High peak-to-average ratio (PAPR) is a major drawback in orthogonal frequency division multiplexing (OFDM) systems. Despite that non-linear transformation techniques achieve low PAPR, it meanwhile degrades the bit error rate (BER) performance. To tackle this issue, deep learning based tightly coupled architectures have been proposed, but their significant computational overheads make them impractical for real-world deployments. This paper explores the ability of separated architecture to efficiently reduce PAPR while maintaining a good BER performance. Specifically, we analyze the distribution of OFDM signals to find an interesting phenomenon that the transmitter can achieve a large PAPR reduction (about 2.4 dB) as long as masking a small percentage (0.55%) of high-PAPR symbols. Driven by this finding, we propose a mask-based scheme including a PAPR reduction module at the transmitter and a mask-location assisted signal reconstruction module at the receiver. To capture the mask-location information (MLI) at the receiver, we outline two ways tailored for scenarios with different communication quality and computing capabilities, respectively. Simulation results reveal that our proposed scheme can achieve satisfactory BER performance with substantially reduced PAPR against conventional schemes. Moreover, the robustness of our scheme is demonstrated across various channels, including an additional white Gaussian noise channel, the Extended Pedestrian A channel and the Extended Vehicular A channel.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

In semantic communication (SemCom), the gain of deep learning-based joint source and channel coding (JSCC) has been proved to partly come from the analog transmission of semantic features extracted directly from the source signal. While, the current mainstream digital communication system design results in the gains almost vanishing when bit-oriented modulation (e.g., QPSK) is employed. To tackle this problem, we propose a Semantic-Oriented Modulation (SOM) method that enables direct mapping from an analog value of a semantic feature to a sequence of discrete values without bit conversion. SOM employs a hierarchical design to mitigate discretization loss and optimizes resource allocation by exploiting the varying significance of semantic features, reducing the data volume by 29.17%. We provide an analysis that reveals the impact of modulation orders and hierarchical layers, guiding the SOM’s design. Simulations in four tasks show that SOM outperforms JSCC with bit-oriented modulation, and the implementation of the Software Defined Radio (SDR) platform confirms its compatibility with existing systems and its potential for latency reduction.

The rapid expansion and resource competition among proliferating Low Earth Orbit (LEO) satellite constellations have escalated adversarial jamming threats, critically challenging network reliability. However, most adaptive frequency hopping (AFH) strategies suffer from slow convergence and high switching overhead when confronted with dense and dynamic jamming scenarios. To tackle this problem, we utilize the temporal correlation of jamming from the fixed satellite orbits. Specifically, we derive the conditional outage probabilities between the adjacent time slots to characterize the temporal correlation of jamming. On this basis, the temporal correlation of jamming adaptive frequency-hopping (TCJ-AFH) strategy is proposed by integrating the correlation into the state exploration process of AFH. Simulation results demonstrate that the TCJ-AFH strategy achieves a 39.7% improvement in the average outage probability under jammed time and a 33.9% reduction in the frequency switching count versus the Q-learning baseline to accelerate the convergence process and minimize overhead, revealing its effectiveness in balancing performance and complexity under dense and dynamic jamming scenarios.

With the proliferation of smart devices and the expansion of human production and living areas, low Earth orbit (LEO) satellite computing is needed to meet the computing demands over a wide area. Due to the limited resources that a single LEO satellite can carry, it is essential to realize intersatellite collaborative computing to achieve efficient onboard processing. However, the high spatiotemporal dynamics of satellite networks pose significant challenges to the establishment of connection and offloading decisions in collaborative computing. Facing these challenges, we propose a multilayer LEO satellite collaborative computing framework by integrating LEO satellites from different orbits. Considering the continuity of task generation, we establish a temporal model for on-board processing. On this basis, we optimize intersatellite offloading decisions to minimize the average system cost over a long-term timescale. To address the constantly changing environment information and the strict constraints on task completion time, we propose using the proximal policy optimization (PF-PPO) algorithm to solve the problem. Extensive simulation results illustrate the effectiveness of the proposed algorithm, which can achieve lower system costs compared with benchmark methods under different conditions. It is also proved that our algorithm has stable performance for the system over a long-term continuous timescale.

Low Earth orbit (LEO) satellite system has revolutionized the way to provide wireless seamless access on a global scale. One of the primary limitations is the low data rates resulting from Doppler shifts induced by the high mobility of LEO satellites. Although orthogonal time frequency space (OTFS) modulation has been proposed to deal with the serious Doppler problem by converting a time-variant fading channel in the time-frequency (TF) domain into a time-invariant channel in the delay-Doppler (DD) domain, it needs to be reconsidered in the LEO satellite system due to the facts that the scale of Doppler axes is not big enough and the velocity of satellite is too fast. In this paper, we analyze two interferences caused by Doppler that will be produced in OTFS-based LEO satellite system. Specifically, we establish a system model of LEO satellite-to-ground communication, involving the fractional Doppler interference (FDI) from the non-integer Doppler tap, and the other is the squint Doppler interference (SDI) from the frequency-dependent Doppler. By deriving the closed-form expressions of FDI and SDI respectively, we find that the simplest but most practical solution to mitigate interference is to increase the value of DD plane bins. Finally, numerical results showcase the significant impact of Doppler on transmission signals by quantifying the signal-to-interference (SIR) ratio and bit error rate (BER) and highlight the dominance of an applicable number of bins on alleviating Doppler in OTFS-based LEO satellite system.

