With the growing demands for marine environmental monitoring, collaborative operations, and marine resource development, high-speed and reliable Internet of Things (IoT) communication has become essential for maritime activities. This paper presents a wideband channel measurement campaign at 3.3 GHz in complex nearshore environments to investigate ship-to-ship (S2S) channel characteristics. The study systematically examines time-frequency stationarity, large-scale fading (LSF), dispersion, and small-scale fading (SSF). Results show that the mean stationarity distance reaches 22.48 m, with an average stationarity bandwidth exceeding 39.95 MHz. The statistical close-in (CI) model provides superior accuracy for path loss modeling, while the shadow fading autocorrelation exhibits a periodic oscillatory pattern, captured by a proposed improved model. Time–frequency–angular analysis indicates weak overall dispersion, with stronger dispersion in complex nearshore environments compared to open sea. Furthermore, RMS delay spread, Doppler spread, and angular spread are accurately modeled by Lognormal, Weibull, and Lognormal distributions, respectively. The Rician distribution dominates SSF envelope modeling, with the K-factor following a Normal distribution (mean 14.28–16.03 dB). These findings provide valuable insights for the design and evaluation of maritime IoT communication systems in nearshore environments.

With the development of 6G and beyond, space-air-ground integrated networks (SAGINs) have become a key factor in promoting high-speed seamless connectivity for users. In this paper, we studied a heterogeneous SAGIN in the millimeter wave (mmWave) band, which comprises unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and low earth orbit satellite as aerial base stations (ABSs) in a scenario featuring partial damage to ground base stations (GBSs). Under the constraint of limited system power resources, we optimize the deployment of ABSs and the association and scheduling of users. We formulate a mixed-integer nonlinear programming (MINLP) problem aimed at maximizing the scheduling of users with quality of service (QoS) requirements and propose a block coordinate descent (BCD) based sequential quadratic programming-minimum rate ratio (SQP-MQR) algorithm to solve this problem. Simulation results demonstrate the superiority of our proposed algorithm in scheduling users with QoS requirements compared to other selected schemes in the heterogeneous SAGIN.

LEO satellite communication systems experience significant space-to-ground propagation delays, which lead to outdated channel state information (CSI). As a consequence, the CSI available at a given time instance reflects the channel conditions from several time instances earlier, a phenomenon known as channel aging. Therefore, accurate channel prediction is crucial. Existing channel prediction methods often rely on sequential models that predict the CSI at each future time instance based on the CSI of only the previous time instance. However, this approach tends to suffer from error accumulation over time, resulting in degraded prediction accuracy as the prediction horizon extends. To address this challenge, we propose a Transformer-Long Short-Term Memory (T-LSTM) channel predictor that combines the Transformer and LSTM architectures for future channel prediction. The model predicts the CSI at the current time instance by utilizing a sequence of outdated CSI from several instances earlier. Simulation experiments conducted on a practical doubly selective satellite channel model demonstrate that the proposed T-LSTM model effectively mitigates the impact of channel aging. Moreover, it improves the prediction accuracy of satellite communication channels affected by long propagation delays, specifically compared to LSTM and Transformer methods operating individually.

In this paper, a measurement campaign is conducted to investigate unmanned surface vehicle (USV) communication channels in complex nearshore environments. The experiment setup includes a fixed offshore platform as the transmitter and a mobile vessel as the receiver. Based on the collected data, this study analyzes and models the stationarity and fading characteristics of the channel. The results show that the values for the 1st percentile, 50th percentile, and 90th percentile of the channel’s stationary distance (threshold=0.8) are 0.412 m, 5.99 m, and 23.19 m, respectively. Path loss analysis indicates that the close-in free-space reference path loss with a path loss exponent of 2.407 provides an accurate representation, while shadow fading follows a normal distribution with a mean of zero and a standard deviation of 4.55 dB. To better capture the autocorrelation of shadow fading, a novel model is proposed, improving accuracy by 35% compared to the conventional exponential model. Additionally, small-scale fading envelope analysis using the Akaike information criterion (AIC) confirms that the Rician distribution is the most suitable model. The K-factor of the Rician distribution can be statistically modeled as a bimodal Gaussian distribution, with a goodness-of-fit (GoF) value of 0.026.

