The user-centric access network (UCAN) has emerged as a promising solution to address the growing demand for high-quality wireless services, but its dynamic clustering faces challenges such as high computational complexity, partial observability at access points (APs), and scalability limitations of centralized control. This paper proposes a multi-agent reinforcement learning (MARL) clustering framework that explicitly addresses the credit assignment problem in decentralized clustering using value decomposition methods. We formulate user-centric clustering as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to jointly optimize minimum spectral efficiency (SE) for fairness and average SE for system efficiency. The proposed framework learns to decompose global Q-function estimation into local Q-functions for individual APs during training, thereby resolving the multi-agent credit assignment challenge and enabling fully distributed execution based solely on local observations. We implement two value decomposition methods: the linear VDN method, which uses additive decomposition, and the nonlinear QMIX method, which employs mixing networks with global state conditioning through hypernetworks. Extensive simulations across static and dynamic scenarios demonstrate that the proposed methods significantly outperform independent learning, MARL baseline lacking explicit credit assignment, and heuristic baselines, achieving superior user fairness while maintaining competitive average performance. The framework exhibits robust generalization across varying mobility speeds and candidate pool sizes. This work establishes value decomposition as an effective clustering solution for practical user-centric network deployments, offering scalable, distributed clustering with provable coordination guarantees.

In massive multiple-input multiple-output (mMIMO) frequency division duplex (FDD) systems, user equipment (UEs) must feed back the channel state information (CSI) to the base station (BS) over the uplink to enhance spectral efficiency. However, in the millimeter wave (mmWave) or higher frequency bands, the significant increase in the number of transmit antennas results in prohibitive feedback overhead. To tackle this issue, we propose a deep learning based multi-user CSI compression framework called CoTransNet. For users in a neighboring area, CSI can be decomposed into two components: one is shared by all users in the area and another is unique to each individual UE. By leveraging shared information, CoTransNet extracts correlations among channels and eliminates redundant feedback information. Experimental results indicate that employing the CoTransNet architecture leads to an average improvement of approximately 4.98% in squared generalized cosine similarity (SGCS) across diverse feedback budgets and a reduction of about 3.54 dB in normalized mean-squared error (NMSE).

Cellular vehicle-to-everything (C-V2X) communication is essential for supporting diverse vehicle applications in cooperative vehicle-infrastructure systems (CVISs). However, various factors, such as building obstructions, signal blockages, and network conditions, severely affect the latency and reliability of V2V communication. To address this challenge, we propose a novel dynamic method for communication mode selection and resource allocation in C-V2X, termed MSRA, which enables vehicles to dynamically select and utilize different communication modes, such as V2V, Vehicle-to-Infrastructure (V2I), and Vehicle-to-Network (V2N) for direct, relay, or forwarding communication, based on real-time conditions. To this end, we formulate the joint optimization problem of mode selection and resource allocation as a Markov decision process (MDP) and propose a solution based on multiagent reinforcement learning (MARL) with centralized training and decentralized execution. Specifically, each vehicle acts as an agent, independently selecting communication modes and resources based on real-time network status and communication link quality, aiming to satisfy the latency and reliability requirements of V2V communication while maximizing the capacity of V2N communication based on heterogeneous Quality-of-Service (QoS) requirements. Simulation results indicate that the proposed algorithm significantly outperforms other decentralized baselines and demonstrates superior performance under various conditions.

Internet of Vehicles (IoV) supported by terrestrial networks can satisfy the necessities of multiple computation-intensive applications. However, current terrestrial networks and resource management mechanisms may only partially guarantee vehicle and in-vehicle user equipment (VUE)’s quality of service due to the limited coverage of roadside units (RSU), especially in remote areas. This paper investigates vehicular edge computing (VEC) in satellite-terrestrial integrated networks with multiple low-earth orbit (LEO) satellites, ground RSUs, and VUEs. In remote areas without RSU coverage, VUEs can offload their partial tasks to satellites to save energy and guarantee latency. We aim to minimize VUEs’ weighted sum energy consumption by jointly optimizing VUEs’ association, data partition, computing resource allocation, power control, and bandwidth assignment under the constraints of maximum tolerant latency, maximum number of outage time slots, computation capacity at each satellite and each RSU, and maximum allowable transmission power at VUEs. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems. We efficiently solve each sub-problem by utilizing variable substitutions, the difference of convex functions algorithms, the Lagrangian dual method, and the Karush-Kuhn-Tucke conditions. Simulation results show that the introduced satellite-terrestrial integrated networks-enabled VEC scheme significantly reduces VUEs’ energy consumption compared to other schemes.