Semantic communication (SemCom) lies in the emphasis on ensuring the correct semantic understanding rather than error-free bit transmission. However, traditional hybrid automatic repeat request (HARQ) mechanism relies on a bit-level check, and it cannot effectively address errors at the semantic level. In this paper, we propose a semantic codebook-based HARQ (SCB-HARQ) mechanism for the reliable and efficient SemCom. To enable semantic-level error detection, SCB-HARQ leverages a shared semantic codebook trained offline at both the transmitter and receiver. This codebook serves as prior information for evaluating the distortion of received semantic features, quantifying the extent to which they deviate from the intended meaning. To reduce the transmission overhead of features in the codebook, a weighted semantic feature index (WSFI) clustering method is introduced to map features into a compact index representation. Then, a masked rate-adaptive joint source-channel coding (JSCC) method is proposed to locate and retransmit the distorted features. The simulation results demonstrate that the proposed SCB-HARQ outperforms the traditional HARQ mechanism, achieving a 46.29% improvement in image reconstruction performance while reducing the transmission data volume by 51.27%.

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: “Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?”. Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.

Semantic communications rely on deep neural networks (DNNs) to reduce the amount of transmitted data by only transmitting the semantics of data rather than the whole data, showing the potential on image transmission even in low signal-to-noise-ratio (SNR) conditions. However, the performance deficiency happens once the real-time data do not follow the independent identical distribution (i.i.d) with the training dataset, which results from the poor generalization of DNNs. To tackle this problem, a promising solution is to align the real-time data to follow the similar distribution with the training dataset at the feature level. Thus, we propose a one-shot data adaptive semantic communication (ODASC), where domain adaptation is incorporated as a pre-processing module to cope with domain shift by aligning the distribution between the real-time data and the training dataset. Image transmission is considered as a case study to demonstrate the big plus of ODASC on data recovery and task accuracy.

Active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASTARS) warrants in-depth research to enhance the reliability and efficiency of sixth-generation wireless communication. This paper investigates the performance of ASTARS-assisted uplink communication networks. We derive new closed-form expressions for the outage probability, considering both perfect and imperfect successive interference cancellation scenarios, and subsequently calculate the throughput and energy efficiency. Numerical results indicate that ASTARS-assisted networks significantly outperform passive counterpart networks.

Satellite being as a relay in non-terrestrial network (NTN) has been commonly considered as a feasible way to expand the coverage. However, one of the main challenges faced by satellite communications is the poor communication quality of the satellite-terrestrial link due to the long transmission distance. Semantic communication have shown initial advantages on transmission performance at poor channel quality (e.g., limited bandwidth or low signal-to-noise ratio), which provides us an idea to address the challenge faced by satellite communications. Current semantic communication mainly focuses on end-to-end communication (i.e., one-hop communication), but two-hop semantic communication in NTN, where satellite acts as a relay, has rarely been explored. In this paper, we devise a two-hop semantic communication system for NTN. Especially, two forwarding protocols, i.e., semantic amplify-and-forward (SAF) and semantic decode-and-forward (SDF), are proposed. On top of this technical contributions, this work provides a new insightful application for semantic communication. The simulation results demonstrate that the proposed two-hop semantic communication system for NTN achieve better recovery performance than traditional approaches.

The mobile communication network is gradually shifting from the deployment of dedicated physical facilities to the deployment of virtualized network functions on general infrastructure. Taking advantage of this transformation, operators can provide customized services to better meet the demands of vertical industries. However, the participation of vertical industry tenants introduces a new attack surface to the mobile networks. The centralized access control scheme currently employed in the 5G system has a high risk of single-point failure and privacy leakage. And it is inefficient when the scheme is applied in distributed core networks. To secure the mobile communication network and protect the privacy of vertical industry tenants, we propose a distributed policy-hidden (DPH) framework, which achieves flexible cross-trust-domain authorization with lower latency. Besides, Attribute-based Encryption is introduced to protect long-distance communications between network functions. Our evaluation demonstrates that DPH is suitable for virtualized 5G networks and effectively reduces the latency of authorization procedures.

Integrated Sensing and Communication (ISAC) can reuse the same spectrum and hardware resources for communication and radar sensing, which is a promising technology to alleviate spectrum congestion. Recently, there has been an increasing research interest in the physical layer secrecy aspects of ISAC systems. This paper studies a basic multiple-input single-output ISAC secrecy system, where a multi-antenna base station transmits a unified signal to sense a point target and communicate to a single-antenna receiver in the presence of$K$single antenna eavesdroppers. Under this setup, a sensing signal-to-noise ratio (SNR) constrained secrecy rate maximization problem has been formulated. We showed that the problem can be reformulated as a convex semidefinite programming problem and proved that beamforming is the optimal transmission scheme. We derived two low-complexity semiclosed-form optimal beamforming solutions and one suboptimal closed-form solution. Numerical results demonstrate that the proposed solutions attain the optimum of the sensing SNR constrained secrecy rate maximization problem and have lower computational complexity than the existing method.

