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.

This paper studies movable pinching-antenna (PA) assisted communications, where a single PA can be dynamically placed along a dielectric waveguide to serve multiple users via orthogonal or non-orthogonal multiple access (OMA/NOMA). In contrast to the ideal-lossless waveguide model commonly assumed, our work explicitly incorporates waveguide-power loss, thereby introducing a fundamental trade-off with free-space path loss that jointly determines system performance. By deriving the lower-bound-based suboptimal PA position for two- and three-user scenarios under both with and without waveguide-power loss, we establish a general positioning guideline applicable to multi-user scenarios. Furthermore, we perform a comparative analysis between the proposed single-PA scheme and a static multi-PA scheme. Analytical and simulation results derive the conditions determining which scheme is superior, revealing that the trade-off is fundamentally governed by the relative dominance between the waveguide-power loss and the free-space path loss. Simulation results validating our analytical derivations also confirm that the proposed PA positioning rule in NOMA-based PASS holds for both successive interference cancellation (SIC) decoding orders, demonstrating its robustness regardless of whether the near user decodes the far user’s signal first or vice versa.

The reconfigurable intelligent surface (RIS) has been widely studied in far-field communications (FFC), and further extended to near-field communications (NFC). With the distinct electromagnetic properties in FFC and NFC, it remains debatable whether the RIS deployment strategies established for FFC are still applicable to NFC. To bridge this gap, we examine RIS-aided cooperative relaying in NFC through two representative configurations, single-RIS and multi-RIS relaying, in which a decode-and-forward relay forwards data from the source to the user with the assistance of RIS For the single RIS deployment, we show that the achievable rates with RIS deployed near the base station (BS) or near relay are completely different. It is revealed that, with a single-antenna relay, the multi-RIS is significantly preferable over the single RIS in NFC without the requirement of massive RIS reflection elements, which is completely different from that of FFC. Furthermore, we derive closed-form expressions for the achievable rates under both single-antenna and multi-antenna relay configurations to explicitly determine the distance threshold. The presented analytical results demonstrate that locating RIS near the relay yields substantial performance gains when the transmission distance exceeds a certain threshold. This conclusion is validated by numerical simulations, which systematically illustrates the distinct impact of RIS deployment strategies in near-field versus far-field regions.

In this letter, we investigate the wireless communication system assisted by hybrid intelligent reflecting surface (RIS), which is composed of both passive and active elements integrated reflection-type amplifiers. Different from conventional hybrid RIS system, the amplifying power used at active RIS is considered as one part of the total power for overall system. Under this condition, we present the exact closed-form expression of the outage probability (OP) and investigate the optimal allocations of the RIS elements and power budget for signal-to-interference-plus-noise ratio (SINR) maximization. In addition, we analyze the amplification factor and corresponding dynamic noise, as well as derive the threshold of the amplification factor for the proposed scheme. Finally, simulation and numerical results show that the proposed hybrid RIS scheme is significantly preferable over the conventional RIS scheme if the amplification factor of the proposed scheme is smaller than that of the threshold.

With the flourishing development of sixth-generation wireless networks, the demand of spectrum efficiency rapidly increases, in order to support the demands of high-quality data transmission and connections of massive users. Among various promising technologies, the technology of near-field communications provides a great potential to address such an issue due to the unique spherical-wave channels for electromagnetic (EM) propagation. In this article, multiple-input-single-output (MISO) near-field communications is comprehensively studied to clarify the influences of the shape and structure of uniform planar array (UPA) on the system performance, considering three cases with uniform linear array (ULA), rectangular UPA, and circular UPA. In particular, we reveal the properties of rapid deterioration for signal-to-noise ratio (SNR) from the reduced projection aperture in near-field communications and investigate the single spherical crown antenna design and spherical crown antenna array design in order to address this issue. Moreover, we also characterize the role of antenna projection aperture in detail, and theoretically analyze the shape of UPA, yielding the corresponding exact closed-form expressions for SNR and outage probability (OP). Based on these above analytical results, we find out that adjusting the spacing between adjacent antennas to control the relative angle between user and selected antennas is an efficient way to improve the projection aperture of antenna and SNR. Simulation results are shown to well match analytical results, which validate the correctness of our analysis, clarifying that our proposed designs outperform the conventional works and illustrating a better stability for angle variations.

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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.

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.