Intelligent transportation systems demand efficient resource allocation and task offloading to ensure low-latency, high-bandwidth vehicular services. The dynamic nature of vehicular environments, characterized by high mobility and extensive interactions among vehicles, necessitates considering time-varying statistical regularities, especially in scenarios with sharp variations. Despite the widespread use of traditional reinforcement learning for resource allocation, its limitations in generalization and interpretability are evident. To overcome these challenges, we propose an Intelligence-based Reinforcement Learning (IRL) algorithm. This algorithm utilizes active inference to infer the real world and maintain an internal model by minimizing free energy. Enhancing the efficiency of active inference, we incorporate prior knowledge as macro guidance, ensuring more accurate and efficient training. By constructing an intelligence-based model, we eliminate the need for designing reward functions, aligning better with human thinking, and providing a method to reflect the learning, information transmission and intelligence accumulation processes. This approach also allows for quantifying intelligence to a certain extent. Considering the dynamic and uncertain nature of vehicular scenarios, we apply the IRL algorithm to environments with constantly changing parameters. Extensive simulations confirm the effectiveness of IRL, significantly improving the generalization and interpretability of intelligent models in vehicular networks.

The satellite-terrestrial integrated networks (STINs) bring a wider range of communication connections to 6G. Due to the reduction in the base station coverage and the number of users continues to increase, user-centric access network (UCAN) has become a more common access method. However, more access options and more complex co-frequency interference make communication resource allocation more difficult. To address this issue, this paper proposes a multi-objective optimization method for communication resources based on heterogeneous graph representation in UCAN. Firstly, this method utilizes heterogeneous graphs to represent network topology and communication resources, extracting a mixture of discrete and continuous decision space from the attributes of nodes and edges. Secondly, a multi-objective optimization model is established to simultaneously optimize the interference, throughput, and energy consumption in UCAN through user access selection and power control, and solved using algorithm. Finally, compared with the benchmark methods, the superiority of the proposed method is demonstrated.

The integration of satellite and terrestrial networks has ushered in a new era of communication capabilities. In satellite-terrestrial integrated network (STIN), full-frequency reuse introduces substantial inter-satellite interference and inter-beam interference, leading to a reduction in system capacity. Considering the extensive coverage area and high mobility of low Earth orbit (LEO) satellites, the acquisition of satellite-ground channel state information (CSI) is inherently imprecise, significantly affecting the performance gains of beamforming techniques. We propose a Graph Attention Network(GAT)-based approach to reduce system interference by optimizing beamforming. In detail, we represent the network topology as a graph, where nodes represent desired links and edges signify interference links. This transforms the NP-hard problem into a graph optimization task. In GAT, it can uncover the concealed influences of inter-satellite and inter-beam interference. This entails learning high-dimensional feature information from networks with similar structures while disregarding state variations in low-dimensional spaces. This approach enhances system robustness. Through simulation results, our proposed beamforming method demonstrates effective interference suppression with low computational complexity, leading to increased system throughput. Moreover, the method exhibits robustness in the presence of corrupted input data.

The widespread employ of Internet of Things (IoT) devices relies on the massive deployment of sensor nodes and data collection timely. Benefit from development of low-Earth orbit (LEO) satellite technology, the LEO Satellite is considered an effective way for achieving wider coverage to terrestrial IoT devices in remote area. However, it is challenging to obtain perfect channel state information (CSI) in LEO satellite system because estimation error and longer round trip time in practice. To overcome this problem, we propose max–min fairness (MMF) robust beamforming deterministic uncertainty model of imperfect CSI convex-concave optimization algorithm (D-ICCA) and stochastic uncertainty model of imperfect CSI convex-concave optimization algorithm (S-ICCA) in LEO satellite communication system, respectively. MMF optimization problems are formulated under the constraints of the maximum per-antennas power constraint in the LEO satellite communication system. In deterministic uncertainty model of imperfect CSI, we obtain the lower bound of CSI by the Cauchy–Schwarz inequality first. Then, we transform the formulated MMF optimization problem to a series of standard convex problem and solve convex optimization subproblems by adopting the alternating direction method of multipliers (ADMM) to obtain suboptimal robust beamforming vectors. In stochastic uncertainty model of imperfect CSI, we model the MMF optimization problem with stochastic phase error and solve the MMF optimization problem via ADMM with closed-from solution to obtain suboptimal robust beamforming vectors. Finally, simulation results demonstrate that the proposed D-ICCA and S-ICCA beamforming algorithms can achieve better performance than ConADMM and FFA-SCA without robust design.

Joint communications and sensing (JCAS) have the potential to improve the overall energy, cost and frequency efficiency of Internet-of-Things (IoT) systems. As a first effort, we propose to optimize the MIMO-OFDM data symbols carried by sub-carriers for better time-and spatial-domain signal orthogonality. This can reduce inter-target and inter-antenna interference, enabling high-quality sensing. We establish an optimization problem that modifies data symbols on sub-carriers to enhance the above-mentioned signal orthogonality. We also develop an efficient algorithm to solve the problem based on the majorization-minimization framework. Moreover, we discover unique signal structures and features from the newly modeled problem, which substantially reduce the complexity of majorizing the objective function. We also develop new projectors to enforce the feasibility of the obtained solution. Simulations show that to achieve the same sensing performance, the optimized waveform can reduce the signal-to-noise ratio (SNR) requirement by 3~4.5 dB compared with the original waveform, while the SNR loss for the uncoded bit error rate is only 1~1.5 dB.