The forthcoming 6G has necessitated more stringent requirements for network latency communication (0.1-1 ms). For this purpose, the Ultra-low Latency Mobile Network (ULMN) integrates open-loop communication, ultra-low latency mobile network association and the cell-free structure towards the minimum end-to-end latency, thus meeting the 6G network latency requirements. However, the ULMN cancels control signaling interactions and traditional centralized resource allocation mode, so both the sender and receiver cannot obtain real-time and accurate channel state information (CSI) in uplink and downlink transmission, which cannot guide reasonable resource allocation and seriously damage network reliability. Uplink communication is more challenging than downlink communication, because a large number of users will make transmission decisions autonomously and blindly to choose channels with poor quality and the same resources to transmit data, resulting in resource conflicts. Therefore, this paper focuses on machine-initiated uplink transmission in ULMN and exploits a reliability-guaranteed distributed resource management scheme. Specifically, we design an novel Multi-Agent Deep Deterministic Policy Gradient based Uplink Resource Management (MADDPG-URM) algorithm to maximize the average probability of success per transmission over time, where senders use historical transmission experience and additional sensor information for resource occupation, association decision-making and power control in a distributed way. The simulation results show that the proposed MADDPG-URM algorithm is more suitable for high network load than radio resource utilization information generation strategies with AN assistance (AA-RRUI) and random algorithm.

Deep learning based semantic communication (DLSC) systems have shown great potential of making wireless networks significantly more efficient by only transmitting the semantics of the data. However, the open nature of wireless channel and fragileness of neural models cause DLSC systems extremely vulnerable to various attacks. Traditional wireless physical layer key (PLK), which relies on reciprocal channel and randomness characteristics between two legitimate users, holds the promise of securing DLSC. The main challenge lies in generating secret keys in the static environment with ultra-low/zero rate. Different from prior efforts that use relays or reconfigurable intelligent surfaces (RIS) to manipulate wireless channels, this paper proposes a novel physical layer semantic encryption scheme by exploring the randomness of bilingual evaluation understudy (BLEU) scores in the field of machine translation, and additionally presents a novel semantic obfuscation mechanism to provide further physical layer protections. Specifically, 1) we calculate the BLEU scores and corresponding weights of the DLSC system. Then, we generate semantic keys (SKey) by feeding the weighted sum of the scores into a hash function. 2) Equipped with the SKey, our proposed subcarrier obfuscation is able to further secure semantic communications with a dynamic dummy data insertion mechanism. Experiments show the effectiveness of our method, especially in the static wireless environment.

Aerial and terrestrial heterogeneous networks (Het-Nets), millimeter wave (mmWave) communications and multi-antenna techniques are viewed as promising components of the solution for future communications. By leveraging the power of stochastic geometry, this paper provides an effective framework for modeling and analyzing an aerial and terrestrial HetNet, where the terrestrial base stations (TBSs) and the UAV base stations (UBSs) co-exist, and the UBSs are equipped with multi-antenna operating at mmWave frequency band. By modeling the TBSs and UBSs as Poisson point processes (PPPs), the expressions for the coverage probability and the average data rate of the typical user of both tiers are derived. Monte Carlo simulations are then performed to validate the analytical expressions. The effects of some relevant parameters on network performance are discussed in detail, and the proposed model can provide some insights into the deployment and optimization of real aerial and terrestrial HetNets.

Blockchain-based non-terrestrial networks (NTN) are expected to ensure the reliability of data in the process of data sharing between untrusted entities. Nevertheless, the low transactions per second (TPS) of blockchain makes it unable to support a large number of data sharing tasks in NTN. One of the key factors for low TPS is the single-chain structure of blockchain. In this way, block generation and block verification are only permitted to be executed sequentially, that means only a single valid block can be generated in the same period. To resolve this dilemma, we propose a twin-chain practical byzantine fault tolerance consensus strategy (TPBFT), in which the consensus process is executed in parallel at two aspects. The first parallel aspect permits block generation and block verification being executed in parallel by twin-chain structure. The second parallel aspect is that the block can be generated with sub-blocks being generated in parallel by replacing transactions with sub-blocks. Finally, the effectiveness of TPBFT is verified by simulation, and the TPS can reach up to 3×109, which means the problem of low TPS of blockchain-based NTN is solved.

The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV toward large scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this article focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data are abstracted from text form into a graph-structured data form. At the same time, graph differential privacy (DP) together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph-structure transformation, an accelerated nodes and edges combined graph DP (ACGDP) algorithm is proposed. Based on the simulations of real-world data that combine electric and nonelectric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph DP processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers’ income. Drivers’ satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first presentMobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we proposeSemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the real-world effects for robust training and evaluation, we further introduce a novel adversarial training method$\texttt {SemMixed}$to harden the ESC againstSemAdvattacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., ourMobileSCcan even be more robust than classical block-wise communication systems in the low SNR regime.

