Multi-vehicle collaborative sensing has emerged as a new paradigm to boost the safety of autonomous vehicles. The cornerstone of this vision is the real-time and accurate registration of mmWave radar point clouds among multiple vehicles. To accomplish this, we designmmReg, an innovative system capable of achieving centimeter-level and real-time sensing fusion between vehicles.mmRegconsists of three major components: (i) aSAR imaging-driven point cloud generationcomponent leverages SAR imaging to image sparse and disordered radar point clouds to generate high-quality point clouds; (ii) amotion-aware frame synchronizationcomponent can achieve the spatio-temporal alignment of point clouds between vehicles for effectively mitigating the impact of asynchronous radar frames; (iii) ashared object-based registrationcomponent can capture and understand the unique global position of shared objects, supporting real-time and accurate registration. We implement and evaluatemmRegon CARLA and real-world campus datasets. The results demonstrate thatmmRegcan improve the vehicle’s sensing range by 117% in an average of 99.91 ms, achieving a 4.82x improvement in accuracy.

Video transmission, as one of the indispensable core services in the Internet of Vehicles, is confronted with numerous challenges such as the dynamics in the Internet of Vehicles environment, the limited resources, and the demand of vehicles for high-quality user Experience (Quality of Experience, QoE). In real-time video streaming scenarios, multicast optimization strategies, including convex optimization, game theory, stochastic optimization, etc., usually only group based on channel quality without considering task types and priorities, which may lead to the mixture of urgent tasks and ordinary tasks, affecting QoE. Therefore, we propose a task-aware three-stage collaborative optimization framework (TARA-IoV). The objective is to prioritize critical mission video services under limited bandwidth while maximizing the overall QoE. The framework first performs dynamic and adaptive vehicle grouping based on multi-dimensional features including task type, priority, geographical location, and channel state. Second, it conducts QoE-driven video quality layer selection leveraging Scalable Video Coding (SVC). Finally, it employs a deep reinforcement learning agent to dynamically allocate bandwidth with explicit task priority awareness under resource constraints. Evaluations on a real-world vehicle trajectory dataset demonstrate that TARA-IoV achieves improved QoE performance and more stable video delivery compared with existing schemes.

Digital twin (DT) migration technology is pivotal for ensuring seamless synchronization between physical entities and their virtual counterparts. However, in complex urban environments, high device mobility, dynamic network topology, and uncertain wireless channel conditions often lead to suboptimal migration decisions. Such inefficiencies exacerbate resource contention and diminish migration timeliness, thereby increasing DT state deviation and compromising service quality. To address these challenges, this paper proposes an AP group-enhanced digital twin edge network model, where multi-antenna access points (APs) are integrated as relay nodes to optimize channel quality and transmission reliability. Building on this, a Group-Collaborative Multi-Agent Proximal Policy Optimization (GC-MAPPO) migration strategy is presented. The strategy formulates the migration problem as a partially observable Markov decision process (POMDP). Specifically, the K-means++ clustering algorithm is first employed to construct optimal AP collaborative groups for mobile devices; subsequently, the MAPPO algorithm is utilized to derive optimal migration policies in dynamic environments. Experimental results demonstrate that, compared to existing baselines, the proposed GC-MAPPO scheme reduces the average synchronization delay by 6.69% to 37.05% and decreases DT state deviation by 29.24% to 82.91%.

3D object detection is a crucial task for autonomous vehicles to perceive traffic environments. Existing methods typically employ tightly coupled feature fusion strategies with fixed sensor combinations, but it fails to adequately capture the modality-specific characteristics, resulting in suboptimal object detection performance. To this end, we propose Chameleon, a novel multi-sensor decoupling system capable of selecting the appropriate sensor combination for object detection across diverse traffic conditions, which consists of two key components: (i) anuncertainty-aware contribution calculationcomponent leverages the uncertainty perceived by sensors to predict the parameters of traffic factors, followed by evaluating their importance; (ii) amutual information-enhanced sensor combination optimizationcomponent leverages mutual information calculations to enhance the mixture of experts and improve the reliability of sensor combination predictions, followed by achieving adaptive multi-sensor decoupling to ensure accurate 3D object detection while reducing inference latency. We implement and evaluate Chameleon using the nuScenes and nuScenes-C datasets. The experimental results show that Chameleon achieves average improvements of 1.10% in the mAP and 0.61% in the NDS compared to the state-of-the-art method across various traffic scenes.