In intelligent communication networks, efficient and timely data delivery is critical, particularly with the rise of applications like IoT, autonomous driving, and Industry 4.0. Age of Information (AoI) has become a vital metric for assessing data freshness, especially in high-frequency millimeter-wave (mmWave) bands, which are prone to signal blockages. Recent advancements in Reconfigurable Intelligent Surfaces (RIS) offer promising solutions for enhancing signal propagation in these environments. This paper addresses the AoI optimization problem in RIS-assisted multi-cell mmWave communication systems. We propose a joint optimization strategy involving base station scheduling, user equipment (UE) selection, and RIS phase configuration to minimize system-wide AoI. Through problem decomposition, we derive two sub-problems focused on reducing power consumption via RIS phase adjustments and minimizing AoI through a joint scheduling algorithm. Simulation results reveal that the proposed scheme significantly reduces weighted AoI across various configurations, demonstrating the advantages of RIS-assisted setups over conventional mmWave communication systems without RIS or with randomly configured RIS elements.

With the rapid advancement of intelligent railway systems, a high-quality train-ground communication system is crucial. However, ensuring reliable wireless communication in ultra-high-speed environments remains a significant challenge due to severe Doppler effects, frequent inter-cell handovers, and diverse QoS demands. Existing solutions, such as soft/hard handover schemes, lack the flexibility to adapt to dynamic conditions, leading to service interruptions and suboptimal performance. In this paper, we propose a dynamic resource allocation strategy based on dual base station coordination, utilizing millimeter-wave (mmWave) technology and real-time train position prediction. This approach dynamically optimizes user association and spectrum allocation to accommodate the rapid movement of trains, reducing service interruptions and ensuring continuous high-quality communication. Simulation results show that our algorithm improves system QoS satisfaction by 37.5%-68.8% and achieves spectrum utilization rates between 76% and 90%, outperforming the comparison schemes. These results validate the effectiveness of our approach in addressing high-speed mobility challenges.

The deployment of space–air–ground integrated networks (SAGIN) is critical for addressing signal coverage challenges in high-speed railway (HSR) tunnel scenarios. However, when aerial access networks and ground base stations (BSs) simultaneously serve trains, co-frequency interference can severely degrade system performance, preventing the satisfaction of Quality of Service (QoS) requirements for flows. To address this issue, this article formulates an optimization problem aimed at maximizing the number of scheduled flows through transmission optimization for HSR tunnel communications in SAGIN. Subsequently, a link selection algorithm is proposed to identify valid transmitter-receiver associations by filtering potential link combinations based on the signal-to-interference-plus-noise ratio (SINR) threshold, ensuring that only feasible associations are selected to meet each flow’s QoS requirement. To improve the number of successfully scheduled flows, a graph theory-based transmission optimization (GTTO) algorithm is developed. This approach effectively avoids the simultaneous transmission of highly interfering links by introducing an interference factor and reorders the scheduling sequence of flows to prioritize those associated with links that have higher SINR. The proposed method is evaluated through simulations, demonstrating its ability to substantially improve the number of scheduled flows and system throughput under varying conditions, such as different train speeds, QoS requirements, airship altitudes, tunnel lengths, and the number of mobile relays (MRs). The results also demonstrate the superiority of the proposed method over conventional approaches, achieving reliable communication and enhanced performance across HSR tunnel scenarios.