In heterogeneous Internet of Things (IoT) networks, various communication technologies lead to different transmission ranges of nodes, and they can cooperatively provide seamless communication. Unfortunately, the flexible communication mode offers more chances for malware to invade the IoT devices. To maintain its cyber security, two key issues, i.e. the critical threshold of the onset of malware propagation and the lowest-cost defense strategy after it occurs, need to be addressed. To solve the first challenge, we construct a dynamics model by using the degree-based mean-field theory and point process theory, to study the malware propagation-defense process among heterogeneous IoT devices. We analytically derive the closed-form expression of the critical malware transmission rate which can be used to predict whether the malware can propagate or not. For the latter problem, we investigate the equivalent conditions of degree-related patching strategies in defense effectiveness, which can be used to determine the lowest-cost one more directly. In addition, We compare different transmission ways of malware, and find that the malware infection is hard to avoid under the way of centralized transmission, but the decentralized transmission has a greater risk of large-scale infection. We also explore how the immunization measures and the different communication frequencies affect malware propagation. The results show that it is significant to identify important devices for immunization and infected cluster may form in some cases. Our results offer a theoretical foundation to predict and defense malware propagation in heterogeneous IoT networks.

In the future mobile edge networks, the Internet of things (IoT) applications will be latency-sensitive and computationally intensive. Given the resource limitation of IoT devices, mobile edge computing (MEC) servers are critical to support the efficient processing of IoT tasks. Since MEC servers attached to the ground base stations are generally deployed in fixed locations and vulnerable to physical damage, the unmanned aerial vehicle (UAV) and satellite-assisted MEC framework has been proposed to leverage the flexibility of UAVs and the broad coverage of satellites. However, efficient utilization of the UAV/satellite resources is challenging for the static ground IoT devices because of the dynamic in terms of aerial and space network topology and IoT task arrival rates. To adapt to the changing environment and utilize the interaction among multiple UAVs, we propose a multi-agent deep deterministic policy gradient (MADDPG) framework to jointly optimize the traveling routes of multi-UAVs and the offloading decision of IoT devices. To minimize the processing cost in terms of task processing latency and energy consumption of IoT devices, cooperative UAVs can help find the optimal task offloading location for each IoT device. Simulation results show the proposed algorithm based on MADDPG can averagely decrease 20% of the above processing cost compared with the benchmark approach.

In the emerging Internet-of-Things (IoT) paradigm, the freshness of sensory information plays a crucial role in online data-analyzing and application-level decision-making. As the tailor-made performance metric of information freshness, the age of information (AoI) depends on the data transmission efficiency and data update frequency, which are energy demanding for IoT devices with limited battery capacity. To alleviate the energy constraints of low-power IoT devices, we propose an AoI-oriented unmanned aerial vehicle (UAVs)-enabled wireless power transmission scheme, where UAVs are deployed to wirelessly charge IoT devices. With the harvested energy, the devices will upload their fresh information to UAVs. The proposed system aims for sustainable IoT networks with practical device-specific energy limitation, which has been long neglected by existing AoI optimization works. In addition, to explore the influence of dynamic time-varying channels on AoI, a practical line-of-sight (LoS)/NLoS channel model is established to accurately depict the dynamic channel characteristics and precisely capture the efficiency of both data transmission and energy harvesting. To achieve the optimal system-level AoI under dynamic channel conditions, a novel deep reinforcement learning-based proactive UAV trajectory planning (PUTP) algorithm is proposed to automatically adjust the UAV fight policy according to the channel variations and the trade-off between the energy transmission and data collection. Extensive simulation results demonstrate that the proposed PUPT algorithm can significantly reduce the AoI by approximately 20% to 65% compared to three other existing trajectory planning algorithms.

The sensitive information of vehicles which is closely related to the safety of transportation makes the privacy problems in the vehicular networks a popular concern. The development of artificial intelligence (AI) has led the Internet of Vehicles (IoV) to the next phase of intelligence, the Social IoV (SIoV). For social purposes, vehicles may upload captured images with more sensitive information. As a result, the privacy problem is even more serious in SIoV. The exited studies of privacy-preserving methods in vehicular networks mainly consider the spontaneous privacy disclosure. However, the main privacy leakage in real life comes from peer disclosure rather than spontaneous privacy disclosure, which is usually ignored. Therefore, this article innovatively presents a decentralized scheme for solving the peer disclosure issues in SIoV, which, to the best of our knowledge, is the first research in SIoV peer disclosure discussion. A directed acyclic graph (DAG)-based mutual supervision (Dmsv) algorithm is designed for mutually distrustful vehicles. It is verified that our proposed algorithm reduces at least 72% multidimentional privacy loss of image entropy leakage probability when compared with nonpeer disclosure prevention. The decentralized scheme also achieves a relatively low delay of around 24 s from the transaction generation to a network-wide consensus. This article provides a feasibility of applying DAG in a mobile system and provides guidance on how the transportation situation influences the confirmation delay and how to adjust the incentive mechanism according to the transportation situation to maintain robustness.