Millimeter wave radar has recently emerged as a promising modality for enabling pervasive gesture recognition while protecting user privacy. However, personalized user behaviors, interference from unexpected actions, and long-term variability in user gestures significantly degrade the accuracy of gesture and user identity recognition, thereby compromising the quality of user experiences. To this end, we designmmGeswith four key modules: (i) afine-grained feature extractorextracts micro-level features from the time-series radar data to identify users' personalized behaviors; (ii) auser-specific feature classifierextracts coarse-grained features from a global perspective, followed by analyzing the micro-details of gesture features to recognize the user; (iii) avoting-based multi-user recognizerretrieves all pre-trained models from the user model database, followed by obtaining the probability indicators of each to return recognition results; (iv) alifelong learning modelretains previous knowledge while adjusting its feature selection capabilities using newly collected data to adapt to gesture changes. We implement and evaluatemmGesusing three self-collected real-world radar datasets, demonstrating its superior performance compared to other state-of-the-art gesture recognition methods.

Infrastructure-assisted autonomous driving has become a new paradigm that enables autonomous vehicles to fuse sensor data and improve driving safety, where a key enabling technology for achieving this vision is to real-time and accurate registering 3-D mmWave radar point clouds between the infrastructure and the vehicle. To this end, we proposeArtemis, a novel lightweight system capable of achieving real-time registration with decimeter-level localization.Artemisconsists of three components: 1) a modal association-based salient object extraction component leverages the complementary advantages of cameras and radars to extract semantics and areas of salient objects for radar point clouds; 2) a salient object shape construction component extracts the shape contour of salient objects based on their inherent geometries; and 3) a contour-guided 3-D point cloud registration component combines two key strategies, keypoint matching strategy and early exit strategy, to quickly select keypoints and transformation directions for achieving accurate registration in real-time. We implement and evaluateArtemiswith two multiview datasets collected in the CARLA platform and campus. The experiment results show thatArtemisachieves an average registration error of 0.33 m within 32.26 ms.

With the wide application of emerging technologies such as ultra-high definition video in Vehicular Edge Computing (VEC), the massive heterogeneous video data generated by vehicles have put forward higher requirements for real-time performance, energy efficiency and accuracy of processing. However, higher video analysis accuracy often leads to an increase in delay and energy consumption. How to balance the relationship between the three is an urgent problem to be solved. Meanwhile, the balanced or fixed bandwidth allocation mechanism adopted by most studies often ignores the characteristic differences of video tasks, resulting in inefficient resource allocation. At the same time, the security risks in the Internet of vehicles cannot be ignored. In order to deal with these challenges, this paper proposed a distributed task offloading framework combining Analytic Hierarchy Process (AHP) and Deep Deterministic Policy Gradient (DDPG). An adaptive bandwidth allocation mechanism based on the characteristics of video tasks is designed, and an improved blockchain consensus mechanism is introduced to ensure the optimal offloading decision in a trusted environment. Experimental results show that compared with the existing offloading schemes, the proposed algorithm reduces the task offloading delay by about 7.54%, reduces the energy consumption by about 6.37%, and improves the accuracy of video analysis by about 5.02% while ensuring security.

Multi-modal data-driven object detection typically depends on large-scale datasets. However, under adverse weather conditions, these datasets often exhibit a long-tail distribution, resulting in a substantial decline in detection performance and posing risks to system safety in applications such as autonomous driving. To this end, we propose Cerberus, a real-time and high-accuracy object detection system, which consists of two key components: (i) asimilarity-driven proposal extractioncomponent, which utilizes pre-trained detectors and convolutional neural networks to enhance low-quality features and suppress noise across image, LiDAR, and radar data, followed by aiming to extract and refine multi-modal features, thereby improving the quality of object proposals; (ii) anadaptive weight-based box enhancementcomponent, which integrates a frozen-weight regionbased convolutional network sub-network with a sensor fusion mechanism, followed by refining bounding boxes by normalizing confidence vectors and dynamically weighting multi-modal features, ensuring accurate detection under adverse weather conditions. Experimental results demonstrate that Cerberus surpasses existing baselines, yielding performance gains of 2.7% in the mean average precision and 2.0% in the nuScenes detection score under adverse weather conditions.

