This paper investigates physical layer security (PLS) in wireless interference networks. Specifically, we consider confidential transmission from a legitimate transmitter (Alice) to a legitimate receiver (Bob), in the presence of non-colluding passive eavesdroppers (Eves), as well as multiple legitimate transceivers. To mitigate interference at legitimate receivers and enhance PLS, artificial noise (AN) aided interference alignment (IA) is explored. However, the conventional leakage minimization (LM) based IA may exhibit confidential signal cancellation phenomenon. We theoretically analyze the cause and then establish a condition under which this phenomenon will occur almost surely. Moreover, we propose a means of avoiding this phenomenon by integrating the max-eigenmode beamforming (MEB) into the traditional LM based IA. By assuming that only statistical channel state informations (CSIs) of Eves and local CSIs of legitimate users are available, we derive a closed form expression for the secrecy outage probability (SOP), and establish a condition under which positive secrecy rate is achievable. To enhance security performance, an SOP constrained secrecy rate maximization (SRM) problem is formulated and an efficient numerical method is developed for the optimal solution. Numerical results demonstrate the effectiveness and the usefulness of the proposed approach.

Bike-sharing demand prediction involves complex, dynamic spatio-temporal dependencies and various influencing factors, thus becomes one of technical challenges in intelligent transportation systems. Existing methods often rely on predefined adjacency matrices based on distance or road connectivity, and typically ignore multi-scale temporal features and external factors such as weather, holidays, social events, and so on. To address these limitations, we propose a model based on dynamic time warping (DTW) and spatio-temporal graph attention network (GAT) to improve the accuracy of bike-sharing demand prediction. In the proposed model, we use a data-driven approach to construct an adjacency matrix that effectively reflects the real dependencies between bike-sharing stations, and temporal attention mechanism is integrated with graph attention network to capture dynamic spatio-temporal correlations hidden in the data. Moreover, multi-scale temporal gated convolutions are applied to fuse short-term and long-term temporal features. The experimental results demonstrate that our proposed model significantly outperforms recent baseline methods in terms of MAE and RMSE evaluation metrics. Meanwhile, we find that the external factors of weather, public facilities and traffic accidents have different influence on results, and the weather has the greatest impact on bike-sharing demand.

Reconfigurable Intelligent Surfaces (RIS) and Un-manned Aerial Vehicles (UAVs) have emerged as promising tech-nologies for the 6th-Generation (6G) network. The integration of RIS with the UAV (RIS-UAV) can enhance ground communication by providing a 360°panoramic reflection. Existing RIS-UAV mainly considers passive elements which suffer from double path loss problems. This motivates the use of the hybrid RIS-UAV equipped with both active and passive RIS elements. This paper investigates the energy efficiency maximisation problem for the hybrid RIS-UAV by optimising the placement of the UAV, subject to the UAV's permitted altitude range. The non-convex optimisation problem is addressed using Particle Swarm Optimisation (PSO) tool and distributed learning algorithm. The numerical results show that the proposed distributed learning algorithm is preferred when optimising the energy efficiency of the hybrid RIS-UAV system. In addition, hybrid RIS-UAV outperforms the fully passive RIS-UAV and the active amplify-and-forward (AF) relay in terms of energy efficiency of the system.

Currently, phishing attacks are posing great damage to the online community. As traditions, attack detection strategies are not effective against this new type of threat. Hence, there is a need for advanced attack detection techniques. In this context, this research proposed a hybrid deep learning and big data-based technique for phishing attack detection approach. Our proposed approach used Conv2d layers in sequence for analysis of the incoming traffic and predict its behavior. We used different parameters to measure our proposed approach. Through the use of the cuckoo optimization algorithm, the propsed approach achieves a high accuracy of 92%.

Image style transfer is a challenging task in computational vision. Existing algorithms transfer the color and texture of style images by controlling the neural network's feature layers. However, they fail to control the strength of textures in different regions of the content image. To address this issue, we propose a training method that uses a loss function to constrain the style intensity in different regions. This method guides the transfer strength of style features in different regions based on the gradient relationship between style and content images. Additionally, we introduce a novel feature fusion method that linearly transforms content features to resemble style features while preserving their semantic relationships. Extensive experiments have demonstrated the effectiveness of our proposed approach.

Global or local style transfer often relies on matrix transformations [1], [2], [3], [4] [5]. In any scale of the image feature space, the representation of color can be seen as the projection result of the features at that scale onto different coordinate bases [1], [5]. Numerous studies have also demonstrated the validity of this approach. Starting from early non-neural network-based image style transfer algorithms, there have been approaches that utilize translation and scaling transformations to transform the coordinate bases of images, and most recently, the work of Neural Style Transfer [neural style preset] employs a simple network structure to predict the matrix parameters of affine transformations.We believe that if we want to solve for the global affine transformation matrix, we can completely abandon neural networks and directly solve it through the statistical features of the original image. First, we employ the Euler method to iteratively optimize the squared loss of the covariance matrix, quickly obtaining the optimal transformation matrix. Then, based on this, we adopt a stepwise transformation approach, decomposing the affine transformation into translation, rotation, and scaling transformations. We first determine the translation transformation based on the statistical differences between the content image and the style image. Then, by leveraging the distance-preserving property of rotation transformations, we provide the analytical form of the rotation matrix. Furthermore, we use basis transformation to nonlinearly blend and superimpose image channels, and solve for the optimal rotation matrix under different blending modes. Finally, we linearly combine the rotation matrices under different blending modes to obtain the final approximate result. Extensive experiments have demonstrated that the approximate solution can achieve results comparable to numerical solutions in image and video color transfer tasks. Meanwhile, our algorithm achieves near-optimal results in terms of runtime and quality trade-offs compared to existing algorithms. This advantage is particularly prominent in video realistic style transfer tasks.

In the field of NetSoftIoT, where network softwarization converges with the proliferation of IoT devices, ensuring robust security in SDN environments is paramount. This paper presents a novel approach that integrates RandomForest for optimized feature selection and LSTM networks for attack detection. Our methodology capitalizes on the LSTM's sequential data processing capability to discern patterns indicative of DDoS attacks within network traffic with an accuracy of 83 %. Leveraging a dataset comprising varied traffic types, our model demonstrated precision in identifying DDoS traffic with a recall of 0.94. The results, validated by confusion matrices and classification reports, indicate the model's efficacy in maintaining network integrity against malicious threats. This study advances the frontier of cybersecurity in SDN, crucial for the burgeoning landscape of IoT applications.

With the proliferation of connected devices in the Internet of Things (IoT), cybersecurity threats have increased. Identifying malicious attacks in IoT requires advanced techniques tailored to this ecosystem. Existing algorithms have limited effectiveness in detecting obfuscated IoT malware. This study proposes the elucidating cybersecurity-promulgated malware taxonomy (ECMT) framework, combining memory analysis and ensemble machine learning (ML), to enhance IoT malware categorization. ECMT integrates support vector classification, quadratic discriminant analysis, and AdaBoost on forensic artifacts from memory dumps to improve detection across families, such as ransomware, spyware, and trojans. ECMT can enable intrusion prevention, information protection, and cybercrime deterrence in IoT environments. Experiments on a balanced data set indicate AdaBoost achieved 96% accuracy, demonstrating ECMT‘s capabilities against complex IoT threats. The integrated approach provides automated, adaptable detection scalable to large IoT deployments through efficient linear models and robust ensemble learning. ECMT addresses concept drift and interpretability via retraining and explanation techniques. Results highlight advanced memory analysis and optimized ML classifiers as a promising solution for robust IoT malware detection despite adversaries’ evolving tactics. Further research can extend platform support, harden models against attacks, and refine streaming input. ECMT establishes a foundation for IoT security by unifying memory forensics, optimized neural architectures, and tailored ensemble learning.

With the increasing rates of interconnected Internet of Things (IoT) devices within Software-Defined Networking (SDN) environments, distributed denial of service (DDoS) attacks have become increasingly common. As a result of this challenge, novel detection and classification methods must be developed based on the unique characteristics of SDN-supported IoT networks. This paper proposes a novel approach to detecting and categorizing DDoS attacks that has been optimized specifically for such environments. As part of our methodology, we integrate convolutional neural networks (CNN) and long-short-term memory (LSTM) models into a multilevel deep neural network architecture. With this hybrid architecture, complex spatial and temporal patterns can be automatically extracted from raw network traffic data to facilitate comprehensive analysis and accurate identification of DDoS attacks. We validate the efficacy and superiority of our proposed approach over traditional machine learning algorithms by conducting rigorous experiments on real-world datasets. Our findings underscore the potential of the multi-level deep neural network approach as a robust and scalable solution for mitigating DDoS attacks in SDN-supported IoT networks. By improving network security and resilience to evolving threats, our methodology contributes to safeguarding critical infrastructures in the era of interconnected IoT ecosystems.