Cellular vehicle-to-everything (C-V2X) communication is a crucial fundamental technology to serve diverse vehicular applications. However, the diversity of communications services in vehicular networks poses a challenge for designing an intelligent and efficient resource allocation framework. In this paper, a deep reinforcement learning (DRL)-based resource allocation framework is developed to meet the heterogeneous Quality of Service (QoS) requirements in heterogeneous vehicular communications networks, by training an agent to jointly optimize the sub-band and transmission power allocation. In particular, the resource allocation framework is formulated by considering the requirements of two key communication modes, the latency and reliability requirements of direct communication mode and the high-capacity requirements of cellular communication mode. Moreover, the interference of fast channel variations in high mobility vehicular environments is fully considered and modeled. Simulation results show that the proposed algorithm outperforms other baseline algorithms while achieving near-optimal performance.

In this paper, an integrated intelligent reflecting surface (IRS)-unmanned aerial vehicle (UAV) communication scheme is proposed where the IRS is mounted on the UAV as a mobile relay between the base station (BS) and the ground user. We present two schemes to maximize the spectrum efficiency (SE) and the energy efficiency (EE) of the system by jointly optimizing the active beamforming, passive beamforming and UAV trajectory. First, to tackle the SE maximization problem, we divide it into three sub-problems to optimize the variables iteratively. For the active and passive beamforming, the closed-form solutions can be directly derived. The suboptimal trajectory design can be obtained by utilizing the successive convex approximation (SCA). Furthermore, considering the limited on-broad energy of UAV, a scheme to maximize the EE is proposed. The optimal active beamforming and the passive beamforming can be similarly obtained. For the non-convex fractional programing of trajectory optimization, it can be solved via the Dinkelbach’s method. Numerical results demonstrate that the effectiveness of the proposed algorithms.

The autonomous cooperation of multiple unmanned aerial vehicles (UAVs) will effectively improve the efficiency of task completion and meet the needs of task diversity. The information interaction and mutual control between UAVs restrict the autonomy of multi-UAVs. The number and selection of control units not only affect the equipment cost of the network but also affect the stability of the network. Link information, such as bandwidth resources and the computing power of the control unit, also affect the resource adjustment of other related links. In order to improve the efficiency and stability of UAVs computing first network, considering the number of control units and link information, this article creatively puts forward the concepts of incidence control, sign incidence control, and incidence complete control. The minimum incidence control number and incidence complete control number of general graphs are calculated. When the network topology of UAV meets the requirements of path, circle, and star, the exact value of the sign incidence control number is calculated. When the network topology of UAV meets the requirements of the complete graph, complete bipartite graph, and wheel graph, the bounds of the sign incidence control number are calculated. The number of control units of the UAV network is further determined.

Integrating unmanned aerial vehicles (UAVs) into emergency communications is a promising way to accomplish efficient network recovery with the advantages of UAV flexibility. To ensure information forwarding from the disaster area, this article considers an emergency communication scenario where a UAV provides uplink relaying services based on nonorthogonal multiple access (NOMA) for a set of disconnected ground wireless access points (APs) under the urban environment. To maximize the system uplink throughput, the UAV altitude, power control, as well as the bandwidth allocation between the access and backhaul links are jointly optimized. Especially, the constraint for the uplink rate fairness is also considered. Our formulated problem is nonconvex due to the complex uplink co-channel interference under the Line-of-Sight (LoS) probability-based Air-to-Ground (AtG) channel. To tackle this issue, we change our formulated problem into an equivalent form by coping with the information-causality and fairness constraints. Then, a joint altitude and resource allocation (JARA) algorithm is developed, which iteratively solves the altitude optimization subproblem and resource optimization subproblem until convergence. For each subproblem, we further introduce auxiliary variables so that it can be solved by using the successive convex approximation (SCA) method. Finally, two benchmarks are used for the throughput comparison, and simulation results verify that the system uplink throughput of our proposed algorithm is improved through the AtG LoS propagation advantage, uplink power control, as well as the bandwidth allocation between the access and backhaul links.

Unmanned aerial vehicles (UAVs) have been widely employed in wireless communications, and the performance can be enhanced with the assistance of intelligent reflecting surface (IRS). However, the finite energy of UAVs greatly limits the endurance and becomes a bottleneck for IRS-UAV communications. In this paper, an integrated IRS-UAV communication scheme is proposed where the IRS is mounted on the UAV to connect the base station and the ground user. We present two schemes to maximize the spectrum efficiency (SE) and the energy efficiency (EE) of the system by jointly optimizing the active beamforming, passive beamforming and UAV trajectory. First, to tackle the SE maximization problem, we divide it into three subproblems to optimize the variables iteratively. For the active and passive beamforming, the closed-form solutions can be directly derived. The suboptimal trajectory design can be obtained by utilizing the successive convex approximation. Furthermore, considering the limited on- broad energy of UAV, a scheme to maximize the EE is proposed. The optimal active beamforming and the passive beamforming can be similarly obtained. For the non-convex fractional programming of trajectory optimization, it can be solved via the Dinkelbach’s method. Numerical results demonstrate that the proposed algorithms are effective for the IRS-UAV networks to maximize the SE and EE, respectively.

