first introduces the concept and key technologies of the HSR IoT, and then analyzes the HSR’s requirements for the IoTs. Based on these concepts, a system architecture of the HSR IoT is proposed to expand the in-depth applications of IoT in various fields of the HSR industry. Finally, future development trends of the HSR IoT are discussed. This article is expected to provide some useful insights into the study and evolution of HSR IoT.

Abstract With the diversity of the communication technology and the heterogeneity of the computation resources at network edge, both the edge cloud and peer devices (collaborators) can be scavenged to provide computation resources for the resource-limited Internet-of-Things (IoT) devices. In this paper, a novel cooperative computing paradigm is proposed, in which the computation resources of IoT device, opportunistically idle collaborators and dedicated edge cloud are fully exploited. Computation/offloading assistance is provided by collaborators at idle/busy states, respectively. Considering the channel randomness and opportunistic computation resource share of collaborators, we study the stochastic offloading control for an IoT device, regarding how much computation load is processed locally, offloaded to the edge cloud and a collaborator. The problem is formulated into a finite horizon Markov decision problem with the objective of minimizing the expected total energy consumption of the IoT device and the collaborator, subject to satisfying the hard computation deadline constraint. Optimal offloading policy is derived based on the stochastic optimization theory, which demonstrates that the energy consumption can be reduced by a proportional factor through the cooperative computing. More energy saving is achieved with better wireless channel condition or higher computation energy efficiency of collaborators. Simulation results validate the optimality of the proposed policy and the efficiency of the cooperative computing between end devices and edge cloud, compared to several other offloading schemes.

Since the radio frequency identification (RFID) technology was put forward,it has been widely used in the transportation,logistics,industry and business due to its convenience and high efficiency.As a carrier for storing recognizable data,the RFID tags play a crucial role in the Internet of things (IoT).More and more functions and modules are embedded in the RFID tags and developed into smart tags in different application fields.Recently,wireless batteryless smart tags have arisen and grown rapidly with the development of the IoT and various emerging backscatter technologies.Wireless batteryless smart tags use the passive backscatter technology to harvest the energy and transmit information through radio frequency signals.Starting from the RFID technology,the history of the RFID and smart tags was introduced briefly,the differences between traditional smart tags and emerging wireless smart tags were compared,the advantages of wireless batteryless smart tags were concluded,several specific applications of it in different fields were listed,and the open challenges were analyzed.

Based on the scenario of railway Internet of things,the system outage performance of the relay network was analyzed,which was assisted with the power beacon (PB),using the half-duplex decode-and-forward relay mode over the Rician fading channel.In view of the equipment supply problems in railway scenes,such as difficult battery replacing,high management overhead,difficult wiring,etc.,the wireless energy collection was applied to railway scenes,providing sufficient and stable energy for the energy-constrained wireless equipment,thus solving the equipment supply problems in traditional railways.Considering that the source node and the relay node in the network were energy limited nodes,and the energy of the information transmission came from the radio frequency signal transmitted from the PB deployed on the train.The source node and the relay node collected energy from the PB,and the source node sent information to the relay node by using the collected energy,and then the relay node forwarded the information received to the destination node.The closed-form expressions of the network end-to-end outage probability and the throughput were derived.The analytical results were demonstrated by Monte Carlo simulation.And based on this analysis,the impact of the ratio of transmitting power of the PB to noise,the Rician K-factor,the time switching factor,transmission rate and relative position on the end-to-end outage probability and throughput were studied.When the train is in the middle of the source node and the relay node,the system can obtain better outage and throughput performance.

In order to implement unmanned aerial vehicle (UAV)-based wireless communications, a better understanding of wireless channels and the corresponding channel characterizations are critical. In this paper, air-to-ground (AG) channel measurements are carried out at some candidate frequencies, i.e., 1 GHz, 4 GHz, 12 GHz, and 24 GHz. With measurement data, the crucial channel parameters are comprehensively analyzed. Firstly, based on the channel model in the 3rd Generation Partnership Project (3GPP), the essential coefficients for modeling path loss, including path loss exponents (PLEs) and height-dependent factors, are obtained for AG channels. Then, a novel autocorrelation model for shadow fading is proposed. Besides, the small-scale fading is statistically analyzed, where the log-logistic distribution is found as the best fit among popular distributions. Moreover, the second-order statistical characteristics, including the level crossing rate (LCR) and average fade duration (AFD), are extracted to describe in-depth the fading behavior. Overall, the results and findings in this paper are essential for realizing reliable communications in AG wireless systems.