Federated learning (FL) has been extensively utilized in distributed learning scenarios for the Internet of Vehicles (IoV). However, two key challenges exist: 1) gradients are frequently transmitted between vehicles during FL training, which reduces the communication timeliness between the traffic participants and 2) the fixed gradient compression method cannot sufficiently adapt to the dynamic IoV network topology. Therefore, we design an adaptive vehicle clustering method constructed according to multiple attributes, such as computational resources, communication distance, and latency. Accordingly, we propose a dynamic gradient compression strategy that filters similar local training models between vehicles and uses the Wasserstein distance to compute a sparsity threshold. This threshold acts as a dynamic compression factor that compresses gradient model parameters, reducing redundant parameter transmission. Furthermore, we conduct experiments using two datasets to evaluate the proposed strategy’s effectiveness. The compression ratio improved by 132- and 178-fold compared to the baselines, and the aggregated accuracy increased by an average of 10.13%. Additionally, experiments incorporating communication noise revealed that the aggregation model of the signal noise ratio is -29 dB.

In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) as well as cameras of the vehicle to support the large-capacity and reliable transmission of high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communications links among RSU and different vehicles are blocked or connected. We firstly utilize the 3D detection technique to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method, and utilize the joint statistical distribution of the residual transmission distance and the residual multi-hop latency to optimize the total transmission latency. Simulation results show that the proposed vision based link state identification method significantly outperforms the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

Leveraging cameras and LiDARs has been proved as an effective way to achieve beam management without training overhead for millimeter wave (mmWave) communications. However, existing methods place sensors at base station (BS) and/or mobile station (MS), which may induce privacy concerns and augment communications system expenses. In this paper, we propose a novel third-party camera aided mmWave beam management framework and self-build an RFSoC prototype for validation. Specifically, we design third-party camera aided beam alignment and blockage prediction algorithms that could work with random third-party perspective. Then, we leverage the proposed beam alignment to design initial access and beam recovery, and utilize the proposed blockage prediction to realize failure prediction. Hand-off from mmWave to sub-6G communications is conducted once the blockage is predicted. To validate the proposed framework, we develop an RFSoC prototype from scratch independently. The real-world real-time experimental results show that the beam alignment achieves over 98% in top-5 accuracy and reduces the time consumption to below$1/50$of that incurred by exhaustive beam sweeping. Meanwhile, the prototype maintains 410 MHz dynamic mmWave communications, and can seamlessly switch to sub-6G communications before a mmWave blockage happens.

In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) to support the large-capacity and reliable transmission of the high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communication links between RSU and different vehicles are blocked or connected. The 3D detection technique is firstly used to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method. The joint statistical distribution of the residual transmission distance and the residual multi-hop latency is used to optimize the total multi-hop latency. Simulation results show that the proposed vision based link state identification method can significantly outperform the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

In this paper, we investigate the covertness and secrecy guarantees of wireless communications in an active attacker scenario where attackers perform detection/eavesdropping and jamming simultaneously. Both detection and eavesdropping attacks need to be counteracted, such that the covertness and secrecy guarantees in wireless communications can be achieved. To understand the covertness and secrecy performances, we provide theoretical modeling for covertness outage probability and secrecy outage probability, respectively. Based on the the-oretical model, we conduct theoretical analysis to identify the covert secrecy rate (CSR) under power control (PC)-based secure transmission scheme. Extensive numerical results are provided to illustrate the achievable performances and also reveal the impact of the active attackers on the CSR.

