As intelligent transportation systems (ITS) become more integrated into modern infrastructure, vehicular communication systems face increasing cybersecurity threats. Traditional centralized intrusion detection system (IDS) has significant limitations in terms of the scalability, privacy preservation, and trust establishment, which are critical challenges in ITS environments. To address these issues, this paper proposes an intrusion detection method tailored specifically for ITS, integrating federated learning (FL) and blockchain technology to create a secure, scalable, and privacy-preserving threat detection system for the Internet of Vehicle (IoV). The proposed method utilizes FL for distributed model training, avoiding the sharing of raw data and employing encryption techniques to protect user privacy at the edge devices. Blockchain technology ensures the integrity and tamper-proof nature of model updates and fosters trust between entities. Moreover, to accommodate the heterogeneous and dynamic data environment in IoV, the method supports both independent and identically distributed (IID) and non-IID data scenarios, enhancing the system’s adaptability and robustness. Given the computational limitations of vehicular devices, this work incorporates knowledge distillation and lightweight model designs, effectively reducing the local computational burden. Experimental results demonstrate the significant effectiveness of the proposed approach: on the CICIDS2017 dataset, the model achieves an accuracy of 97.10% with 11,904 parameters and a memory consumption of 0.045MB; on the Car-Hacking dataset, it achieves an accuracy of 99.10% with 9,989 parameters and a memory consumption of 0.038MB; on the CICIoV2024 dataset, it achieves an accuracy of 99.65% with 9,861 parameters and a memory consumption of 0.038MB. Compared to traditional baseline models, the proposed approach significantly reduces both the number of training parameters and memory consumption, while maintaining high accuracy, making it highly suitable for deployment in resource-constrained vehicular environments within ITS.

Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

This article studies the secrecy performance for a dual-hop nonorthogonal multiple access (NOMA)-aided unmanned aerial vehicle (UAV) network in the face of a passive untrusted far user (UFU). A new artificial noise (AN) scheme is proposed, where AN generated in the first hop can be used to encrypt confidential signals by XOR operation in the second hop. Based on the proposed AN (PAN) scheme, we analyze the exact and asymptotic outage probabilities (OPs) for both NOMA users and intercept probability (IP) for trusted near user (TNU). Simulation results verify the correctness of our theoretical analysis and demonstrate that the PAN scheme significantly improves the security for TNU at the cost of negligible reliability of UFU compared to the benchmark schemes.

Since adopting the spherical wave model in near-field, the imperfect resolution of beamfocusing has significantly enabled non-orthogonal multiple access (NOMA) technique to enhance spectrum efficiency, where one user’s beam can be served for additional users. However, as additional users sharing the same radio resources to enhance network density, physical layer security (PLS) becomes increasingly critical, especially in scenarios that eavesdroppers could potentially intercept communications intended for legitimate users. To address such issue, a comprehensive investigation of the resolution is proposed to distinguish NOMA users and eavesdroppers within a near-field communication (NFC) framework, considering both angular and distance domains. Furthermore, by leveraging the resolution capabilities, we individually consider the positions of NOMA users and eavesdroppers, which significantly impacts the power allocations, decoding orders and security performances. Moreover, our simulation results verify the analytical results and demonstrate the improvement of the average secrecy rate as well as secrecy outage probability (SOP), offering a novel perspective to enhance future NFC systems.

This paper proposes a blockchain-integrated federated learning (FL) framework tailored for secure, efficient, and energy-aware model training in edge computing environments. The framework follows an offload-train-aggregate paradigm where edge devices transmit local datasets to proximate servers for localized model updates. A reputation-driven RAFT consensus protocol is incorporated to achieve reliable, low-latency, and lightweight blockchain coordination while preserving privacy and accountability. To overcome the inherent mixed-integer nonlinear programming (MINLP) complexity, we develop a two-stage cross-layer optimization strategy. In the first stage, an alternating direction method of multipliers (ADMM)-based feedback control scheme jointly allocates bandwidth and computation resources under energy and delay constraints. In the second stage, server selection and sub-band assignment are modeled as a bipartite matching problem and solved via the Hungarian algorithm, guided by a convergence-aware performance bound. Extensive simulations demonstrate that our framework significantly improves learning accuracy, uplink throughput, and energy efficiency over state-of-the-art FL baselines. It also exhibits strong robustness to network fragmentation and resource heterogeneity, making it well suited for practical edge environments.