With the rapid development of 6G communication and the wide deployment of high-speed rail (HSR), it becomes essential to enhance the utilization of HSR communication resources while ensuring the requirements of communication-sensitive users for high reliability and low latency. Meanwhile, the development of integrated sensing and communication (ISAC), brings more inspiration for smart HSR. In this background, we model an ISAC and hyper-reliable low-latency communication (HRLLC) system for UAV-assisted HSR. We formulate a mixed integer nonlinear programming problem (MINLP) with the objective of maximizing the fair sum rate while satisfying the minimum radar sensing requirement. To solve this problem of nonconvex and high coupling, we propose a hybrid particle swarm optimization-genetic algorithm (PSO-GA) that combines the fast convergence of PSO-only (PSO) and the strong global search ability of GA, with parameter-free penalty functions. Through careful design, PSO-GA dynamically balances the exploration and development capabilities. It achieves the best overall performance with a faster convergence speed than existing algorithms. An average improvement of 29%, 57%, and 42% has been achieved with different numbers of passengers, total transmission power, and number of resource blocks. This article supports the future development of intelligent HSR communication.

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 the space-air-ground integrated network (SAGIN), high-speed railway (HSR) communication is expected to be enhanced by simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS). However, when both aerial platforms and ground base stations (BSs) provide services to HSR user equipments (UEs), severe signal interference can arise, leading to the system failing to meet the quality of service (QoS) requirements of the flows. In this paper, we introduce the optimization problem of transmission scheduling for STAR-RIS assisted train-to-ground communications in SAGIN. To address this issue, a phase optimization algorithm of passive STAR-RIS is proposed to improve the channel quality of HSR UEs. Furthermore, a coalition game algorithm is proposed to associate HSR UEs with the optimal links that minimize inter-flow interference. Finally, a QoS-aware flow scheduling algorithm is proposed to optimize the order of the flows for the selected links. Simulation results confirm that the proposed scheduling scheme effectively increases the number of completed flows and total transmitted bits for STAR-RIS assisted train-to-ground communications in SAGIN, outperforming traditional methods.

Reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communication is emerging as a key technology to support the ultra-high data rates in future sixth-generation networks. However, the acquisition of accurate channel state information (CSI) in such systems is challenging due to the passive nature of RIS and the hybrid beamforming architecture typically employed in THz systems. To address these challenges, we propose a novel low-complexity two-phase channel estimation scheme for RIS-assisted THz systems with beam split effect. In the proposed scheme, we first estimate the full CSI over a small subset of subcarriers (SCs), then extract angular information at both the base station and RIS. Subsequently, we recover the full CSI across remaining SCs by determining the corresponding spatial directions and angle-excluded coefficients. Theoretical analysis and simulation results demonstrate that the proposed method achieves superior performance in terms of normalized mean-square error while significantly reducing computational complexity compared to existing algorithms.

Emerging as an important enabling technology for smart factories, ultra-reliable low latency communications (URLLC) have attracted extensive attention from academia and industry. In this paper, we aim to improve the performance of downlink URLLC in a smart factory. We first construct the system model based on the 5G New Radio (NR) standard, which specifies the modulation scheme, resource block structure and achievable data rates under finite blocklength codes (FBC). Next, since it is challenging to fulfill all transmission requests with limited radio and power resources, we formulate the problem to maximize the network throughput while considering delay and reliability constraints. This is a mixed integer non-convex nonlinear problem that is difficult to solve directly. To be tractable, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution. Specifically, the flow scheduling sub-problem is transformed into a matching game (MG) and solved by a delayed acceptance-based algorithm. Also a local water-filling algorithm is utilized to solve the power allocation sub-problem. Simulation results reveal that our proposed scheme outperforms other benchmark schemes.

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals encounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.

As research on integrated sensing and communication (ISAC) progresses, it has been discovered that ISAC can be effectively utilized to enhance the security of wireless communications. Its sensing function can assist in both eavesdropping detection and physical-layer security techniques. In this article, our focus lies on addressing the security challenges associated with high-speed train-to-ground communication using ISAC technology. We explore a novel secure communication scheme. Specifically, we exploit the sensing capabilities of ISAC to detect eavesdropping at the receiving end and subsequently establish a signal blind zone at the location where eavesdropping occurs through beamforming and waveform optimization techniques. This approach ensures the achievement of secure wireless communication. Mathematically modeling the problem as an optimization problem, we derive a lower bound for simplification purposes. Subsequently, we employ an alternating optimization algorithm to iteratively find suboptimal solutions for the optimization variables. Through extensive simulation experiments and comparative analysis, we demonstrate that our proposed algorithm not only guarantees communication security but also outperforms existing algorithms in terms of efficiency.