In recent years, with the spectrum resources in low frequency bands becoming increasingly insufficient, the carrier frequency of wireless communication has started its evolution to the Terahertz (THz) frequency band, and meanwhile the THz communication system has become one of the research hot spots. In this paper, we investigate the problem of concurrent transmission scheduling for THz wireless backhaul network. To increase the weighted sum of completed flows with their quality of service (QoS) requirements satisfied in THz wireless backhaul network, we propose an algorithm of QoS-aware bandwidth allocation and concurrent scheduling (IHQB). Considering the priority of the flow, the reciprocal of the priority is taken as the weight of the flow. In this algorithm, QoS awareness and bandwidth allocation are exploited to achieve more successfully scheduled flows and higher network throughput. The concurrent scheduling with the maximum independent set (MIS) is allocated to different frequency bands. And the mentioned MIS is obtained from the conflict graph which has been established under the conditions of half-duplex and interference threshold. Simulation results show that IHQB performs better than other algorithms in terms of the weighted sum of completed flows and system throughput, especially compared with STDMA.

In recent years, the intelligent transport system (ITS) has been developed rapidly because of global urbanization and industrialization, which is considered as the key enabling technology to improve road safety, traffic efficiency, and driving experience. To achieve these goals, vehicles need to be equipped with a large number of sensors to enable the generation and exchange of high-rate data streams. Recently, millimeter-wave (mmWave) technology has been introduced as a means of meeting such a high data rate requirement. In this paper, a comprehensive study on the channel characteristics for vehicle-to-infrastructure (V2I) link in mmWave band (22.1-23.1 GHz) for various road environments and deployment configurations is conducted. The self-developed ray-tracing (RT) simulator is employed with the calibrated electromagnetic (EM) parameters. The three-dimensional (3D) environment models are reconstructed from the OpenStreetMap (OSM). In the simulations, not only the vehicle user equipment (UE) moves, but also the other vehicles such as cars, delivery vans, and buses move around the vehicle UE. Moreover, the impacts of the receiver (Rx) multiple antennas and beam switching technologies at the vehicle UE are evaluated as well. The channel parameters of the V2I link in mmWave band, including received power, Rician $K$ -factor, root-mean-square delay spread, and angular spreads are explored in the target scenarios under different simulation deployments. This work aims to help the researchers understand the channel characteristics of the V2I links in mmWave band and support the link-level and system-level design for future vehicular communications.

In this article, we analyze the signal-to-interference ratio (SIR) coverage probability of a multi-cell downlink system with random traffic arrivals. Based on a theoretical model, in which the base stations (BSs) and devices are deployed as independent Poisson point processes (PPPs), we first present the sufficient and necessary conditions of the ε-stable region for the queueing system. Then, by taking into account the impact of spatially queueing interactions among BSs, we focus on the steady status of the queues and introduce a two-queue-length approximated model for the system. Specifically, in the studied new model, the BSs are separated into two sets in the steady state: the long-queue BS set and the short-queue BS set. The locations of the former are modeled by a Neyman-Scott process and those of the latter are modeled as a residual hole process. By applying the first-order statistic approximation, we further approximate the hole process by a homogeneous PPP with the same density. To model the deployment of the BSs precisely, the related parameters in the approximated model are fitted. Using tools from stochastic geometry and queueing theory, we derive the SIR coverage probability in the steady state. An iterative algorithm is proposed to calculate the active probability of the BSs. To reduce the computational complexity, Beta distribution is applied to approximate the probability density function of the service rate in each iteration of the algorithm. Finally, the effect of the fitting parameters and the accuracy of our analysis are presented via Monte Carlo simulations.