Consensus is one of the most important issues of a blockchain system because it is a necessary process to reach an agreement between a group of separated nodes that do not trust each other in a decentralized framework. Most existing blockchain consensus works assume that the time of block propagation among separated nodes during the consensus process is ignorable, i.e., a block always successfully reaches every participating node during a period of time that is far shorter than the mining time. However, when blockchain is used in vehicularad hocnetworks (VANETs), the block propagation time is no longer negligible since the dynamic connectivity of the moving nodes in a wireless environment brings opportunistic communication to blockchain consensus. In this article, we study the impact of mobility on block propagation under the single-chain structure in VANET. Specifically, we investigate the dynamics of block propagation from the macroscopic view and derive the closed-form expression of the single-block propagation time. Then, we characterize the blockchain forking as the multiblock competitive propagation. In this way, an approximate result on multiblock propagation time is discussed. An interesting finding is that higher mobility and more moving vehicles can speed up the block propagation. In addition, we also discover that distinct propagation capabilities of moving nodes contribute to the forking reduction in the blockchain consensus.

In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of concern. The privacy problem is even more severe in autonomous driving business than the other businesses in VEC such as ordinary navigation. Federated learning (FL), which is a privacy-preserved strategy proposed by Google, has become a hot trend to solve the privacy problem in many fields including VEC. Therefore, we introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter only with the help of MEC server. Moreover, different from the common assumption of honest MEC server and honest vehicle in former studies, we take the malicious MEC servers and malicious vehicles into account. First, we consider honest-but-curious MEC server and malicious vehicles and propose a traceable identity-based privacy preserving scheme to protect the vehicular message privacy where improved Dijk-Gentry-Halevi-Vaikutanathan (DGHV) algorithm is proposed and a blockchain-based Reputation-based Incentive Autonomous Driving Mechanism (RIADM) is adopted. Further, when the case comes to the non-credibility of both parties where semi-honest MEC server and malicious vehicles are considered, we propose an anonymous identity-based privacy preserving scheme to protect the identity privacy of vehicles with Zero-Knowledge Proof (ZKP). Based on the simulation of virtual autonomous driving based on real-world road images, it is verified that our proposes scheme can reduce 73.7 % training loss of autonomous driving, increase the accuracy to around 5.55 % while keeps effective privacy of message and identity under the threat of dishonest MEC server and vehicles.

The development of car-sharing system using electric vehicles (EVs) is a promising solution to mitigate traffic pressure and reduce carbon emissions. As the market scale of car-sharing expands gradually, the electricity refueling of EVs in car-sharing system becomes quite vital. We propose the concept of car-sharing battery swapping station (CSBSS), which has both the functions of a car-sharing station and a battery swapping station. An individual CSBSS is modeled as a coupled queuing network. Deep Q-Network (DQN) is implemented in this paper to control the charging operation of replaced batteries (RBs). The proposed charging control strategy can fetch more profit than the baseline scheme. Moreover, the simulation results show that more profits can be obtained by adjusting the state of charge (SOC) threshold or the total number of batteries. Our work provides a practical perspective and guidance for the problem of refueling car-sharing EVs.

Vehicular edge computing (VEC) is a promising paradigm to satisfy the ever-growing computing demands by offloading computation tasks to vehicles equipped with computing servers. One of the major challenges in VEC system is the highly dynamic and uncertain moving route of vehicular servers. In order to address this challenge, a particular kind of vehicles (i.e., buses) is adopted as moving servers with the pre-designated route and timetable. On this basis, a fluctuation-aware learningbased computation offloading (FALCO) algorithm based on multi-armed bandit (MAB) theory is proposed. Specifically, base stations (BSs) are regarded as agents to learn the state of moving server so as to construct a stable observation set in the dynamic vehicular environment. In addition, the softmax function is applied to indicate the probability for each decision, which provides more flexible policies for obtaining better results. Simulation results demonstrate that our proposed FALCO algorithm can improve delay performance compared with the other existing learning algorithms.

Facing the dramatic increase of mobile devices and the scarcity of spectrum resources, grant-free nonorthogonal multiple access (NOMA) emerges as an enabling technology for massive access, which also reduces signaling overhead and access latency effectively. However, in grant-free NOMA systems, the collisions resulting from uncoordinated resource selection can cause severe interference and reduce system throughput. In this article, we apply deep reinforcement learning (DRL) in the decision making for grant-free NOMA systems, to mitigate collisions and improve the system throughput in an unknown network environment. To reduce collisions in the frequency domain and the computational complexity of DRL, subchannel and device clustering are first designed, where a cluster of devices compete for a cluster of subchannels following grant-free NOMA. Furthermore, discrete uplink power control is proposed to reduce intracluster collisions. Then, the long-term cluster throughput maximization problem is formulated as a partially observable Markov decision process (POMDP). To address the POMDP, a DRL-based grant-free NOMA algorithm is proposed to learn about the network contention status and output subchannel and received power-level selection with less collisions. The numerical results verify the effectiveness of the proposed algorithm and reveal that DRL-based grant-free NOMA outperforms slotted ALOHA NOMA with 32.9% and 156% performance gain on the system throughput when the number of devices is twice and five times that of the subchannels, respectively. When the number of devices is five times that of the subchannels, the success access probability of DRL-based grant-free NOMA is above 85%, compared to 33% in the slotted ALOHA NOMA system.

Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology.