Abstract To effectively extract and classify the information from reports or documents and protect the privacy of the extracted results, we propose a privacy classification named Word Embedding Combination Privacy-preserving Support Vector Machine (WECPPSVM) model to classify the text. In addition, this paper also proposes the Privacy-preserving Distribution and Independent Frequent Subsequence Extraction Algorithm (PPDIFSEA), which calculates the degree of independence of the training data input to the classification model by training the Deep Belief Network(DBN) in PPDIFSEA, then obtains the Privacy Boundary(PB). PB is an indispensable condition for both data sampling and privacy noise generation. And this model can protect privacy by injecting the privacy noise into the classification result, this method can interfere with the background knowledge-based privacy attack. Our quantitative analysis shows that the WECPPSVM proposed in this paper can approach mainstream text classification algorithms in terms of text classification accuracy while preserving privacy without increasing computational complexity. In addition, the fusion study and privacy threat evaluation also verify that the proposed PPDIFSEA method combined with WECPPSVM achieves an acceptable level of classification accuracy and privacy protection.

Smart video surveillance plays a significant role in public security via storing a huge amount of continual stream data, evaluates them, and generates warns where undesirable human activities are performed. Recognition of human activities in video surveillance has faced many challenges such as optimal evaluation of human activities under growing volumes of streaming data with complex computation and huge time processing complexity. To tackle these challenges we introduce a lightweighted spatial-deep features integration using multilayer GRU (SDIGRU). First, we extract spatial and deep features from frames sequence of realistic human activity videos via utilizing a lightweight MobileNetV2 model and then integrate those spatial-deep features. Although deep features can be used for human activity recognition, they contain only the high-level appearance, which is insufficient to correctly identify the particular activity of human. Thus, we jointly apply deep information with spatial appearance to produce detailed level information. Furthermore, we select rich informative features from spatial-deep appearances. Then, we train multilayer gated recurrent unit (GRU) and feed informative features to learn the temporal dynamics of human activity frames sequence at each time step of GRU. We conduct our experiments on benchmark YouTube11, HMDB51, and UCF101 datasets of human activity recognition. The empirical results show that our method achieved significant recognition performance with low computational complexity and quick response. Finally, we compare the results with existing state-of-the-art techniques, which show the effectiveness of our method.

With the further development of Unmanned Aerial Vehicle (UAV) technologies, research on multi-UAV formations have also received more attention. Unmanned Aerial Vehicles (UAVs) cooperate with each other to form a formation group, which can give full play to the advantages that a single UAV does not have, and more capable of working in multi-task scenarios. Based on the Sierpinski fractal structure, this paper proposes a hybrid formation control architecture. To address the aggregation problem of UAV swarms, a multi-agent deep reinforcement learning (MADRL) method is used for aggregation control. To improve the efficiency, accuracy, effectiveness and scalability of MADRL in environments with a large number of UAVs, a multi-intelligent deep deterministic policy gradient method based on attention mechanism and adaptive accuracy (3A-MADDPG) is proposed. The method enables each agent to selectively focus on the information of other agents, with adaptive learning rate to dynamically learn its own critic network. The algorithm has a large learning rate in the early stage and converges quickly. The later stage of the algorithm has small learning rate and high accuracy, and combined with the fixed variable hybrid reward function designed in the paper, so the UAV can fly to the center of the expected region more accurately, making the whole cluster more accurate. Experiments show that the 3A algorithm proposed in the paper converges faster and achieves higher accuracy than the benchmark algorithm, whether it is the cluster aggregation formation or the separation and aggregation of sub-formations.

Cyber-Physical-Social System (CPSS) big data, the term that has been popularized over the past decade, is different from other types of big data because and is often used to refer to data sets that are too large or complex to be analyzed by traditional means. CPSS big data is specified as global historical and local real-time data. Due to the vast and heterogeneous nature of CPSS, this big data requires authentication control and security protocols. Blockchain technology has emerged as a promising solution for building secure and decentralized access control frameworks that facilitate data sharing and collaboration in CPSS. However, existing blockchain-based authentication control frameworks have scalability, privacy, and usability limitations. In this context, we proposed a secure authentication technique for CPSS that provide security to the system. Our proposed approach is lightweight and secure against different type of cyber attacks, such as replay attacks, and session hijacking attacks.

We investigate the physical layer security (PLS) in multi-user interference networks. In particular, we consider secure transmission from a legitimate source (Alice) to a legitimate destination (Bob), coexisting with multiple passive eavesdroppers (Eves), as well as multiple legitimate transceivers. By assuming that only statistical channel state informations (CSIs) of Eves and local CSIs of legitimate users are available, we propose an artificial noise (AN) assisted interference alignment (IA) for security enhancement. Unlike traditional IA based security approaches which may result in secret signal cancellation, we design a modified alternating minimization (AM) scheme to overcome this threat, by incorporating the max-eigenmode beamforming (MEB) for secure transmission. Moreover, by partitioning the IA equation into three independent subsets and their combinations, a much tighter necessary condition for IA feasibility is established. We also provide guiding insights into the practical system designs, including useful guidelines for the selection of the dimension of AN. Furthermore, the power allocation ratio between the secret signal and the AN signal is optimized to minimize the secrecy outage probability (SOP), subject to a minimum secrecy rate constraint. Numerical results demonstrate that our design can enhance both quality and security of the secret signal, and thus is suitable and reliable for PLS in interference networks.

The satellite–terrestrial integrated network (STIN) has been envisioned as an emerging architecture to provide global anytime anywhere network access, and satisfy transmission requirements of high-capacity backhaul data. However, the integration of satellite and terrestrial networks will aggravate the diversity of base stations (BSs) in backhaul delay and capacity, as well as coverage area, which makes it difficult for users with diverse requirements to access the most suitable service BS. As an effort to address the above problems, a distributed user association with grouping (DUAG) mechanism is proposed via the interaction between BSs and users to maximize the sum rate and balance the load of STIN while meeting the user’s demand by user grouping. First, the transmission characteristics of terrestrial and backhaul links are analyzed after constructing a STIN model, which consists of satellite, three types of BSs, and the variety of intelligent terminals. Then, the user association problems are formulated to maximize the sum rate and balance the load of STIN via jointly considering the backhaul capacity of BSs and mobility and delay of users. Meanwhile, the DUAG mechanism is proposed to associate users with the most suitable service BSs. In DUAG, a greedy-based user association algorithm with user grouping is developed for maximizing the sum rate via giving priority to users with high data rate, and a matching algorithm with user grouping is designed for balancing the load by means of performing multiple iterations between users and BSs. Simulation results demonstrate that the proposed DUAG can maximize the sum rate and balance the load of STIN while guaranteeing the delay demand of user with the increase of user density.

Residing in the proximity of end devices, edge computing (EC) holds great potential to provide low-latency, energy-efficient, and secure services, which has become an essential part of the Industrial Internet of Things (IIoT). To future accelerate task processing and reduce service latency, this work proposes an online incentive-driven task allocation scheme to stimulate collaborative computing among EC servers and IIoT devices. To better serve dynamic and heterogeneous tasks in terms of profiles and importance, EC servers (including neighboring servers) and IIoT devices with available resources can cooperatively process the tasks. Considering the heterogeneity of computing resources in edge servers and industrial IoT devices, we formulate a task allocation problem, which is NP hard. An online incentive-driven task allocation algorithm is proposed to this NP-hard problem, which will optimize task assignment strategies to maximize system utility, promote faster computing, and stimulate collaborative computing. Theoretical analyses show that the online incentive algorithm can satisfy incentive compatibility, individual rationality, computational efficiency, and feasibility. The results demonstrate that the proposed task allocation scheme with collaborative EC achieves superior performance and effectiveness.

Recent years have witnessed considerable progress in the application of Internet of Things (IoT) technology in smart transportation systems. The wider presence of Wi-Fi networks in subway gates allows passengers to use the quick response (QR) code of mobile phone applications for entrance. The network established by gates has become a medium which connects stations and passengers. However, in addition to directly monitoring the passenger flow, the potential application of the smart card usage data collected by the gates remains an open topic. Although there are several clustering-based works devoted to revealing passengers’ travel behavior patterns, research on the social attributes of subway passengers is very limited. To fill the gap, this article proposes a novel method to mine similarity information of passengers by leveraging passengers’ communication behaviors hidden in subway card usage data. Passengers are first organized as a graph, which not only reflects the interactions between them but also incorporates the context information of subway stations. Then, the node embedding is used to encode the information contained in the graph and with the use of cosine similarity, the similarity between two passengers is measured. Extensive experiments on two real-world location-based social network data sets and extended experiments on a Shanghai subway data set are conducted. The results show that the proposed method can effectively improve the accuracy of similarity measurement and provide social features that are distinguishable from travel behavior patterns.