With the further investigation and utilization of the extremely large-scale antenna array (ELAA) and Terahertz (THz) band, as well as the increasingly stringent standards of regulatory agencies, the near-field communications are evolving into the norm of intelligent transportation systems. In this work, we propose a novel beamfocusing scheme to improve the system performance for the reconfigurable intelligent surface (RIS)-aided hybrid near- and far-field communications with multi-unmanned aerial vehicle (UAV). Compared to conventional schemes without considering the near-field beam pattern with a finite depth, in our proposed scheme, the RIS only serves one UAV, while other UAVs are within the radiation range of the concentrated signal energy by beamfocusing. Specifically, we study the polar radius and angular deviations between UAVs with a given beamfocusing gain, aiming to reveal the impact of RIS structure on beamfocusing gain. We also make a fair comparison between the proposed scheme and allocated RIS scheme, deriving the feasible reference distance. In addition, we study the feasibility for different RIS structures focusing on the beamfocusing gain, which has the potential to improve the system performance. Simulation results demonstrate that the proposed scheme is always superior than that of the allocated RIS scheme within the feasible reference distance.

The increasing connectivity and automation in the Internet of vehicles (IoV) have significantly heightened the risk of network attacks, making intrusion detection systems (IDS) a crucial component of security measures in intelligent transportation systems (ITS). To address this challenge, we propose an advanced intrusion detection method integrating knowledge graph (KG) and artificial intelligence (AI) techniques, termed IDS-IKGAI, to enhance the security of IoV infrastructures. In our proposed scheme, we first preprocess an intrusion detection dataset specific to IoV, i.e., feature selection and extraction, that can be represented as triples using the resource description framework (RDF). These RDF triples are used to construct a knowledge graph, capturing the semantic relationships among the features. Next, we map the knowledge graph to a vector space, to build a labeled dataset for machine learning. To train and predict potential intrusions, the random forest (RF) and light gradient boosting machine learning (LightGBM) algorithms are investigated. Experimental evaluations demonstrate the effectiveness of our proposed scheme, with around F1 scores of 99.99% for RF and 99.93% for LightGBM, outperforming conventional benchmark models.

Like deep neural network (DNN)-based classifiers, DNN-based trackers are also vulnerable to adversarial attacks that degrade the tracking performance by adding adversarial perturbations to the input videos. This paper proposes a detection method for the first time to assist the tracker in detecting adversarial attacks. The adversarial perturbations in the visual object tracking task are invisible but are effective at attacking trackers. This naturally creates challenges in detecting attacks in the spatial pixel domain. To this end, we innovatively transfer the detection of adversarial attacks from the spatial domain to the frequency domain. Specifically, we first theoretically prove that the perturbations are added mainly to the high-frequency band of the video. Then, from the empirical studies, we conclude that the low-frequency band contributes most to the tracking performance and is most robust against adversarial attacks. According to the theoretical proof and empirical conclusion, we finally design an unsupervised adversarial detection framework, which mainly contains a frequency decomposition module (FDM), a target tracker (TT) with its mirror tracker (MT), and a discriminant module (DM). For an input video, the TT is fed the full-frequency video, whereas the MT takes as input the low-frequency video that is decomposed by the FDM. The DM discriminates the input video as adversarial or natural by comparing the racking performance differences between the two trackers. The whole detection process is performed along with the tracking phase, and all the modules in the framework require no training on adversarial examples. Extensive experiments demonstrate that our adversarial detection framework can effectively detect mainstream adversarial attacks in the tracking field. It can also be flexibly integrated with many trackers, including anchor-based and anchor-free trackers. More importantly, the trackers integrated with the detection framework can still maintain near-original tracking performance.

In the rapidly evolving field of wireless communications, the emergence of 5G and the progression towards 6G technologies highlight the demands for innovative frameworks capable of enhancing network performance under complex environmental factors. According to this trend, this work introduces a novel system model for aerial reconfigurable intelligent surface (ARIS)-assisted wireless communications, engineered to adeptly manage dynamic environmental influences such as fluctuating wind patterns and variable weather conditions. By treating these influences as random variables, we further introduce unpredictable variations in ARIS orientation (roll, yaw, and pitch), affecting network efficiency and reliability. To mitigate these environmental perturbations and uncertainties in the channel state information (CSI), we propose a robust beamforming strategy to reshape the original optimization problem into a form that is easier to analyze and solve, by employing stochastic optimization, successive convex approximation (SCA), and block coordinate descent (BCD) techniques to optimize active beamforming vectors and ARIS phase shifts. Comprehensive simulations rigorously evaluate the performance of our proposed algorithms across diverse conditions, including aerial disturbances, varying channel states, and different RIS element configurations. The results underscore the efficacy of our beamforming design in boosting the resilience and dependability of ARIS-enhanced wireless networks.