The development of plug-in electric vehicles (PEVs) brings lucrative opportunities for charging station operators (CSOs). To attract more CSOs to the PEV market, provision of reasonable pricing policy is of great importance. However, dynamic environments and uncertain behavior of competitors make the pricing problem of CSOs challenging. In this paper, we focus on the dynamic pricing policy for maximizing the long-term profits of CSOs. Firstly, we propose a hierarchical framework to describe the economic association of PEV market, which is composed of smart grid, CSOs and charging stations (CSs) serving PEVs from top to bottom. Next, we leverage the Markov game to model the layer of CSOs as a competitive market. Finally, we design a dynamic pricing policy algorithm (DPPA) based on multi-agent reinforcement learning to achieve higher long-term profits of CSOs. Based on the real data of PEVs in Beijing, the experiment results show that DPPA has a significant improvement in long-term profit of CSOs, and the improvement gains increase over time. Moreover, DPPA can reduce the profit loss of CSOs effectively while involving more competitors.

Vehicular edge computing (VEC) is a potential solution to meet delay-sensitive and computation-intensive vehicular applications demands. However, the nature of mobility brings a significant challenge, i.e., when and where to offload task can capture the optimal delay performance for VEC. To address this challenge, firstly, we characterizes the temporal-spatial correlation for VEC system. With the aid of this, we formulate the energy-constrainted delay minimization problem as a mixed integer nonlinear programming (MINLP). Due to the fact that it is hard to tackle the features about non-convex and coupling of designed MINLP problem, this paper transforms the original problem into task placement sub-problem and delayed offloading sub-problem. Specifically, the former is solved by two-stage decision tree algorithm and the latter is obtained by dynamic programming technique. On this basis, a temporal-spatial computation offloading scheme is proposed. Simulations demonstrate that our proposed scheme achieves superior delay performance and provides useful guides for choosing suitable schemes in different conditions.

Blockchain has been considered as a promising technology to improve the efficiency of data sharing in Vehicular Edge Computing (VEC) by data mining among RSUs and vehicles. In order to obtain more mining reward, RSUs and vehicles will compete to accomplish the data sharing mining task. However, it is easier for RSUs equipped with stronger computing capabilities to win the mining reward. In this condition, some vehicles provide their own computing resources to form a shared resource pool for the competition with RSUs. Meanwhile, the competition among the vehicles sharing the resource pool is non-negligible since some selfish vehicles divide the group reward but without providing resources. In this way, we provide a multilayer game model that involving a deformed N Iterated Prisoners Dilemma (NIPD) among vehicles and a bargain game between resource pool and RSUs. Further, we use multi-agent Deep Q Network (DQN) to achieve the equilibrium between vehicles and RSUs in mining. Finally, numerical results show the optimal strategy can attain a stable data sharing mining system in VEC network.

Due to the exponential growth of data traffic and high computational requirements, mobile edge computing (MEC) has become a promising approach to improve system performance in 5G networks. Latency and energy consumption are two important performance metrics for MEC. Due to the inhibition of buildings and other factors, the path loss in different directions are not the same, which leads to that the coverage regions of BSs are not circles. Integral geometry is an effective approach to analyze the system performance of irregular-shape region and is more suitable for the realistic scenario. In this paper, we propose MEC networks with both static UEs and a dynamic vehicle and analyze the average computation latency and energy consumption in both networks. Our difficulty lies in the handover in dynamic network. Our simulation results validate the analytical results well and our results shows that the density of static UEs and the speed of dynamic vehicle have main effect to average latency and energy consumption. The results provide useful guidelines for performance analysis of MEC network.

Grant-free transmission is considered as a promising technology to support sporadic data transmission in massive machine-type communications (mMTC). Due to the distributed manner, high collision probability is an inherent drawback of grant-free access techniques. Non-orthogonal multiple access (NOMA) is expected to be used in uplink grant-free transmission, multiplying connection opportunities by exploiting power domain resources. However, it is usually applied for coordinated transmissions where the base station performs coordination with full channel state information, which is not suitable for grant-free techniques. In this paper, we propose a novel distributed layered grant-free NOMA framework. Under this framework, we divide the cell into different layers based on predetermined inter-layer received power difference. A distributed layered grant-free NOMA based hybrid transmission scheme is proposed to reduce collision probability. Moreover, we derive the closed-form expression of connection throughput. A joint access control and NOMA layer selection (JACNLS) algorithm is proposed to solve the connection throughput optimization problem. The numerical and simulation results reveal that, when the system is overloaded, our proposed scheme outperforms the grant-free-only scheme by three orders of magnitude in terms of expected connection throughput and outperforms coordinated OMA transmission schemes by 31.25% with only 0.0189% signaling overhead of the latter.