Federated learning (FL) is a popular edge learning approach that utilizes local data and computing resources of network edge devices to train machine learning (ML) models while preserving users’ privacy. Nevertheless, performing efficient learning tasks on the devices and achieving longer battery life are primary challenges faced by federated learning. In this paper, we are the first to study the application of heterogeneous computing (HC) and wireless power transfer (WPT) to federated learning to address these challenges. Especially, we propose a heterogeneous computation and resource allocation framework based on a heterogeneous mobile architecture to achieve effective implementation of FL. To minimize the energy consumption of smart devices and maximize their harvesting energy simultaneously, we formulate an optimization problem featuring multidimensional control, which jointly considers time splitting for WPT, dataset size allocation, transmit power allocation and subcarrier assignment during communications, and processor frequency of processing units (central processing unit (CPU) and graphics processing unit (GPU)). However, the major obstacle is how to design a proper algorithm to solve this optimization problem efficiently. For this purpose, we decouple the optimization variables so as to achieve high efficiency in deriving its solution. Particularly, we first compute the optimal processor frequency and dataset size allocation via employing the Lagrangian dual method, followed by finding the closed-form solution to the optimal time splitting allocation, and finally attain the optimal subcarrier assignment as well as transmit power for transmissions through an iteration algorithm. To evaluate the performance of our proposed scheme, we set up four baseline schemes as comparison, and simulation results show that the proposed scheme converges quite fast and better enhance the energy efficiency of the wireless powered FL system compared with the baseline schemes.

Rescue assignments strategy are crucial for multiple Autonomous Underwater Vehicle (multi-AUV) systems in three dimensional (3-D) complex underwater environments. Considering the requirements of rescue missions, multi-AUV systems need to be cost-effective, fast-rescuing, and less concerned about the relationship between rescue missions. The real-time rescue plays a vital role in the multi-AUV system with the characteristics mentioned above. In this paper, we propose an efficient Reward acting on Reinforcement Learning and Particle Swarm Optimization (R-RLPSO), to provide a strategy of real-time rescue assignment for the multi-AUV system in the 3-D underwater environment. This strategy consists of the following three parts. Firstly, we present a reward-based real-time rescue assignment algorithm. Secondly, we propose an Attraction Rescue Area containing a Rescue Area. For the waypoints in each Attraction Rescue Area, the reward is calculated by a linear reward function. Thirdly, to speed up the convergence of the R-RLPSO and mark the rescue states of Attraction Rescue Area and rescue area, we develop a Reward Coefficient based on the reward of all Attraction Rescue Areas and Rescue Areas. Finally, simulation results show that the system based on R-RLPSO is more cost-effective and time-saving than that of based on comparison algorithms ISOM and IACO.

Sensors in wireless sensor network (WSN) are usually deployed in the wild or even hostile circumstance. What is worse, most of the sensors have limited communication bandwidth, computation resources and energy. Therefore, it is challenging to ensure the security of WSN without decreasing its network performance. Network coding (NC) is a promising way for improving communication capability in WSN, e.g., high throughput, robustness and low-energy. Nevertheless, network coding is vulnerable to malicious attacks. Presently, many secure detections, such as information theoretic-based or cryptographic-based techniques, have been proposed to deal with a single type of attack, but are incapable of resisting the joint attacks, e.g., the union of pollution attacks and replay attacks. In this paper, a secure detection service is presented. It is deployed on every node of WSN to monitor, manage and control the messages passing through them in real-time. In the service, a lightweight timestamp-based message authentication code, namely TMAC, is designed with Exclusive OR network coding. Based on TMAC and time synchronization technique, a joint detection is implemented to resist pollution attacks and replay attacks synchronously. The correctness of the detection service is proved. Finally, the performance evaluation shows that the detection scheme brings negligible extra-expense in communication bandwidth and computational complexity compared to MAC-based schemes, and consumes energy lowly compared to other joint detection schemes.

Infrared image target detection has always been a hot topic of research, but there is still little research on infrared image target detection in the field of transportation. In this paper, we use the idea of transfer learning to transfer the target detection framework in the visible domain of deep learning to the infrared domain, and propose the target detection model CMF Net based on multi-scale feature fusion. CMF Net uses two multi-scale feature extraction mechanisms and features fusion, so that the final output feature map of the backbone network contains not only low-level visual features which are beneficial to target localization, but also high-level semantic features which are beneficial to target recognition, and can adapt to multi-scale features of the target. The experiment verified the advantages of CMF Net, and its mAP on the test data of the infrared image data set FLIR reached about 71%. This result is an increase of about 13% compared to Faster R-CNN, an increase of about 6% compared to YOLO3, and an increase of about 17% compared to SSD.

In order to perform competitive privacy-guaranteed object detection, we propose an end-to-end model called Privacy-preserving Deep Transformation Self-attention (PPDTSA). This model ensures the privacy of the inference results. It has a low-complexity hierarchical structure with a relatively small number of hyper-parameters. Consistency of prediction is achieved through the encoding and decoding blocks of the self-attention mechanism which enables points of interest to be located. Focus loss is estimated based on foreground-background imbalance. The remaining dense blocks enable image details to be retained and the Region Of Interest to be expanded. At the same time, the objects detected in the image are protected through the privacy noise volume which is specified by the user. Experimental results demonstrate that PPDTSA achieves superior performance on the MOT20 dataset compared with three other state-of-the-art object detection models.

Traffic load-balancing is important to data center performance. However, existing data center load-balancing solutions are either limited to simple topologies or cannot provide satisfactory performance. In this paper, we propose a multipath-aware TCP (MA-TCP) which can sense the path migration of TCP flows. With this new mechanism, the reduction in TCP congestion window due to packet reordering during the path migration can be avoided. This, in turn, makes the path migration more timely as soon as the original path is congested. Furthermore, if the new path is congested (again), the flow can securely continue to migrate without worrying about transmitting rate reduction. Through NS-3 simulations, we show that MA-TCP achieves better flow completion time (FCT) than existing data center load-balancing solutions.

Mobile-edge computing (MEC) is a promising technology, which allows reducing latency and energy consumption, thereby making the user experience better. Although MEC can support various types of services, differentiated Quality-of-Service (QoS) requirements bring difficulties and challenges to the allocation of radio resources and computing resources of the MEC system. In this article, we jointly optimize subchannel allocation, as well as the local central processing unit (CPU) speed scaling, user association, subcarrier assignment, power allocation, and video quality decision for MEC systems to study the total cost saving problem. Considering the traffic variations, we develop an online algorithm by using the Lyapunov optimization technique to solve this problem, referred to as dynamic subchannel allocation and resource allocation (DSARA). Particularly, the proposed DSARA algorithm only needs to track the state of the current network without requiring any prior knowledge. Besides, we prove that our proposed algorithm can asymptotically achieve the minimum total cost value (such as minimizing the power consumption and maximizing quality satisfaction). Simulation results show that the DSARA can achieve a good tradeoff between the total cost and delay, and outperforms the existing schemes in terms of the total cost expenditure.

As a promising new paradigm, Vehicular Edge Computing (VEC) can improve the QoS of vehicular applications by computation offloading. However, with more and more computation-intensive vehicular applications, VEC servers face the challenges of limited resources. In this paper, we study how to effectively and economically utilize the idle resources in volunteer vehicles to handle the overloaded tasks in VEC servers. First, we present a model of volunteer assisted vehicular edge computing, in which the cost and utility functions are defined for requesting vehicles and VEC servers, and volunteer vehicles are encouraged to assist the overloaded VEC servers via obtaining rewards from VEC servers. Then, based on Stackelberg game, we analyze the interactions between requesting vehicles and VEC servers, and find the optimal strategies for them. Furthermore, we prove theoretically that the Stackelberg game between requesting vehicles and VEC servers has a unique Stackelberg equilibrium, and propose a fast searching algorithm based on genetic algorithm to find the best pricing strategy for the VEC server. In addition, to maximize the reward of volunteer vehicles, we propose the volunteer task assignment algorithm for optimal mapping between the tasks and volunteer alliances. Finally, the effectiveness of the proposed scheme is demonstrated through a large number of simulations. Compared with other schemes, the proposed scheme can reduce the offloading cost of vehicles and improve the utility of VEC servers.

Word-of-Mouth (WoM) mode, as a new mode of task sensing in crowdsourcing, shows high efficiency in building contributor groups. To better tap the potential of WoM mobile crowdsourcing, the underlying rationale of interactions among contributors needs to be well understood. In this article, we analyze the behavior of contributors based on the Stackelberg game, and find optimal strategies for contributors. We consider two different crowdsourcing tasks announcement methods: 1) one-time parallel and 2) multitime sequential announcement ways, which form two different market scenarios. Then, we formulate two-stage and multistage contributor game models for the two scenarios, respectively. The backward induction approach is used to analyze each game, and the problems to find the optimal strategies for contributors are transformed into optimization problems. Furthermore, the Lagrange multiplier and Karush-Kuhn-Tucker (KKT) methods are used to solve the optimization problems. We theoretically prove that Stackelberg equilibrium exists and is unique. Based on the proposed theory, we design algorithms to compute the profit-maximizing contribution quantity of sensing data for each contributor. Finally, we present the detailed experimental analysis and the experimental result shows the effectiveness of the proposed algorithms.