Deploying reconfigurable intelligent surfaces (RIS) near source and destination is of practical interest for improving the link quality of vehicle-to-vehicle (V2V) wireless systems. However, the accurate channel modeling and RIS tile deployment constitute a pair of challenges to evaluate the system performance such as error performance, capacity, etc. In this paper, we investigate the double-RIS channel characteristics and propose a geometry-based triple-cylinder model, where the RIS sub-surface/tile is enabled to assist V2V systems and other elements are turned off. To determine tile locations on RIS surface, we formulate an optimization problem by maximizing the end-to-end channel gain and solve it using gradient ascent (GA) method. Following this, channel correlation function, channel capacity, and outage probability are derived according to the proposed model. Five typical mobile scenarios are discussed to validate the convergence of proposed GA algorithm, where the results show that channel gain can converge to its maximum with optimized tile locations. In addition, channel correlation under different parameters and conditions are explored. Simulation results validate the enhanced channel capacity and outage probability obtained by optimizing the tile locations.

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.

At present, in order to make use of sufficient spectrum resources to provide even better quality of service and with the development of millimeter wave (mm-wave) communications technology, high speed railway (HSR) communication systems have also taken mm-wave frequency band into consideration. However, since the train runs with high speed as well as the operating environment is complex and dynamic, which may cause the communication link blockage issue for a while. To solve the problem, we adopt the emerging innovative intelligent reflecting surface (IRS) technology to enhance the robustness of mm-wave HSR communication system by introducing a reflection link. Therefore, when the direct communication link is blocked in some case, the reflection link can ensure that the communication is not interrupted. In this paper, we focus on maximizing the number of flows meeting their QoS requirements. A robust IRS-assisted scheduling scheme is proposed under the constraints of half duplex transmission, transmit power, IRS phase shift and limited time slots. As the formulated problem is non-convex, it is difficult to solve directly. Therefore, we divide it into four subproblems, and utilize alternative optimization method to get a sub-optimal solution. The simulation results show that compared with the other three baseline schemes, the IRS-assisted transmission scheduling algorithm proposed in this paper can improve the system performance effectively.

Unmanned Aerial Vehicle (UAV) swarm can be employed to collect time-sensitive data of ground sensors in remote and hostile areas. Inadequate design of UAVs' trajectories and data collection schedule incur delay and negatively impact the information freshness of ground sensors. This paper aims to jointly optimize the trajectories and data collection schedules of multiple UAVs to minimize the average Age of Information (AoI), adapting to the AoI of the ground sensors, and the trajectories of the UAVs. The optimization is formulated as a multi-agent Markov decision process (MMDP), where network states consist of AoI at the ground sensors and the flight trajectories. In practice, a multi-UAV-assisted sensor network contains a large number of network states and actions in MMDP. Exploring the actions of multiple agents in a large state space results in considerable training uncertainties that destabilize the AoI minimization. For stabilizing the formulated MMDP, we propose an onboard Proximal Policy Optimization-based flight resource allocation scheme (PPO-FRAS), which conducts an on-policy learning to optimize the trajectories of the UAVs and data collection schedule of the ground sensors. Numerical results show that the proposed PPO-FRAS achieves 28% and 59% lower AoI than the existing trajectory planning solution based on Deep Q-Network and the greedy algorithm, respectively.

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.

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.