Device-to-device (D2D) communication and femtocell are introduced as underlays to the cellular systems by reusing the cellular resources, where Stackelberg game framework is well suited to jointly consider the utility maximization of the macrocell, D2D and femtocell. In this paper, we study transmit power allocation schemes for D2D transmitters and femtocell users to improve the performance of the system. In our scheme, macrocell system is considered as the leader while the D2D pair and femtocell system are considered as two followers who buy the same channel resource from the leader, forming a one-leader-two-followers Stackelberg game. After analysing the procedure, we first obtain the optimal price for the leader and the corresponding optimal transmit power for both followers. Then we propose an iterative power allocation algorithm(IPAA), which will finally construct a Stackelberg equilibrium. Finally, we perform computer simulations to study the performance of the IPAA. It is shown that the IPAA is effective in distributed power allocation and macrocell protection for the spectrum-sharing-based three-tier networks. To make it practical, we demonstrate the IPAA is also efficient in Log-Normal Shadowing Model.

Along with the increasing data demands in vehicular networks (VNs), how to satisfy large-amount data transmission under the requirements of low delay and high reliability has gained a lot of attentions. In this paper, we investigate the key performance metrics when vehicles download the required data by hybrid Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) patterns in multi-lane scenario. Analytical results are derived on the transmission time and the number of handovers. In addition, in order to realize the minimum number of handovers in V2V transmission process, we put forward a Maximum Single Download Time (MSDT) handover strategy. Finally, simulations and discussions are presented to validate the performance of the proposed strategy under different transmission models, and to prove its advantage by comparing with some other typical strategies.

Caching the popular multimedia contents at the wireless edge such as mobile devices, has been identified as a key technology to unleash the ultimate potential of wireless networks. In addition to the physical link condition, social relationships are also of great importance for effectiveness enhancement of the D2D-based wireless caching network. In this paper, we analyze theoretically the performances of the cache-enabled D2D underlaid cellular network with the heterogeneous caching placement, while taking the role of social interaction into account. In this scenario, we firstly derive the achievable average ergodic rate that satisfies a successful content delivery by utilizing the social interaction statistics along with channel statistics with the aid of stochastic geometry. Then the successful transmission probability under the constraint of social interaction is further derived. Based on the expression, we reveal the impact of physical layer and content-related parameters on the network performance. Simulation results validate the accuracy of theoretical analysis and provide the condition when the network performance can benefit from D2D caching.

To alleviate the burden on power grid in heterogeneous cellular networks, energy harvesting (EH) has inspired thorough research in both academia and industry. However, the mismatched distribution of energy storage and traffic load leads to a bad result that the renewable energy can not be utilized sufficiently. To tackle this issue, we propose an energy-biased-received-power (EBRP) scheme (i.e., users incline to associate with BSs at higher energy level) for energy-load balancing and on-grid energy saving. Taking into account the dynamic EH behavior, a performance analysis of coverage probability and energy efficiency is conducted in a two-tier heterogeneous network with EH enabled personal cells. Numerical and simulation results are provided along with the maximum-biased-received-power (MBRP) scheme. Numerical results show that the reduction of maximum transmit power at base stations is favorable for enhancing energy efficiency. Moreover, it is revealed that the proposed scheme shows superiority in energy-load balancing when the energy efficiency is comparable with that of the MBRP scheme.

Machine-type communications (MTC), considered as one of potential technologies in 5G, has attracted more and more attention in recent years. Massive MTC connection density but with small data rate leads to preamble deficiency and network congestion in the network. In order to solve this problem, we jointly utilize the back off strategy and the hierarchical access control method. We formulate a Quality of Experience (QoE)- oriented bandwidth resource allocation problem as a MINLP (Mixed Integer Non-linear problem) under the constraint of limited preambles. This problem is proven to be NP-hard and has extremely high complexity. To reduce the complexity, a two-step iterative algorithm is proposed with the aid of removal algorithm and convex optimization theory. Simulation result shows that out proposed method can effectively improve the QoE gain compared with typical algorithm.

Future mobile networks are converging toward heterogeneous multi-tier networks, where various classes of base stations (BS) are deployed based on user demand.So it is quite necessary to utilize the BSs resources rationally when BSs are sufficient.In this paper, we develop a more realistic model that fully considering the inter-tier dependence and the dependence between users and BSs, where the macro base stations (MBSs) are distributed according to a homogeneous Poisson point process (PPP) and the small base stations (SBSs) follows a Matern cluster process (MCP) whose parent points are located in the positions of the MBSs in order to offload the users from the over-loaded MBSs.We also assume the users are just randomly located in the circles centered at the MBSs.Under this model, we derive the association probability and the average ergodic rate by stochastic geometry.An interesting result that the density of MBS and the radius of the clusters jointly affect the association probabilities in a joint form is obtained.We also observe that using the clustered SBSs results in aggressive offloading compared with previous cellular networks.

In this article, a network model incorporating both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions is proposed to investigate impacts of blockages in urban areas on heterogeneous network coverage performance. Results show that co-existence of NLOS and LOS transmissions has a significant impact on network performance. We find in urban areas, that deploying more BSs in different tiers is better than merely deploying all BSs in the same tier in terms of coverage probability.

Abstract In the paper, we study the problem of secure connectivity for colluding eavesdroppers using relay selection in random wireless networks, where the relay nodes and eavesdroppers are all randomly distributed according to two independent Poisson point process. The decode‐and‐forward and randomize‐and‐forward two relay strategies are considered, and a new metric is defined for best relay selection and random relay selection. We derive closed‐form expressions for the secrecy outage probability for the two relay strategies. In particular, the effect of power allocation ratio and the maximum ratio combing at the destination node on the secrecy outage probability is demonstrated for the decode‐and‐forward relay strategy. Numerical results illustrate the secrecy performance gains with collaborative transmit diversity. © 2016 The Authors. Wireless Communications and Mobile Computing Published by John Wiley & Sons Ltd.