Leveraging various sensors, i.e., cameras and Li-DARs, is deemed as a promising way to realize fast millimeter wave (mmWave) beam alignment with no frequency overhead. However, previous methods that place extra sensors at base station (BS) and mobile station (MS) may augment communication system expenses and also induce privacy concerns. In this paper, we propose a novel beam alignment framework that utilizes images taken by camera placed at third-party perspective. We design a deep neural network that extracts and fuses the position and orientation of mobile user in the third-party images to infer the optimal beam pair, and we establish real-world datasets to train and evaluate the proposed network. Specifically, we design a mmWave light-weight beam sweeping system to reduce time expenses and system complexity during dataset collection. To calculate the time consumption of the third-party camera aided beam alignment framework, we independently implement a mmWave communication prototype with 410MHz bandwidth OFDM baseband according to IEEE 802.11 protocol on Xilinx RFSoC. The experimental results show the third-party camera aided beam alignment approach achieves over 98% in top-5 accuracy, and reduces the mmWave beam alignment time consumption to below 1/50 of that incurred by exhaustive beam sweeping.

Query exchange in the Social Internet of Vehicles (SIoV) can protect users’ trajectory information. However, this method lacks an appropriate incentive mechanism, which leads to cooperative users refusing to participate in query exchange. In order to provide cooperative users with incentives to participate in query exchange, this paper proposes a smart contract-based query exchange (SC-QE) trajectory privacy protection method. By creating a many-to-many smart contract, the method encourages the cooperative users to bid to the requesting users. Subsequently, in order to select a Best Similarity Deviation User (BSDU) for the requesting user to perform query exchange, the users in the smart contract are modeled as a weighted bipartite graph, and the matching between the requesting users and BSDUs is realized by means of a weighted bipartite graph best matching algorithm. Following successful verification of the query exchange transaction in the smart contract, the base station distributes rewards to the BSDU and uploads the query exchange transaction to the consortium blockchain. Experimental results show that compared with the deviation-based query exchange (DQE) method, the proposed method reduces the user processing time by 12% while increasing the continuous anonymous success rate by 29%. Therefore, the proposed method can reduce the service query time and improve the level of trajectory privacy protection.

With the emergence of a large number of computing resource-intensive applications and a variety of content delivery services, data in Internet of Vehicles (IoV) is exploding. In order to improve the service performance of IoV, Vehicle Edge Computing (VEC) accelerates the response process of content requests and reduces the backhaul burden of the base station by caching content at the nodes of the edge network. However, the existing caching strategies are usually affected by high computing and communication overhead, and can not well capture the dynamic changes and content popularity of the vehicle network. In order to solve these problems, we design a novel Cooperative Caching scheme by using Mobility Prediction and Consistent Hash for Federated Learning (called CMCF), which integrates mobility prediction and consistent hashing into the content caching scheme, and uses the federated learning framework to optimize the cached content, then we use deep reinforcement learning algorithm to develop the optimal cooperative caching policy to reduce the average delay of content transmission. Extensive simulation results prove the superiority of our method. Compared with other advanced caching schemes, CMCF can increase the cache hit rate by 8.7% and reduce the average content delivery delay by 17.8%.

With the rapid advancement of in-vehicle communication technology, vehicular edge caching has garnered considerable attention as a pivotal technology to improve the efficiency of data transmission. However, existing studies often overlook the issues of increased average content access latency and decreased caching hit rate, stemming from the conflict between limited storage space in in-vehicle edge servers and vehicle mobility. To address these issues, this paper proposes a Multi-agent Federated Deep Reinforcement Learning based Collaborative Caching Strategy (MFDRL-CCS), leveraging Vehicle-to-Vehicle (V2V) communications. Specifically, we first perform vehicle connectivity prediction based on Recurrent Neural Network (RNN) considering the characteristics of vehicle nodes and their interrelations. Then, the optimal caching vehicle is selected based on the connectivity between vehicle nodes and the density of vehicle nodes. Meanwhile, a Multi-Head Attention Popularity Prediction (MHAPP) model is also constructed, which amalgamates multi-dimensional features, including historical popularity, social relationships, and geographic location, to predict content popularity. Finally, the edge collaborative caching model is formulated as a Markov Decision Process (MDP). Under the multi-agent competitive deep Q-learning framework, each vehicle learns the optimal caching strategy through an independent Q-network to maximize long-term rewards, and uses federated learning to train the caching replacement algorithm in a distributed manner. Compared to existing caching policies, the caching policy proposed in this paper improves the caching hit rate by approximately 19.8% and reduces the content access latency by about 12.5%.

