Resource allocation is paramount to improve spectral efficiency in spectrum-sharing networks. However, numerous existing resource allocation schemes, especially those based on deep reinforcement learning techniques, overlook the impact of time-variant channel quality caused by high dynamics of wireless environment and heterogeneous action space due to discrete actions and continuous parameters, which may significantly degrade the desired system performance. To tackle these issues, in this paper, two intelligent resource allocation schemes that can jointly optimize channel allocation and transmit power in a dynamic spectrum-sharing network are proposed. In particular, an intelligent framework, enhanced by channel prediction, is first proposed to capitalize fully on the latent evolutionary characteristics of time-varying channels, facilitating efficient resource allocation design. Subsequently, a hybrid action representation-based intelligent framework is proposed to learn the latent dependence between channel allocation and transmit power for each secondary user. Simulation results demonstrate that our proposed schemes achieve superior performance compared with several benchmark schemes, highlighting that the sum rate can be improved by exploiting latent channel characteristics and latent hybrid actions dependence.

Precisely modeling radio propagation in complex environments has been a significant challenge, especially with the advent of 5G and beyond networks, where managing massive antenna arrays demands more detailed information. Traditional methods, such as empirical models and ray tracing, often fall short, either due to insufficient details or because of challenges for real-time applications. Inspired by the newly proposed 3D Gaussian Splatting method in the computer vision domain, which outperforms other methods in reconstructing optical radiance fields, we propose RF-3DGS, a novel approach that enables precise site-specific reconstruction of radio radiance fields from sparse samples. RF-3DGS offers high efficiency, requiring only a few minutes for training and achieving fast inference for any arbitrary receiver pose within milliseconds. Furthermore, RF-3DGS can provide fine-grained Spatial Channel State Information (Spatial-CSI) of these paths, including the channel gain, the delay, the angle of arrival (AoA), and the angle of departure (AoD). Our experiments, calibrated through real-world measurements, demonstrate that RF-3DGS not only significantly improves reconstruction quality, training efficiency, and rendering speed compared to state-of-the-art methods, but also holds great potential for supporting wireless communication and advanced applications such as Integrated Sensing and Communication (ISAC). Code and dataset are available athttps://github.com/SunLab-UGA/RF-3DGS

High peak-to-average power ratio (PAPR) has long posed a challenge for multi-carrier systems, impacting amplifier efficiency and overall system performance. This paper introduces dynamic angle fractional Fourier division multiplexing (DA-FrFDM), an innovative transmission framework that significantly reduces PAPR by dynamically selecting an optimal transform angle in the fractional Fourier domain. By exploiting the dual nature of PAPR across time and frequency domains, DA-FrFDM adaptively maps signals from an intermediate domain to time domain that minimizes peak fluctuations while preserving average power. A tailored optimization algorithm is developed to efficiently determine the angle that minimizes PAPR for each signal block. Simulation results demonstrate that DA-FrFDM outperforms state-of-the-art PAPR mitigation techniques such as clipping, selective mapping, and partial transmitted sequence, achieving superior PAPR reduction for both Gaussian and QAM signals. These findings highlight DA-FrFDM as a promising solution for enhancing power efficiency in future multi-carrier systems.

The proliferation of mobile devices and online interactions have been threatened by different cyberattacks, where phishing attacks and malicious Uniform Resource Locators (URLs) pose significant risks to user security. Traditional phishing URL detection methods primarily rely on URL string-based features, which attackers often manipulate to evade detection. To address these limitations, we propose a novel graph-based machine learning model for phishing URL detection, integrating both URL structure and network-level features such as IP addresses and authoritative name servers. Our approach leverages Loopy Belief Propagation (LBP) with an enhanced convergence strategy to enable effective message passing and stable classification in the presence of complex graph structures. Additionally, we introduce a refined edge potential mechanism that dynamically adapts based on entity similarity and label relationships to further improve classification accuracy. Comprehensive experiments on real-world datasets demonstrate our model's effectiveness by achieving F1 score of up to$\text{9 8. 7 7 \%}$. This robust and reproducible method advances phishing detection capabilities, offering enhanced reliability and valuable insights in the field of cybersecurity.

Federated learning (FL) has emerged as a distributed machine learning (ML) technique that can protect local data privacy for participating clients and improve system efficiency. Instead of sharing raw data, FL exchanges intermediate learning parameters, such as gradients, among clients. This article presents an efficient wireless communication approach tailored for FL parameter transmission, especially for Internet of Things (IoT) devices, to facilitate model aggregation. Our study considers practical wireless channels that can lead to random bit errors, substantially affecting FL performance. Motivated by empirical gradient value distribution, we introduce a novel received bit masking method that confines received gradient values within prescribed limits. Moreover, given the intrinsic error resilience of ML gradients, our approach enables the delivery of approximate gradient values with errors without resorting to extensive error correction coding or retransmission. This strategy reduces computational overhead at both the transmitter and the receiver and minimizes communication latency. Consequently, our scheme is particularly well-suited for resource-constrained IoT devices. Our simulations demonstrate that our proposed scheme can effectively mitigate random bit errors in FL performance, achieving similar learning objectives but with the 50% air time required by existing methods involving error correction and retransmission.

Semantic communication is envisioned as a potential communication paradigm enabled by artificial intelligence and is promising to break the Shannon limit for future 6G networks. This paradigm benefits uninhabited aerial vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting task-relevant semantic information. However, resource allocation in the multiple collaborative UAV scenarios remains unexplored, particularly regarding multi-modal semantic communication. To tackle this challenge, this paper investigates a semantic-aware intelligent resource allocation method for multi-UAV-assisted semantic communication networks in the UAV image-sensing task-oriented scenario. A multi-modal semantic communication framework with multi-UAV relay collaboration is developed. At the semantic level, a novel quality of experience (QoE) and the transmission cost model are introduced, based on which a semantic-aware resource allocation problem is formulated, aiming to maximize QoE while minimizing the transmission cost by jointly optimizing the UAV trajectory, the spectrum bandwidth, the transmit power and the number of the transmitted semantic symbols. To deal with optimization challenges involving hybrid variables and coordination among UAVs, a multi-UAV hybrid decision-controlled deep reinforcement learning (DRL) scheme is proposed. Simulation results demonstrate the effectiveness of the proposed scheme compared with the benchmark schemes in achieving a good balance between the QoE and the transmission cost.

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing. Link state map (LSM) is one particular type of CKM that aims to learn the location-specific line-of-sight (LoS) link probability between the transmitter and the receiver at all possible locations, which provides the prior information to enhance the communication quality of dynamic networks. This paper investigates the LSM construction for cellular-connected unmanned aerial vehicles (UAVs) by utilizing both the expert empirical mathematical model and the measurement data. Specifically, we first model the LSM as a binary spatial random field and its initial distribution is obtained by the empirical model. Then we propose an effective binary Bayesian filter to sequentially update the LSM by using the channel measurement. To efficiently update the LSM, we establish the spatial correlation models of LoS probability on the location pairs in both the distance and angular domains, which are adopted in the Bayesian filter for updating the probabilities at locations without measurements. Simulation results demonstrate the effectiveness of the proposed algorithm for LSM construction, which significantly outperforms the benchmark scheme, especially when the measurements are sparse.

Accurate channel estimation is required for various multiple input multiple output (MIMO) implementations in the next-generation wireless communication systems. Recently, Artificial Intelligence (AI) techniques have been introduced for channel state information (CSI) processing in channel estimation because of their accuracy and relatively low complexity compared to the traditional approaches. However, these AI-supported CSI processing models are usually developed with a fixed training dataset. Therefore, the performance of such approaches cannot be guaranteed in new environments. This paper focuses on enhancing AI-supported channel estimation methods with a 2-stage open set recognition scheme. New environments are detected in the first stage by identifying different characteristics between testing and training data. In the second stage, new data is filtered and further categorized to each individual environment. Simulation results using four different environment settings demonstrate that the proposed method can greatly enhance the usability of AI-supported channel estimation.

Semantic communication is of crucial importance for the next-generation wireless communication networks. Recent advancements have primarily benefited from the design of semantic communication systems based on deep learning. Nevertheless, these deep learning-based systems are vulnerable to certain security attacks, particularly backdoor attacks. A novel attack paradigm, backdoor attacks on semantic symbols (BASS), targets reconstruction tasks by manipulating the reconstructed source data or features. However, the perceivable risks associated with BASS have not been thoroughly explored. This paper investigates the perceivable risks of BASS in the context of computer vision tasks. A transform-based methodology is designed to improve the stealthiness of the poisoned reconstructed target samples in the training dataset. In addition, while various hidden triggers have been studied for traditional backdoor attacks, they cannot be applied to BASS directly due to the unaligned model problem. To address this, an iterative hidden trigger generation (IHTG) algorithm is proposed. The simulation results demonstrate the effectiveness of the proposed methods in addressing the perceivable risks in BASS.

A vehicular communication network allows vehicles on the road to be connected by wireless links, providing road safety in vehicular environments. Vehicular communication network is vulnerable to various types of attacks. Cryptographic techniques are used to prevent attacks such as message modification or vehicle impersonation. However, cryptographic techniques are not enough to protect against insider attacks where an attacking vehicle has already been authenticated in the network. Vehicular network safety services rely on periodic broadcasts of basic safety messages (BSMs) from vehicles in the network that contain important information about the vehicles such as position, speed, received signal strength (RSSI) etc. Malicious vehicles can inject false position information in a BSM to commit a position falsification attack which is one of the most dangerous insider attacks in vehicular networks. Position falsification attacks can lead to traffic jams or accidents given false position information from vehicles in the network. A misbehavior detection system (MDS) is an efficient way to detect such attacks and mitigate their impact. Existing MDSs require a large amount of features which increases the computational complexity to detect these attacks. In this paper, we propose a novel grid-based misbehavior detection system which utilizes the position information from the BSMs. Our model is tested on a publicly available dataset and is applied using five classification algorithms based on supervised learning. Our model performs multi-classification and is found to be superior compared to other existing methods that deal with position falsification attacks.

Massive multiple input multiple output (MIMO) systems are integral to next-generation wireless technologies due to their ability to meet the growing demands of throughput and support a plethora of applications. An efficient operation of massive MIMO requires accurate channel state information (CSI). In a frequency division duplex (FDD) MIMO system, the base station can rely on CSI feedback that user equipment (UE) estimates from downlink CSI from orthogonal pilot sequences. Recently, artificial intelligence (AI), i.e., deep learning approaches, have been introduced to compress and reconstruct CSI matrices at UE and the base station, respectively. However, these existing approaches still rely on channel estimation at the UE side, which introduces additional errors in the autoencoder design. To address these issues, we propose to implement the autoencoder that processes the pilot sequences directly to avoid excessive processing errors. Moreover, a higher compression can be achieved due to the lower error. Evaluation results demonstrate that the proposed scheme can significantly reduce the communication overhead by using a higher compression ratio while maintaining high CSI reconstruction performance in addition to lower bit error rates compared to the existing deep learning approach.

Asynchronous federated learning aims to solve the straggler problem in an environment with heterogeneity, where certain clients may possess limited computational capacities, potentially leading to model aggregation delay. The core concept behind asynchronous federated learning is to empower the server to aggregate the model as soon as it receives an update from any client without waiting for updates from multiple clients or adhering to a predetermined waiting time, which is typical in synchronous mode. Because of the asynchronous setting, a potential concern is the emergence of a stale model issue, wherein slow clients might employ an outdated local model for their data training. Consequently, when these locally trained models are uploaded to the server, they may impede the convergence of the global training. Therefore, effective model aggregation strategies play a significant role in updating the global model. Besides, client scheduling is critical when heterogeneous clients with diversified computing capacities participate in the federated learning process. This work first investigates the impact of the convergence of asynchronous federated learning mode when adopting the aggregation coefficient in synchronous mode. Effective aggregation solutions that can achieve the same convergence result as in the synchronous mode are proposed, followed by an improved aggregation method with client scheduling. The simulation results in various cases demonstrate that the proposed algorithm converges with a similar level of accuracy as the classical synchronous federated learning algorithm but effectively accelerates the learning process, especially in its early stage.