Reconfigurable intelligent surfaces (RISs) achieve high passive beamforming gains for signal enhancement or interference nulling by dynamically adjusting their reflection coefficients. Their employment is particularly appealing for improving both the wireless security and the efficiency of radio frequency (RF)-based wireless power transfer. Motivated by this, we conceive and investigate a RIS-assisted secure simultaneous wireless information and power transfer (SWIPT) system designed for information and power transfer from a base station (BS) to an information user (IU) and to multiple energy users (EUs), respectively. Moreover, the EUs are also potential eavesdroppers that may overhear the communication between the BS and IU. We adopttwo-timescaletransmission for reducing the signal processing complexity as well as channel training overhead, and aim for maximizing the average worst-case secrecy rate achieved by the IU. This is achieved by jointly optimizing theshort-termtransmit beamforming vectors at the BS (including information and energy beams) as well as thelong-termphase shifts at the RIS, under the energy harvesting constraints considered at the EUs and the power constraint at the BS. The stochastic optimization problem formulated is non-convex with intricately coupled variables, and is non-smooth due to the existence of multiple EUs/eavesdroppers. No standard optimization approach is available for this challenging scenario. To tackle this challenge, we propose a smooth approximation aided stochastic successive convex approximation (SA-SSCA) algorithm. Furthermore, a low-complexity heuristic algorithm is proposed for reducing the computational complexity without unduly eroding the performance. Simulation results show the efficiency of the RIS in securing SWIPT systems. The significant performance gains achieved by our proposed algorithms over the relevant benchmark schemes are also demonstrated.

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.

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.

With the evolution of 5G, 6G and beyond, device-to-device (D2D) communications have been developed as an energy-, and spectrum-efficient solution. However, D2D links are allowed to share the same spectrum resources with cellular links, which will bring significant interference to those cellular links. Fortunately, an emerging technique called reconfigurable intelligent surface (RIS), can mitigate aggravated interference caused by D2D links by adjusting phase shifts of the surface to create favorable beam steering. In this paper, we study an RIS-assisted single cell uplink communication scenario, where a cellular link and multiple D2D links share the same spectrum and an RIS is adopted to mitigate the mutual interference. The problem of maximizing total system rate is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of the surface. To obtain practical solutions, we capitalize on alternating maximization and the problem is decomposed into two sub-problems. For the power allocation, the problem is a difference of concave functions (DC) problem, which is solved with the gradient descent method. For the phase shift optimization, a local search algorithm is utilized. Simulation results show that deploying the RIS with optimized phase shifts can effectively eliminate the interference in D2D networks.

In this paper, we focus on the reconfigurable intelligent surface (RIS) assisted single-cell uplink communication network scenario. In the network, one cellular link and multiple device-to-device (D2D) links sharing the same spectrum resources combine direct and reflective channel transmissions with the assistance of the RIS, which is adopted to alleviate the interference by fully using the beamforming capability. Subjected to qualityof-service (QoS) and total power constraints, a system sumrate maximization problem is formulated by jointly optimizing transmission powers of all links and discrete phase shifts of all RIS elements. Since it is a mixed integer non-convex non-linear problem, we decompose it into two sub-problems, and apply the alternating optimization to obtain a sub-optimal solution efficiently. For the power allocation sub-problem, it is a difference of concave functions (DC) problem, which is transformed to a convex one by the multivariate Taylor expansion and then solved with the gradient descent method. For the phase shift subproblem, a local search algorithm is utilized. Simulation results verify that our proposed scheme can eliminate the interference of D2D networks better than the scheme without RIS and other benchmark schemes.

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.

In this paper, we investigate a QoE-aware coordinated caching scheme to adaptively satisfy the diversified video streaming requirements in a high-speed railway network, which consists of multiple cells endowed with cache storage capacity. However, traditional QoS-based video streaming schemes may not accurately reflect the influence of video adaption on user satisfaction. What's more, dynamic user demands and frequent handover impose serious challenges in terms of unknown uncertainties and short dwelling time. To overcome these challenges, we first define a perceptual QoE considering both video quality and video stalling. Second, we formulate a long-term optimization problem to maximize the perceptual QoE, with the cache storage and system stability constraints. Then, we propose a distributed online algorithm based on the Lyapunov optimization theory, which does not require future system information and is efficient for implementation. Finally, theoretical analysis and simulation results are presented to validate the effectiveness of the proposed algorithm.