This paper investigates cooperative forwarding in large highly dynamic vehicular networks. Unlike traditional coordinated cooperative forwarding schemes that require a large amount of coordination information to be exchanged before making the forwarding decision, this paper proposes an uncoordinated cooperative forwarding scheme where each node, a random transmission range, decides whether or not to forward a received packet independently based on a forwarding probability determined by its own location. Analytical results are derived on the successful end-to-end transmission probability and the expected number of forwarding nodes involved in the cooperative forwarding process. The multi-hop correlations and multi-path correlations, which constitute major challenges in the analysis, are carefully considered in our analysis. Simulations are conducted to establish the performance of the proposed scheme assuming different forwarding probability functions. In addition to developing an uncoordinated cooperative forwarding scheme, which is particularly suited for the highly dynamic vehicular networks, this paper also makes important theoretical contributions on analyzing the connectivity of networks with nodes of variable and random transmission ranges.

In cooperative localization, distributed algorithm is more attractive than its centralized counterpart due to the low complexity and robustness. However, the position ambiguity of cooperative mobile terminal (MT) is usually neglected by related works, causing the loss of accuracy in distributed algorithms. In this paper, we establish an element-wise-weighted total least-squares (EW-TLS) model for distributed cooperative localization. Considering the impact of position ambiguity and distance measurement error, this localization model yields an optimal consistent estimator. Meanwhile, a general geometric dilution of precision (G-GDOP) metric is derived to estimate MT's coordinate error. Then a distributed cooperative localization algorithm is proposed by integrating the EW-TLS model with the G-GDOP metric. Simulation results indicate that the proposed algorithm outperforms traditional methods in high accuracy and strong robustness.

Distributed cooperative localisation, owing to its low computational burden, is more attractive in wireless networks than the centralised paradigm. However, the distributed algorithms tend to have worse location accuracy among existing works. In this study, the authors focus on the performance dissimilarity between distributed and centralised localisation algorithms, and analyse the Cramér–Rao lower bound. Then, their performance relationship is investigated, and verified by theoretical proof and extensive simulations. Furthermore, it is observed that the position ambiguity of cooperative mobile terminal is the primary cause of performance loss in the distributed algorithm, in contrast with the centralised one. To suppress the impact of position ambiguity, the authors establish an element‐wise‐weighted total least‐squares model and devise the corresponding distributed localisation algorithm. Numerical simulations indicate that the proposed algorithm outperforms traditional methods.

In this paper, we propose a novel compressive sensing (CS) based indoor positioning approach, which uses the signal strength differentials (SSDs) as location fingerprints (LFs). The target location is regarded as an unknown sparse location vector in the discrete spatial domain. Then it just takes a little number of online noisy SSD measurements for the exact recovery of the sparse location vector by solving an ℓ1-minimization program. In order to mitigate the influences of large measurements noise on the recovery accuracy, an LF space denosing algorithm is proposed to discriminate the localization contribution rate of every LF according to its SSD variation. Moreover, an LF space filtering strategy is also exploited to lower the high computational complexity of the CS recovery algorithm. Both experimental results and simulations demonstrate that we achieve remarkable improvements on the positioning performance of the CS based approach by using the two proposed algorithms.

In restricted communication environment, multiple-target localization is of important practical significance. So the cooperative group localization (CGL) was firstly put forward, which has been verified the effectiveness on localization performance gain and simultaneous multiple-target localization in ill conditions. However, it exists two inherent difficulties: the strict demand for CGL topology and the high complexity. By the rational use of information to relax restrictions on topology, and by dividing the complex problem into some simple local ones, the factor graph (FG) together with the sum-product algorithm is a perfect candidate for the problems above. This paper firstly proposes a novel FG-based CGL algorithm. Numerical results indicate that compared with the existing CGL algorithm, the proposed algorithm not only performs better in relaxing CGL topology requirement, but also enjoys high localization accuracy under low complexity.

In cognitive radio (CR) networks, two main challenges faced by cooperative spectrum sensing are low detection performance and long detection time in low SNR scenario. To the best of our knowledge, little study analyzes these two problems simultaneously and thoroughly. Therefore, this paper proposes a novel spectrum sensing scheme, termed multistep detection (MD) scheme, which aims to resolve the above two problems. For MD scheme which consists of five steps, we focus on the following two key parts: two-threshold detection and iteration detection. For the two-threshold detection, we make theoretical analysis about the establishment of the two thresholds. And for the iteration detection, a novel spectrum sensing algorithm is proposed to improve the detection performance in low SNR. The iteration algorithm utilizes the following property to improve the signal's SNR: the variance of the mean of independent and identity Gaussian random variables is 1/K of one variable's variance. The simulation results indicate that the proposed MD scheme outperforms the traditional cooperative scheme both on the detection performance and the detection time significantly.