A vehicular network is susceptible to various security flaws and attacks. Cryptographic techniques are used in vehicular networks but these alone cannot provide proper security to the network. Identifying various types of attacks is necessary to secure vehicular communication networks. This work is focused on both binary and multi-class attack detection in vehicular networks. A publicly available dataset, VeReMi-Extension is used to detect these attacks. This dataset has been reformulated to generate novel features aimed at detecting attacks in vehicular networks accurately. Machine learning-based methods have been applied to the reformulated dataset for the detection of attacks in vehicular networks. The extensive simulation results show that the proposed scheme can detect more than 99% attacks both for binary and multi-class scenarios which is an impressive performance to enhance the security in vehicular networks.

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to the serious challenge, namely, the finite computation, energy and communication resources, which limits the achievable detection performance. In order to overcome this challenge, a UAV cognitive semantic communication system is proposed by exploiting knowledge graph. Moreover, a multi-scale compression network is designed for semantic compression to reduce data transmission volume while guaranteeing the detection performance. Furthermore, an object detection scheme is proposed by using the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that compared to the benchmark systems, our proposed system has superior detection accuracy, communication robustness and computation efficiency even under high compression rates and low signal-to-noise ratio (SNR) conditions.

Millimeter wave (mmWave) communications are highly affected by blockage, whereas the emerging reconfigurable intelligent surface (RIS) has the potential to overcome this issue. This paper proposes a Neyman-Pearson (N-P) criterion-based blockage-aware algorithm to improve resilience to blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for RIS-assisted mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. Specifically, we propose an accelerated projected gradient descent (PGD) algorithm to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we formulate a new Nesterov momentum acceleration scheme to speed up the convergence rate. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate performance.

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency.

Semantic communication is of crucial importance for the next-generation wireless communication networks. The existing works have developed semantic communication frameworks based on deep learning. However, systems powered by deep learning are vulnerable to threats such as backdoor attacks and adver-sarial attacks. This paper delves into backdoor attacks targeting deep learning-enabled semantic communication systems. Since current works on backdoor attacks are not tailored for semantic communication scenarios, a new backdoor attack paradigm on semantic symbols (BASS) is introduced, based on which the corresponding defense measures are designed. Specifically, a training framework is proposed to prevent BASS. Additionally, reverse engineering-based and pruning-based defense strategies are designed to protect against backdoor attacks in semantic communication. Simulation results demonstrate the effectiveness of both the proposed attack paradigm and the defense strategies.

The global surge in electric vehicle (EV) adoption demands a corresponding expansion of smart EV charging stations, leveraging intelligent management for efficient EV charging. Intelligent EV charging management can efficiently control EV charging scheduling and power flow among the electrical power grid, renewable energy sources, and battery energy storage systems (BESS) to achieve minimized charging costs. This paper proposes the use of the State-Action-Reward-State-Action (SARSA) reinforcement learning (RL) algorithm for power scheduling in a grid-tied model integrating photovoltaics (PV) and BESS of an EV charging station, specifically for fast charging. SARSA RL is selected for its significant potential to effectively manage the complexities associated with power scheduling, thus facilitating the scheduling and optimization of power flow in this scenario. The system employs a day-ahead planning approach, adopting forecasts for solar power generation, grid tariff, and EV charging demand as EV load consumption predictions. In addition, a Markov decision process is used to formulate the optimization problem. The simulation results conclude that the SARSA RL algorithm achieves good rewards by minimizing the cost associated with power scheduling.

Unmanned aerial vehicle (UAV) swarms have found widespread applications in executing high-complexity and remote-risk missions. However, the limited onboard resources and energy of UAV swarms may hinder their support for computation-intensive yet delay-sensitive applications, especially when faced with exponentially growing big data. This article focuses on investigating a UAV swarm-assisted aerial–ground collaborative computing system, where one UAV swarm is divided into different groups and collaborates with a remote ground base station (BS) to provide computation services for ground smart mobile devices (SMDs). A comprehensive optimization framework is presented to maximize the system’s computation efficiency by jointly designing group formation, UAV trajectories, and resource allocation. The formulated problem involves a fractional structure with nonlinear coupling of different variables, rendering it highly nonconvex. To address this challenge, we propose a Dinkelbach-based looped iterative optimization (DLIO) algorithm. Specifically, Dinkelbach’s method is initially adopted to reformulate the original problem into a parametric structure, which is then decomposed into subproblems for group formation, resource allocation, and UAVs’ trajectory scheduling. Subsequently, these subproblems are addressed by a looped iterative optimization (LIO) algorithm. The outer loop determines group forming and resource allocation, while the inner loop utilizes the method of successive convex approximation (SCA) to solve trajectory scheduling for UAVs. Simulation results validate the effectiveness of our proposed DLIO algorithm, ensuring rapid convergence and significant improvements in the system’s computation efficiency compared to other benchmarks.

The success of the Internet of Things has recently spread to the industrial sector, commonly referred to as Industrial IoT (IIoT). IIoT, which has a far-reaching impact on the operation of industries around the world, is recognized as a key enabler for the fourth industrial revolution. It has the potential to prompt economic growth and global competitiveness, in terms of improving productivity, efficiency, and so on.

In this paper, we investigate a sensing-enabled integrated space-air-ground (SAG) data collection network, in which an unmanned aerial vehicle (UAV) can not only work singly to sense data from multiple targets but also collaborate with a low-earth orbit (LEO) satellite to collect communication data from multiple users. Since the coverage of the UAV is much smaller than that of the LEO satellite, we first determine the set of usable users and targets for the UAV by analyzing the signal-to-noise ratios between the UAV and the users and targets. Based on this, we pose an optimization problem designed to maximize the total amount of data collected in the network while satisfying the constraints of UAV energy consumption, memory capacity, and minimum amount of sensor data per target. Moreover, considering that the network consists of three layers and the UAV has dual functions of communication and sensing, this problem is solved by jointly optimizing the scheduling of the users’ data upload scheme, the UAV trajectory, and the allocation of communication and sensing time. However, the formulated problem is a mixed integer nonlinear programming (MINLP) problem, so it is difficult to find the optimal solution. Therefore, we further design an alternating iterative optimization algorithm (AIOA) framework to find an appropriate solution. Specifically, we alternately optimize the UAV trajectory, time allocation strategy, and data upload schedule in each iteration. Finally, simulation experiments validate the effectiveness of the AIOA and its superiority over other benchmarks in terms of the amount of data collected.

Driven by the ever-increasing requirements of ultrahigh spectral efficiency, ultralow latency, and massive connectivity, the forefront of wireless research calls for the design of advanced next-generation multiple access schemes to facilitate the provisioning of these stringent demands. This inspires the embrace of nonorthogonal multiple access (NOMA) in future wireless communication networks. Nevertheless, the support of massive access via NOMA leads to additional security threats due to the open nature of the air interface, the broadcast characteristic of radio propagation, and the intertwined relationship among paired NOMA users. To address this specific challenge, the superimposed transmission of NOMA can be explored as new opportunities for security-aware design; for example, multiuser interference inherent in NOMA can be constructively engineered to benefit communication secrecy and privacy. The purpose of this tutorial is to provide a comprehensive overview of the state-of-the-art physical layer security techniques that guarantee wireless security and privacy for NOMA networks, along with the opportunities, technical challenges, and future research trends.

Semantic communication (SemCom) is envisioned as a promising technique to break through the Shannon limit. However, semantic inference and semantic error correction have not been well studied. Moreover, error correction methods of existing SemCom frameworks are inexplicable and inflexible, which limits the achievable performance. In this paper, to tackle this issue, a knowledge graph (KG) is exploited to develop SemCom systems. Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios. Moreover, a simple, general, and interpretable semantic alignment algorithm for semantic information detection is proposed. Furthermore, an effective semantic correction algorithm is proposed by mining the inference rule from the KG. Additionally, the pre-trained model is fine-tuned to recover semantic information. For the multi-user cognitive SemCom system, a message recovery algorithm is proposed to distinguish the messages of different users by matching the knowledge level and the context at the destination. Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive SemCom systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability. Finally, we present realistic single-user and multi-user cognitive SemCom systems results by building a software-defined radio prototype system.

A robust transmission scheme for intelligent reflecting surface (IRS) aided cognitive radio systems is designed to provide security against internal eavesdropping. By considering the secondary receiver as an internal eavesdropper, a total transmit power (TTP) minimization problem is formulated. Through a novel optimization strategy which jointly considers the transmit beamforming vector at the primary transmitter, the transmit beamforming vector at the secondary transmitter and the phase shifts at the IRS, a solution to the formulated problem is presented. By considering perfect channel state information (CSI) and imperfect CSI scenarios, two optimization algorithms are proposed both aiming at reducing the TTP. For the former scenario, instead of applying the commonly used inner approximation (IA) algorithm which cannot handle maximum allowable leaked rate constraint, a novel penalty-based IA algorithm is proposed. For the latter scenario, a non-convex robust optimization problem is formulated. Because of its non-convex nature, a novel upper-bounding technique and S-procedure are used to transform it into a more tractable form which is then solved by deriving of a penalty convex-concave procedure based alternating optimization algorithm. Performance results show that, as compared to other baseline schemes, the proposed algorithms significantly reduce the TTP.

Millimeter wave (mmWave) communications are sensitive to blockage over radio propagation paths. The emerging paradigm of reconfigurable intelligent surface (RIS) has the potential to overcome this issue by its ability to arbitrarily reflect the incident signals toward desired directions. This paper proposes a Neyman-Pearson (NP) criterion-based blockage-aware algorithm to improve communication resilience against blockage in mobile mmWave multiple input multiple output (MIMO) systems. By virtue of this pragmatic blockage-aware technique, we further propose an outage-constrained beamforming design for downlink mmWave MIMO transmission to achieve outage probability minimization and achievable rate maximization. To minimize the outage probability, a robust RIS beamformer with variant beamwidth is designed to combat uncertain channel state information (CSI). For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization. Particularly, we leverage a subspace constraint to reduce the scope of the projection operation and formulate a new Nesterov momentum acceleration scheme to speed up the convergence process of PGD. Extensive experiments confirm the effectiveness of the proposed blockage-aware approach, and the proposed accelerated PGD algorithm outperforms a number of representative baseline algorithms in terms of the achievable rate.

Semantic communication is envisioned as a potential communication paradigm based on artificial intelligence that holds the promise of breaking the Shannon limit for future 6G networks. This paradigm offers a promising opportunity for Unmanned Aerial Vehicles (UAVs) to conserve communication resources and minimize latency by only transmitting relevant semantic information. Despite the promising potential of UAV semantic communication networks, resource allocation in this context remains largely unexplored, particularly regarding multi-modal communication that adjusts the types of transmitted information (image, text, video, etc.) according to the task objectives and the available resources. This paper addresses the semantic-oriented resource allocation for multi-modal semantic communication with a focus on the UAV image-sensing task-oriented scenario. Firstly, a multi-modal semantic communication for the original image-sensing tasks of UAVs is designed. Subsequently, a semantic-level resource allocation problem based on the approximate semantic entropy and the semantic rate is formulated in terms of the transmit power allocation, channel assignment, and the number of transmitted semantic symbols. To solve the problem formulated, which involves a hybrid discrete-continuous action space, a novel algorithm called Hybrid-Decision-Controlled Deep Reinforcement Learning-based Semantic Communication Allocation (HDCD-SC) is introduced. The simulation results demonstrate that the proposed HDCD-SC algorithm can dynamically adjust the transmission modal according to the available resources, and achieve better performance in terms of latency, amount of semantic information, and notable reductions in energy and bandwidth costs when compared to other benchmarks.