Device-to-device (D2D) communication can significantly improve network coverage and spectral efficiency. Meanwhile, non-orthogonal multiple access (NOMA) has recently been integrated with D2D communication to further improve connection density and satisfy explosive data rate requirements of end users. Considering quality of experience (QoE) has become an important indicator from the user perspective, in this paper, we study the QoE-driven resource allocation problem in a device-to-device (D2D) underlaying NOMA cellular network coexisting with D2D pairs and NOMA-based cellular users (CUs). Our target is to maximize the sum mean opinion scores (MOSs) of all users while guaranteeing the minimum QoE requirement of each CU and D2D pair, by jointly optimizing subchannel assignment and power allocation at CUs and D2D pairs. Since this problem is mixed-integer and non-convex, we first transform it into an equivalent yet more tractable form. Then, a two-stage iterative algorithm based on the alternating optimization framework and constrained concave-convex procedure technique is proposed to optimize subchannel assignment and power allocation alternately. Simulation results show that the proposed scheme outperforms the orthogonal multiple access solution and three NOMA based benchmark schemes in terms of QoE performance.

Software-defined networking (SDN) is a cornerstone of next-generation networks and has already led to numerous advantages for data-center networks and wide-area networks. However, SDN is not widely adopted in constrained networks, such as Wireless Sensor Networks (WSN), due to excessive control overhead, lossy medium, and in-band control channels. Therefore, a key challenge to enable Software-Defined Wireless Sensor Networks (SD-WSN) is to reduce the number of control messages required to configure the data plane. In this paper, we propose a cluster-based flow control approach in hybrid SDNs. Our approach is hybrid in the sense that it takes advantage of distributed legacy routing and centralized SDN routing. In addition, it makes a trade-off between the granularity of flow control and the communication overhead induced by the SDN controller. The approach partitions a network into clusters with minimum number of border nodes. Instead of handling the individual flows of each node, the SDN controller only manages incoming and outgoing traffic flows of clusters through border nodes, while the flows inside each cluster are controlled by a distributed legacy WSN routing algorithm. Our proof-of-concept implementations in both software and hardware show that our approach is efficient with respect to reducing the number of nodes that must be managed and the number of control messages. In comparison to benchmark solutions with and without clustering, our solution reduces communication costs for flow configuration in an SD-WSN at least by 27% and at most by 88% respectively, without degrading packet delay nor delivery rate.

Abstract Dynamic multipath routing gives users flexibility over the transmitting path to deliver messages for security. Several protocols have already presented, such as multipath TCP, random routing mutation. However, they often limited to scalability, which mainly reflects on two aspects of network resource consumption and compossibility with other main transfer protocols. In this paper, to enhance the security of data delivery in wide area networks, a novel multipath routing scheme for end‐to‐end communication is proposed. The basic idea is concurrently using two paths (a fixed path and a changing path) to deliver the data of a pair, the fixed routing path undertakes the main delivery tasks, and the continually changing routing path only transfers a little data of the flow during the whole communication period. For systematically achieving security, Information Dispersal Algorithm disperses the flow data over the two paths for resisting anyone routing path compromised. Further, the method can maintain more routing path change since the burst network resource requirement decreases significantly at the routing mutation moment. Meanwhile, it brings a small amount of bandwidth disturbance over the traffic statuses of the network, thus alleviating instead of worsening the compossibility with other protocols.

The current security method of Internet-of-Vehicles (IoV) systems is rare, which makes it vulnerable to various attacks. The malicious and unauthorized nodes can easily invade the IoV systems to destroy the integrity, availability, and confidentiality of information resources shared among vehicles. Indeed, access control mechanism can remedy this. However, as a static method, it cannot timely response to these attacks. To solve this problem, we propose an intelligent edge-chain-enabled access control framework with vehicle nodes and roadside units (RSUs) in this study. In our scenario, vehicle nodes act as lightweight nodes, whereas RUSs serve as full and edge nodes to provide access control services. Considering the low accuracy of risk prediction due to limited training sets, we leverage a generative adversarial networks (GANs) to convert the risk prediction to a sequence generation. Moreover, aiming at the problems of gradient disappearance and mode collapse existed in the original GANs, we devise a Wasserstein combined GANs (WCGANs). Simulation results demonstrate that WCGAN has higher prediction accuracy than the original GANs. Additionally, it can also improve the accuracy of access control of risk prediction-based access control (RPBAC) model.

As a promising paradigm, VANET has been attracting more and more attention from the industry and academia recently. However, due to rapid movement of vehicles and highly dynamic topology, designing efficient MAC protocol for VANET is still challenging. In this paper, we propose MAC-AC, a novel TDMA-based distributed MAC protocol designed specifically for a vehicular ad hoc network. In MAC-AC, when multiple vehicles compete for the same time slot within their two-hop communication range, one of contending vehicle can obtain this time slot by a simple method, which increases the success probability of vehicles accessing channel. Analysis results are presented to demonstrate the efficiency of MAC-AC and compare it to ADHOC MAC, an existing MAC protocol based on TDMA.

High definition map (HDM) is essential to autonomous driving vehicles for path planning and driving decision-making. However, the data of HDM is quite different from other applications. HDM data has the characteristics such as large amount of basic data, frequent updating with small volume, accurate geolocation, etc. In this paper, aiming to reduce the energy consumption and offloading delay, we propose a cluster-based strategy for HDM data offloading by combining the characteristics of HDM data and the mobility of vehicles. Simulation results show that the proposed strategy performs well in the energy consumption and delay.