Pull Request (PR) is a major contributor to external developers of open-source projects in GitHub. PR reviewing is an important part of open-source software developments to ensure the quality of project. Recommending suitable candidates of reviewer to the new PRs will make the PR reviewing more efficient. However, there is not a mechanism of automatic reviewer recommendation for PR in GitHub. In this paper, we propose an automatic core-reviewer recommendation approach, which combines PR topic model with collaborators in the social network. First PR topics will be extracted from PRs by the latent Dirichlet allocation, and then the collaborator–PR network will be constructed with the connection between collaborators and PRs, and the influence of each collaborator will be calculated via the improved PageRank algorithm which combines with HITS. Finally, the relationship between topics and collaborators will also be built by the history of PR reviewing. When a new PR presents, a collaborator will be chosen as a core reviewer according to the influence of collaborators and the relationship between the new PR and collaborators. The experiment results show in the matching score calculation processing, the influence of collaborators shows higher than that with the expert, and the recommendation precision is better than 70%.

Code review is an important way to improve software quality and ensure project security. Pull Request (PR), as an important method of collaborative code modification in GitHub open source software community platform, is very important to find a suitable code reviewer to improve code modification efficiency for Pull Request submitted by code modifiers. In order to solve this problem, we have proposed a review recommendation algorithm based on Pull Request topic model and reviewer's influence. This algorithm has not only extracted the topic information of PR through Latent Dirichlet Allocation (LDA) method, but also analyzed the professional knowledge influence of reviewers through influence network. What’s more, it has combined the topic information of reviewers to find the appropriate PR reviewers. The experimental results based on GitHub show that the algorithm is more efficient, which can effectively reduce the time of code review and improve the recommendation accuracy.

The integration knowledge of wearable sensors, wireless communications, and artificial intelligence have brought forth the smart health systems, which empower the consumer’s to make a difference to their well-being by connecting data to personalized analysis to timely insights. Therefore, the real-time data obtained directly reflects the personal status of interest and can be used in a variety of healthcare applications in the Internet of Things (IoT), from preventive treatment to diagnostics and rehabilitation, as well as in virtual and augmented reality environments.

The papers in this special section focus on mobile cloud computing. The papers address variety of interesting topics covering different aspects of the Mobile Cloud, such as process offloading, work sharing, performance enhancement of Mobile Clouds, security issues in Mobile Clouds, and applications of Mobile Clouds.

In the Service-Function-Chaining (SFC) enabled networks, various sophisticated policy-aware network functions, such as intrusion detection, access control and unified threat management, can be realized in either physical middleboxes or virtualized network function (VNF) appliances. In this paper, we study the service chaining towards the hybrid SFC clouds, where both physical appliances and VNF appliances provide services collaboratively. In such hybrid SFC networks, the challenge is how to efficiently steer the service chains for traffic demands while matching their individual policy chains concurrently such that a utility associated with the total admitted traffic rate and the induced overheads can be maximized. We find such problem has not been well solved so far. To this end, we devise a Markov Approximation (MA) based algorithm. The approximation property of the proposed algorithm is also proved. Extensive evaluation results show that the proposed MA algorithm can yield near-optimal solutions and outperform other benchmark algorithms significantly.

With the growing popularization of Electric Vehicles (EVs), charging management has become an increasingly important research problem in smart cities. Different from plug-in charging technology, we alternatively enable the battery switch technology to provide fast EV charging (reduce the service waiting time from tens of minutes to a few minutes), by facilitating the switchable (fully-recharged) batteries maintained at CSs and also the batteries cycling procure to refresh their availability. Nevertheless, potential hot spot may still happen at CSs, due to running out of switchable batteries as well as long batteries charging queue. With this concern, we next propose a reservation based EV charging management scheme to alleviate such situation, considering EVs' anticipated charging reservations (including arrival time, expected charging time) to coordinate EVs' charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our enabling technology, in terms of minimized waiting time for the battery switch as the benefit of EV drivers, and higher number of batteries switched as the benefit of CSs.

Software-Defined networking (SDN), as a new paradigm, fixes the shortage that traditional network does not support the dynamic, scalable computing and storage needs of more computing environments. SDN, however, also faces security problems such as vulnerable to DDoS attacks. DDoS attacks are well-known and powerful attacks. DDoS detection and DDoS traffic separation for SDN environments are still an open research issue. DDoS attacks in SDN environments will not only bring damage to target server, but also takes exact impact on SDN system. In this paper, we identify a new type DDoS attack, specifically aiming SDN environment, which is harder to be detected. We propose a novel real-time DDoS detection scheme for SDN environment, by using Principal Component Analysis (PCA) scheme to analyze the network status on traffic packets data. We separate the network into different parts, to reduce the total calculation burden. We compare our scheme with sample entropy, showed our scheme achieves better detecting ability for DDoS attacks.

A lot of Pull Requests (PRs) appear in Github everyday, and thus it is a very important work to review these PRs quickly in Github. Labeling PRs according to the PRs classification can improve the success rate and the review efficiency. However, recent research works have shown that most of the PRs are not labeled, and if the PR is labeled, it is done manually. To solve this problem, we propose a supervised classification model combined with supervised topics model and Naive Bayes classifier, which can make the PR be classified automatically. The method creates a one-one relationship between labels and PRs, and the approach classifies most PRs automatically with the only label which record the closest topic of PRs. The experimental results show that the proposed model can reach a precision of 60% in majority situation. The proposed model may support a better result via adjusting the parameters in case.

In order to cope with the increasing number of cyber attacks, network operators must monitor the whole network situations in real time. Traditional network monitoring method that usually works on a single machine, however, is no longer suitable for the huge traffic data nowadays due to its poor processing ability. In this paper, we propose a machine-learning based online Internet traffic monitoring system using Spark Streaming, a stream- processing-based big data framework, to detect DDoS attacks in real time. The system consists of three parts, collector, messaging system and stream processor. We use a correlation-based feature selection method and choose 4 most necessary network features in our machine- learning-based DDoS detection algorithm. We verify the result of feature selection method by a comparative experiment and compare the detection accuracy of 3 machine learning methods - Naïve Bayes, Logistic Regression and Decision Tree. Finally, we conduct experiments in a cluster with the standalone mode, showing that our system can detect 3 typical DDoS attacks - TCP flooding, UDP flooding and ICMP flooding at the accuracy of more than 99.3%. It also shows the system performs well even for large Internet traffic.

A new method for attack detection of smart grids with wind power generators using reservoir computing (RC) is introduced in this paper. RC is an energy-efficient computing paradigm within the field of neuromorphic computing and the delayed feedback networks (DFNs) implementation of RC has shown superior performance in many classification tasks. The combination of temporal encoding, DFN, and a multilayer perceptron (MLP) as the output readout layer is shown to yield performance improvement over existing attack detection methods such as MLPs, support vector machines (SVM), and conventional state vector estimation (SVE) in terms of attack detection in smart grids. The proposed algorithms are shown to be more robust than MLP and SVE in dealing with different variables such as the amplitude of the attack, attack types, and the number of compromised measurements in smart grids. The attack detection rate for the proposed RC-based system is higher than 99%, based on the accuracy metric for the average of 10 000 simulations.

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

By utilizing the radio channel information to detect spoofing attacks, channel based physical layer (PHY‐layer) enhanced authentication can be exploited in light‐weight securing 5G wireless communications. One major obstacle in the application of the PHY‐layer authentication is its detection rate. In this paper, a novel authentication method is developed to detect spoofing attacks without a special test threshold while a trained model is used to determine whether the user is legal or illegal. Unlike the threshold test PHY‐layer authentication method, the proposed AdaBoost based PHY‐layer authentication algorithm increases the authentication rate with one‐dimensional test statistic feature. In addition, a two‐dimensional test statistic features authentication model is presented for further improvement of detection rate. To evaluate the feasibility of our algorithm, we implement the PHY‐layer spoofing detectors in multiple‐input multiple‐output (MIMO) system over universal software radio peripherals (USRP). Extensive experiences show that the proposed methods yield the high performance without compromising the computing complexity.

This paper presents a multi‐time channel prediction system based on backpropagation (BP) neural network with multi‐hidden layers, which can predict channel information effectively and benefit for massive MIMO performance, power control, and artificial noise physical layer security scheme design. Meanwhile, an early stopping strategy to avoid the overfitting of BP neural network is introduced. By comparing the predicted normalized mean square error (NMSE), the simulation results show that the performances of the proposed scheme are extremely improved. Moreover, a sparse channel sample construction method is proposed, which saves system resources effectively without weakening performances.