Automated Internet of Things (IoT) devices generate a considerable amount of data continuously. However, an IoT network can be vulnerable to botnet attacks, where a group of IoT devices can be infected by malware and form a botnet. Recently, Artificial Intelligence (AI) algorithms have been introduced to detect and resist such botnet attacks in IoT networks. However, most of the existing Deep Learning-based algorithms are designed and implemented in a centralized manner. Therefore, these approaches can be sub-optimal in detecting zero-day botnet attacks against a group of IoT devices. Besides, a centralized AI approach requires sharing of data traces from the IoT devices for training purposes, which jeopardizes user privacy. To tackle these issues in this paper, we propose a federated learning based framework for a zero-day botnet attack detection model, where a new aggregation algorithm for the IoT devices is developed so that a better model aggregation can be achieved without compromising user privacy. Evaluations are conducted on an open dataset, i.e., the N-BaIoT. The evaluation results demonstrate that the proposed learning framework with the new aggregation algorithm outperforms the existing baseline aggregation algorithms in federated learning for zero-day botnet attack detection in IoT networks.

This paper studies a multi-tier cache-aided relaying network, where the destination$D$is randomly located in the network and it requests files from the source$S$through the help of cache-aided base station (BS) and$N$relays. In this system, the multi-tier architecture imposes a significant impact on the system collaborative caching and file delivery, which brings a big challenge to the system performance evaluation and optimization. To address this problem, we first evaluate the system performance by deriving analytical outage probability expression, through fully taking into account the random location of the destination and different file delivery modes related to the file caching status. We then perform the asymptotic analysis on the system outage probability when the signal-to-noise ratio (SNR) is high, to enclose some important and meaningful insights on the network. We further optimize the caching strategies among the relays and BS, to improve the network outage probability. Simulations are performed to show the effectiveness of the derived analytical and asymptotic outage probability for the proposed caching strategy. In particular, the proposed caching is superior to the conventional caching strategies such as the most popular content (MPC) and equal probability caching (EPC) strategies.

In this paper, we study short-packet communications in multi-hop networks with wireless energy transfer, where relay nodes harvest energy from power beacons to transmit short packets to multiple destinations. It is proposed a novel cooperative beamforming relay selection (CRS) scheme which incorporates partial relay selection and distributed multiuser beamforming to achieve a high-reliable transmission in two consecutive hops. A closed-form expression for the average block error rate (BLER) of the CRS scheme is derived, based on which an asymptotic analysis is also carried out. To achieve optimal channel uses allocation, we formulate a fairness end-to-end throughput maximization problem which is generally NP-hard due to the non-concavity of the objective function and mixed-integer constraints. To solve this challenging problem efficiently, we first relax channel uses to be continuous and transform the relaxed problem into an equivalent non-convex one, but with a more tractable form. We then develop a low-complexity iterative algorithm relying on inner approximation framework to convexify non-convex parts that converges to at least a locally optimal solution. Towards real-time settings, we design an efficient deep convolutional neural network (CNN) with multiscale-accumulation connections to achieve the sub-optimal solution of the relaxed problem via real-time inference processes. Numerical results are presented to verify the analytical derivations and to demonstrate performance improvements of the CRS scheme over the benchmark ones in terms of BLER, reliability, latency, and throughput in various settings. Moreover, the designed CNN provides the lowest root-mean-square error compared to the state-of-the-art deep learning approaches while the CNN-aided optimization framework estimates accurately the optimal channel uses allocation with low execution time.

Orthogonal frequency division multiplexing (OFDM) systems have been widely applied in practice since OFDM has diverse outstanding advantages. However, their performance improvement is confronted with bottlenecks. In this paper, in order to tackle this issue, intelligent resource allocation driven by reinforcement learning is studied in intelligent reflecting surface (IRS) enhanced OFDM systems. The system sum rate is maximized by jointly optimizing the subcarrier allocation, the transmit beamforming of the base station and the phase shift of the IRS. An intelligent resource allocation scheme based on combining deep Q networks (DQN) and deep deterministic policy-gradient (DDPG) is proposed to tackle the formulated challenging non-convex problems. In order to further improve the spectrum efficiency, spectrum sharing is considered in the IRS-enhanced OFDM system. The secondary users sum rate maximization framework is formulated by jointly optimizing the channel allocation, the transmit beamforming of the secondary base station (SBS) and the phase shift of the IRS. Dueling double deep Q networks (D3QN) and twin delayed deep deterministic policy gradient (TD3) are exploited to tackle the hybrid action space issue under interference. Simulation results demonstrate that our proposed schemes can significantly improve the transmission rate compared to the benchmark schemes.

Motivated by the ever-increasing concerns on per-sonal data privacy and the rapidly growing data volume at local clients, federated learning (FL) has emerged as a new machine learning setting. An FL system is comprised of a central parame-ter server and multiple local clients. It keeps data at local clients and learns a centralized model by sharing the model parameters learned locally. No local data needs to be shared, and privacy can be well protected. Nevertheless, since it is the model instead of the raw data that is shared, the system can be exposed to the poisoning model attacks launched by malicious clients. Furthermore, it is challenging to identify malicious clients since no local client data is available on the server. Besides, membership inference attacks can still be performed by using the uploaded model to estimate the client's local data, leading to privacy disclosure. In this work, we first propose a model update based federated averaging algorithm to defend against Byzantine attacks such as additive noise attacks and sign-flipping attacks. The individual client model initialization method is presented to provide further privacy protections from the membership inference attacks by hiding the individual local machine learning model. When combining these two schemes, privacy and security can be both effectively enhanced. The proposed schemes are proved to converge experimentally under non-IID data distribution when there are no attacks. Under Byzantine attacks, the proposed schemes perform much better than the classical model based FedAvg algorithm.

The massive multi-input-multi-output (MIMO) system will greatly enhance the performance of the next-generation wireless communications for many applications e.g., high-mobility users. The orthogonal time frequency space (OTFS) is a promising technique for high-mobility massive MIMO use cases. However, the MIMO-OTFS system requires accurate downlink channel information for optimal performance. This paper studies an uplink-aided downlink channel estimation scheme targeting high-mobility user scenario based on a frequency division duplex massive MIMO-OTFS system. Most of the existing work over-looks the change in the delay, Doppler, and angle domain during a channel estimation process due to high mobility. In this work, we analyze the reciprocity between an uplink and a downlink channel and derive the estimation error due to the latency in processing the uplink channel estimates. Simulation results demonstrate that an uplink channel may change significantly in a high-mobility massive MIMO-OTFS system, given a reasonably small amount of processing latency. Such a change will lead to high error in downlink channel estimation. With the proof of concept, our future work will focus on refining the channel estimation framework with a reduction of the processing latency.

In this paper, we study the resource allocation problem for an intelligent reflecting surface (IRS)-assisted OFDM system. The system sum rate maximization framework is formulated by jointly optimizing subcarrier allocation, base station transmit beamforming and IRS phase shift. Considering the continuous and discrete hybrid action space characteristics of the optimization variables, we propose an efficient resource allocation algorithm combining multiple deep Q networks (MDQN) and deep deterministic policy-gradient (DDPG) to deal with this issue. In our algorithm, MDQN are employed to solve the problem of large discrete action space, while DDPG is introduced to tackle the continuous action allocation. Compared with the traditional approaches, our proposed MDQN-DDPG based algorithm has the advantage of continuous behavior improvement through learning from the environment. Simulation results demonstrate superior performance of our design in terms of system sum rate compared with the benchmark schemes.

In this paper, we design an efficient deep convolutional neural network (CNN) to improve and predict the performance of energy harvesting (EH) short-packet communications in multi-hop cognitive Internet-of-Things (IoT) networks. Specifically, we propose a Sum-EH scheme that allows IoT nodes to harvest energy from either a power beacon or primary transmitters to improve not only packet transmissions but also energy harvesting capabilities. We then build a novel deep CNN framework with feature enhancement-collection blocks based on the proposed Sum-EH scheme to simultaneously estimate the block error rate (BLER) and throughput with high accuracy and low execution time. Simulation results show that the proposed CNN framework achieves almost exactly the BLER and throughput of Sum-EH one, while it considerably reduces computational complexity, suggesting a real-time setting for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 1.33 × 10-2on the considered dataset, which exhibits the lowest RMSE compared to the deep neural network and state-of-the-art machine learning approaches.

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NO-MA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated. An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes.

Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, the existing semantic communication frameworks do not involve inference and error correction, which limits the achievable performance. In this paper, in order to tackle this issue, a cognitive semantic communication framework is proposed by exploiting knowledge graph. Moreover, a simple, general and interpretable solution for semantic information detection is developed by exploiting triples as semantic symbols. It also allows the receiver to correct errors occurring at the symbolic level. Furthermore, the pre-trained model is fine-tuned to recover semantic information, which overcomes the drawback that a fixed bit length coding is used to encode sentences of different lengths. Simulation results on the public WebNLG corpus show that our proposed system is superior to other benchmark systems in terms of the data compression rate and the reliability of communication.

Mobile crowdsensing enables collaborative data sensing between cloud server and mobile nodes. To participate in the sensing task, mobile nodes upload their locations to the centralized cloud for task allocation. However, revealing locations to an untrusted cloud results in privacy leakage, such as trajectories tracking and home address exposal, threatening the personal security. Obfuscation and cryptography based schemes are two main solutions to protect the location privacy. However, these schemes may either degrade the accuracy of task allocation or rely on some strong assumptions. Thus, how to protect location privacy without strong assumptions while remaining high accuracy in task allocation is challenging. In this paper, we propose a secure protocol for edge-assisted mobile crowdsensing, which removes the assumption that the cloud cannot collude with mobile nodes. Specifically, we deploy homomorphic encryption among service requestor, cloud server and edge nodes in a collaborative manner. Benefiting from the additive property of the cryptosystem, the cloud is able to securely calculate the mobile node’s travel distance while knowing nothing about the mobile mode’s location and task location. Based on the protocol, two types of location-dependent task allocation, travel distance based task allocation and spatial distribution based task allocation, can be implemented with location privacy preservation. Experimental results show the effectiveness of our work in task allocation. In addition, comprehensive privacy discussion indicates that the proposed protocol is secure from the collusion between cloud and mobile nodes, while preserving the task location and location privacy of mobile nodes.

Spectrum prediction is of crucial importance for realizing the cognitive Internet of Things to tackle the spectrum scarcity problem. Deep-learning-based spectrum prediction methods have attracted extensive attention due to their superior accuracy. However, the training speed of deep networks is low and the architecture of traditional networks is uninterpretable. In order to tackle these problems, a radio frequency machine-learning-driven spectrum prediction scheme is proposed by exploiting a novel model-enabled autoregressive (AR) network. A cell with only two parameters is exploited in each layer of the AR, which accelerates the network training. Moreover, the domain knowledge of the AR structure enables our proposed scheme to be explainable. Simulation results show that our proposed scheme has the best prediction accuracy than the long short-term memory (LSTM)-based scheme and the AR scheme. It is also shown that its convergence speed is higher than that of the LSTM-based scheme.

Mobile edge computing (MEC) provides wireless devices (WDs) more computing capability and lower latency by allowing them to offload their computation tasks to a nearby more powerful server. Furthermore, adopting the relaying technique can effectively improve the offloading performance, especially when the wireless channel conditions between WD and MEC server are poor. In this article, we consider a multiuser relay-aided MEC system targeting at minimizing the energy consumption. The relay can either execute the computation task by itself or offload the task to the MEC server. Under the partial computation offloading mode, an energy minimization problem is investigated by jointly optimizing transmit power, offloading time duration, and central processing unit (CPU) frequencies. To solve the nonconvex optimization problem, an iterative algorithm based on successive convex approximation (SCA) is developed. Furthermore, the closed-form expressions for the optimal transmission powers and the CPU frequencies are derived. The simulation results show that the proposed scheme can achieve a lower energy consumption than other benchmark schemes.

Mobile-edge computing (MEC) has recently emerged as a promising technology in the 5G era. It is deemed an effective paradigm to support computation intensive and delay-critical applications even at energy-constrained and computation-limited Internet of Things (IoT) devices. To effectively exploit the performance benefits enabled by MEC, it is imperative to jointly allocate radio and computational resources by considering nonstationary computation demands, user mobility, and wireless fading channels. This article aims to study the tradeoff between energy efficiency (EE) and service delay for multiuser multiserver MEC-enabled IoT systems when provisioning offloading services in a user mobility scenario. Particularly, we formulate a stochastic optimization problem with the objective of minimizing the long-term average network EE with the constraints of the task queue stability, peak transmit power, maximum CPU-cycle frequency, and maximum user number. To tackle the problem, we propose an online offloading and resource allocation algorithm by transforming the original problem into several individual subproblems in each time slot based on the Lyapunov optimization theory, which are then solved by convex decomposition and submodular methods. Theoretical analysis proves that the proposed algorithm can achieve a$[O(1/V), O(V)]$tradeoff between EE and service delay. Simulation results verify the theoretical analysis and demonstrate our proposed algorithm can offer much better EE-delay performance in task offloading challenges, compared to several baselines.