Cybertwin technology introduces a novel paradigm employing digital twins to model complex physical systems within a cyber environment, thus enhancing communication, collaboration, and decision-making capabilities. By harnessing advanced technologies, such as reconfigurable intelligent surfaces (RISs) and multiaccess edge computing (MEC), seamless interaction between physical and virtual entities is facilitated. In this article, we propose a cybertwin-driven edge computing framework that leverages RIS technology, complemented by an efficient computing offloading strategy to support large-scale Internet of Everything (IoE) applications. Specifically, the proposed strategy focuses on a multicell system where numerous randomly distributed end users have the option to offload delay-sensitive and computing-intensive tasks to edge computing nodes. The offloading channels are enhanced by RISs through passive beamforming, while cybertwin technology directs resource cooperation among multicells and allocates computing and communication resources. Our main objective is to optimize the system’s utility with respect to task completion latency and energy consumption reduction. To achieve this goal, we conduct the joint optimization of task offloading and resource allocation. Furthermore, we develop a joint task offloading and resource allocation (JTORA) algorithm to derive optimal solutions for passive beamforming design, computing offloading decisions, communication resource scheduling, and computing capacity allocation. The simulation results demonstrate the superiority of the proposed algorithm over benchmark schemes in terms of edge computing efficiency. Furthermore, the system utility can be further enhanced by increasing the number of embedded RIS elements.

This article investigates physical-layer security (PLS) for an energy-harvesting cognitive network with nonorthogonal multiple access (EHCN-NOMA), where a cognitive base station (CBS) communicates with two cognitive users named cognitive near user (CNU) and cognitive far user (CFU) by adopting the NOMA principle under an active or passive eavesdropper (Eve) attack. By means of energy harvesters, the CBS collects the radio frequency energy from the primary source (PS) by a time-switching protocol; meanwhile, the cognitive transmit power is limited by the interference threshold of primary destination (PD) to guarantee the Quality of Service (QoS) of primary transmissions. To evaluate the impact of active and/or passive eavesdroppings on system performance, we derive closed-form expressions in terms of outage probabilities (OPs), intercept probabilities (IPs), as well as effective secrecy throughputs (ESTs). The numerical results verify the correctness of our theoretical derivations and confirm that there exists a tradeoff between security and reliability for the EHCN-NOMA transmissions. Moreover, the EST improvement depends on the competition between security and reliability. Furthermore, the maximum EST performance can be achieved for both eavesdropping scenarios by adjusting the time allocation between that of the energy transfer phase and the information transmission phase.

This letter investigates secrecy performance for cognitive transmissions, where a secondary user (SU) shares same spectrum with a primary user (PU) simultaneously ensuring the Quality of Service (QoS) of primary transmissions. Additionally, an eavesdropper (Eve) overhears cognitive transmissions from SU to base station (BS). To against eavesdropping attacks, a novel artificial noise-aided rate splitting (ANRS) scheme is proposed, where PU emits artificial noise to confuse Eve and SU adopts rate splitting (RS). The numerical results of secrecy outage probability indicates that the ANRS scheme achieves better secrecy performance than that of AN without RS (ANWRS) and of RS without AN (RSWAN) schemes.