User alignment refers to linking a user's accounts across multiple social networks, which is important for studying community discovery, recommendation systems, and other related fields. However, existing methods primarily perform user alignment by correlating user features, neglecting the causal relationship between network topology and user alignment, which makes it challenging to achieve superior user alignment accuracy and generalization capabilities. Therefore, we propose a counterfactual inference-based social network user-alignment algorithm (CINUA). This improves user connection retention due to the non-Euclidean geometric characterization of hyperbolic spaces. The similarity of aligned users is augmented using a hyperbolic graph attention network. User-feature embedding and fusion facilitate user relevance mining. Furthermore, there are causal relationships between network topology structure and user linkages. In various communities, there are some highly similar user pairs, and based on counterfactual inference, the network topology is adjusted to enhance sample diversity. Multilevel factual and counterfactual networks are constructed through iterative diffusion based on user alignment and their linkages. By integrating the users’ causal features in multiple networks, the accuracy and generalization capabilities of the user alignment model are effectively improved. In this article, the experimental results indicate that CINUA achieves a user alignment accuracy improvement of 5.98% and 3.03%, on two datasets respectively compared to the baseline methods on average. CINUA can achieve favorable alignment results even when the training dataset is small. This demonstrates that our algorithm can ensure both user alignment accuracy and generalization capability.

Aiming to solve the problem of interruption of normal communication service caused by the damage of ground communication facilities after disaster, an Air-Ground Integrated Mobile Edge Network (AWMEN) offloading model was established under the constraints of communication security, energy consumption and coverage. The traditional method needs to be re-iterated every time the preset environmental state changes, which will waste a lot of communication resources and computing resources, greatly reduce the efficiency, and face the risk of data privacy disclosure. However, the deep reinforcement learning method under the federated learning framework will be more flexible and applicable to dynamic scenarios. A Markov decision process model is constructed based on the unmanned aerial vehicles (UAV) and the environment. The experience trajectory is designed by interacting with the external environment, and the optimal offloading strategy is obtained. The Twin Delayed Deep Deterministic Policy Gradient of behavior cloning (TD3-BC-R) is compared with baseline method (0-1 mode), Actor-Critic (AC-R), Deep Deterministic Policy Gradient (DDPG-R) and Twin Delayed Deep Deterministic Policy Gradient (TD3-R), the experiment shows that, The total time cost of TD3-BC-R is reduced by more than 1/3, and low latency transmission is also achieved.

As the industry related to intelligent vehicles becomes increasingly mature, emerging in-vehicle applications and services are mushrooming. They have strict requirements on service response delay and network bandwidth. In the face of dynamic environment in the Internet of Vehicles (IoV), how to design an adaptive edge caching strategy becomes a challenge. To cope with this challenge, some researchers have introduced optimization methods based on learning algorithms in cooperative caching. However, general learning algorithms tend to waste bandwidth and computing resources on repetitive task. To this end, we make full use of the idle resources in the road to build a cooperative caching system and propose a Social-Aware Decentralized Cooperative caching (SADC) for IoV. This strategy uses the federated learning framework to train the collaborative caching algorithm based on Deep Reinforcement Learning (DRL). Among them, the Road Side Unit (RSU) is responsible for the training and updating of the global model, and vehicles use local data to provide local updates to the RSU, which then averages the updates provided by all vehicles to improve the shared model. In addition, we use the social network of vehicle users to obtain vehicle contact rates in different areas. The SADC strategy can reduce the content transmission latency and response time, thereby improving the experience quality of vehicle users. Compared with traditional caching strategies, this strategy reduces the average content access delay by about 20%. We also demonstrate the effectiveness of this strategy using an extensive set of experiments.

The emerging backscatter communication technology is recognized as a promising solution to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor network with backscatter communication technology can monitor the environment in remote areas without battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To tackle this challenge, we propose a multi-UAV-aided data collection scenario where the unmanned aerial vehicle (UAV) can fly close to the backscatter sensor node (BSN) to activate it and then collects the data. We aim to minimize the total flight time of the rechargeable UAVs when the collection mission is finished. During the data collection process, the UAVs can return to the charging station to recharge itself when the energy of UAV is not sufficient to complete the mission. To reduce the complexity of the task, we first use the Gaussian mixture model clustering method to divide the BSNs into multiple clusters. Then we consider the deterministic boundary and ambiguous boundary for the UAV flying regions, respectively. For the deterministic boundary scenario, we propose a single-agent deep option learning (SADOL) algorithm, where each UAV cannot fly beyond the deterministic boundary. For the ambiguous boundary scenario, we propose a multiagent deep option learning (MADOL) algorithm to enable the UAVs to cooperatively learn the ambiguous BSNs assignment. In the simulation, we compare the proposed algorithms with multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) algorithms, which proves the proposed algorithms can achieve better performance.