In this paper, we propose an energy-efficient joint resource allocation and power control scheme for downlink multi-cell orthogonal frequency division multiple access (OFDMA) networks with imperfect channel state information (CSI). Considering the maximum allowed transmit power, the tolerable outage probability, and the fact that each resource block is allocated to at most one user in each cell, we formulate the energy-efficient joint resource allocation and power control problem as a probabilistic mixed non-convex fractional programming problem, which is hard to be tackled. In order to solve the problem efficiently, we first substitute the probabilistic constraints into the objective function and then introduce new auxiliary variables to transform the original problem into a difference of two convex functions (D.C.) programming problem. As a result, we resort to D.C. algorithm to obtain a solution satisfying Karush-Kuhn-Tucker conditions of the D.C. problem. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

As the social coding is becoming increasingly popular, understanding the influence of developers can benefit various applications, such as advertisement for new projects and innovations. However, most existing works have focused only on ranking influential nodes in non-weighted and homogeneous networks, which are not able to transfer proper importance scores to the real important node. To rank developers in Github, we define developer's influence on the capacity of attracting attention which can be measured by the number of followers obtained in the future. We further defined a new method, DevRank, which ranks the developers by influence propagation through heterogeneous network constructed according to user behaviors, including "commit" and "follow". Our experiment compares the performance between DevRank and some other link analysis algorithms, the results have shown that DevRank can improve the ranking accuracy.

Pull Request (PR) is the main method for code contributions from the external contributors in GitHub. PR review is an essential part of open source software developments to maintain the quality of software. Matching a new PR for an appropriate integrator will make the PR reviewing more effective. However, PR and integrator matching are now organized manually in GitHub. To make this process more efficient, we propose a Topic-based Integrator Matching Algorithm (TIMA) to predict highly relevant collaborators(the core developers) as the integrator to incoming PRs . TIMA takes full advantage of the textual semantics of PRs. To define the relationships between topics and collaborators, TIMA builds a relation matrix about topic and collaborators. According to the relevance between topics and collaborators, TIMA matches the suitable collaborators as the PR integrator.

Due to the explosive growth of Internet traffic, network operators must be able to monitor the whole network situations and manage their network resources in an efficient way. Traditional network analysis method that works on a single machine are no longer suitable for this huge traffic data due to its poor processing ability. Some big data frameworks, such as Hadoop and Spark, can handle such analysis job even for large network traffic, but they are inherently designed for offline data analysis. In this paper, we treat the online network analysis as a stream analysis problem and use Spark Streaming to cope with the high-speed Internet traffic data in real time. The system consists of two parts, collector and stream processor. Firstly, several collectors capture network traffic data from switches through mirrored ports and send the packet information to a central stream processor which is a cluster running Spark Streaming. Then, the stream processor analyzes the input data streams and calculates Internet performance metrics. We take TCP performance monitoring as an example to show how network measurement can be done using the stream processing platform. Finally, we conducted typical experiments in a cluster of 3 computers with the standalone mode, showing that our system performs well in huge Internet traffic measurement and monitoring.

Recommendation is widely used to deal with information overloading by suggesting items based on historical information of users. One of the most popular recommendation techniques is matrix factorization (MF), in which the preferences of users are estimated by dot products of their real latent factors between users and items. Although MF can achieve high recommendation accuracy, it suffers from efficiency issues when making preferences ranking in real space. Hash retrieval technique can be applied to recommender systems to speed up preferences ranking. Due to the existence of discrete constraints in learning hash codes, it is possible to exploit a two-stage learning procedure according to most existing methods. This two-stage procedure consists of relaxed optimization by discarding discrete constraints and subsequent binary quantization. However, existing methods have not been able to well handle the change of dot product arising from quantization. To this end, we propose a dot-product based preference preserved hashing method, which quantizes both norm and cosine similarity in dot product respectively. We also design an algorithm to optimize the bit length for norm quantization. Based on the evaluation to several datasets, the proposed framework shows consistent superiority to the competing baselines even though only using shorter binary code.

The benefits of network layer cooperation cognitive radio networks have been gradually recognized in recent years. In this paper we consider the network layer cooperation in cognitive radio network, whereby primary users select some secondary users to relay packets, in return for more favourable spectrum access rules for secondary users. Under this cooperation scheme, we investigate how to enlarge the throughput of the whole network, where a QoS(Quality of Service)-guaranteed adaptive cooperation scheme is developed. Our scheme can guarantee the QoS demand of primary users and update its frequency division cooperation scheme dynamically according to the status of nodes. Our algorithm requires knowledge of only instantaneous queue lengths at secondary nodes and the predictable end-to-end delay. Simulation results reveal that our proposed scheme significantly outperforms previous works in terms of throughput.

Recommender systems have been widely used to deal with information overload, by suggesting relevant items that match users' personal interest. One of the most popular recommendation techniques is matrix factorization (MF). The inner products of learned latent factors between users and items can estimate users' preferences for items with high accuracy, but the preferences ranking is time consuming. Thus, hashing-based fast search technologies were exploited in recommender systems. However, most previous approaches consist of two stages: continuous latent factor learning and binary quantization, but they didn't well deal with the change of inner product arising from quantization. To this end, in this paper, we propose a constraint free preference preserving hashing method, which quantizes both norm and similarity in dot product. We also design an algorithm to optimize the bit length for norm quantization. The performance of our method is evaluated on three real world datasets. The results confirm that the proposed model can improve recommendation performance by 11%-15%, as compared with the state-of-the-art hashing approaches.

In the context of Software-Defined Networking (SDN), various sophisticated policy-aware network functions such as intrusion detection, access control and load balancer, can be realized via specified middlebox devices. However, high congestions may occur in specific bottleneck links if middlebox selection and traffic routing are not well jointly planed. To this end, we study a joint optimization of MiddleBox Selection and Routing (MBSR) problem with the objective to maximize the throughput for a specified set of sessions in an SDN network. In order to solve this NP-hard problem, we design a polynomial algorithm using the Markov approximation technique. Numerical results show that the proposed Markov approximation based algorithm outperforms other benchmark algorithms significantly and generates near-optimal solutions.

A massive number of the fifth generation (5G) terminals in the network brings challenges to contention-based random access, especially for machine-type communications (MTC). Based on the extreme traffic model defined by 3rd Generation Partnership Project (3GPP), available solutions, cannot support all the MTC devices access the network successfully. To solve the problem, we firstly analyze the objective of massive access control and propose a concept of distribution reshaping, with which data arrival distribution with highly coordinated manner can actually be changed into random access attempt distribution without a coordinated manner. Both the relevant application layer and radio interface schemes are proposed. Simulation results show that the proposed radio interface algorithm notably outperforms relevant existing approaches under various metrics. Moreover, it can support 10 times the number of devices access the network successfully within averaged access delay of 90 milliseconds. Once the application layer scheme works together with the radio interface scheme, higher throughputs and lower access delays are obtained.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks with max-min fairness, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs to improve the frequency reuse factor, and the minimum individual energy efficiency (EE) is maximized. This is a generalized fractional programming (GFP) problem, and is hard to tackle due to its non-concave nature, which means the complexity of global optimal solution is unaffordable. In order to give sub-optimal solution with reasonable complexity, we first transform the GFP problem into equivalent optimization problem in a parametric subtractive form, and then add constraints on the co-channel interferences to convert the non-concave GFP problem into concave one. The sub-optimal solution, which can be obtained through solving the deduced concave problem based on sophisticated convex optimization methods, gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

The stability of links in mobile cognitive networks (MCNets) is significantly affected by primary user activities and node mobility, which makes topology control and stable routing more challenging than that in traditional wireless networks. In multi-channel multi-hop MCNets, it will become worse. In this paper, we propose a primary user activity prediction model to reveal channel utilization patterns of primary users. Next, we put forward a novel routing metric Primary user activity Prediction based Stability Metric (PPSM) to quantitatively capture the affect of primary user activities and node mobility. Finally, we propose and implement a Primary user activity Prediction based Joint Topology Control and Stable Routing (PP-JTCSR) protocol for maximizing network throughput based on our primary user activity prediction model, which can find out the most stable and the shortest path between a source and a destination. NS2-based simulation results demonstrate that our PP-JTCSR protocol can generate stable topology through predicting link and path duration quantitatively, and outperforms related proposals in terms of path stability and average throughput.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs and co-channel interference caused by resource sharing becomes a significant challenge. We consider both the total energy efficiency (EE) and individual EE optimization problems, which are fractional programming and generalized fractional programming problems, respectively, and are hard to tackle due to their non-concave nature. We first transform them into equivalent optimization problems in parametric subtractive forms, which fit in a class of non-concave optimization methods known as difference of two concave functions programming, and then solve them using Dinkelbach and branch-and-bound methods to give global optimal solutions. Due to the unaffordable complexity of the global optimal solution, we further propose sub-optimal schemes through adding constraints on the interferences to convert the non-concave problems into concave ones and to give sub-optimal solutions with reasonable complexity. The sub-optimal solution gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed schemes.