With the emergence of computation-intensive vehicular applications, computation offloading based on mobile-edge computing (MEC) has become a promising paradigm in resource-constrained vehicular cloud networks (VCNs). However, when doing computation offloading in a VCN, malicious service providers can cause serious security concerns on the content offloading. To address that in this article, a blockchain-based secure computation offloading scheduling scheme is proposed. It embraces the blockchain-based trust management paradigm and smart contract-enabled deep reinforcement learning (DRL) algorithm. As for the trust management, the long-term reputation and short-term trust variability are jointly considered. Specifically, a novel three-valued subjective logic (3VSL) scheme is adopted to obtain a more comprehensive reputation, and the statistics of behavioral transitions can provide a short-term trust variability to timely capture the malicious behaviors. In addition, to securely update, validate, and store the trust information, we propose a hierarchical blockchain framework that comprises vehicular blockchain, roadside unit (RSU) blockchain, and cloud blockchain. Furthermore, a smart contract-enabled DRL algorithm is proposed to implement the secure and intelligent computation offloading scheduling in a VCN. Simulations are conducted to verify the effectiveness of the proposed scheme.

The industrial Internet of Things (IIoT) is one of the key applications in 5G heterogeneous networks. To support high energy efficiency (EE) and reliability of IIoT equipment, it is important to design an efficient resource allocation algorithm in dynamic and complex environments. However, most of the studies on 5G heterogeneous IIoT networks did not address the transceiver hardware impairment (HWI) issues (e.g., phase noises, amplifier nonlinearities, and quantization errors) and the corresponding algorithms may not be applicable in practice. To this end, in this article, we investigate a realistic beamforming algorithm in a multicell downlink multiple-input single-output heterogeneous IIoT network by incorporating HWIs in our design. In particular, a beamforming design problem is formulated as a nonconvex optimization problem for maximizing the total EE of all equipment subject to the quality of service constraints of the IIoT equipment in both the macrocell and femtocells and the maximum transmit power constraints of base stations. In light of the intractability of the considered problem, we develop an EE-based iterative beamforming algorithm to tackle the formulated problem by employing the semidefinite relaxation method, Dinkelbach’s method, and the successive convex approximation method. Simulation results show that the proposed algorithm can achieve higher EE and bring less interference power to the macrocell IIoT equipment by comparing it with baseline algorithms.

Non-orthogonal multiple access (NOMA) is a promising candidate for the sixth generation wireless communication networks due to its high spectrum efficiency (SE), energy efficiency (EE), and better connectivity. It can be applied in cognitive radio networks (CRNs) to further improve SE and user connectivity. However, the interference caused by spectrum sharing and the utilization of non-orthogonal resources can downgrade the achievable performance. In order to tackle this issue, intelligent reflecting surface (IRS) is exploited in a downlink multiple-input-single-output (MISO) CRN with NOMA. To realize a desirable tradeoff between SE and EE, a multi-objective optimization (MOO) framework is formulated under both the perfect and imperfect channel state information (CSI). An iterative block coordinate descent (BCD)-based algorithm is exploited to optimize the beamforming design and IRS reflection coefficients iteratively under the perfect CSI case. A safe approximation and the$ \mathcal {S}$-procedure are used to address the non-convex infinite inequality constraints of the problem under the imperfect CSI case. Simulation results demonstrate that the proposed scheme can achieve a better balance between SE and EE than baseline schemes. Moreover, it is shown that both SE and EE of the proposed algorithm under the imperfect CSI can be significantly improved by exploiting IRS.

In this paper, we consider a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple Internet-of-Things (IoT) nodes harvest energy from the energy signals transmitted by a power beacon (PB) and utilize the harvested energy for local computing and task offloading via hybrid backscatter communication (BackCom) and active transmission (AT). Considering the limited computation capacity of the MEC server and the quality-of-service (QoS) and energy-causality constraints per IoT node, we propose two resource allocation schemes to maximize the total computation bits of all the IoT nodes and the system computation energy efficiency (EE), respectively, by jointly optimizing the computation frequency and time of the MEC server and each IoT node, the transmit power of the PB and each IoT node, and the BackCom power reflection coefficient and the time for energy harvesting (EH), BackCom, and AT of each IoT node. The non-convex computation bits maximization problem is transformed to a convex one by introducing a series of auxiliary variables and proof by contradiction, and then solved by the existing convex tools. The system computation EE maximization is a non-convex nonlinear programming problem. We propose a two-layer iterative algorithm to solve it optimally and devise a reduced-complexity iterative algorithm to solve it sub-optimally by leveraging the block coordinate decent technique. Computer simulations validate the convergence of the proposed iterative algorithms and their superior performance over the benchmark schemes in terms of the computation bits or EE.

Mobile edge computing (MEC) has recently emerged as an enabling technology to support computation-intensive and delay-critical applications for energy-constrained and computation-limited Internet of Things (IoT). Due to the time-varying channels and dynamic task patterns, there exist many challenges to make efficient and effective computation offloading decisions, especially in the multi-server multi-user IoT networks, where the decisions involve both continuous and discrete actions. In this paper, we investigate computation task offloading in a dynamic environment and formulate a task offloading problem to minimize the average long-term service cost in terms of power consumption and buffering delay. To enhance the estimation of the long-term cost, we propose a deep reinforcement learning based algorithm, where deep deterministic policy gradient (DDPG) and dueling double deep Q networks (D3QN) are invoked to tackle continuous and discrete action domains, respectively. Simulation results validate that the proposed DDPG-D3QN algorithm exhibits better stability and faster convergence than the existing methods, and the average system service cost is decreased obviously.

As a popular scenario of mobile crowdsensing, edge computing assisted vehicular crowdsensing (EVCS) encourages vehicles to participate in sensing data with the equipped devices. Due to the vehicular mobility, vehicles may dynamically enter and leave the coverage area of an edge node, leading to recurrent task allocations that consume excessive communication and computational resources. How to avoid recurring recruitment in task allocation is challenging. In this paper, we propose an optimization framework to facilitate task allocation by utilizing the cooperation between edge nodes. The proposed framework avoids complicated recruitment procedures while maximizing the connection time between the recruited vehicles and the edge node. Due to the NP-hardness of the formulated optimization problem, we design a reinforcement learning based algorithm to solve the problem with high accuracy and efficiency. Simulation results show the effectiveness of our proposed framework.

Beamforming design is crucial to reconfigurable intelligent surface (RIS)-aided communication networks. However, most of the existing works assume ideal hardware and perfect channel state information (CSI), which are unrealistic assumptions in practical systems. In order to improve system robustness and user fairness, in this paper, we firstly study the max-min energy efficiency problem for RIS-aided heterogeneous networks under non-ideal hardware and imperfect CSI. Specifically, the joint optimization of transmit beamforming vectors of femto base stations (FBSs) and the phase shift matrices of RISs is formulated as a nonconvex problem to maximize the minimum energy efficiency of femtocells subject to the constraints of the maximum transmit power of FBSs, the maximum cross-tier interference power of macrocell users, the minimum rates of femtocell users, and unit modulus of RISs. To facilitate the design, we develop an iterative block coordinate descent-based algorithm which exploits the semidefinite relaxation, the S-procedure, the successive convex approximation method, and the singular value decomposition method. Simulation results demonstrate the superiority of the proposed algorithm.

Energy efficiency (EE) is a crucial performance metric in wireless-powered backscatter communication networks (WP-BackComNets) for achieving a good tradeoff between data rates and the overall energy consumption, which however has not been sufficiently exploited by the existing works. In this paper, an EE-based maximization resource allocation (RA) problem is studied in a downlink orthogonal frequency division multiple access-based WP-BackComNet, where the circuit power consumption of the backscatter device, the minimum energy harvesting (EH) constraint, and the maximum transmit power constraint of the power station are considered. To deal with the non-convex problem, we firstly transform it into an equivalently subtractive form via Dinkelbach's method. Then, we apply a variable substitution approach to transform the non-convex problem into a convex one, where the closed-form solutions of the reflection coefficient, the transmit power, and the EH time are deduced by using Lagrange dual method. Simulation results demonstrate that the proposed algorithm can achieve better EE performance than other benchmark algorithms.

In recent years, computation offloading has been considered as a promising technology to support computation- intensive vehicular applications. In this paper, we mainly focus on computation offloading in a vehicular cloud network (VCN), in which both vehicles and infrastructures with resource availability are defined as resource providers. However, due to the dynamically changing on-board resource distribution and the uncoordinated offloading strategies among vehicles, the computation offloading problem in a VCN is very challenging. In this paper we first model the problem as a multi-agent multi- armed bandit problem. We then propose a reshaped upper confidence bound (UCB) algorithm to estimate the on-board resource distribution with the reward estimation. We further utilize a novel multi-agent reinforcement learning algorithm to manage the computation offloading in a VCN. Simulation results demonstrate the performance gains by using the proposed algorithm.

The development of artificial intelligence, wireless communication and smart sensor platform facilities the emergence of multitude of novel vehicular applications in recent years. These new vehicular applications usually are realized with BIG models, which are normally delay sensitive and computation intensive. To alleviate the heavy pressure on the resource-constrained vehicles, computation offloading has been regarded as a promising approach to circumvent this challenge. In this paper, we take the deep learning model as an BIG model example to investigate the computation offloading in a vehicular cloud network. Specifically, task division technology is utilized to decomposed the BIG model into several components, in which there are dependencies between multiple components. To satisfy the requirements of delay-sensitive vehicular applications, it is crucial to propose an efficient computation offloading scheme. To solve it, a novel deep reinforcement learning algorithm is proposed, wherein a common deep learning model is maintained by all agents. The reward mechanism is elaborately designed to combine the long-term reward and short-term reward. In the final, the proposed algorithm’s effectiveness is verified by the experimental simulations.

Cognitive radio (CR) and energy harvesting (EH) have been deemed two promising technologies in the spectrum-scarce and energy-limited wireless networks. In this paper, the cooperative cognitive radio network (CRN) with EH is considered, where a secondary user (SU) close to the secondary base station (SBS) employs power splitting for EH and assists to relay the data for another SU far away from the SBS. A SU sum-rate maximization problem is formulated under the constraints of the power budget at the SBS, the interference threshold of the primary network, and SU QoS. To tackle this problem, a resource allocation algorithm based on an improved deep neural network (DNN) is proposed. In order to accelerate the convergence of the DNN loss function, transfer learning is exploited to initialize the DNN weights. The loss between the DNN output and the optimal transmit power obtained by the conventional solution is stored in the memory pool, where the samples with large losses are used to train the DNN. Simulation results show the efficiency of our proposed DNN-based resource allocation scheme, which outperforms the normal DNN-based resource allocation and conventional resource allocation scheme in terms of the computation time.

A new machine learning (ML) technique termed as federated learning (FL) aims to preserve data at the edge devices and to only exchange ML model parameters in the learning process. FL not only reduces the communication needs but also helps to protect the local privacy. Although FL has these advantages, it can still experience large communication latency when there are massive edge devices connected to the central parameter server (PS) and/or millions of model parameters involved in the learning process. Over-the-air computation (AirComp) is capable of computing while transmitting data by allowing multiple devices to send data simultaneously by using analog modulation. To achieve good performance in FL through AirComp, user scheduling plays a critical role. In this paper, we investigate and compare different user scheduling policies, which are based on various criteria such as wireless channel conditions and the significance of model updates. Receiver beamforming is applied to minimize the mean-square-error (MSE) of the distortion of function aggregation result via AirComp. Simulation results show that scheduling based on the significance of model updates has smaller fluctuations in the training process while scheduling based on channel condition has the advantage on energy efficiency.