Accurate path loss (PL) prediction is essential for predicting transmitter coverage and optimizing wireless network performance. Traditional PL models are difficult to cope with the development trend of diversity, time-varying and mass wireless channels. In this work, the most widely used multilayer perceptron (MLP) neural network in artificial neural network (ANN) is employed to accurately predict PL. Three types of environmental features are defined and extracted, which describe the propagation environment only by considering limited environmental types instead of complex 3D environment modeling. Principal component analysis (PCA) is used to generate the low-dimensional environmental features, and eliminate redundant information among similar environmental types. Moreover, the information of base station (BS) and the receiver (Rx), including 3D locations, frequency, the transmitted power of BS, the antenna information, the feeder loss, and the received power of all the locations are obtained from the measurements. Different environmental features are combined with the information of BS and Rx to construct seven datasets for PL prediction models based on MLP neural networks. The impacts of the number of neurons in the hidden layer, the number of hidden layers, the number of training samples, and environmental features on PL prediction models are explored by considering the absolute value of mean error (AME), the mean absolute error (MAE), the standard deviation (STD) of error, the correlation coefficient, and the time ratio, respectively. This work aims to understand the propagation characteristics of radio waves, which can provide a theoretical basis for wireless network optimization and communication system design.

In the fifth generation (5G) era, dense deployment of small cells and full-duplex (FD) technology applications are two key features of millimeter-wave (mmWave) wireless communication systems, which offer the opportunity to meet the explosive growth of data service requirements. It is the beamforming and advances in analog as well as digital self-interference (SI) cancellation schemes that improve the network capacity in mmWave wireless backhaul networks. To achieve power saving and further network performance optimization, we propose a FD concurrent transmission mechanism employed in a mmWave backhaul network. Contention graph is constructed in consideration of multi-user interference (MUI), SI, and FD transmission constraints. Then flow-grouping and power control algorithms are proposed based on the contention graph. We evaluate the performance of the proposed algorithm in terms of energy consumption, achievable network throughput, and energy efficiency. The impact of the interference threshold on the system performance is also investigated when the distribution of base stations (BSs), traffic loads, and maximum transmission powers change. Simulation results illustrate that with proper SI cancellation and interference threshold, the proposed concurrent mechanism outperforms time-division multiple access (TDMA), half-duplex (HD) concurrent transmission, and FD concurrent transmission without power control.

In the vision of intelligent transportation, vehicles are expected to feature with advanced applications, such as automatic road enforcement, dynamic traffic light sequence, and autonomous driving. Therefore, real-time and fast dynamic information exchanges are required, and vehicle-to-everything (V2X) communications are highly demanded. In this work, the channel characteristics of vehicular communication are analyzed in the millimeter-wave (mmWave) band at 22.1-23.1 GHz. Specifically, two types of links (the satellite link and the terrestrial link) are considered in urban and highway scenarios with different weather conditions. The ray-tracing simulator together with calibrated electromagnetic parameters is employed to practically generate wideband channels. The key channel parameters of each link including the received power, Rician K -factor, root-mean-square delay spread, and angular spreads are explored. The co-channel interferences between the two links are analyzed as well. The observations and conclusions of this work can be useful for the design of V2X communication technologies.

The millimeter wave (mmWave) has been extensively utilized for its rich spectrum resource. With the development of self-interference (SI) cancellation technology, full-duplex (FD) communications have become possible recently. In order to get larger network capacity, we introduce FD communications into mmWave wireless network. However, mmWave links are easily blocked by obstacles, and the blockage makes some flows fail to be transmitted directly. For the communication problems of shadow coverage area, a relay path selection algorithm is proposed to select optimal multi-hop paths for these blocked flows. This proposed algorithm not only can relay the blocked flows by other unblocked links, but also select the appropriate relay paths to minimize transmission time of the entire transmission process. To further improve the system transmission efficiency, we develop a concurrent scheduling algorithm by using FD communications. The scheduling algorithm continuously schedules the links until the throughput requirements of flows are accommodated. Through extensive simulations at 60 GHz mmWave wireless network, the proposed scheme can solve the problem for a certain degree of blocked flows and complete the transmission with the least time consumption. Compared with other existing schemes, the proposed scheme shows superior performances on relaying capability and scheduling duration.