As vehicular communications for beyond fifth‐generation (B5G) and sixth‐generation (6G) is picking up interests from academia and industry recently, more and more research and development have been devoted towards the establishment of vehicular communications for B5G and 6G that is capable of supporting the ever more intelligent transportation systems. One key facilitating the design and improvement of vehicular communications for B5G and 6G is channel modelling, which is widely regarded as the foundation of all communication and networking systems. In this paper, the authors focus on the research and analysis of B5G and 6G vehicular channel measurements and modelling. By emphasising the new requirements and challenges that the emerging B5G and 6G technologies and frequency bands bring to vehicular communication channel measurements and modelling, they present an overview of the existing work and identify the limitations therein, and provide guidelines on the channel model development and adoption for various system development and verification objectives. Finally, future challenges related to vehicular channel measurements, modelling, and their application for B5G and 6G are addressed.

Cellular vehicle-to-everything (C-V2X) is an important enabling technology for autonomous driving and intelligent transportation systems. It evolves from long-term evolution (LTE)-V2X to new radio (NR)-V2X, which will coexist and be complementary with each other to provide low-latency, high-reliability, and high-throughput communications for various C-V2X applications. In this article, a vision of C-V2X is presented. The requirements of the basic road safety and advanced applications, the architecture, the key technologies, and the standards of C-V2X are introduced, highlighting the technical evolution path from LTE-V2X to NR-V2X. Especially, based on the continual and active promotion of C-V2X research, field testing, and development in China, the related works and progresses are also presented. Finally, the trends of C-V2X applications with technical challenges are envisioned.

Vehicular communication networks have emerged to enable numerous vehicular data services and applications. Conventional vehicularad hocnetworks (VANETs) are often operated in thead hocmode and mainly focus on road safety applications based on the connection between vehicles and roadside units (RSUs). To support vehicular communications, dedicated shortrange communication (DSRC) and car-to-car communication consortium (C2C-CC) have been initiated in the United States and Europe, respectively. With the new era of the Internet of Things (IoT), the conventional VANETs have evolved to the Internet of Vehicles (IoV). In IoV, each vehicle is envisioned as an intelligent object, equipped with sensing platforms, computing facilities, control units, and storages and is connected to any entity (other vehicles, RSUs, charging/gas stations, cloud, and so on) via vehicle-to-everything (V2X) communications. Intelligent vehicles can take different roles, i.e., being both a client and a server, taking and providing big data services, leading to numerous new IoV applications, from assisted/autonomous driving and platooning, secure information sharing and learning to traffic control and optimization.

Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul. In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access. However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO's original users. Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing. Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently. The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed.

Non-orthogonal multiple access (NOMA) has been recently considered in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. In addition, simultaneous wireless information and power transfer (SWIPT) is a promising solution to maximize the energy efficiency. In this paper, for the first time, we investigate the integration of SWIPT in mmWave massive MIMO-NOMA systems. As mmWave massive MIMO will likely use hybrid precoding (HP) to significantly reduce the number of required radio-frequency (RF) chains without an obvious performance loss, where the fully digital precoder is decomposed into a high-dimensional analog precoder and a low-dimensional digital precoder, we propose to apply SWIPT in HP-based MIMO-NOMA systems, where each user can extract both information and energy from the received RF signals by using a power splitting receiver. Specifically, the cluster-head selection algorithm is proposed to select one user for each beam at first, and then the analog precoding is designed according to the selected cluster heads for all beams. After that, user grouping is performed based on the correlation of users' equivalent channels. Then, the digital precoding is designed by selecting users with the strongest equivalent channel gain in each beam. Finally, the achievable sum rate is maximized by jointly optimizing power allocation for mmWave massive MIMO-NOMA and power splitting factors for SWIPT, and an iterative optimization algorithm is developed to solve the non-convex problem. Simulation results show that the proposed HP-based MIMO-NOMA with SWIPT can achieve higher spectrum and energy efficiency compared with HP-based MIMO-OMA with SWIPT.

Distributed mobile cloud computing (MCC) is the new paradigm for providing ubiquitous cloud resources to mobile users with low latency. Mobility is an important factor in distributed MCC which may incur intermittent connectivity and consequently fail computation offloading requests. Latest researches on human mobility show that mobility of users present inherent patterns, periodicity, and predictability. This motivates us to propose a mobile access prediction algorithm based on tail matching subsequence, whose effectiveness and accuracy is validated by experiments using reality mobility dataset. Then MAGA, a mobility-aware offloading decision method for distributed MCC is proposed in this paper for single-job, multicomponent, and multisite offloading scenario. The proposed mobile access prediction is used in MAGA for cloudlet reliability estimation. An integer encoding-based adaptive genetic algorithm is used for offloading decision. Experiment results show the performance advantages of MAGA.