Mobile-edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate the EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras, such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this article, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and EH. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the central process unit frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on the Lyapunov optimization and semidefinite programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of the user mobility, EH model, and channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Energy-efficient design and secure communications are of crucial importance in wireless communication networks. However, the energy efficiency achieved by using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix under both the perfect channel state information (CSI) and the imperfect CSI. In order to tackle the challenging non-convex fractional problems, an algorithm based on semidefinite programming (SDP) relaxation is proposed for solving energy efficiency maximization problem under the perfect CSI case while an alternate optimization algorithm based onS-procedure is used for solving the problem under the imperfect CSI case. Simulation results demonstrate that the proposed design outperforms the benchmark schemes in term of energy efficiency. Moreover, the tradeoff between energy efficiency and the secrecy rate is found in the IRS-assisted MISO network. Furthermore, it is shown that IRS can help improve energy efficiency even with the uncertainty of the CSI.

Visible light communication (VLC) is a promising technique in the fifth and beyond wireless communication networks. In this paper, a secure multiple-input single-output VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support energy-limited devices taking into account a practical non-linear energy harvesting model. Specifically, the optimal beamforming design problems for minimizing transmit power and maximizing the minimum secrecy rate are studied under the imperfect channel state information (CSI). S-Procedure and a bisection search is applied to tackle challenging non-convex problems and to obtain efficient resource allocation algorithm. It is proved that optimal beamforming schemes can be obtained. It is found that there is a non-trivial trade-off between the average harvested power and the minimum secrecy rate. Moreover, we show that the quality of CSI has a significant impact on achievable performance.

Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected to a central parameter server (PS) and iteratively download/upload data from/to the PS. Due to limited bandwidth, only a subset of connected devices can be scheduled in each round. There are usually millions of parameters in the state-of-art machine learning models such as deep learning, resulting in a high computation complexity as well as a high communication burden on collecting/distributing data for training. To improve communication efficiency and make the training model converge faster, we propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate under practical constraints during the entire learning process. NOMA allows multiple users to transmit on the same channel simultaneously. The user scheduling problem is transformed into a maximum-weight independent set problem that can be solved using graph theory. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks than other existing schemes within the same learning time.

Edge caching is a new paradigm that has been exploited over the past several years to reduce the load for the core network and to enhance the content delivery performance. Many existing caching solutions only consider homogeneous caching placement due to the immense complexity associated with the heterogeneous caching models. Unlike these legacy modeling paradigms, this paper considers heterogeneous content preference of the users with heterogeneous caching models at the edge nodes. Besides, aiming to maximize the cache hit ratio (CHR) in a two-tier heterogeneous network, we let the edge nodes collaborate. However, due to complex combinatorial decision variables, the formulated problem is hard to solve in the polynomial time. Moreover, there does not even exist a ready-touse tool or software to solve the problem. We propose a modified particle swarm optimization (M-PSO) algorithm that efficiently solves the complex constraint problem in a reasonable time. Using numerical analysis and simulation, we validate that the proposed algorithm significantly enhances the CHR performance when comparing to that of the existing baseline caching schemes.

While next-generation wireless networks intend leveraging edge caching for enhanced spectral efficiency, quality of service, end-to-end latency, content sharing cost, etc., several aspects of it are yet to be addressed to make it a reality. One of the fundamental mysteries in a cache-enabled network is predicting what content to cache and where to cache so that high caching content availability is accomplished. For simplicity, most of the legacy systems utilize a static estimation - based on Zipf distribution, which, in reality, may not be adequate to capture the dynamic behaviors of the contents popularities. Forecasting user's preferences can proactively allocate caching resources and cache the needed contents, which is especially important in a dynamic environment with real-time service needs. Motivated by this, we propose a long short-term memory (LSTM) based sequential model that is capable of capturing the temporal dynamics of the users' preferences for the available contents in the content library. Besides, for a more efficient edge caching solution, different nodes in proximity can collaborate to help each other. Based on the forecast, a non-convex optimization problem is formulated to minimize content sharing costs among these nodes. Moreover, a greedy algorithm is used to achieve a sub-optimal solution. Using extensive simulation and analysis, we validate that the proposed algorithm performs better than other existing schemes.

With the development of intelligent transportation system (ITS), a multitude of novel vehicular applications have been emerging. There is an urgent need for simultaneously supporting multi-tasks across a group of vehicles in a vehicular network, forming a typical multi-task multi-agent (MTMA) environment. Deep Reinforcement Learning (DRL) is deemed a promising approach to solving the highly complicated MTMA problem. However, owing to the extraordinarily growing computational complexity as well as the explosively increasing dimension of state and action spaces in the MTMA environment, the value functions in the DRL are usually bulky and could be difficult to be learned efficiently. In this way, by virtue of the correlations among multiple vehicular tasks, we adopt the value-decomposition mechanism (VDM) to decompose the complicated value function into several small pieces and then compute each sub-function separately. The proposed paradigm can yield great speed-up in learning and help substantially with a smaller state and action space but without degrading the performance. In this work, we consider an MTMA environment with three vehicular tasks to demonstrate the effectiveness of the proposed mechanism with simulation results.

With the explosive growth of the computation intensive vehicular applications, the demand for computational resource in vehicular networks has increased dramatically. However some vehicular networks may be deployed in an environment that lack resource-rich facilities to support computationally expensive vehicular applications. In this work we propose a new scheme that enables computational resource sharing among vehicles in vehicular cloud network (VCN), which can be formulated as a complex multi-knapsack problem. In order to solve it, a deep reinforcement learning (DRL) algorithm is developed. Considering the non-stationary behavior brought in by the parallel learning and exploring processes among vehicles, computational resource sharing in such a vehicular network is a typical multiagent problem, therefore we model the problem with a Markov game problem. In addition, to tackle the heterogeneity property of the computational resources, a multi-hot encoding scheme is designed to standardize the action space in DRL. Furthermore, we propose a centralized training and decentralized execution framework that can be solved by a multi-agent deep deterministic policy gradient (MADDPG) algorithm. The numerical simulation results demonstrate the effectiveness of the proposed scheme.

Energy-efficient design and secure communications are of crucial importance in the future wireless communication networks. However, the energy efficiency when using physical layer security can be limited by the channel conditions. In order to tackle this problem, an intelligent reflecting surface (IRS) assisted multiple input single output (MISO) network with independent cooperative jamming is studied in this paper. The energy efficiency is maximized by jointly designing the transmit and jamming beamforming and IRS phase-shift matrix. An alternative optimization algorithm is proposed based on semidefinite programing (SDP) relaxation for solving the challenging non-convex fractional optimization problem. Simulation results demonstrate that our proposed design outperforms the benchmark schemes in term of energy efficiency. The study sheds light on the tradeoff between energy efficiency and the secrecy rate in the IRS-assisted MISO network.

Fairness among different users and system robustness are key issues in the future communication network design. A robust max-min fairness energy efficiency (EE) maximization problem in a downlink non-orthogonal multiple access (NOMA) heterogeneous network is studied when channel state information and interference power are uncertain. A worst-case EE of the small cell is maximized by jointly optimizing the transmit power and cell association under the bounded channel uncertainty model, subject to constraints on the cross-tier interference power, maximum transmit power, and the minimum rate requirement of each small-cell user. The formulated robust max-min fairness EE problem is a mixed-integer and non-convex programming problem with infinite inequality constraints. An iterative resource allocation algorithm is designed based on the proposed power allocation and cell association scheme. Simulation results demonstrate the effectiveness of the proposed robust resource allocation scheme and its improvement over existing schemes.

Cognitive radio is a promising technology to improve spectral efficiency. However, communication security of a secondary network is limited by its transmit power and channel fading. In order to tackle this issue, by exploiting the high flexibility and the possibility of establishing line-of-sight links, a cognitive unmanned aerial vehicle (UAV) communication network is studied. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAVs trajectory and transmit power. Our formulated problem takes into account practical imperfect location estimation. To solve the non-convex problem, an iterative suboptimal algorithm based on the Bernstein-type inequalities is presented. Our simulation results demonstrate that the proposed scheme can improve the secure communication performance significantly compared to a benchmark scheme based on fixed trajectory.

Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existing attackers, it is very challenging to offload computation tasks securely and efficiently. In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations. To tackle the problem, a dynamic secure task offloading and resource allocation algorithm is proposed based on Lyapunov optimization theory. A stochastic non-convex problem is formulated to jointly optimize the local-CPU frequency and transmit power, aiming at maximizing the network energy efficiency, which is defined as the ratio of the long-term average secure rate to the long-term average power consumption of all users. The formulated problem is decomposed into the deterministic sub-problems in each time slot. The optimal local CPU-cycle and the transmit power of each user can be given in the closed-from. Simulation results evaluate the impacts of different parameters on the efficiency metrics and demonstrate that the proposed method can achieve better performance compared with other benchmark methods in terms of energy efficiency.

Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.

Vehicular networks are expected to significantly improve the efficiency and safety of transportation system. The number of connected vehicles is estimated to exceed 200 millions by 2020. With the rapid developed technologies such as the fifth generation cellular networks, various real-time network applications will be applied in vehicular networks. Considering the limited capability of devices installed on vehicles, cloud computing can be used to provide sufficient storage and computational power. However, the numerous vehicles with massive applications will generate tremendous volume of data and exhaust finite bandwidth. It is difficult for the conventional cloud computing paradigm to satisfy stringent quality of service requirements of vehicular networks, especially for delay-sensitive applications. Then vehicular fog computing, in which servers are allocated close to vehicles, has been proposed to reduce the latency. Vehicular fog computing is still in its early stage. How to provide reliable and secure fog service to client vehicles has not been well addressed. In this paper, we propose a scalable and efficient security provision scheme based on Chinese Remainder Theory. Simulation results demonstrate that the proposed scheme significantly reduces the authentication delay.

Internet-of-Things (IoT) applications have been rapidly deployed into pervasive environment, where both challenges and opportunities abound. On the one hand, a large number of IoT devices and their rich functions contribute significant volumes of data, which has brought tremendous convenience to the daily lives of end users. On the other hand, the heterogeneous IoT devices and a large amount of private information transmitted through networks also bring serious security and privacy issues. It is a big challenge to model IoT systems and trust relationships between different entities with a large number of heterogeneous IoT devices. In this article, we study a general IoT system architecture with consideration of heterogeneous IoT devices. Different trust models are proposed and analyzed based on the trust relationships between different entities in the IoT system. We propose a flexible and efficient authentication scheme with a consideration of heterogeneous IoT devices based on the least trust-required model. The proposed scheme provides security and privacy to resource-limited IoT devices flexibly and efficiently by utilizing IoT devices with better storage and computational ability. Moreover, secure data transmission is presented with contextual privacy and data integrity services. The proposed scheme achieves not only the mutual authentication, initial session key agreement, and data integrity but also anonymity, contextual privacy, forward security, end-to-end security, and key escrow resilience. Security analysis is presented to provide verification of the proposed protocol and security objectives. Moreover, performance evaluation is presented with comparison to the other schemes in terms of security features, computational overhead, and communication overhead. The performance comparisons show that our proposed scheme provides flexible and efficient security by consideration of heterogeneous IoT devices. With the higher proportion of resource-limited IoT devices, our proposed scheme outperforms other similar schemes.

The ever-growing demand of the Internet of Things (IoT) imposes great challenges in the existing cellular systems and calls for novel approaches for the wireless network design. In this article, we develop a joint energy and computation optimization paradigm in an IoT network. The tasks collected at local IoT devices can be computed at hierarchical mobile-edge computing facilities. Both nonorthogonal multiple access (NOMA) and frequency-division multiple access (FDMA) are used for computation offloading. The system model considers both long-term and short-term system behaviors and makes the best decision for energy consumption and computation efficiency. The long short-term memory (LSTM) network is applied to predict the long-term workload, based on which the number of active process units in the edge layer is optimized. In the short-term model, a resource optimization problem is formulated. Due to the dynamic arrival workload and nonconvex features of the problem, the Lyapunov optimization approach and successive convex approximation for the low-complexity method are applied to solve this problem. The simulation results show that the proposed scheme can significantly improve the delay and energy consumption performance.