With the rapid development of mobile applications, mobile edge computing (MEC), which provides various cloud resources (e.g., computation and storage resources) closer to mobile and IoT devices for computation offloading, has been broadly studied in both academia and industry. However, due to the limited coverage of static edge servers, the traditional MEC technology performs badly in a nowadays environment. To adapt the diverse demands, in this paper, we propose a novel mobile edge mechanism with a vehicle-mounted edge (V-edge) deployed. Aiming at maximizing completed tasks of V-edge with sensitive deadline, the problem of joint path planning and resource allocation is formulated into a mixed integer nonlinear program (MINLP). By utilizing the piecewise linear approximation and linear relaxation, we transform the MINLP into a mixed integer linear program (MILP). To obtain the nearoptimal solution, we further develop a gap-adjusted branch & bound algorithm, also called GA-B&B algorithm. Moreover, we propose a low-complexity L-step lookahead branch scheme (referred to as L-step scheme) for efficient scheduling in large-scale scenarios. Extensive evaluations demonstrate the superior performance of the proposed scheme compared with the traditional static edge mechanism. Furthermore, the proposed L-step scheme achieves close performance to the near-optimal solution, and significantly improves the task completion percentage of state-of-the-art schemes by over 10 percent.

In this paper, we investigate the edge caching and content delivery problem for both high-speed train (HST) passengers and low-mobility cellular users. Under multi-dimensional resources constraints, we formulate an optimization problem to minimize the content retrieval delay of HST passengers and meanwhile guarantee the delay requirements of cellular users. As the formulated problem is a mixed-integer nonconvex optimization problem, which is intractable directly, we propose an efficient iterative algorithm that optimizes the three decision variables (i.e., content placement, subchannel allocation, and transmission power allocation) alternately. In specific, Lagrangian multiplier is introduced to convert the constrained optimization, which transforms the content caching problem into a Lagrangian relaxed knapsack problem. Afterwards, the subchannel assignment problem is solved by the Hungarian algorithm with polynomial time complexity, and the power allocation strategy is obtained by the bisection method. Extensive simulations are carried out and results demonstrate that our proposed caching strategy can reduce the content retrieval delay by up to 25% in comparison with the benchmark strategy.

Multiuser multiple input multiple output (MU-MIMO) millimeter wave (mmWave) communication is considered as a key technology to provide multi-gigabit train-to-ground wireless connections in the high-speed railway (HSR) system. Considering the power consumption and hardware constraint, the hybrid beamforming (BF), which combines analog BF and digital BF, is widely adopted in the MU-MIMO mmWave systems. In this paper, we target on an efficient hybrid BF structure design in HSR scenario with taking the practical HSR mmWave channel model into consideration. Specifically, the hybrid BF design aims at maximizing the overall throughput and is formulated as an optimization problem which is proved to be nonconvex and NP-hard. Therefore, a suboptimal yet efficient two-stage solution is proposed, where a weighted minimum mean square error (WMMSE) based beamforming strategy is exploited to devise the hybrid beamformer at the base station (BS) at the first stage, and the orthogonal matching pursuit (OMP) approach is leveraged to decouple the digital BF and analog BF at BS at the second stage. Simulation results demonstrate that higher overall throughput can be achieved by the proposed hybrid BF scheme compared to other state-of-the-art benchmarks.

With the explosive growth of mobile traffic, millimeter wave (mmWave) systems have gained considerable attention from both academia and industry. Although relaying and concurrent transmissions have been utilized to improve the system performance, dynamics due to human mobility are still challenges for mmWave systems. In this paper, we propose a high throughput service scheduling (HTSS) scheme, which exploits multi- hop relay and concurrent transmissions to enhance throughput with the consideration of human mobility. In HTSS, we develop a relay path planning algorithm to establish multi-hop relay paths, and then utilize a global time scheduling algorithm to compute transmission scheme to achieve mobility-aware optimization. Through extensive evaluations under realistic human mobility trajectories, we demonstrate the superior performance of HTSS in terms of the system throughput compared with the state-of-the-art schemes.

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.

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.