As the advent of cognitive radios,blind encoder identification has attracted increasingly attentions since it plays an important role.The existing works mainly focus on additive white Gaussian noise (AWGN) channel,while the blind identification in multipath scenarios has not been sufficiently investigated.Considering the blind low density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel,a likelihood-based classifier was proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters.Then,an average log-likelihood ratio (LLR) estimator was adopted 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.

Abstract This paper investigates the multi-cell coordinated beamforming (MCBF) design for secure simultaneous wireless information and power transfer (SWIPT) in both centralized and distributed manners. In each cell, one transmitter serves multiple information receivers (IRs) and energy receivers (ERs) with the non-linear energy harvesting (EH) model. Meanwhile, several eavesdroppers (Eves) intend to intercept the confidential information transmitted for IRs. To achieve a secure transmission, the artificial noise (AN) is embedded in the transmit signals of each transmitter. The proposed design is formulated into a power-minimization problem to guarantee the IRs’ information and ERs’ energy requirements while avoiding the information being intercepted by Eves. Since the problem is non-convex and not easy to solve, a solution method based on semi-definition relaxation (SDR) is proposed and the global optimum is proved to be guaranteed with full channel state information (CSI). We further present a distributed AN-aided MCBF for the system by using alternating direction method of multipliers (ADMM), with which each transmitter is able to calculate its own beamforming vectors and AN covariance matrix based on its local CSI. Simulation results show that our proposed distributed design converges to the global optimum obtained by the centralized one. It is also shown that by employing AN, the total required power of the system is reduced and the effect of AN on the system performance decreases with increment of transmit antennas. Compared with traditional linear EH model, optimizing the system under the non-linear EH one avoids false output power at the ERs and saves power at the transmitter.

More people prefer using rail traffic for travel or for commuting owing to its convenience and flexibility. The railway scenario has become an important communication scenario in the fifth generation era. The communication system should be designed to support high‐data‐rate demands with seamless connectivity at a high mobility. In this paper, the channel characteristics are studied and modeled for the railway tunnel scenario with straight and curved route shapes. On the basis of measurements using the “Mobile Hotspot Network” system, a three‐dimensional ray tracer (RT) is calibrated and validated for the target scenarios. More channel characteristics are explored via RT simulations at 25.25 GHz with a 500‐MHz bandwidth. The key channel parameters are extracted, provided, and incorporated into a 3rd‐Generation‐Partnership‐Project‐like stochastic channel generator. The necessary channel information can be practically realized, which can support the link‐level and system‐level design of the communication system in similar scenarios.

In this paper, a massive multiple-input multiple-output (MIMO) channel measurement campaign with 256-element virtual rectangular array at the base station was conducted. The typical hotspot scenario, subway station, is considered, and the measurements were conducted at 6 GHz with a bandwidth of 100 MHz. A hybrid clustering approach is proposed to characterize the cluster evolution over the large-scale array. In the hybrid approach, we apply the space-alternating generalized expectation maximization algorithm to estimate the multipath components (MPCs), and use the multipath component distance-based tracking algorithm and the KPowerMeans algorithm for MPCs tracking and clustering. A cluster partition algorithm is further proposed to adjudge the clusters association over the array, and output the final clustering results. Under such a scheme, cluster-based model parameters are provided with detailed analysis. The extracted parameters include overall angle distribution, global angular spread, inter-cluster parameters, and intra-cluster parameters. The obtained model parameters can be fed into the new channel simulator for massive MIMO. This is useful for the design and application of the practical massive MIMO system in the future.

The development of self-interference (SI) cancelation technology makes full-duplex (FD) communication possible. Considering the quality of service (QoS) of flows in small cells densely deployed scenario with limited time slot resources, this paper introduces the FD communication into the concurrent scheduling problem of millimeter-wave wireless backhaul network. We propose a QoS-aware FD concurrent scheduling algorithm to maximize the number of flows with their QoS requirements satisfied. Based on the contention graph, the algorithm makes full use of the FD condition. Both residual SI and multi-user interference are considered. Besides, it also fully considers the QoS requirements of flows and ensures the flows can be transmitted at high rates. Extensive simulations at 60 GHz demonstrate that with high SI cancelation level and appropriate contention threshold, the proposed FD algorithm can achieve superior performance in terms of the number of flows with their QoS requirements satisfied and the system throughput compared with other state-of-the-art schemes.