Resource allocation (RA) is a key technique to guarantee the quality of service of users and maximize the system capacity in cognitive nonorthogonal multiple access (NOMA) networks. However, traditional RA approaches have been proposed under perfect channel state information (CSI) which is too ideal in practical systems due to the impact of link delays, quantization errors, etc. In this article, with imperfect CSI, a downlink robust RA algorithm is proposed for robust transmission to maximize the sum energy efficiency (EE) of secondary users (SUs) under channel uncertainties. Meanwhile, it protects the minimum data rates of SUs, satisfying the maximum transmission power constraints of base stations and the maximum interference temperature constraint of each primary user (PU). The formulated problem is nonconvex, thus challenging to solve. For delay-tolerant services, to deal with the intractability of the problem caused by outage probability constraints, the closed-form expressions of outage probabilities of SUs and PUs are derived under Gaussian CSI error models. For delay-sensitive services, the robust constraints with bounded uncertainty sets are transformed into convex ones. Based on successive convex approximation and the parameter transformation approach, the original problem is converted into a closed-form geometric programming problem solved by dual decomposition methods and subgradient methods. Additionally, users’ outage probabilities and the minimum required transmit power under the two modeling approaches are analyzed. Computational complexity and sensitivity analysis are provided. The simulation results show the proposed algorithm serves good robustness and EE.

Visible light communication (VLC) is a promising technology for the next generation wireless communication systems due to its high spectral efficiency and energy efficiency. In this article, a secure multiple-input single-output (MISO) VLC network is studied, where simultaneous lightwave information and power transfer (SLIPT) is exploited to support multiple energy-limited devices tacking into account a practical non-linear energy harvesting model. The transmit power minimization and the minimum secrecy rate maximization problems are formulated under both perfect and imperfect channel state information, respectively. To further improve the user connectivity, those problems are also investigated in MISO-VLC networks with non-orthogonal multiple access (NOMA). To tackle these challenging non-convex problems, semidefinite program relaxation and $-Procedure are exploited. It is proved that optimal beamforming schemes can be obtained for the considered two types problems in MISO-VLC SLIPT networks, while a sub-optimal solution can be obtained for the transmit power minimization problem in those networks with NOMA. It is found that there is a non-trivial trade-off between the average harvested power and maximum-minimum secrecy rate. Moreover, it is shown that the performance achieved with NOMA outperforms that of conventional orthogonal multiple access in MISO-VLC SLIPT networks, despite the existence of imperfect channel state information.

Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate constraints, the computation latency constraints and the secrecy outage probability constraints. The semi-closed form expression for the optimal solution is derived. We then investigate the secrecy outage probability minimization problem by taking the priority of two users into account, and characterize the optimal secrecy offloading rates and power allocations with closed-form expressions. Numerical results demonstrate that the performance of our proposed design are better than those of the alternative benchmark schemes.

Non-orthogonal multiple access (NOMA) based mobile edge computing (MEC) networks can enhance delay-constrained computation. Most of the existing works focus on the energy or delay minimization, and the study on NOMA based MEC in terms of successful computation probability has been limited, which motivates this work. In particular, randomly deployed edge computing users are modeled as the homogeneous Poisson Point Process and are divided into two groups namely center group (C-group) and edge group (E-group) for uplink NOMA grouping. We propose a new hybrid offloading scheme in a NOMA-based MEC network that can operate in three different modes, namely partial offloading, complete local computation, and complete offloading. We firstly consider a NOMA grouping scenario where a user from the C-group and a user from the E-group are each given a fixed location. The probability that the computation can be completed within the given delay budget is derived and the optimal parameters, i.e., the time for offloading, the power allocation, and the offloading ratios for the two fixed users, are obtained. It reveals that the optimal offloading ratios are determined by the difference of the computational capability between the edge computing user and the MEC server, and that the locations of users have a big impact on the successful computation probability. Inspired by this, we further study three distance-dependent NOMA grouping schemes in the MEC offloading. Specifically, we provide closed-form mathematical expressions of the successful computation probability and its partial optimal solutions for these three schemes. Simulation results verify the accuracy of the analytical results and compare the performance of the proposed offloading scheme with the existing schemes. Insights on the pros and the cons of different user selection schemes are also provided.

Cognitive radio is a promising technology to improve spectral efficiency. However, the secure performance of a secondary network achieved by using physical layer security techniques is limited by its transmit power and channel fading. In order to tackle this issue, a cognitive unmanned aerial vehicle (UAV) communication network is studied by exploiting the high flexibility of a UAV and the possibility of establishing line-of-sight links. The average secrecy rate of the secondary network is maximized by robustly optimizing the UAV's trajectory and transmit power. Our problem formulation takes into account two practical inaccurate location estimation cases, namely, the worst case and the outage-constrained case. In order to solve those challenging non-convex problems, an iterative algorithm based on S-Procedure is proposed for the worst case while an iterative algorithm based on Bernstein-type inequalities is proposed for the outage-constrained case. The proposed algorithms can obtain effective suboptimal solutions of the corresponding problems. Our simulation results demonstrate that the algorithm under the outage-constrained case can achieve a higher average secrecy rate with a low computational complexity compared to that of the algorithm under the worst case. Moreover, the proposed schemes can improve the secure communication performance significantly compared to other benchmark schemes.

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. Resource allocation strategies for maximizing the computation efficiency are critically important. In this paper, computation efficiency maximization problems are formulated in wireless-powered MEC networks under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) are considered and evaluated for offloading. The energy harvesting time, the local computing frequency, and the offloading time and power are jointly optimized to maximize the computation efficiency under the max-min fairness criterion. Two iterative algorithms and two alternative optimization algorithms are respectively proposed to address the non-convex problems formulated in this paper. Simulation results show that the proposed resource allocation schemes outperform the benchmark schemes in terms of user fairness. Moreover, a tradeoff is elucidated between the achievable computation efficiency and the total number of computed bits. Furthermore, simulation results demonstrate that the partial computation offloading mode outperforms the binary computation offloading mode and NOMA outperforms TDMA in terms of computation efficiency.

Large-scale multi-antenna systems can effectively improve data transmission reliability and throughput for smart grid. However, the massive number of antennas and radio frequency (RF) chains also result in high complexity and energy cost. In this paper, we develop a new performance benchmark named energy economic efficiency for measuring the time-average throughput per energy cost. Then, we investigate how to maximize long-term energy economic efficiency via the joint optimization of communication and energy resource allocation. The formulated joint optimization problem is NP-hard because it not only involves long-term nonlinear optimization objective and constraints, but also involves both integer and continuous optimization variables. Next, we propose an online joint antenna selection and power control algorithm by combining nonlinear fractional programming, Lyapunov optimization, and bisection method. The proposed algorithm can achieve bounded performance deviation from the optimum performance without requiring the prior knowledge of future channel state information (CSI), energy arrival, and electricity price. Finally, a comprehensive theoretical analysis is provided, and the proposed algorithm is verified through simulations under various system configurations.

Cellular based vehicle-to-everything (V2X) communications have recently gained more interest from both academia and industry. However, there exist many challenges in cellular-based V2X communications in which resource allocation is one of the main challenges. In this paper, we propose a graph and deep reinforcement learning-based resource allocations in which channels for vehicular communications are assigned in a centralized manner by the base station whereas vehicular user equipment uses deep reinforcement learning for distributed power control. Graph-based channel allocation includes a weighted bipartite matching and clustering scheme and relies on strictly limited channel state information (CSI). Whereas, power selection is performed using deep reinforcement learning where each agent selects the transmission power to maximize the aggregated V2V data rate. Our proposed scheme relies on realistic channel assumption with minimum transmission overhead. In addition, we have also performed simulations and have shown that our scheme is better compared to previous schemes in terms of sum V2V and sum V2I capacity.

With the emerging applications of vehicular networks, how to provide sufficient communication and computation supports are the two most important challenges for vehicular communication systems. Cloud-based vehicular networks and mobile edge computing frameworks have been proposed to relieve the computing burden of vehicles. However, for time-sensitive and computation-intensive applications with large input data size, e.g. image aided navigation, the data transmission process occupies large bandwidth, which may degrade the quality of service of all network users, especially in high density scenarios. Thus, in this paper we formulate a computation offloading problem for these time-sensitive and computation-intensive applications to minimize the data transmitted to the server. We propose two approaches to solve the formulated problem, i.e. a graph theory based method and a heuristic algorithm. Simulation results demonstrate that both algorithms can achieve near optimal solutions and greatly reduce the data volume transmitted to the server.

Low latency and energy efficiency are two important performance requirements in various fifth-generation (5G) wireless networks. In order to jointly design the two performance requirements, in this paper a new performance metric called effective energy efficiency (EEE) is defined as the ratio of the effective capacity (EC) to the total power consumption in a cellular network with underlaid device to device (D2D) communications. We aim to maximize the EEE of the D2D network subject to the D2D device power constraints and the minimum rate constraint of the cellular network. Due to the non-convexity of the problem, we propose a two-stage difference-of-two-concave (DC) function approach to solve this problem. Towards that end, we first introduce an auxiliary variable to transfer the fractional objective function into a subtractive form. We then propose a successive convex approximation (SCA) algorithm to iteratively solve the resulting non-convex problem. The convergence and the global optimality of the proposed SCA algorithm are both analyzed. The numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

Machine learning plays a vital role in the detection of network anomalies. In this paper, we first briefly examine the different categories of machine learning models, regarding to the acquisition of data label. With the support of fog computing, we then propose data-driven network intelligence for anomaly detection. The proposed framework includes fog enabled infrastructure and fog assisted artificial intelligence (AI) engine. Fog enabled infrastructure provides efficient computing resources for the selection of optimal learning model and sampling ratio. Fog assisted AI engine trains effective and robust semi-supervised learning models for detecting anomalies. We demonstrate that the optimal learning model achieves high detection accuracy and effective computational performance, with the close cooperation between infrastructure and AI engine in a fog computing environment.

Energy-efficient computation is of crucial importance in mobile edge computing (MEC) networks. However, few investigations have studied resource allocation strategies for maximizing the computation efficiency. A computation efficiency maximization framework is established in wireless-powered MEC networks relying on non-orthogonal multiple access (NOMA) under both partial and binary computation offloading modes. A practical non-linear energy harvesting model is considered. The energy harvesting time, the local computing frequency, the operation mode selection, the offloading time and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. An iterative algorithm and an alternative optimization algorithm are proposed to solve the formulated challenging non-convex problems. Simulation results show that our proposed resource allocation schemes outperform the benchmark schemes in terms of computation efficiency. Moreover, a tradeoff is elucidated between the computation efficiency and the computation throughput.

This paper studies a multiple-input single-output non-orthogonal multiple access cognitive radio network relying on simultaneous wireless information and power transfer. A realistic non-linear energy harvesting model is applied and a power splitting architecture is adopted at each secondary user (SU). Since it is difficult to obtain perfect channel state information (CSI) in practice, instead either a bounded or Gaussian CSI error model is considered. Our robust beamforming and power splitting ratio are jointly designed for two problems with different objectives, namely, that of minimizing the transmission power of the cognitive base station and that of maximizing the total harvested energy of the SUs, respectively. The optimization problems are challenging to solve, mainly because of the non-linear structure of the energy harvesting and CSI errors models. We converted them into convex forms by using semi-definite relaxation. For the minimum transmission power problem, we obtain the rank-2 solution under the bounded CSI error model, while for the maximum energy harvesting problem, a two-loop procedure using a 1-D search is proposed. Our simulation results show that the proposed scheme significantly outperforms its traditional orthogonal multiple access counterpart. Furthermore, the performance using the Gaussian CSI error model is generally better than that using the bounded CSI error model.

This letter focuses on the design of an optimal resource allocation scheme to maximize the energy efficiency (EE) in a simultaneous wireless information and power transfer (SWIPT) enabled two-way decode-and-forward (DF) relay network under a non-linear energy harvesting model. In particular, we formulate an optimization problem by jointly optimizing the transmit powers of two source nodes, the power-splitting (PS) ratios of the relay, and the time for the source-relay transmission, under multiple constraints including the transmit power constraints at sources and the minimum rate requirement. Although the formulated problem is non-convex, an iterative algorithm is developed to obtain the optimal resource allocation. Simulation results verify the proposed algorithm and show that the designed resource allocation scheme is superior to other benchmark schemes in terms of EE.