5G has been developing at high speed since 2012 and has become a global economic driver. In this paper, we offer a survey of 5G covering visions, requirements, roadmap, key technologies, standardization, frequency management, technology trials, industrial ecology, and a list of main 5G contributors. We also point out the contributions to 5G from China, aiming to be ‘globally leading in 5G’ by acting as a main 5G contributor in standardization and promoting/enhancing the Chinese 5G industry. Finally, progress on 5G is reviewed mixed with our rethinking of 5G.

The ultradense network (UDN) is one of the most promising technologies in the fifth generation (5G) to address the network system capacity issue. It can enhance spatial reuse through the flexible, intensive deployment of small base stations. A universal 5G UDN architecture is necessary to realize the autonomous and dynamic deployment of small base stations. However, the security of the 5G UDN is still in its infancy, and the data communication security among the network entities is facing new challenges. In this paper, we proposed a new security based on implicit certificate (IC) scheme; the scheme solves the security problem among the access points (APs) in a dynamic APs group (APG) and between the AP and user equipment (UE). We present each phase regarding how two network entities obtain the Elliptic Curve Qu‐Vanstone (ECQV) implicit certificate scheme, verify each other’s identity, and share keys in an UDN. Finally, we extensively analyze our lightweight security communication model in terms of security and performance. The simulation on network bandwidth evaluation is also conducted to prove the efficiency of the solution.

Safety messages propagation is the major task for Vehicular Cyber‐Physical Systems in order to improve the safety of roads and passengers. However, reducing traffic and car accidents can only be achieved by disseminating safety messages in a timely manner with high reliability. Although mathematical modeling of the delay of safety messages is extremely beneficial, analyzing the safety messages propagation is considerably complex due to the high dynamics of vehicles. Moreover, most previous works assume vehicles drive independently and the interaction between vehicles is not taken into consideration. In this paper, we proposed an analytical model to describe the performance of safety messages propagation in the VCPSs under platoon‐based driving pattern. Infrastructure‐less and RSU‐supported scenarios are evaluated independently. The analytical model also takes into account different transmission situations and various system parameters, such as communication range, traffic flow, and platoon size. The effectiveness of the analytical model is verified through simulation and the impacts of different parameters on the expected transmission delay are investigated. The results will help determine the system design parameters to satisfy the delay requirement for safety applications in VCPSs.

The demands for vehicular Internet access are proliferating. To enable vehicular communications, roadside units (RSUs) can be deployed along the roadside to provide wireless coverage and network access for driving‐thru vehicles and the performance of vehicle to RSU communications have been studied in multiple contexts. However, there is not still an efficient media access control (MAC) scheme specific for the sparse highway environment. In this study, the authors investigate the MAC scheme of drive‐thru Internet in a sparse highway environment by a Markov chain encountering model. The analytical model incorporates the high‐node mobility with the modelling of distributed coordination function (DCF) and unveils the impacts of mobility velocity and number of vehicles on the throughput. On the basis of the model, they develop a new MAC scheme and show that when vehicle number is small the proposed MAC scheme can obtain higher throughput and mitigate the impacts of vehicle mobility on the system throughput, which is desirable for the sparse highway environment. Using extensive simulations, they validate the accuracy of the analytical model and effectiveness of the proposed MAC scheme.

Combining tiny sensors and wireless communication technology, wireless body area network (WBAN) is one of the most promising fields. Wearable and implantable sensors are utilized for collecting the physiological data to achieve continuously monitoring of people’s physical conditions. However, due to the openness of wireless environment and the significance and privacy of people’s physiological data, WBAN is vulnerable to various attacks; thus, strict security mechanisms are required to enable a secure WBAN. In this article, we mainly focus on a survey on the security issues in WBAN, including securing internal communication in WBAN and securing communication between WBAN and external users. For each part, we discuss and identify the security goals to be achieved. Meanwhile, relevant security solutions in existing research on WBAN are presented and their applicability is analyzed.

Being one of core characteristics for 5G cellular networks, ultra‐dense small cell network is an effective approach to reuse the spectrum and achieve high data rate transmission in the wireless communication networks. However, because of sharing the spectrum resources, the interference problem among the macrocell base stations (MBS) and the small cell base stations (SCBSs) is hard to address. In this study, the authors model the scenario as a Stackelberg game, where the MBS act as the leader and all SCBSs act as followers. In the game, the MBS set its interference penalty price first, based on the prices the MBS then determines its channel allocation schemes to all SCBSs. Observing the interference penalty price and the amount of allocated channels performed by the MBS, each SCBS then determines its transmit power to achieve its optimal utility. Because of the first‐move advantage, the MBS is able to predict the reactions of each SCBS and make optimal strategies. Simulation results show the correctness of the analysis and the significant benefits when the power control and channel allocation are jointly considered in the proposed schemes.

Diverse applications in vehicular network present specific requirements and challenges on wireless access technology. Although considered as the first standard, IEEE 802.11p shows the obvious drawbacks and is still in the field-trial stage. In this paper, we propose long-term evolution (LTE)-V as a systematic and integrated V2X solution based on time-division LTE (TD-LTE) 4G. LTE-V includes two modes: 1) LTE-V-direct and 2) LTE-V-cell. Comparing to IEEE 802.11p, LTE-V-direct is a new decentralized architecture which modifies TD-LTE physical layer and try to keep commonality as possible to provide short range direct communication, low latency, and high reliability improvements. By leveraging the centralized architecture with native features of TD-LTE, LTE-V-cell optimizes radio resource management for better supporting V2I. LTE-V-direct and LTE-V-cell coordinate with each other to provide an integrated V2X solution. Performance simulations based on sufficient scenarios and the prototype system with typical cases are presented. Finally, future works of LTE-V are envisioned.