We consider a wireless wideband downlink transmission for high-speed railway. In this scenario, the wireless channel is double selective, i.e., multipath and highly time-varying, and involves non-Gaussian noise at the receiver side. Conventional receiver algorithms designed for Gaussian noise and time-invariant channels are inefficient especially for impulsive noise and time-varying multipath channels, which are the main challenges in high-speed railway. They may lead to communication outage and involve large carrier offsets, respectively. Regarding the challenges, a synthesis scheme is proposed to resolve these issues in this paper. First, in order to mitigate the impulsive noise, three impulsive noise detection algorithms are adopted to discriminate impulsive noise from the received signal. The blanking algorithm is used to suppress the discriminated noise. Then, the refined signal goes through a well-designed filtering, which transforms the received signal with varying frequency offsets into an angle domain. The advantage in the angle domain is that the frequency offset in each angle becomes nearly constant. Thus, the frequency offsets can be estimated within each angle to realize accurate estimation. In the simulation, three different impulsive noise models are considered to evaluate the communication reliability in this circumstance. The simulation results show that the proposed scheme significantly improves the reliable performance of the high-speed railway communication systems.

As an extension of cloud computing, fog computing at the edge of networks provides low latency, location awareness, and real-time interactions. At the same time, millimeter-wave communications are able to provide directional multi-gigabit transmission rates with large available bandwidth. Based on the user mobile trajectories in a region, several activity hotspots that users pass by frequently can be obtained. By caching popular content at the edge nodes near the hotspots, users can download the cached content directly at a short distance, and the user experience can be significantly improved. Considering multiple hotspots in a region, how to efficiently schedule the transmission for the caching at edge nodes becomes a key problem. In this paper, we focus on the problem of mobility-aware transmission scheduling for caching at edge nodes near hotspots and utilize the multi-hop relaying and concurrent transmissions to achieve better performance. After formulating the optimal scheduling problem as a stochastic nonlinear mixed integer program, we propose a mobility-aware caching scheduling scheme, called Multi-Hop Relaying-based Caching, where multi-hop D2D paths are established for edge nodes, and concurrent transmissions are exploited in the scheduling of caching at edge nodes. Extensive performance evaluation demonstrates that MHRC achieves more than $1\times $ higher expected cached data amount compared with the state-of-the-art schemes.

This paper presents results from an extensive measurement campaign of multi-user channels with large-scale antenna arrays (MULAs) in a subway station environment. Based on the measurements, two spatial separation metrics are characterized. The variance of user separability and temporal behavior are investigated. Furthermore, a MULA channel model with transmit correlation is proposed, which ensures that the MULA channels can be generated with a given degree of compatibility. The proposed MULA model is validated by comparing the distributions of the sum rate capacity and the condition number to measurements.

Wireless power transfer is able to provide sustainable and relatively stable energy supply for battery-powered wireless sensor networks. This paper investigates how to optimally design a wireless powered sensor network with minimal power requirements. To this end, we formulate an optimization problem to minimize the total energy consumption at two remote radio units (RRUs) by jointly optimizing energy beamforming and time assignment, where the circuit energy consumption including basic circuit and information processing energy consumption at sensors is taken into account in order to achieve a more practical and general system design. To solve this non-convex optimization problem, an efficient solution method is presented on the basis of variable substitutions and semidefinite relaxation technique. We analyze the optimality of our proposed solution method. When the number of sensors is not more than four, the rank-one constraint is always guaranteed. When it is larger than four, we show that with our proposed solution method via simulations, an approximate global optimal result can be achieved. Simulation results also show that by jointly optimizing the energy beamforming and time assignment, the system required power can be greatly reduced, while the energy beamforming has greater effect than time assignment on the proposed system. Moreover, it is shown that for fixed non-zero circuit energy consumption (including the static part and the dynamic part), the total energy consumption at the RRUs almost linearly increases with the increment of transmission rate requirement. Besides, the total energy consumption at two RRUs caused by different numbers of enjoyers and collaborators is also discussed.