Cloud computing is an Internet-based computing paradigm which may share resources to provide on-demand services to devices. Integrating smart grid into cloud computing is emerging and promising, and this integration may execute in the cloud based hierarchical multi-user data-shared environment, where security issues such as data confidentiality and manager authority may arise in the smart grid system. In order to provide safe and secure grid operations, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three layers structure are proposed in this paper. In power grids, the system can be controlled and monitored by the enormous sensitive data which may be from sensor nodes, smart measurement units and soon on. The novel scheme mainly focus on the data process, storage and access to ensure the managers with legal authorities to get corresponding sensitive data and restrict illegal managers and unauthorized legal managers from accessing the data.

In this paper, we investigate the energy-efficient resource allocation problem for the device-to- device (D2D) communications overlaying LTE networks, where the D2D user equipment (UE) shares the spectrum with the cellular UE in an orthogonal way such that the interference between them is completely eliminated. We consider both the non- orthogonal and orthogonal resource allocation strategies for D2D communications, where the resources allocated to different D2D pairs are non-orthogonal and orthogonal respectively. In the non-orthogonal strategy, the interference exists among different D2D pairs, and the resource allocation only concerns the transmit power control for each D2D pair; whereas in the orthogonal strategy, there is no interference, and the resource allocation concerns both the resource block (RB) allocation and the transmit power control. In the two strategies, the related resource allocation problems are firstly formulated as a fractional programming (FP) problem and a mixed-integer nonlinear fractional programming (MINLFP) problem respectively, both of which are then transformed into equivalent optimization problems in parametric subtractive form by exploiting the property of FP. As the transformed equivalent problems are non-concave, we develop the sub-optimal energy-efficient resource allocation schemes by solving them based on Dinkelbach and Powell-Hestenes-Rockafellar augmented Lagrangian methods. Simulation results demonstrate the effectiveness of the proposed schemes and show that the non-orthogonal strategy outperforms the orthogonal one in terms of the energy efficiency.

This paper first provides a brief survey on existing traffic offloading techniques in wireless networks. Particularly as a case study, we put forward an online reinforcement learning framework for the problem of traffic offloading in a stochastic heterogeneous cellular network (HCN), where the time-varying traffic in the network can be offloaded to nearby small cells. Our aim is to minimize the total discounted energy consumption of the HCN while maintaining the quality-of-service (QoS) experienced by mobile users. For each cell (i.e., a macro cell or a small cell), the energy consumption is determined by its system load, which is coupled with system loads in other cells due to the sharing over a common frequency band. We model the energy-aware traffic offloading problem in such HCNs as a discrete-time Markov decision process (DTMDP). Based on the traffic observations and the traffic offloading operations, the network controller gradually optimizes the traffic offloading strategy with no prior knowledge of the DTMDP statistics. Such a model-free learning framework is important, particularly when the state space is huge. In order to solve the curse of dimensionality, we design a centralized Q-learning with compact state representation algorithm, which is named QC-learning. Moreover, a decentralized version of the QC-learning is developed based on the fact the macro base stations (BSs) can independently manage the operations of local small-cell BSs through making use of the global network state information obtained from the network controller. Simulations are conducted to show the effectiveness of the derived centralized and decentralized QC-learning algorithms in balancing the tradeoff between energy saving and QoS satisfaction.

The articles in this special issue focus on new and emerging technologies in the communications industry.

This paper investigates the joint transmitter and receiver optimization for the energy efficiency (EE) in orthogonal frequency-division multiple-access (OFDMA) systems. We first establish a holistic power dissipation model for OFDMA systems, including the transmission power, signal processing power, and circuit power from both the transmitter and receiver sides, while existing works only consider the one side power consumption and also fail to capture the impact of subcarriers and users on the system EE. The EE maximization problem is formulated as a combinatorial fractional problem that is NP-hard. To make it tractable, we transform the problem of fractional form into a subtractive-form one by using the Dinkelbach transformation and then propose a joint optimization method, which leads to the asymptotically optimal solution. To reduce the computational complexity, we decompose the joint optimization into two consecutive steps, where the key idea lies in exploring the inherent fractional structure of the introduced individual EE and the system EE. In addition, we provide a sufficient condition under which our proposed two-step method is optimal. Numerical results demonstrate the effectiveness of proposed methods, and the effect of imperfect channel state information is also characterized.

This paper considers a basic MIMO information-energy broadcast system, where a multi-antenna transmitter transmits information and energy simultaneously to a multi-antenna information receiver and a dual-functional multi-antenna energy receiver which is also capable of decoding information. Due to the open nature of wireless medium and the dual purpose of information and energy transmission, secure information transmission while ensuring efficient energy harvesting is a critical issue for such a broadcast system. Providing that physical layer security techniques are adopted for secure transmission, we study beamforming design to maximize the achievable secrecy rate subject to a total power constraint and an energy harvesting constraint. First, based on semidefinite relaxation, we propose global optimal solutions to the secrecy rate maximization (SRM) problem in the single-stream case and a specific full-stream case. Then, we propose inexact block coordinate descent (IBCD) algorithm to tackle the SRM problem of general case with arbitrary number of streams. We prove that the IBCD algorithm can monotonically converge to a Karush-Kuhn-Tucker (KKT) solution to the SRM problem. Furthermore, we extend the IBCD algorithm to the joint beamforming and artificial noise design problem. Finally, simulations are performed to validate the effectiveness of the proposed beamforming algorithms.

In this paper, we investigate space-time network coding (STNC) in cooperative multiple-input multiple-output networks, where U users communicate with a common destination D with the aid of R decode-and-forward relays. The transmit antenna selection with maximal-ratio combining (TAS/MRC) is adopted in user-destination and relay-destination links where a single transmit antenna that maximizes the instantaneous received signal-to-noise ratio is selected and fed back to transmitter by receiver and all the receive antennas are combined with MRC. In the presence of perfect feedback, we derive new exact and asymptotic closed-form expressions for the outage probability (OP) and the symbol error rate (SER) of STNC with TAS/MRC in independent but not necessarily identically distributed Rayleigh fading channels. We demonstrate that STNC with TAS/MRC guarantees full diversity order. To quantify the impact of delayed feedback, we further derive new exact and asymptotic OP and SER expressions in closed form. We prove that the delayed feedback degrades the full diversity order to (R + 1)ND, where ND is the antenna number of the destination D. Numerical and Monte Carlo simulation results are provided to demonstrate the accuracy of our theoretical analysis and evaluate the impact of network parameters on the performance of STNC with TAS/MRC.

In this paper, we investigate the resource allocation for joint transmitter and receiver energy efficiency maximization in orthogonal frequency division multiple access (OFDMA) systems. An elaborate power dissipation model is proposed for OFDMA systems considering the transmission power from the base station side, the signal processing power and radio frequency (RF) circuit power from both sides. Then we formulate the energy efficiency maximization problem and propose a two-step method based on the relationship analysis of the single subcarrier-user (SU) pair energy efficiency and system energy efficiency. Specifically, we first pair each subcarrier with the user resulting in highest SU pair energy efficiency, which is motivated by a special case study. Then, we propose a linear complexity scheme by exploring the inherent fractional structure of the system energy efficiency, which is proved to be optimal for the power allocation with given SU pairing in the first step. Finally, we provide a sufficient condition under which our proposed two-step method is globally optimal. Numerical results demonstrate the effectiveness of the proposed method and we also find that exploiting more user diversity is not always beneficial from the perspective of energy efficiency.

This paper investigates the optimal energy-efficient transmission policy of multi-channels in energy harvesting systems. We configure the transmitter with the active mode in which the energy cost includes the basic operation cost and transmission cost and signal processing cost, while with the sleep mode only counting the basic operation cost. Then the energy efficiency maximization problem of joint transmission time and power allocation is formulated and studied in the offline manner. Based on the fractional optimization theory, we transform the original fractional optimization problem into a series of subtractive-form optimization problems which are then further transformed into convex optimization problems. Then characteristics of the optimal solution are described based on the analysis of transmission time and power allocation. Finally, a special case without considering the basic operation cost as previous works assumed is studied. We find that the optimal policy results a best subchannel scheduling which can be viewed as the peaky transmission. Through this, the energy cost of the sleep mode in previous case can be interpreted as the switching operation cost.