Very large multiple input multiple output (MIMO) system (referred to as massive MIMO) is currently investigated as a novel technology to enhance system throughput for future cellular systems. In this paper, we study a practical implementation of massive MIMO technology in LTE-Advanced systems. Firstly, two three dimensional(3D) dynamic beamforming schemes are proposed to realize full spatial resolution of the elevation as well as the traditional azimuth domain. These schemes are implemented through combining a couple of precoder matrix indictor (horizontal PMI and vertical PMI) during the channel state information (CSI) feedback stage. Furthermore, we also propose a limited feedback precoding algorithm named PMI-based regularized zero-forcing precoding (P-RZF) to achieve multi-user MIMO (MU-MIMO). System level simulation is performed to evaluate the proposed schemes.

Satellite networks provide global coverage and can take a wide range of multimedia services. Routing algorithm performance is critical for satellite network business and is always considered an important step for using the satellite networks. Although a lot of research has been done on the performance of satellite routing algorithm in normal condition, the performance under node failure scenarios is seldom discussed. In this paper, number of tests is designed to evaluate delay, loss rate performances of routing algorithms that include SPF, DRA, and SGRP. The key of the tests is focusing on the performance in single satellite node failure and random node failures scenarios. Moreover, a novel error path measure is proposed and realized to analyze the convergence performance of these algorithms. It is found that, node failure in the minimum horizontal ring may bring about more error paths. DRA performance may suffer severe degeneration at large damage condition.

Software-defined networking has emerged as a promising solution for supporting dynamic network functions and intelligent applications through decoupling control plane from forwarding plane. OpenFlow is the first standardized open management interface of SDN architecture. But it is unrealistic to simply swaping out conventional networks for new infrastructure. How to integrate OpenFlow with existing networks is still a serious challenge. We propose a tunnel splicing mechanism for heterogeneous network with MPLS and OpenFlow routers. Two key mechanisms were suggested: first, abstract the underlying network devices into uniformed nodes in order to shield the details of various equipments, second, strip the manipulation of flow table and lable switch table from controller and fulfill it in an independent module. This new paradigm has been developed on Linux system and tests have been carried out in experiment networks. The emulation results proved its feasibility and efficiency.

Biasing cell association (BCA) is an effective and easy-to-implement load balancing technology in small cell networks (SCN). The existing studies are mainly respect to the static BCA strategy which is not appropriate for the non-uniform traffic distribution scenarios. In this paper, we address the problem of load balancing in SCN where the small cell base stations (SCBSs) are underlaid deployed within the macro cell base station (MBS) coverage. We design a dynamic BCA scheme for SCN where each BS aims to balancing its load. To be specific, the light-loaded BS tends to attract more users while the heavy-loaded BS tends to discharge the current users. Firstly, a quantum-behaved particle swarm optimization based algorithm is proposed towards the suboptimal solution for dynamic BCA. And then we put forward a distributed User-SCBS side BCA scheme. At last, simulation results highlight that the proposed dynamic BCA scheme can highly improve the system spectral efficiency and reduce the average delay.

This paper discusses the problem of minimizing the energy used to transmit packets over a memoryless channel, with a constraint on the delay suffered by packets and a constraint on peak transmitter power. The problem is studied within discontinuous reception (DRX) supported system. Specifically, we seek a DRX-compatible transmission policy that can solve the problem without the knowledge of fading distribution and packet arrival process. To achieve this goal, we formulate the problem as a variation of finite horizon classical secretary problem (CSP) with arbitrary monotonic utility. We present key structure property of optimal solution, and utilize it to obtain the optimal policy which can be described as a threshold rule. Moreover, we also propose an off-line calculation method to obtain the thresholds with low computing complexity. With the help of the thresholds, the optimal policy can make transmission decision only based on the relative rank of channel state over present time slot and the inter-packet deadlines. The performance of the optimal transmission policy is studied via simulation. The results show that the optimal transmission policy can work efficiently in DRX-supported system with low packet drop rate and high energy efficiency.

Internet of Things (IoT), which will create a huge network of billions or trillions of “Things” communicating with one another, are facing many technical and application challenges. This paper introduces the status of IoT development in China, including policies, R&D plans, applications, and standardization. With China's perspective, this paper depicts such challenges on technologies, applications, and standardization, and also proposes an open and general IoT architecture consisting of three platforms to meet the architecture challenge. Finally, this paper discusses the opportunity and prospect of IoT.