Named Data Networking (NDN) can effectively deal with the rapid development of mobile video services. For NDN, selecting a suitable forwarding interface according to the current network status can improve the efficiency of mobile video communication and can also avoid attacks to improve communication security. For this reason, we propose a stochastic adaptive forwarding strategy based on deep reinforcement learning (SAF-DRL) for secure mobile video communications in NDN. For each available forwarding interface, we introduce the twin delayed deep deterministic policy gradient algorithm to obtain a more robust forwarding strategy. Moreover, we conduct various numerical experiments to validate the performance of SAF-DRL. Compared with BR, RFA, SAF, and AFSndn forwarding strategies, the results show that SAF-DRL can reduce the delivery time and the average number of lost packets to improve the performance of NDN.

With the rapid development of the Internet of Things (IoT) in 4G/5G deployments, the massive amount of network data generated by users has exploded, which has not only brought a revolution to human’s living, but also caused some malicious actors to utilize these data to attack the privacy of ordinary users. Therefore, it is crucial to identify the entity users behind multiple virtual accounts. Due to the low precision of user identification in the many-to-many mechanism of user identification, a random forest confirmation algorithm based on stable marriage matching (RFCA-SMM) is proposed in this study. It consists of three key steps: we first employ the stable marriage matching model to calculate the similarity between multiple users and utilize a scoring model to calculate the overall similarity of the users, after which candidate matching pairs are selected; second, we construct the random forest model that exploits a user similarity vector training set; afterward, the candidate matching pairs combine the secondary confirmation of the random forest model, which both improve the precision of the many-to-many user identification and protect private user data in the IoT. Extensive experiments are provided to demonstrate that the proposed algorithm improves precision rate, recall rate, and F-Measure (F1), as well as Area Under Curve (AUC).

In this paper, we design a compressed sensing (CS) based channel estimation method for millimeter wave (mmWave) massive MIMO systems and investigate the impact of dual-wideband effect (frequency-wideband and spatial-wideband) that appears in large array communications. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the basis mismatch problem. Hence, the proposed method achieves better channel estimation accuracy compared to most state-of-the-art algorithms that rely on on-grid CS approach. Moreover, the proposed method could successfully handle the spatial-wideband effect (sometimes known as beam squint effect) for wideband massive MIMO communications that was previously ignored by many existing literatures. Simulation results are provided to demonstrate the superior performance of the proposed method.

In this paper, we derive the closed-form capacity expression as well as the capacity-achieving input distribution for an ambient backscatter system with memoryless binary input and binary output (BIBO) channel. The discrete inputs are restricted to two mass points and the outputs are binary results obtained from energy detection with certain threshold. To show the influence of the signal to noise ratio (SNR) on the capacity, a closed-form tight capacity ceiling is also derived when SNR turns relatively large. Simulations are provided to corroborate the theoretical studies. Interestingly, simulations show: (i) the detection threshold maximizing the capacity is the same to the one from the maximum likelihood detector; (ii) the capacity is achieved by a uniform distribution for the inputs.

Due to the varieties of services and the openness of network architectures, great challenges for information security of the 5G systems are posed. Although there exist various and heterogeneous security communication mechanisms, it is imperative to develop a more general and more ubiquitous authentication method for data security. In this paper, we propose for the 5G networks a novel multimedia authentication framework, which is based upon the trusted content representation (TCR). The framework is general and suitable for various multimedia contents, e.g., text, audio, and video. The generality of the framework is achieved by the TCR technique, which authenticates the contents’ semantics in both high and low levels. Analysis shows that the authentication framework is able to authenticate multimedia contents effectively in terms of active and passive authenticating ways.