This paper develops a tractable analysis framework to evaluate the reliability and security performance of cooperative non-orthogonal multiple access (co-NOMA) in cognitive networks, where both a primary base station (PBS) and a NOMA-strong primary user (PU) send confidential messages to multiple uniformly distributed PUs in the presence of randomly located external eavesdroppers. For constricting the interference to the PUs imposed by cognitive femto base stations (CFBSs), a mobile association scheme is introduced. Moreover, an eavesdropper-exclusion zone is introduced around the PBS for improving the secrecy performance of the primary networks. To characterize the security-reliability tradeoff of the considered network, we first derive the activation probability of CFBSs and the conditional probability density function associated with the distance between the relay user and other PUs. Then, the connection outage probability (COP) and the secrecy outage probability (SOP) of each PU with NOMA (co-NOMA) or non-cooperative NOMA (nco-NOMA) are separately derived to obtain the overall COP and SOP in the primary networks. Finally, the tradeoff between COP and SOP with co-NOMA (identified as transmission SOP) is investigated for simultaneously reflecting the security and reliability. Numerical results demonstrate the performance improvements of the proposed co-NOMA scheme in comparison to that of the nco-NOMA scheme in terms of different parameters. Furthermore, the security-reliability tradeoff performance of co-NOMA is shown.

Joint optimal resource allocation and probabilistic caching design is conceived for device-to-device (D2D) communications in a heterogeneous wireless network (HetNet) relying on full-duplex (FD) relays. In particular, popular contents can be cached at user devices and at relays that are located close to users. A user may request the contents of interest from another user via D2D communications and also from a nearby relay equipped with FD radios. If the requested contents are not found in the buffers of other users/relays within the coverage range, users may opt for connecting to the macro base station (MBS) via a relay by using an FD communication. Furthermore, we propose a beneficial mobility-aware coded caching philosophy for D2D communications in the HetNet considered. Especially, we model the mobility pattern of users as discrete random jumps and exploit coded caching for improving the throughput attained. Subsequently, we develop mathematical models for analyzing the throughput in the presence of edge caching, where both the system-level co-channel interference and the FD self-interference are considered. We circumvent the high complexity of stochastic optimization by developing low-complexity optimization. Finally, numerical results are presented to illustrate the theoretical findings developed in this paper and quantify the throughput gains attained.

Simultaneous wireless information and power transfer (SWIPT) has been recognized as a promising approach to improving the performance of energy constrained networks. In this paper, we investigate a SWIPT based three-step two-way decode-and-forward (DF) relay network with a non-linear energy harvester equipped at the relay. As most existing works require instantaneous channel state information (CSI) while CSI is not fully utilized when designing power splitting (PS) schemes, there exists an opportunity for enhancement by exploiting CSI for PS design. To this end, we propose a novel heterogeneous PS scheme, where the PS ratios are dynamically changed according to instantaneous channel gains. In particular, we derive the closed-form expressions of the optimal PS ratios to maximize the capacity of the investigated network and analyze the outage probability with the optimal dynamic PS ratios based on the non-linear energy harvesting (EH) model. The results provide valuable insights into the effect of various system parameters, such as transmit power of the source, source transmission rate, and source to relay distance on the performance of the investigated network. The results show that our proposed PS scheme outperforms the existing schemes.

In 5G wireless networks, the highly growing concern of data privacy from end users drives security and privacy fundamental and strong requirements for information services. End users will always expect and constantly demand efficient and effective privacy-preserving based security services in 5G communications. Those services should not only adjust the levels of security protection, but also optimize the entire secure data communication process from the users to an untrusted cloud, via multiple fog nodes. In this new paradigm, a list of options should be open at the side of fog nodes, so that they can adjust the desired level of enhanced security protection for users' data intelligently and dynamically. In this way, the load of computational overhead for enhanced security protection at the user side will be greatly reduced. The requested options of those security services will be provided based on learning the contributed attributes, and further be enforced by fog nodes, where the data will be subsequently adjusted in a suitable way. The idea of Quality of Protection (QoP) can be applied at the fog nodes in 5G networks, so that fog nodes can supply different levels of security protection to different data protection demands. In this paper, we propose a privacy-preserving data preprocessing scheme for fog computing in 5G network security. Specifically, this work is conducted in the perspective of QoP, aiming to preserve the security service option learned from attributes and to enable fog nodes to supply different levels of privacy protection services with different security demands from users.

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. However, resource allocation strategies for maximizing the computation efficiency have not been fully investigated. In this paper a computation efficiency maximization problem is formulated in the wireless-powered MEC network under a practical non-linear energy harvesting model. The energy harvesting time, the local computing frequency, the offtoading time, and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. The problem is non-convex and challenging to solve. An iterative algorithm is proposed to solve this problem. Simulation results show that our proposed resource allocation scheme outperforms the benchmark schemes in terms of the computation efficiency and verify the efficiency of our proposed algorithm. A tradeoff is elucidated between the achievable computation efficiency and the computation bits.

Public safety communications provide effective communications amongst the first responders and victims in public safety scenarios. Device-to-device (D2D) communication is a technique that can be used to enhance network coverage in cellular networks. In this paper, we propose a novel D2D clustering scheme to expand cellular coverage for public safety communications. In the proposed scheme, cluster heads are selected from a group of public safety user equipment based on different metrics such as remaining battery power, SINR, number of discovered out of coverage devices and mobility. Each cluster head provides synchronization, radio resource management information and coverage to its cluster members. The simulation results demonstrate that our proposed scheme outperforms previous methods in terms of coverage percentage and energy consumption.

Public safety communication aims to provide efficient mission critical and first responder communication scenarios. Device-to-device (D2D) proximity services are designed to offload massive traffic from base stations and extend the coverage area. Utilizing relay to provide network services for user equipment (UE) that out of coverage is one of the most important attributes of proximity services. Existing works mainly focus on the transmission rate and energy efficiency for relay selection. In this paper, we propose a relay selection scheme that targets to extend the connection time in public safety communications. In particular, the proposed scheme takes into consideration the remaining battery capacity and communication capability of each UE. The system level simulation results show that the proposed scheme can prolong the connection time for the UE that is out of coverage.

This paper investigates a wireless powered sensor network, where multiple sensor nodes are deployed to monitor a certain external environment. A multiantenna power station (PS) provides the power to these sensor nodes during wireless energy transfer phase, and consequently the sensor nodes employ the harvested energy to transmit their own monitoring information to a fusion center during wireless information transfer (WIT) phase. The goal is to maximize the system sum throughput of the sensor network, where two different scenarios are considered, i.e., PS and the sensor nodes belong to the same or different service operator(s). For the first scenario, we propose a global optimal solution to jointly design the energy beamforming and time allocation. We further develop a closed-form solution for the proposed sum throughput maximization. For the second scenario in which the PS and the sensor nodes belong to different service operators, energy incentives are required for the PS to assist the sensor network. Specifically, the sensor network needs to pay in order to purchase the energy services released from the PS to support WIT. In this case, this paper exploits this hierarchical energy interaction, which is known as energy trading. We propose a quadratic energy trading-based Stackelberg game, linear energy trading-based Stackelberg game, and social welfare scheme, in which we derive the Stackelberg equilibrium for the formulated games, and the optimal solution for the social welfare scheme. Finally, numerical results are provided to validate the performance of our proposed schemes.

This paper studies a cognitive small cell downlink network, where one cognitive base station (CBS) transmits information to a cognitive user and transfers energy to energy harvesting receivers (EHRs). The spectrum sensing interval, the spectrum sensing time, and the beamforming matrices of the CBS are jointly optimized to maximize the energy efficiency (EE) of the CBS and to minimize the energy cost of the CBS under both the bounded channel state information (CSI) model and the probabilistic CSI model. The interference constraints of the macrocell users, the secrecy rate constraint, the transmit power constraint of the CBS and the energy harvesting constraints of the EHRs are all considered in this paper. All the three formulated optimization problems are nonconvex, for which semidefinite relaxation, a 2-D line search method, S-Procedure, and Bernstein-type inequalities are exploited. This paper also derives the conditions under which the EE maximization problem and the energy cost of the CBS minimization problem using the bounded CSI model have rank-one solutions. Simulation results demonstrate that the proposed algorithms have significant gain on the CBS EE under the perfect CSI and also gain on the CBS energy cost under imperfect CSI, all compared to the benchmark scheme.

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.

Mobile-edge computing (MEC) and wireless power transfer are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly impacted by the severe propagation loss. In order to address this issue, an unmanned aerial vehicle (UAV)-enabled MEC wireless-powered system is studied in this paper. The computation rate maximization problems in a UAV-enabled MEC wireless powered system are investigated under both partial and binary computation offloading modes, subject to the energy-harvesting causal constraint and the UAV's speed constraint. These problems are non-convex and challenging to solve. A two-stage algorithm and a three-stage alternative algorithm are, respectively, proposed for solving the formulated problems. The closed-form expressions for the optimal central processing unit frequencies, user offloading time, and user transmit power are derived. The optimal selection scheme on whether users choose to locally compute or offload computation tasks is proposed for the binary computation offloading mode. Simulation results show that our proposed resource allocation schemes outperform other benchmark schemes. The results also demonstrate that the proposed schemes converge fast and have low computational complexity.

This paper studies the joint optimal resource allocation and probabilistic caching design for device-to-device (D2D) communications in a wireless heterogeneous network with full-duplex (FD) relays. In particular, popular contents can be cached at user devices as well as at relays that are located close to users. A user can request contents from another user via D2D communications and also from a nearby relay equipped with FD radios. In the case that there is a caching miss (i.e., the requested contents are not found at the other users/relays within the coverage range), users can connect to the base station via a relay by using the FD communication technology. Subsequently, we develop mathematical models to analyze the throughput performance with edge caching where both cochannel system level interference and FD self-interference are considered. Due to the high complexity of stochastic optimization, we develop low-complexity optimization formulation by decomposing the original problem into three simple subproblems that can be efficiently solved. Finally, numerical results are presented to illustrate developed theoretical findings in the paper and significant performance gains of the throughput performance.

Heterogeneous cellular network has become an important network architecture to improve network spectral efficiency. This paper works on a theoretical framework for analyzing outage probability and spectral efficiency in a two-tier heterogeneous network with joint processing cooperation. The heterogeneous network consists of high power nodes (HPNs) and low power nodes (LPNs). A range expansion association scheme is used to extend the coverage range of LPNs and to help achieve load balancing. Cooperation is applied to LPN cell edge users that are subject to strong interference from HPNs. Joint processing cooperation is formed between the serving LPN and nearest HPN and data is transmitted to the user simultaneously from these two cooperative nodes by taking radio resources from both. Study shows that cooperation can greatly improve LPN cell edge outage performance. When range expansion area increases, the average per user spectral efficiency of the whole system with cooperation remains steady while the average per user spectral efficiency without cooperation decreases.

This paper presents an energy efficient and QoS aware cell zooming scheme assisted by low power Relay Base Stations (RBSs). Cell zooming is a promising technology for future cellular networks in order to achieve a higher energy efficiency, while maintain QoS of the mobile users. However, the benefit of cell zooming scheme is limited by the increased outage probability, pertaining to the fact that new coverage holes can be formed and inter-cell interference also increases as a result of increased transmission power. In this paper we propose a relay-assisted cell zooming algorithm that achieves energy efficiency in cellular networks while maintain QoS of the users. We analyze energy efficiency and resource consumption of the proposed scheme and compare the performance with that of the basic cell zooming scheme.