We investigate the energy-efficient resource allocation problem for the downlink in long-term evolution heterogeneous networks through maximizing the energy efficiency (EE) under the per-user throughput and per-eNB power constraints in this paper. We demonstrate that EE is an increasing function in channel gain, and that EE is continuously differentiable and strictly quasiconcave in transmit power associated with each resource block (RB). Due to the non-convexity of the optimization problem, we develop a two-step resource allocation scheme composed by RB allocation and transmit power control. In the first step, we allocate RBs to users through maximizing the minimum EE of individual user and satisfying the throughput requirement of each user. In the second step, by enforcing the per-user throughput and per-eNB power constraints and exploiting the strict quasiconcavity of EE in transmit power associated with each RB, the power control algorithm is developed to maximize the EE. Simulation results demonstrate the effectiveness of the proposed resource allocation scheme and show that it greatly improves the EE compared with conventional spectral-efficient scheme.

An (N, K) codebook C is a set of N unit-norm complex vectors in \BBCK. Optimal codebooks meeting the Welch bound with equality are desirable in a number of areas. However, it is very difficult to construct such optimal codebooks. There have been a number of attempts to construct codebooks nearly meeting the Welch bound with equality, i.e., the maximal cross-correlation amplitude Imax(C) is slightly higher than the Welch bound equality, but asymptotically achieves it for large enough N. In this paper, using difference sets and the product of Abelian groups, we propose new constructions of codebooks nearly meeting the Welch bound with equality. Our methods yield many codebooks with new parameters. In some cases, our constructions are comparable to known constructions.

In a content delivery network (CDN), the energy cost is dominated by its geographically distributed data centers (DCs). Generally within a DC, the energy consumption is dominated by its server infrastructure and cooling system, with each contributing approximately half. However, existing research work has been addressing energy efficiency on these two sides separately. In this paper, we jointly optimize the energy consumption of both server infrastructures and cooling systems in a holistic manner. Such an objective is achieved through both strategies of: 1) putting idle servers to sleep within individual DCs; and 2) shutting down idle DCs entirely during off-peak hours. Based on these strategies, we develop a heuristic algorithm, which concentrates user request resolution to fewer DCs, so that some DCs may become completely idle and hence have the opportunity to be shut down to reduce their cooling energy consumption. Meanwhile, QoS constraints are respected in the algorithm to assure service availability and end-to-end delay. Through simulations under realistic scenarios, our algorithm is able to achieve an energy-saving gain of up to 62.1% over an existing CDN energy-saving scheme. This result is bound to be near-optimal by our theoretically-derived lower bound on energy-saving performance.

Energy efficiency has become an increasingly important aspect of wireless communications. Coordinated multipoint (CoMP) processing may be considered as a promising approach to improve the energy efficiency, extend the cell coverage, and increase the communication capacity in wireless cellular networks. However, the CoMP communication systems may require a large amount of channel state information (CSI) feedbacks which would be constrained by the wireless backhaul networks. This paper proposes a control theoretical approach to improve the energy efficiency in cooperative wireless cellular networks with CoMP communications. In particular, we propose a stochastic predictive control algorithm to achieve energy-efficient CoMP transmissions through optimal base station grouping. Moreover, since the channel state information in CoMP is often outdated/lost due to the feedback delays in the backhaul network, our proposed approach is based on a new discrete time predictive function control model to mitigate the impacts of packet delay or loss of channel state information. Simulation results show the effectiveness of the proposed scheme.

In this paper, we investigate the performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) relaying system with orthogonal space-time block code (OSTBC) transmissions over correlated Nakagami-m fading channels. For maximal ratio combining (MRC) receiver at the destination, we provide the compact closed-form expressions for cumulative distribution function (CDF) and probability density function (PDF) of the instantaneous end-to-end signal-to-noise ratio (SNR). In addition, the exact analytical expressions are also derived for the outage probability (OP) and symbol error rate (SER) relying on CDF. Furthermore, we present the asymptotic expressions for OP and SER in the high SNR regime, from which we gain an insight into the system performance and derive the achievable diversity order and array gain. The analytical expressions are validated through Monte-Carlo simulations.

The Long Term Evolution (LTE) may provide ubiquitous mobile broadband services with all IP architecture, however, the quality of service (QoS) of LTE systems is seriously affected by the network congestions, packet losses, jitters, latencies and other QoS issues in all IP networks. Thus it is valuable to investigate and design efficient resource scheduling algorithms to improve the performance of data services and enduser experiences. In this paper we propose an improved radio resource scheduling algorithm over the existing semi-continuous scheduling algorithm for the voice over Internet Protocol (VoIP) data packets. Through mapping the TCP (transmission control protocol) ACK (acknowledgement) packets into a higher priority logical channel, the probability of both the discarded ACK packets and congestions in the wireless channels are reduced. As the result, the scheme may avoid frequently opening the TCP congestion control mechanism. The simulation results have shown the advantages of our proposed algorithm, such as the RTT (Round-Trip Time) packet delay reduction, improved throughput, acceptable stability, desirable performance, and son on.

The issue of improving energy efficiency for size-limited transceivers is considered. Effective transmitter-end and receiver-end coupling matrices are studied. Different from prior works, effective radiation impedances are involved in the expression of effective transmitter-end coupling matrix (ETCM). It is proved that, when transmit antennas are coupled, effective radiation impedances vary with input signals and thus so does ETCM, whereas effective receiver-end coupling matrix (ERCM) does not vary with incident waves even when receive antennas are coupled. Based on the analysis on ETCM and ERCM, antenna selection based transceivers equipped with a compact antenna array is proposed. It is shown that a size-limited transceiver can improve its energy efficiency using a compact antenna array and thanks to receiver-end coupling matrix estimation (RCME) the additional training overhead is negligible.

The optimized weight spectrum sequence (OWSS) is utilized as a design criterion in this paper to determine the periodic puncturing pattern and the non-periodic puncturing pattern of partially systematic RCPT (PS-RCPT) codes for LTE systems. It is shown that the OWSS-criterion based PS-RCPT codes outperform the pseudo-random puncturing (PRP) based PS-RCPT codes. Meanwhile, it is unveiled that, the puncturing ratio of information bits should be carefully determined in PS-RCPT codes generation to achieve reasonable tradeoff between the waterfall region and the error floor region performance.

In this paper, we investigate beamforming approaches for multiple access secrecy channels. This paper investigates the equivalency between beamforming weight solutions under a fixed sum transmit power constraint and those under a fixed secrecy rate when multiple eavesdroppers are available. We propose an advantageous semidefinite-programming based beamforming solution for multiple eavesdroppers in multiple-access wire-tap channels.

Proportional fair (PF) scheduling algorithm has been extensively utilized in many different systems, since it can support both multi-user diversity and system fairness guarantee. Brute-force searching is the optimal scheduling, pairing and ordering algorithm. However, the optimal brute-force searching method is too computationally prohibitive, especially in case of the large number of users. This paper proposes a novel 2-dimensional grid based scheduling scheme for uplink MU-MIMO transmission using either the conventional proportional fairness metric or delta scheduling metric. Comparing to the scheduling approach using the conventional priority based pairing metric, the proposed scheduling algorithm based on delta scheduling metric could obtain approximate 7% gain for both cell and cell-edge throughput gain with neglectful computational complexity increase. With the aid of successive interference cancellation, MU-MIMO with MMSE-SIC receiver could also improve the cell-edge throughput compared to SU-MIMO. The advantage of the proposed scheme is that it can improve the throughput performance without the sacrifice of the user fairness guarantee.

Orthogonal frequency division multiplexing (OFDM) technique has gained increasing popularity in both wired and wireless communication systems. However, in the conventional OFDM systems the insertion of a cyclic prefix (CP) and the transmission of periodic training sequences for purpose of channel estimation decrease the system's spectral efficiency. As an alternative to OFDM, isotropic orthogonal transform algorithm (IOTA)-based multicarrier system adopts a proper pulse shaping with good time and frequency localisation properties to avoid interference and maintain orthogonality in real field among sub-carriers without the use of CP. In this study, the authors propose linearly precoded IOTA-based multicarrier systems to achieve blind channel estimation by utilising the structure of auto-correlation and cross-correlation matrices introduced by precoding. The results show that the proposed IOTA-based multicarrier systems achieve better power and spectral efficiency compared with the conventional OFDM systems.

Using cyclic prefix (CP), the transmission schemes, orthogonal frequency-division multiplexing (OFDM), single carrier block transmission, and time reversal, can be unified as linear block precoding. Considering frequency-selective channels, this paper studies linear block precoding for CP-based multi-point transmission with per-antenna power constraints (PAPCs) under capacity maximization and mean-square-error (MSE) minimization criteria. We show that, the optimal precoders for both criteria could be, but not necessarily, in the form of OFDM transmission (i.e., an inverse discrete fourier transformation (IDFT) matrix multiplying a complex diagonal matrix). Based on the optimal precoder structure, the two problems are simplified to two matrix-free optimization problems for which we prove strong duality holds. Moreover, it is shown that the dual problems can be equivalent to two unconstrained convex optimization problems. Efficient optimum precoding algorithms are proposed for both problems. Simulation results show that the maximum capacity (or minimum MSE) in the PAPC case almost coincides with that in the sum power constraint (SPC) case when the power budgets for each antenna are equal, but a capacity (or MSE) gap exists between the two power constraint cases when the power budgets for each antenna are different.