To fully exploit the potential of reconfigurable intelligent surface (RIS), the controllable channel state information (CSI) should be accurate for its future applications. Unfortunately, in vehicular communications, obtaining exact instantaneous CSI presents substantial challenges. Moreover, even with an instantaneous CSI acquisition, a processing latency for RIS phase shift adaption might occur before the vehicular system reacts to the instantaneous CSI information. To effectively introduce RIS into Internet of vehicle (IoV) networks, we employ a more realistic statistical CSI approach in designing RIS-assisted vehicular communication systems that are robust to the general characteristics of the channel, rather than its instantaneous fluctuations. We present a practical system framework, where a roadside unit employs an RIS to facilitate indirect wireless communications for vehicle-to-vehicle (V2V) communications. Particularly, the direct links between vehicles are susceptible to blockages caused by surrounding obstacles/vehicles. The deployment of RIS is to establish supplementary communication links between a multi-antenna vehicle source (VS) and multiple vehicular users (VUs) as they traverse areas with a poor service coverage. With the objective to maximize the time-averaged sum-rate of VUs, instead of instantaneous CSI, we rely on the delayed statistical CSI feedback to design active beamforming at the VS and passive beamforming at RIS. Moreover, we develop an efficient algorithm, named JAPBNB, which leverages the fractional programming technique to find a stationary solution for the formulated sum-of-logarithms-of-ratio problem. Specifically, a non-convex block coordinate descent (BCD) approach, collaborating with the alternating direction method of multipliers (ADMM), is applied for the joint optimization of active and passive beamforming. Finally, the complexity and convergence of the proposed JAPBNB algorithm are thoroughly discussed and validated. Simulation results demonstrate that the time-averaged sum-rate obtained by the proposed JAPBNB algorithm approaches that obtained by the instantaneous CSI scheme, when the delayed statistical CSI feedback interval is adequately small.

Due to the inherent broadcast characteristics of wireless communications, the data transmission is difficult to be shielded from unintended recipients. Secure communications over wireless channel is regarded as an effective way to overcome this issue in the design of wireless networks. In this paper, a new cooperative relaying system based on partial non-orthogonal multiple access (P-NOMA) is proposed, where two relays help the communications between the source and destination nodes in the presence of an eavesdropper (Eve). Specifically, after receiving the P-NOMA signals transmitted from the base station, the relay nodes decode and forward the receptions under the attack of a passive Eve. To improve the physical layer security (PLS) of the proposed system, we design four cooperative schemes for signals decoded at the relays and destination, where maximum ratio combination (MRC) technique is adopted to both destination and Eve to construe the worst case for achievable secrecy rate. Without lose of generality, the channel conditions (weak or strong), new power allocations, decoding principles and Eve locations are considered in designing forwarding schemes. In particular, to further improve the achievable secrecy rate, we also propose that, if the Eve locating near to the weak relay node, the weak relay only forwards the signal with lower power allocation factor at the base station. The closed-form expressions of the achievable secrecy rates are derived for the proposed schemes over Rayleigh fading and Nakagami-$m$fading channels, which well match the simulation results. By means of the numerical result, it corroborates the superiority of the proposed P-NOMA schemes over the conventional NOMA schemes. It is also revealed that, with an increasing overlap ratio, the advantage in terms of the secrecy rate becomes more remarkable.

Unmanned aerial vehicles (UAVs) are widely applied for service provisioning in many domains, such as topographic mapping and traffic monitoring. These applications are complicated with huge computational resources and extremely low-latency requirements. However, the moderate computational capability and limited energy restrict the local data processing for the UAV. Fortunately, this impediment may be mitigated by utilizing wireless power transfer (WPT) and employing the multiaccess edge computing (MEC) paradigm for offloading demanding computational tasks from the UAV via wireless communications. Particularly, the offloaded information may become compromising by the eavesdropper (Eve) when UAVs offload the computational tasks to MEC servers. To address this issue, a UAV-MEC (UMEC) system with energy harvesting (EH) is studied, where the full-duplex protocol is considered to realize simultaneously receiving confidential data from the UAV and broadcasting the control instructions. It is worth noting that in our proposed scheme, these control instructions also serve as the artificial interference to confuse the Eve. To improve the energy efficiency for offloading, the computational communication resource allocation is optimized to minimize the energy consumption for UAV with the consumed and harvested energy. Specially, the worst case secrecy offloading rate and computation-latency constraint are considered, to further enhance the reliability and security of the proposed system. Since the objective optimization problem is nonconvex, we convert it into a convex one by analytical means. The semiclosed form expressions of the offloading time, offloading data size, and transmit power are, respectively, derived. Moreover, the conditions of nonoffloading, partial, and full offloading are also discussed from a physical perspective. With the specific conditions of activating the above-mentioned three offloading options, numerical results verify the performance of our proposed offloading strategy in various scenarios and show the superiority of our offloading strategy with the existing works in terms of the offloading capacity and energy efficiency.