This paper investigates the optimal energy beamforming and time assignment in radio frequency (RF) energy harvesting (EH) wireless powered sensor networks for smart cities, where sensor nodes (SNs) first harvest energy from a sink node, and then transmit their collected data to the sink node via time-division-multiple-access (TDMA) manner by using the harvested energy. In order to achieve green system design, we formulate a problem to minimize the energy requirement of the sink node to support transmission between the sink node and the SNs under data amount constraint and EH constraint. For practical design, the energy consumed by circuit and information processing is also considered. Since the problem is non-convex, we use semidefinite relaxation (SDR) method to relax it into a convex optimization problem and then solve it efficiently. We theoretically prove that when the number of SNs are not greater than two, the relaxed problem guarantees rank-one constraint and when the number of SNs exceeds two, our obtained results are very close to the optimal ones. Simulation results show that when the data amount is relatively small, the energy consumed by circuit and information processing affects the system performance greatly, but for a relatively large data amount, the energy requirement of the sink node on its own signal processing is affected very limited and the system energy requirement is dominated by the transmit power consumption at the SNs. Furthermore, we also discuss the effects of the other parameters on the system performance, which provide some useful insights in future smart city planning.

Ambient backscatter technology has attracted much attention recently, because it enables battery-free devices, such as tags or sensors, to communicate through wireless energy harvesting and radio backscattering. Existing studies about ambient backscatter assume that the tags have only two states: backscatter or non-backscatter. Actually, some references have shown that the tags can readily realize three states: positive and negative phase backscatter, and non-backscatter. In this paper, we propose a new coding scheme for these tags with three states to improve the throughput of the ambient backscatter communication system. We then design a maximum a posteriori (MAP) detector for the reader to extract binary information from ternary coded signals. We also analyze the detection performance in terms of closed-form bit error rate (BER) expressions. It is found that the proposed coding scheme can improve the throughput of an ambient backscatter system, and there exists an error floor for the BER curve. Finally, simulation results are provided to corroborate our theoretical studies.

In this paper, an integrity-oriented content offloading (ICO) problem with variable modulation and coding schemes is proposed for relay-assisted vehicular sensor network. To accurately describe the link duration properties, a finite-state Markov chain-based model is built by jointly considering the mobility and radio propagation characteristics. Considering the ICO problem is NP-hard, our solution divides it into two offloading sub-problems with direct link and relay-assisted path, respectively. These two sub-problems are solved heuristically, and the corresponding results are combined to achieve a near-optimal result as the ICO scheme. While the analytical model is validated by Monte Carlo simulations and chi-square goodness fit test, the performance of our proposed ICO scheme is verified through simulations and comparisons, showing that our ICO solution can reduce the integral content offloading time compared with two other selected schemes and an upper performance bound.

As one of the most important candidate technologies for the fifth-generation wireless communication systems, massive MIMO technology has been widely studied recently because of the significant improvements it can provide in terms of spectrum efficiency and power efficiency. As the foundation of wireless communication, research on propagation characteristics for massive MIMO channels is of primary importance. This paper investigates the characteristics for massive MIMO channels in an indoor hall scenario at 6-GHz. Channel measurements were conducted with a bandwidth of 200 MHz in both line of sight (LOS) and non-LOS (NLOS) conditions. The statistical parameters in the delay domain were extracted to show the spatial variation along the large-scale antenna array. Based on the measured data, the spatial variation is first defined, and then characterized by using the spatial power delay profile correlation coefficient and spatial channel gain correlation matrix collinearity, and the quasi-stationarity region along the massive MIMO array is estimated. Furthermore, by using the space-alternating generalized expectation-maximization algorithm, the multipath components are extracted and classified according to the propagation environments, which provide more insights to the spatial variation of massive MIMO channels. Finally, the characteristics of the extracted angular parameters are investigated and the fluctuations are modeled, where the spatial variation phenomenon was clearly observed over the large-scale antenna array. The quasi-stationarity distance of the variation was found to range from 2.5 to 32.5 cm, and the LOS case differs from the NLOS case at 6 GHz. These results and discussions are useful for analysis and future modeling for massive MIMO channels, and may contribute to future definitions of channel spatial consistency.