Using cyclic prefix (CP), the transmission schemes, orthogonal frequency-division multiplexing (OFDM), single carrier block transmission, and time reversal, can be unified as linear block precoding. Considering frequency-selective channels, this paper studies linear block precoding for CP-based multi-point transmission with individual antenna power constraints under capacity maximization criterion. We prove that, the optimal precoder could be, but not necessarily, in the form of OFDM transmission (i.e., an inverse discrete fourier transformation (IDFT) matrix multiplying a complex diagonal matrix). Further, we show that the capacity maximization problem with individual power constraints can be simplified as an unconstrained problem, which allows us to solve the problem using the simple gradient decent method. Simulation results show that the proposed precoding method achieves near-optimal performance.

Different detection schemes for multiple-input, multiple-output (MIMO) systems are investigated. By enhancing the interference and noise estimation, we propose a novel MIMO receiver strategy, which is shown to achieve superior performance with moderate increase in computational complexity compared to conventional MIMO detection schemes.

In this paper, we propose high-rate distributed space-time-frequency codes (DSTFCs) to exploit maximum achievable diversity gains over frequency-selective fading channels. The proposed designs achieve full-rate for any number of cooperative nodes, and allow channel variations over multiple OFDM blocks within one DSTFC codeword. We analyze diversity gains of DSTFCs through both conditional and average pairwise error probability (PEP), and we proposes better design criteria based on one-side channel conditional PEP. We show that the difference between the frequency-selective channel orders of source-to-relay and relay-to-destination links may provide extra diversity advantages, thus additional performance gains. Through Monte-Carlo simulations, we demonstrate that proposed high-rate DSTFCs provide notable diversity advantages over existing designs.

This paper extends square M×M linear dispersion codes (LDC) proposed by Hassibi and Hochwald to T×M non-square linear dispersion codes of the same rate M, termed uniform LDC, or U-LDC. This paper establishes a unitary property of arbitrary rectangular U-LDC encoding matrices and determines their connection to the traceless minimal nonorthogonality criterion for space-time codes. The U-LDC are then applied to rapid fading channels by constructing trace-orthonormal versions, or TON-U-LDC for 2L and 4L input symbols, where L is a positive integer. Compared to a variety of state-of-the-art codes, the proposed codes are found to perform well in both block and rapid fading channels. In rapid fading, the symbol-wise time diversity order of a T × M, TON-U-LDC for 2L input symbols is shown to be min (T,2M).

This paper considers multiuser MIMO CDMA systems with high rate space-time linear dispersion codes (LDC) and orthogonal space-time block codes (O-STBC) in time-varying Rayleigh fading MIMO channels. We propose a multi-function process integrating multi-user detection, space-time decoding and symbol demodulation, which can be coupled with soft channel decoding to improve the system performance in an iterative fashion. We show that the space-time coded CDMA systems approach the single-user bound with only two iterations, and full diversity LDCs enable the systems to utilize the time diversity inherent in fast fading channels. The space-time coded CDMA systems are also compared to the MIMO CDMA system based on spatial multiplexing, some recommendations are made on how to design a practical MIMO CDMA system based on the comparative studies.

To improve performance of orthogonal frequency division multiplexing (OFDM) for fading channels, this paper proposes increasing frequency and time diversity using linear dispersion codes (LDC-OFDM). Methods of LDC-OFDM processing are proposed for both zero-padding (ZP) and cyclic-prefix (CP) type guard intervals. A two-step-estimation (TSE) decoding strategy is proposed that decouples symbol estimation from LDC decoding. This paper analyzes the upper bound diversity order of LDC-CP-OFDM, which is equal to the full diversity order available in the channels. A criterion for full frequency-time diversity design is derived, a rate-one code is provided and performance is examined through simulations. This paper also investigates LDC-CP-OFDM and LDC-ZP-OFDM performance under imperfect channel estimation and low complexity receiver structures, respectively. In addition, TSE is shown to have performance close to that of full complexity one-step estimation (OSE).

High-rate space-time-frequency block codes (STFBC) are promising for achieving high bandwidth efficiency, low overhead and latency. Recently, a class of low-complexity STFBC methods based on two stages of complex diversity coding (CDC) have been proposed, known as double linear dispersion STFC(DLD-STFC). This paper investigates two issues related to the performance improvement of high-rate STFCs. First, it is shown that the two CDC stages of DLD-STFC can be interchanged. Two new diversity concepts for analysis of 3-dimensional DLD-STFC are introduced: per dimension diversity order and per dimension symbol-wise diversity order. A sufficient condition for DLD-STFC to achieve full symbol-wise diversity order is provided despite the existence of two CDC stages. Second, the gain obtainable in combining CDC with forward error correction (FEC) for STFC designs is quantified. Through simulations, it is shown that STFC based on the proper combination of CDC and FEC may outperform a variety of other STFC combinations, especially in spatially correlated channels. Further, the choice of the mapping from FEC to DLD-STFC may significantly impact system performance.

This paper proposes a general coordinate-interleaving method for block-based space-time codes or linear dispersion codes, called space-time coordinate interleaving linear dispersion codes (ST-CILDC), which enables not only symbol-level diversity but also coordinate-level diversity for high rate block-based space-time code design. This paper analyzes the upper bound diversity order and provides the analysis results of the upper bound statistical diversity order and average diversity order for ST-CILDC systems. Compared with conventional ST-LDC systems, ST-CILDC systems may show either almost doubled average diversity order or extra coding advantage in time varying channels. With trivial extra complexity over ST-LDC systems, ST-CILDC systems maintain the diversity performance in quasi-static block fading channels, and significantly improve the diversity performance in rapid fading channels

Linear dispersion coded orthogonal frequency division multiplexing (LDC-OFDM) has recently been proposed to improve joint frequency and time diversity. This paper investigates whether LDC are able to support joint frequency and time diversity for single-carrier block communications in time-varying frequency selective fading channels, and proposes linear dispersion coded cyclic-prefix single-carrier modulation (LDC- CP-SCM), which utilizes LDC across multiple CP-SCM blocks. LDC-CP-SCM uses a layered two-stage LDC decoding strategy, and is thus backwards-compatible to CP-SCM systems. This paper analyzes the diversity properties of LDC-CP-SCM, and provides a sufficient condition for LDC-CP-SCM to maximize all available joint frequency and time diversity gain and coding gain. For the LDC considered, simulations show that with and without carrier frequency offset (CFO) effects, LDC-CP-SCM may outperform both CP-SCM and LDC-CP-OFDM in time- varying frequency selective channels. This paper also shows that LDC-CP-SCM with forward error correction (FEC) may outperform CP-SCM with FEC over time.

This paper investigates increasing space, time, and frequency diversity through linear dispersion codes (LDC) in MIMO-OFDM wireless fading channels. Two new types of block-based high-rate space-time-frequency (STF) codes: (1) double linear dispersion space-time-frequency-coding (DLD-STFC), and (2) linear dispersion space-time-frequency-coding (LD-STFC) are proposed. In addition to double LDC encoding, DLD-STFC uses three-stage LDC decoding. The LD-STFC, on the other hand, requires only one LDC procedure across multiple OFDM subcarriers, OFDM blocks and multiple antennas. Both DLD-STFC and LD-STFC are backwards compatible to uncoded MIMO-OFDM systems. Comparison to an extension of a recently proposed LDC-OFDM to MIMO systems, called MIMO-LDC-OFDM, is made in which a single LDC-OFDM codeword is mapped to one transmit antenna. This paper discusses diversity properties of these STF block based designs. An error union bound analysis provides further insights, including more restrictive LDC code design criteria. Compared to other methods of similar complexity, simulations reveal that the bit error rate (BER) performance of full-rate DLD-STFC offers superior performance.

High rate linear dispersion codes (LDC) for space time channels can support arbitrary numbers of transmit and receive antennas. In contrast to the one-to-one transformations used in interleaving, these codes disperse data in linear combinations over space and time. To improve performance of orthogonal frequency division multiplexing (OFDM) for wireless fading channels, this paper investigates increasing frequency and time diversity using LDC. To overcome the requirement of constant channel gains over an entire LDC time interval, a new decoding algorithm for a special subclass of LDC is proposed. The newly proposed LDC-OFDM linearly disperses data over both time and frequency, i.e., over multiple subcarriers and OFDM blocks. Simulations show the bit error rate (BER) performance of rate-one LDC-OFDM with zero padding is superior to that of uncoded OFDM with zero padding. Further, compared to uncoded OFDM, LDC-OFDM may have improved performance without increasing the peak-to-average power ratio (PAPR).