To overcome the inherent defect of centralized data processing in cloud computing, the mobile edge computing (MEC) brings data storage and computing capacities, to the edge closer to end users. However, the uneven distribution of access vehicles, as well the volume of computing data, cause the workload diversity among various mobile edge computing servers (MECSs). In this paper, we propose a hierarchical model with quality of service (QoS)-aware and power-aware resource management for the cooperative edge-computing-based intelligent vehicular network (CEC-IoV), and the system latency and energy efficiency at MECSs are respectively optimized. Specifically, considering the changing response times versus MECSs’ workloads, the Minimum Latency with Migration Loads (MLML) scheme is developed for workload balance among multiple MECSs. By selecting the appropriate response time threshold and migration loads from overloading MECSs to idle MECSs simultaneously, the load-balancing problem can be efficiently solved for multiple MECSs with unbalanced workloads. On the other hand, through performing workload redistribution and dynamic reconfiguration of virtual machines (VMs) instantiated onto the parallel computing platform at one MECS, the energy-efficiency can be also optimized while guaranteeing the QoS requirement on the processing delay. With the latency constraint, the power minimization problem is formulated to be a convex one, and the semi-closed forms for optimal solutions of VMs’ workloads and processing rates are provided using KKT conditions. Compared with the performance obtained by benchmark schemes, numerical results exhibit that our resource management schemes gain lower system latency and higher energy efficiency.

This paper considers a mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) offloads demanding computational tasks to the access point (AP), and AP cooperatively sends jamming noise to protect the confidentiality and secrecy of data transmission. In this system, the computation from UAV is portioned into two parts with one executed locally on UAV and the other offloaded to MEC for processing. To minimize the total energy-consumption of UAV for computing and offloading, we jointly optimize computation and communication resource allocation, subject to secure offloading rate and computation-latency constraints. Although the formulated problem is non-convex, we transform it into a convex problem by analytical means. Numerical results verify the superiority of our proposed scheme in terms of energy-consumption and quantify the performance of our proposed offloading scheme in various scenarios.

The massive connectivity and information exchange in the Internet of Things (IoT) pose great challenges to the bandwidth efficiency. To overcome this issue, this article investigates a cooperative full-duplex device-to-device system with nonorthogonal multiple access (NOMA) and partial decode-and-forward (P-DF). In the proposed system, the base station simultaneously transmits signals to all receiving nodes. Two kinds of scenes are considered depending on the locations of the users, where the cases with and without an eavesdropper are respectively studied. According to the P-DF protocol, three kinds of relay forwarding strategies are studied, where only the correctly decoded symbol will be forwarded to the user equipments to avoid performance loss. For the full-duplex relay, it is allowed to forward its own signal by superposing the partial correctly decoded symbol from the previous phase to other users, to further improve the spectral efficiency. Closed-form expressions of the ergodic sum rate, outage probability and secrecy rate are derived. Simulation results verify the analytical results especially in the high signal-to-noise ratio region, and show the superiority of our proposed P-DF-NOMA scheme in terms of outage probability performance.

Due to the great potential of the combination of machine learning technology and unmanned aerial vehicle (UAV) enabled wireless communications, various optimization algorithms on resource allocation have been proposed for the Internet of Things. UAVs not only can perform the missions under the extreme conditions but also enhance the overall performance of the system as an aerial relay assisting transmission in the public and civil domains, which have been received extensive attentions. However, with the limited capacity and power constraints, they are difficult to support the transmission for the big data information users. In addition, the lack of spectrum resource poses challenges to satisfy the quality of service (QoS) of mobile users in wireless networks. To contribute to these urgent problems, this article first studies the potential and effective applications of UAVs, by introducing the Chinese remainder theorem (CRT) and nonorthogonal multiple access (NOMA) technologies into UAV relay networks. Two scenarios with/without direct transmissions between the source and destination nodes are investigated, following the decomposition and reconstruction mechanisms to satisfy the big data information transmission. Considering the user fairness, we further discuss the effect of the UAV numbers to the overall system capacity. To maximize the system capacity, the designs of transmission protocol and receiver are also discussed, in various channel conditions. Finally, a low complexity and efficient two‐stage power allocation scheme is established for the perspective of users and UAV relays.