The next-generation cellular network is to provide a large variety of services for different kinds of terminals, from traditional voice and data services over mobile phones to small packet transmission over massive machine-type terminals. Although orthogonal-subcarrier-based waveforms are widely used nowadays in many practical systems, they can hardly meet the requirements in the coming 5G networks. Therefore, more flexible waveforms have been proposed to address the unprecedented challenges. In this paper, we will provide comprehensive analysis and comparison for typical waveforms. To offer an insightful analysis, we will not only introduce the basic principles of the waveforms, but also reveal the underlying characteristics. Moreover, a comprehensive comparison in terms of different performance metrics will also be presented in thispaper, which provides an overall understanding of the new waveforms.

Millimeter-wave (mm-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 for the split hybrid beamforming system. The core of our scheme is the beam selection criterion, which is derived from capacity analysis. Furthermore, we propose a beam switching algorithm to ensure the robustness when blockage occurs. Through simulations under different channel conditions, the near-optimal performance of our scheme is demonstrated, and the beam switching algorithm is able to ensure robust network connectivity and achieve higher channel capacity when the blockage is relived.

This paper investigates the optimal power minimization design of simultaneous wireless information and power transfer systems under non-linear energy harvesting (EH) model, where a multi-antenna hybrid access point (H-AP) simultaneously transmits information and power to multiple heterogeneous users, such as information-energy receivers (IERs), information receivers (IRs), and ERs. Power splitting (PS) receiver architecture is adopted at all IERs. In order to achieve green system design, an optimization problem is formulated to minimize the transmit power of H-AP subject to the required signal-to-interference-plusnoise ratios (SINRs) constraints at IERs and IRs, and the harvested energy constrains at IERs and ERs, by jointly optimizing the beamforming vector at H-AP and the PS ratios at IERs. Since the problem is nonconvex and the employ of the non-linear EH model makes it more difficult to solve, the semidefinite relaxation and variable substitutions are used to handle it. For some cases, we theoretically prove that the globe optimal solution can be guaranteed by using our method, and for rest cases, we discuss its optimality via simulations. Numerical results show that although the traditional linear EH model is feasible for the practical EH circuits in some cases, the corresponding system consumes more transmit power than that under the non-linear model EH. Moreover, the effects of the numbers of users, the required SINRs, and the harvested energy on the system transmit power are also discussed.

In future 5G communication systems, supporting high-quality wireless communications in high-mobility scenarios becomes very essential. Although many existing works indicate that increasing transmit power is able to reduce the handover failure probability (referred to as the power greedy scheme) through improving the radio condition or received signal strength, it goes against the requirement of green system design. In this paper, we investigate the effect of power adjustment on handover performance from the perspective of reducing the “uncertainty” in handover procedure in high-speed railway communications systems by embedding it into the existing handover procedure. It is shown that, with power adjustment, the handover performance can be improved without increasing extra energy consumption. The power adjustment-assisted handover scheme is applied to a high-speed railway scenario with distributed antenna system (DAS) cells. Both the blanket transmission-based handover scheme and the remote antenna unit selection transmission-based handover scheme are discussed for DAS cells. To evaluate the performance, the handover probability, handover failure probability, and communication interruption probability associated with two handover schemes are analyzed. Moreover, two new performance metrics, named handover occurrence probability and handover failure occurrence probability, are defined to efficiently evaluate the handover performance versus the position of the train. Both the analytical and the numerical results show that introducing power adjustment into handover gives ability to achieve a better performance compared with the current existing ones. Moreover, the power adjustment-assisted handover scheme is capable of achieving the similar system performance to existing power greedy scheme but without increasing energy consumption.