For an uncrewed aerial vehicle (UAV)-aided energy-harvesting non-orthogonal multiple access (EH-NOMA) network, where an untrusted far user (UFU) overhears the confidential transmissions from base station (BS) to the trusted near user (TNU), how to ensure the security of TNU and achieve the reliability of UFU becomes an open issue. The traditional artificial noise (TAN) scheme just guarantees the security of TNU without focusing on the reliability of UFU, while the non-artificial noise (NAN) scheme only ensures the reliability of UFU but sacrifices the security of TNU. Thus, the TAN and NAN schemes are unable to guarantee the fairness between TNU and UFU. To this end, we employ digital network coding to encrypt confidential signals with artificial noise signals, and the proposed artificial noise (PAN) scheme not only protects the confidential transmissions of TNU but also guarantees the reliability of UFU. We use security-reliability tradeoff (SRT) and interference-reliability tradeoff (IRT) to evaluate the performance of TNU and UFU, respectively. Additionally, we derive the exact and asymptotic expressions of outage probabilities for both TNU and UFU, and the intercept probability for TNU. Numerical results verify the accuracy of our theoretical results and demonstrate that: 1) the SRT and IRT performance of the PAN scheme is better than that of the TAN and NAN schemes; 2) the PAN scheme achieves better secrecy for TNU compared to the TAN scheme. Meanwhile, the reliability of UFU in the PAN scheme is almost the same as the NAN scheme, indicating the PAN scheme simultaneously realizes the security of TNU and the reliability of UFU and the fairness of NOMA users is achieved by the PAN scheme.

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.

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.

As a typical usage scenario for the next-generation mobile communication network, massive Internet of Things (mIoT) is requested to provide high machine-type communication device (MTCD) density service. Nonorthogonal multiple access (NOMA) and mobile-edge computing (MEC) can further enhance the performance of mIoT. Furthermore, to cope with the energy consumption constraint of MTCD, energy harvesting (EH) can be leveraged. In this article, considering the social trusts of MTCDs, we propose an MEC offloading scheme for cellular Internet of Things networks with massive NOMA-aided EH MTCD and several road side units with edge servers randomly distributed in a macrocell. We aim to maximize the total sum rate of the network by jointly considering the processing mode selection, device clustering, subchannel allocation, and power allocation while satisfying the power, energy, latency, and quality of service requirements. To this end, we prove the NP-hardness of the considered optimization problem and decompose it into three subproblems, which can be solved by an iterative algorithm. Numerical results demonstrate the superior performance of the proposed scheme.

Molecular communication (MC) is a bio-inspired communication paradigm, which lays the foundation for the Internet of Bio-NanoThings (IoBNT) in the medical field. As a high energy-efficient information transfer method, MC via diffusion (MCvD) is envisioned as a promising candidate for IoBNT but suffers from low date rates due to the long tail of the channel impulse response (CIR). To this end, the multiple-input–multiple-output (MIMO) technique has been introduced to MCvD. However, the intersymbol interference (ISI) and interlink interference (ILI) deteriorate the bit error rate (BER) performance of MIMO MCvD systems. In this article, molecular type permutation shift keying in the space domain (MTPSK-SD) and time-interleaved MTPSK-SD are proposed for MIMO MCvD systems to improve the BER performance by reducing ILI. The principle of MTPSK-SD is further generalized to the spatiotemporal domain, yielding three spatiotemporal modulation schemes, which can provide desirable BER performance without requiring any CIR information in the communication scenarios affected by different levels of ISI and ILI. Two low-complexity detectors are proposed to obtain different tradeoffs between anti-ILI and anti-ISI performance. Furthermore, a complementary coding scheme, which can effectively reduce the ILI under the considered symmetrical system topology, is designed and applied to all the proposed modulation schemes. Additionally, the BER upper bound is analyzed. Numerical simulations on BER corroborate the analysis and show that the proposed schemes are promising multimolecule modulation alternatives, which outperform the existing MIMO MCvD modulation schemes.

This paper considers a cooperative device-to-device (D2D) system with non-orthogonal multiple access (NOMA). We assume that the base station (BS) can simultaneously communicate with all users to satisfy the full information transmission requirement. In order to characterize the impact of the weak channel and different decoding schemes, two novel decoding strategies are introduced: single signal decoding scheme and maximum ratio combining (MRC) decoding scheme, respectively. With the single signal decoding scheme, the users decode the received signals immediately after the receptions from the BS. On the other hand, the MRC decoding scheme jointly decodes the received signals via MRC until the corresponding phase comes and the users jointly decode the received signals by employing MRC. Considering Rayleigh fading channels, the ergodic sum-rate (SR), outage probability and outage capacity of the proposed D2D-NOMA system are analyzed. Moreover, approximate expressions for the ergodic SR are also provided with a negligible performance loss. Numerical results demonstrate that the ergodic SR and outage probability of the proposed D2D-NOMA scheme overwhelm that of the conventional NOMA schemes. Furthermore, it is also revealed that the system performance including the ergodic SR and outage probability are limited by the weak channel for both the single signal decoding scheme and conventional NOMA schemes, but not for the MRC decoding scheme.

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.

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.