The high‐speed dynamic environment and massive information transmitted via wireless communications in the vehicular ad hoc networks (VANETs) pose a great challenge to privacy and security. To overcome this issue, use of the content‐centric networking (CCN) provides a potential and practical solution. In‐network caching is a main feature for future smart cities, in which the content is mainly placed in network nodes. Therefore, how to effectively select the cache locality and cache content is essential to improve the overall network performance, which is an inevitable trend. With these observations, this article proposes a caching strategy based on the node value and content popularity (NVCP) for the massive VANET scenario. In the proposed NVCP scheme, different from the traditional caching strategies, we evaluate the node value from three aspects: the connectivity, intermediary, and eigenvector centralities, synthetically, since the content with different types of popularity is placed in nodes with different values, resulting in the redundancy deterioration and diversity improvement for the content. The proposed caching strategy is evaluated by the stochastic network topology with multifactors, which provides different impacts on the system performance. Simulation results show that the NVCP outperforms the traditional cache strategies for 6G‐CCN in terms of the cache hit ratio, average hop count, and transmission latency. Moreover, placing the content in the neighbor nodes is also introduced to further improve the utilization of the cache space and achieve better cache performance.

In this paper, we consider an energy-constrained unmanned aerial vehicle (UAV)-enabled mobile relay assisted secure communication system in the presence of a legitimate source-destination pair and multiple eavesdroppers with imperfect locations. The energy-constrained UAV employs the power splitting (PS) scheme to simultaneously receive information and harvest energy from the source, and then exploits the time switching (TS) protocol to perform information relaying. Furthermore, we consider a full-duplex destination node which can simultaneously receive confidential signals from the UAV and cooperatively transmit artificial noise (AN) signals to confuse malicious eavesdroppers. To further enhance the reliability and security of this system, we formulate a worst case secrecy rate maximization problem, which jointly optimizes the position of the UAV, the AN transmit power, as well as the PS and TS ratios. The formulated problem is non-convex and generally intractable. In order to circumvent the non-convexity, we decouple the original optimization problem into three subproblems; this facilitates the design of a suboptimal iterative algorithm. In each iteration, we propose a multi-dimensional search and numerical method to handle the subproblem. Numerical simulation results are provided to demonstrate the effectiveness and superior performance of the proposed joint design versus the conventional schemes in the literature.

In this paper, we study a joint energy harvesting (EH) and task offloading (TO) design for an energy- constrained fog computing network, which consists of a mobile terminal and two fog nodes. The energy- constrained terminal employs time switching (TS) protocol to harvest energy from the signals sent by the circuit-powered fog node, and then exploits the harvested energy to perform local computing and offload computing. Our aim is to minimize the product of energy consumption and time delay with the constraints of TS ratio and EH requirements. To determine the optimal solution of the original non-convex problem, we decouple it into three subproblems based on the time delay assumption and then solve these subproblems through our proposed constraints activation algorithm. Furthermore, we also derive the closed form expressions for the optimal TO and TS ratios. Simulation results are presented to illustrate the effectiveness and superior performance of the proposed joint design against other conventional schemes in the literature.

A genetic algorithm (GA) based QoS perception routing protocol (GABR) is proposed to guarantee the quality of service (QoS) influenced by broken links between vehicles and the failure of packets transmission in a vehicular ad hoc network (VANET). With the observation that all improvable paths are probed by the intersection based routing protocol, the genetic GA is utilized to optimize the global available paths which satisfies the QoS requirement. Moreover, by means of the numerical results, it is shown that the proposed scheme is significantly improved compared with protocols of the intersection based routing (IBR) and connectivity aware routing (CAR) in terms of transmission delay and packet loss rate.

This paper presents a novel time division multiple access-medium access control (TDMA-MAC) protocol for vehicular ad hoc networks (VANETs), in which both cooperative and opportunistic transmissions are employed for enhanced communications. When vehicle density is low, the idle time slots of a licensed VANET channel are used for cooperative transmission through a relay. When the vehicle density is high, cognitive radio technique is applied to seek additional time slots available on the cognitive channels for opportunistic transmission. Simulation results show that the proposed TDMA-MAC protocol can reduce latency and packet loss rate significantly when compared with the existing protocols.

In this paper, the approach to produce the complex wavelet packet basis function is given. A complex wavelet packet basis function based frequency-hopping multiple access communication system, which employs antenna diversity and decision feedback equalization, is proposed. The simulation results show that the new multiple access system outperforms the analogy system based on real wavelet packet basis function. In addition, it has a high spectrum efficiency, high security and good narrow band interference rejection capability. Furthermore, the developed system is also easy to support multirate voice and data services.