The pervasive increasing mobile devices and explosively increasing data traffic pose imminent challenges on wireless network design. Device-to-device (D2D) communication is envisioned to play a key role in the fifth generation cellular networks to efficiently support much larger and more diverse set of devices. This paper investigates the mode selection and resource allocation for D2D communications with dynamic user arrivals and departures. We formulate the optimal resource control problem to minimize the average energy consumption of flow transmission into an infinite horizon average reward Markov decision process. In order to deal with the well-known curse of dimensionality problem and facilitate distributed implementation, we approximate the mode selection Q-factor by the sum of per-queue mode selection Q-factors. Moreover, we apply distributive stochastic online learning to estimate the per-queue Q-factors. Simulation results show that the proposed approach outperforms various existing baseline algorithms.

With the rapid development of wireless communication networks, the need of massive Multiple-Input Multiple-Output (MIMO) to offer sufficient network capacity has become evident. As a part of array signal processing, direction-of-arrival (DoA) estimation is of vital importance to obtain directional information of sources and to enable the 3-D beamforming. In this paper, the performance of DoA estimation for massive MIMO systems is analyzed and compared using a low complexity algorithm. To be specific, 2-D unitary estimation of signal parameters via rotational invariance techniques (U-ESPRIT) algorithm is studied to jointly estimate elevation and azimuth information, and uniform rectangular array is selected to represent massive MIMO systems. The simulation results indicate that the U-ESPRIT algorithm works well for the massive MIMO systems. The performance has been evaluated with various types of antenna configuration and source numbers. Finally, the array resolution is adopted to analyze the performance of elevation and azimuth estimation and how to choose the appropriate antenna array configuration.

In reverberation chambers, the multipath channels with different delay characteristics can be generated by loading the chamber with different amounts of absorbers. This paper investigates the impacts of absorber loadings on the delay characteristics based on realistic measurements. An efficient method for estimating the root-mean-square (rms) delay spread in the chamber is presented. Furthermore, it is found that the chamber loadings also significantly affect the quality of a digitally modulated signal and the corresponding modulation quality measurements are performed.

Physical layer (PHY) security is recently regarded as a promising technique to improve the security performance of wireless communication networks. Current developments in PHY security are often based on the assumption of perfect channel state information (CSI). In this paper, both security and reliability performance for the downlink cloud radio access network with optimal remote radio heads (RRHs) node selection are investigated in a practical scenario by considering channel estimation (CE) errors. In particular, a three-phase transmission scheme is proposed and the linear minimum mean-square error (MMSE) estimation method is utilized to obtain the CSI. Based on the CSI estimates and the statistics of CE errors, the outage probability and intercept probability are derived in closed-form expression to evaluate the security and reliability performance, respectively. In addition, two possible cases (with or without intercepting signals from baseband unit) are considered for the eavesdropper. It is found that the suggested optimal RRHs selection scheme outperforms the nonselection scheme, and that the increasing number of RRHs can lower the outage probability as well as the intercept probability. It is also shown that there exists an optimal training number to minimize the sum of the outage probability and intercept probability. Finally, simulation results are provided to corroborate our proposed studies.

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed.

The indoor radio channels at 15 GHz are investigated based on measurements. The large- and small-scale fading behaviors as well as the delay dispersion characteristics are discussed. It is found that the large-scale fading, Ricean K-factor, and delay spread can be described by log-normal distributions. Furthermore, both autocorrelation and cross correlation properties of the above parameters are analyzed and modeled. These parameters characterize fading and delay behaviors as well as their mutual dependency and can be used as empirical values for future wireless system design and simulation in 15 GHz short-range indoor channels.

Recently, high frequency bands (above 6 GHz) have attracted more attention for the next generation communication systems due to the limited frequency resources below 6 GHz. To reveal the influence of frequency on propagation channels, channel characterization results at 14.6 and 3.6 GHz bands based on measurements in an indoor scenario and in a reverberation chamber are presented. The measurement results indicate minimal differences in path loss exponents, shadow fading standard deviation, root-mean-square (RMS) delay spread and coherence bandwidth for the two frequency bands, while the path loss at 14.6 GHz band is clearly larger than that at the 3.6 GHz band. Furthermore, the underlying factors that influence the channel characteristics are investigated. It is found that the RMS delay spread is independent of the frequency in the scenario where free space propagation and/or reflection are the main mechanisms. Measurements in the reverberation chamber verify this inference.