Heterogeneous cellular networks (HCNs) with millimeter wave (mm-wave) communications included are emerging as a promising candidate for the fifth generation mobile network. With highly directional antenna arrays, mm-wave links are able to provide several Gbps transmission rate. However, mm-wave links are easily blocked without line of sight. On the other hand, device to device (D2D) communications have been proposed to support many content-based applications and need to share resources with users in HCNs to improve spectral reuse and enhance system capacity. Consequently, an efficient resource allocation scheme for D2D pairs among both mm-wave and the cellular carrier band is needed. In this paper, we first formulate the problem of the resource allocation among mm-wave and the cellular band for multiple D2D pairs from the view point of game theory. Then, with the characteristics of cellular and mm-wave communications considered, we propose a coalition formation game to maximize the system sum rate in statistical average sense. We also theoretically prove that our proposed game converges to a Nash-stable equilibrium and further reaches the near-optimal solution with fast convergence rate. Through extensive simulations under various system parameters, we demonstrate the superior performance of our scheme in terms of the system sum rate compared with several other practical schemes.

With the explosive growth of mobile traffic, diverse mobile applications with high throughput demands have gained considerable attention from both academia and industry. However, dynamics due to human mobility impose serious issues on high throughput communications. Although millimeter-wave (mm-wave), relays, and concurrent transmission have been applied for throughput improvement, how to achieve high throughput transmission in mobility-aware scenarios is still challenging. In this paper, we propose a throughput-efficient service scheduling (TESS) scheme, which exploits multi-hop relay and concurrent transmissions with the consideration of human mobility. In TESS, we first propose a mobility-aware transmission scheduling scheme in single mm-wave cell. The proposed scheduling scheme consists of a relay path planning algorithm and a global time scheduling algorithm. In the relay path planning algorithm, we establish multi-hop relay transmission paths from base station to service points. In the global time scheduling algorithm, we schedule concurrent transmission in relay paths. Furthermore, the proposed TESS scheme is extended for multi-cell scenarios. Through extensive performance evaluations under realistic human mobility trajectories, we demonstrate the superior performance of TESS in terms of system throughput compared with the state-of-the-art schemes.

Enhancing throughput gain is an important target in the development of millimeter-wave (mmWave) communications. Spatial reuse is an important approach for throughput enhancement, which works well in mmWave networks due to directional links. In addition, multicast transmissions are highly desirable in mmWave systems, and become feasible with recent advances in antenna technology. In this paper, we address the problem of spatial reuse scheduling with multicast transmissions for mmWave communications. We formulate the problem as a Mixed Integer Non-Linear Programming (MINLP) problem and propose a low-computational-complexity heuristic algorithm, where potential links are fully utilized to exploit spatial reuse gain by dividing multicast demands into multiple unicast/multicast transmissions. Extensive simulation results demonstrate that the proposed scheme achieves near-optimal performance, and obtains high throughput gain with low packet loss rate.

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.

With the explosive growth of mobile data demand, small cells densely deployed underlying the homogeneous macro-cells are emerging as a promising candidate for the fifth generation (5G) mobile network. The backhaul communication for small cells poses a significant challenge, and with huge bandwidth available in the mmWave band, the wireless backhaul at mmWave frequencies can be a promising backhaul solution for small cells. In this paper, we propose the Maximum QoS-aware Independent Set (MQIS) based scheduling algorithm for the mmWave backhaul network of small cells to maximize the number of flows with their QoS requirements satisfied. In the algorithm, concurrent transmissions and the QoS aware priority are exploited to achieve more successfully scheduled flows and higher network throughput. Simulations in the 73 GHz band are conducted to demonstrate the superior performance of our algorithm in terms of the number of successfully scheduled flows and the system throughput compared with other existing schemes.