Quality of Service (QoS) and queue management are critical issues for broadcast scheme of IEEE 802.11 systems in Vehicular Ad hoc Networks (VANETs). However, existing 1-dimensional models of broadcast scheme in VANETs are unable to capture the complete QoS performance and queueing behavior due to the lack of an adequate finite buffer model. We present a 2-dimensional Markov chain that integrates the broadcast scheme of the 802.11 system and queueing processes into one model. The extra dimension, that models the queue length, accurately capture important QoS measures for realistic 802.11 broadcast systems with finite buffer under finite load. We derive an simplified method for solving the steady state probabilities of the Markov chain. The solutions are validated by extensive simulations. Based on this model, we also show numerical results to analyze the performance of the broadcast scheme in VANETs in terms of collision probability, throughput, queue length, and QoS measures, including blocking probability and queueing delay.

In this paper, the original Multiple Input Multiple Output (MIMO) Y channel is extended into a more generalized circumstance, called a generalized MIMO Y channel with general signal demands. The model consists of K(K≥2) nodes each equipped with Mkantennas and an intermediate relay equipped with N antennas. There is no direct link between the nodes. Therefore, supposing that node i and node j (i ≠ j∈{1,2,···, K}) exchange nijsignals, each node has to transmit mk(mk=Σi ≠ kknki, mk≤Mk) signals for other nodes in the MAC phase, and then receive mksignals in the BC phase, via the relay. Then, it's proved that Σk=1Kmkdegrees of freedom (DoF) can be achieved by making use of signal space alignment (SSA) and interference nulling beamforming in this model, when N ≥ 1/2(Σk=1Kmk) and (Mi+Mj) ≥ (N+nij).

MILSA (Mobility and Multihoming supporting Identifier Locator Split Architecture) has been proposed to address the naming and addressing challenges for NGI (next generation Internet), we present several design enhancements for MILSA which include a hybrid architectural design that combines "core-edge separation approach" and "split approach", a security-enabled and logically oriented hierarchical identifier system, a three-level identifier resolution system, a new hierarchical code based design for locator structure, cooperative mechanisms among the three planes in MILSA model to assist mapping and routing, and an integrated MILSA service model. The underlying design rationale is also discussed along with the design descriptions. Further analysis addressing the IRTF (Internet Research Task Force) RRG (Routing Research Group) design goals shows that the enhanced MILSA provides comprehensive benefits in routing scalability, traffic engineering, mobility and multihoming, renumbering, security, and deployability.

Reliability evaluation of wireless sensor networks (WSN) is a critical step in WSN design. To evaluate the reliability of WSN, an algorithm named COBDD is presented in this paper. COBDD executes recursive construction of the ordered binary decision diagram (OBDD) only once, and therefore save much running time when WSN is subject to large number of common causes. Furthermore, it constructs OBDD with node expansion and decrease redundant computations from isomorphic sub-networks. Experiments show that COBDD is an efficient algorithm for the reliability evaluation of WSN.

The integration cellular networks, wireless local area networks (WLANs), and the new paradigm of mobile ad hoc networks (MANETs) is the trend for next generation mobile networks. And the multi-hop routing mechanism is an open and challenge issue in this area. In this paper, a multi-hop routing mechanism with fuzzy estimation of links for heterogeneous wireless networks (HWNs) is proposed. The mechanism comprises neighbor discover, gateway discover and route discovery, which supports the mobile hosts (MHs) outside of the service area to access BS/AP by multi-hop route. The quality of links is addressed and evaluated by comprehensive fuzzy estimation approach based on analytic hierarchy process (AHP). Furthermore, the route maintenance overhead is also analyzed and discussed. Simulations reveal that the proposed routing mechanism can effectively provide valid route and improve the quality of service and performance in HWNs.

BitTorrent is one of the most well known peer-to-peer file sharing applications, accounting for a significant proportion of Internet traffic. Current BitTorrent system builds its overlay network by randomly selecting peers, a fact that has the potential to seriously handicap both individual performance and generate a significant amount of cross-ISP traffic. In this paper, we propose a novel peer selection algorithm called UTAPS which could selects peers within small hop counts and low round trip time (RTT) range by utilizing the knowledge of underlying topology. Consequently, the proximities among peers in the P2P overlay network are enhanced. Simulation results show that the UTAPS algorithm can achieve better individual performance in sense of low download time and reduce the traffics which are injected into ISP backbones.

Network/service survivability is an important problem demanding prompt solution in the design and planning of new telecommunications networks (e.g., SDH networks and ATM networks). Different restoration mechanisms for SDH and ATM networks have already been developed. The previous literature comparing and evaluating the restoration mechanisms only uses a simple method. In order to overcome the shortcomings in the traditional method, this paper proposes the customer-oriented and network engineer-oriented definitions of restoration mechanism availability. In the second definition, the extra spare capacity requirement in the network is taken into account in the availability analysis. Because an exact measurement of the availability is not feasible due to the large number of failure states in the network, an ORDER algorithm (Li and Silverter 1984) to get a reasonable subset to approximately measure the availability is recommended. Then, applications of the definitions are given. One application is how this definition is used by network planner/engineer to construct a cost-effective survivable network. Another application is that a new group of parameters-survivability quality of service (SQoS) is defined and added to CAC (connection admission control) of the ATM survivable network. Finally, a simulation using two definitions is performed. The simulation results prove that two availabilities can evaluate restoration mechanisms more sufficiently and more meaningfully.