60‐GHz millimetre‐wave (mm‐wave) communication is gradually becoming a promising candidate for the next generation wireless system to meet the demands of mobile applications. To compensate for high path loss, directional links are established in mm‐wave communication system, which adds opportunity for spatial reuse. As one of its most promising protocols for commercial production, IEEE 802.11ad standard provides a mechanism to support space‐time division multiple access (STDMA) within a single network. However, the interference level among different co‐channel networks is much lower than that inside a network, which provides greater potential for spatial reuse. In this study, based on the architecture and timing structure of IEEE 802.11ad, the authors propose a spatial reuse strategy among multiple co‐channel networks. They formulate the problem as a mixed‐integer non‐linear programming problem, and then propose an inter‐network STDMA scheduling algorithm, which considers clustering frame structure in IEEE 802.11ad, and combines greedy principle and mutual interference avoidance strategy. Extensive simulation results have shown that the proposed scheme enlarges total throughput in comparison with STDMA inside each network, as well as non‐STDMA scheme. Meanwhile, it achieves the lowest packet loss rate under heavy traffic load.

Abstract Millimeter wave (mmWave) communications in 60 GHz band have become a hot topic in wireless communications. New medium access control (MAC) protocols are needed because of the fundamental differences between mmWave communications and existing other communication systems. In mmWave wireless personal area networks, the channel transmission rates of links vary significantly because of the difference in the distance between nodes, the accuracy of beamforming, and the existence of obstructions. Owning to the directivity of mmWave links, spatial reuse should be exploited to improve network capacity. In this paper, we develop a channel transmission rate aware directional MAC protocol, termed RDMAC, in which both the multirate capability of links and spatial reuse are exploited to improve network performance. RDMAC has two stages. The first stage measures the channel transmission rates of links, and a heuristic algorithm is proposed to compute near‐optimal measurement schedules with respect to the total number of measurements. The second stage accommodates the traffic demand of links, and a heuristic transmission scheduling algorithm is proposed to compute near‐optimal transmission schedules with respect to the total transmission time. Simulations under various traffic modes show that compared with existing protocols, RDMAC has lower network latency, higher network throughput, and also a good fairness performance. Copyright © 2014 John Wiley & Sons, Ltd.

With the explosive growth of mobile data demand, there has been an increasing interest in deploying small cells of higher frequency bands underlying the conventional homogeneous macrocell network, which is usually referred to as heterogeneous cellular networks, to significantly boost the overall network capacity. With vast amounts of spectrum available in the millimeter-wave (mmWave) band, small cells at mmWave frequencies are able to provide multigigabit access data rates, whereas the wireless backhaul in the mmWave band is emerging as a cost-effective solution to provide high backhaul capacity to connect access points of the small cells. In order to operate the mobile network optimally, it is necessary to jointly design the radio access and backhaul networks. Meanwhile, direct transmissions between devices should be also considered to improve system performance and enhance user experience. In this paper, we propose a joint transmission scheduling scheme for the radio access and backhaul of small cells in the mmWave band, termed D2DMAC, where a path selection criterion is designed to enable device-to-device transmissions for performance improvement. In D2DMAC, a concurrent transmission scheduling algorithm is proposed to fully exploit spatial reuse in mmWave networks. Through extensive simulations under various traffic patterns and user deployments, we demonstrate that D2DMAC achieves near-optimal performance in some cases and outperforms other protocols significantly in terms of delay and throughput. Furthermore, we also analyze the impact of path selection on the performance improvement of D2DMAC under different selected parameters.

Recently, millimeter wave communications in 60 GHz band have attracted considerable attention from academia, industry, and standards bodies. To overcome huge propagation loss, high gain directional antennas are used. Consequently, concurrent transmissions (spatial reuse) are enabled within a basic service set (BSS) and even among BSSs, which provides huge potential to improve the network capacity. In this paper, we propose a software-defined mmWave network architecture, where a centralized controller is introduced by abstracting the cross-layer control functions from the network layer to physical layer. Efficient coordination is achieved in this architecture to maximize the spatial reuse.

In order to improve the work efficiency of dynamic measurement, the paper applies CSCW into the field of dynamic measurement. In the process of design and implementation of cooperative work, the Web service technology and XML technology are adopted, solving the heterogeneousness of dynamic measurement platform and the standardization of measurement instruments. On the basis, the work mode and architecture of the cooperative dynamic measurement system are designed and the applied example is given. At last future work is introduced.