This paper applies non-orthogonal multiple access (NOMA) and relaying schemes in a mmWave based wireless heterogeneous system that aims to support Internet of Things (IoT) applications. The system consists of high power base stations, low-power relays, and low-power IoT devices. Due to the ad hoc deployment nature of low-power relays, they have very limited access to wireline power charging facilities. Furthermore, IoT devices normally have limited power and short battery life. The study assumes low-power relays and IoT devices are capable of energy harvest functionality. With the help of relays or IoT devices, downlink NOMA transmission consists of two phases. In the first phase, the BS sends a composite signal to a UE and a selected relay simultaneously by applying NOMA. After receiving the signal, relay or the IoT device split the signal into two parts. One part is for information decoding and the other part is for energy harvesting. In the second phase, the BS sends another message to UE 1 while the relay sends the decoded message to UE 2 by using the harvested energy in phase 1. The outage problem of the proposed scheme is analyzed and simulations results are presented to verify the theoretical results.

Based on data fusion technology, multi-sensor fusion positioning merges several positioning sources together to achieve an optimal positioning result by making full use of all the homogeneous or heterogeneous information from different fusion sensors. However, there has not been much research carried out about the effectiveness evaluation of multi-sensor fusion positioning system. In this paper, a self-feedback effectiveness evaluation algorithm is proposed which can not only evaluate multi-sensor fusion positioning systems, but also improve positioning performance by adopting feedback information. Besides traditional evaluation parameters, confidence level and plug and play capability are proposed as new evaluation parameters to estimate effectiveness of multi-sensor fusion positioning system. Simulations verify the efficiency of proposed evaluation parameters and self-feedback effectiveness evaluation algorithm.

In this paper two different non-orthogonal multiple access (NOMA) relay schemes are analyzed, namely NOMA cooperative scheme and NOMA TDMA scheme. Both schemes apply NOMA in the first stage by sending NOMA superimposed signals to relays. Relays then forward the messages to two UEs in the second stage. In the second stage of NOMA cooperative scheme, two relays form a cooperative pair to simultaneously transmit the decoded signals to their respective recipients. Dirty paper coding is used as precoding to cancel out inter-user interference. The second stage of NOMA TDMA scheme uses TDMA to send to two users in two separate time slots. The outage probability is analyzed for both schemes and the impact of error propagation in the NOMA successive interference cancellation is analyzed and evaluated. Performance study shows that the theoretical analysis matches the simulation results very well. NOMA cooperative scheme achieves an overall better outage performance than NOMA TDMA scheme.

As a promising radio access technology for 5G cellular network, non- orthogonal multiple access (NOMA) has attracted extensive research attention recently. Compared with orthogonal multiple access (OMA), NOMA possesses the potential to further improve system spectrum efficiency. To perform uplink NOMA successfully, the received signal of each user equipment (UE) within a NOMA group at base station needs to be decodable. On the other hand, fractional power control (FPC) is widely applied in the existing wireless networks to mitigate inter-cell interference by giving cell center and cell edge users different target receiving signal power levels. NOMA can exploit this difference in the received powers and group cell edge UEs and cell center UEs into a NOMA group so that further spectrum efficiency can be realized. In this paper, an analytical framework on uplink NOMA with FPC is developed and performance on the system coverage and average user achievable rate is evaluated. The analysis on an OMA access scheme with FPC is also provided for comparison. The analytical results are validated by simulations. The performance study demonstrates that NOMA with FPC can bring considerable capacity gain compared to OMA with FPC.

Due to prominent advantages in local coverage enhancement and indoor cellular capacity improvement, femtocell (FC) is considered as a vital component in long‐term evolution advanced (LTE‐A) heterogeneous networks (HetNets). A two‐tier LTE‐A HetNet with conventional macro base stations in the first tier and femto base stations in the second tier is considered. Under this architecture, a new downlink interference evaluation scheme is proposed, based on which a FC enhanced multi‐target resource allocation algorithm is developed to maximise user equipment (UE) throughput in a dense FC deployment scenario meanwhile providing every UE a minimum signal to interference plus noise rate (SINR) guarantee. The optimal resource allocation problem is formulated as a MAX‐K cut problem based on the graph theory approach. System‐level simulations reveal that intra‐cell interference is greatly reduced and the spectral efficiency is significantly improved by using the proposed resource allocation scheme. Femtocell throughput is elevated significantly without causing much degradation on the macrocell throughput.

To address the ever increasing high data rate and connectivity requirements in the next generation 5G wireless network, novel radio access technologies (RATs) are actively explored to enhance the system spectral efficiency and connectivity. As a promising RAT for 5G cellular networks, non-orthogonal multiple access (NOMA) has attracted extensive research attentions. Compared with the orthogonal multiple access (OMA) that has been widely applied in existing wireless communication systems, NOMA possesses the potential to further improve the system spectral efficiency and connectivity capability. This paper develops analytical frameworks for NOMA downlink and uplink multi-cell wireless systems to evaluate the system outage probability and average achievable rate. In the downlink NOMA system, two different NOMA group pairing schemes are considered, based on which theoretical results on outage and achievable data rates are derived. In the uplink NOMA, revised back-off power control scheme is applied, and outage probability and per UE average achievable rate are derived. As wireless networks turn into more and more densely deployed, inter-cell interference has become a dominant capacity limiting factor but has not been addressed in most of the existing NOMA studies. In this paper, a stochastic geometry approach is used to model a dense wireless system, that supports NOMA on both uplink and downlink, based on which analytical results are derived either in pseudo-closed forms or succinct closed forms and are further validated by simulations. Numerical results demonstrate that NOMA can bring considerable system-wide performance gain compared with OMA on both uplink and downlink when properly designed.

This paper investigates the secure communications of multiuser untrusted amplify-and-forward relay networks in the presence of direct links. In the considered system, one user is selected among multiple ones for secure transmission to the destination node with the help of an untrusted relay node. To reduce the information leakage to the untrusted relay, the paper considers two selection criteria to select the user based on the direct and the relaying links, respectively. The impact of direct and the relaying link on the system secrecy performance is studied by deriving the close-form ergodic secrecy rate (ESR) as well as the asymptotic expression. From the asymptotic expression, it can be found that the asymptotic ESR increases linearly with the logarithm of the average channel gain ratio of the direct link to the relaying link.

To achieve a significant boost on capacity performance in the next generation (5G) cellular network, novel radio access technologies (RAT) are demanded to make the system more spectrum efficient. As a promising multiple access scheme for 5G cellular network, non-orthogonal multiple access (NOMA) has attracted extensive research attention recently. Existing works show that NOMA posses the potential to further improve system spectrum efficiency compared with the orthogonal multiple access (OMA), which is predominantly adopted by existing wireless networks. In this paper, we develop the analytical framework on system coverage and average user achievable rate in a downlink NOMA system. We explicitly consider the inter-cell interference in the study, which is a capacity limiting factor in most wireless networks but less addressed in most existing analytical work for NOMA. Additional to NOMA, the analysis on an OMA access scheme, i.e., orthogonal frequency division multiple access (OFDMA), is also conducted for comparison. Owing to the tractability of Poisson Point Process (PPP) model used in this work, all the analytical results are derived and expressed in a pseudo-closed form or a succinct closed form. The analytical results are validated by simulations and demonstrate that NOMA can bring considerable performance gain compared to OMA when success interference cancellation (SIC) error is low.

This work focuses on the analysis for multicast services in fifth-generation (5G) wireless communication system. We investigate the physical- layer wireless multicast technology in massive multi-input multi-output (MIMO) mutual coupling channel model, and proposed the hybrid unicast/multicast transmission system. The mutual coupling channel model is adopted to describe the channel characteristics under the linear antenna array scenario and the rectangular antenna array scenario. The proposed hybrid transmission scheme adopts multicast beamforming in the multicast groups as well as multi-user MIMO (MU-MIMO) linear precoding in the unicast group to increase system throughput. The null-space method based interference cancellation is further performed between each group to eliminate signal leakage generated from each group. Comparisons between two types of antenna array configurations, different channel models, linear precoding as well as multicast beamforming, and user grouping strategies for multicast services are presented and analyzed by simulation.

Heterogeneous cellular network has become an important network architecture to improve network capacity and spectral efficiency. This paper works on a theoretical framework for analyzing the system spectral efficiency in a heterogeneous network with different association schemes including best power and range expansion associations. Furthermore, a fractional frequency reuse scheme is applied to mitigate the inter-tier interference in a heterogeneous network. Proportional fair bandwidth allocation is used to balance the system spectral efficiency and user fairness. Numerical results show that range expansion with fractional frequency reuse can improve the system spectral efficiency significantly.

Device-to-Device (D2D) communication is a promising technology in the next generation (5G) cellular network as it can significantly improve the system performance by leveraging the proximity of communications and reusing cellular frequency resources. However, this benefit may not be fully exploited if the co-channel interference among D2D users (DUE) and cellular users (CUE) is not properly tackled. In this paper we propose a distance based power control scheme for D2D communication underlaying uplink cellular network to achieve the expected performance gain without generating evident interference to primary CUEs. We analyze the coverage performance of both CUEs and DUEs. Stochastic geometry model, more specifically, Poisson Point Process (PPP) model, is applied to get the tractable analysis results. The analytical results are validated by simulation.

Facing a huge volume of data that quickly generated from big data applications, it is crucial for the information and communication technology (ICT) infrastructure being able to process, aggregate, store, manage and analyze with massive data. When an ICT conducts a centralized data learning task, the information privacy of each local dataset might be violated. In this paper, we consider a secure data learning scheme in which multiple parties would find the predictive models from their overall data, while not revealing its own private data to one another at the same time. Instead of deploying the centralized data learning process which may jeopardize the information privacy, we distribute the centralized data learning tasks to each local learning party as their own local data learning tasks to learn the value from local data. Besides that, we propose an associated secure scheme to show the guarantee of privacy of learning results during the information reassemble and value response process. Evaluation work is provided to verify the privacy of training dataset, as well as the accuracy of learning weights. A case study is presented based on an open metering data analysis.

The paper studies a scheme that jointly considers beamforming based MU-MIMO and NOMA in a downlink cellular network with underlaid D2D users. Two different MU-MIMO beamforming schemes are developed. The first beamforming scheme aims to eliminate the interference caused by different beams while the second one aims to cancel out the interference from base stations to D2D users. An optimization problem is formulated to maximize the total system sum throughput of both cellular users and D2D users. Since the optimization problem is NP hard and difficult to solve, we develop a suboptimal sequential solution by determining the zero-forcing beamforming matrix for MU-MIMO first. Then a user grouping and optimal power allocation algorithm is proposed in order to maximize the capacity of cellular users in each beam. Simulation results show that MUMIMO beamforming, NOMA and D2D together will significantly improve the overall system throughput.

Non-orthogonal multiple access (NOMA) is an emerging technology that can improve system spectral efficiency. In this paper, we propose a downlink multiple-input-multiple- output (MIMO) wireless system that incorporates NOMA. To make the system model more realistic, error propagation in successive interference cancellation (SIC) is considered during the decoding process. We formulate an optimization problem aiming at maximizing system throughput. The imposed error propagation increases the complexity to find a closed-form solution, so a heuristic way is proposed to solve the precoding matrix by an iterative algorithm. Case studies are performed to evaluate the impact of power allocation with residual interference afterwards. Simulation results show the superiority of the proposed precoding design and give an insight on power allocation.

Millimeter wave (mmWave) is recognized as a promising technology for the fifth generation cellular networks as it can increase system capacity significantly due to its abundant spectrum resources. Since mmWave signals are highly sensitive to blockages, received signals can experience severe none-line-of-sight (NLOS) pathloss if there are blockages between a mmWave base-station (mBS) and a user equipment (UE). Relay nodes (RNs) can be used in a mmWave cellular network to help alleviate blockages and provide alternative line-of-sight (LOS) links when blockage occurs. In this paper, we provide coverage study in a mmWave network with various distributions of mBSs, UEs, blockages, and RNs. Both analytical and simulation results show that with the assistance of RNs more LOS links are expected and the network signal-to-noise ratio (SNR) or signal-to-interference-noise ratio (SINR) performance can be improved significantly.

Abstract Smart grid is an intelligent cyber physical system (CPS). The CPS generates a massive amount of data for efficient grid operation. In this paper, a big data‐driven, cloud‐based information and communication technology (ICT) framework for smart grid CPS is proposed. The proposed ICT framework deploys hybrid cloud servers to enhance scalability and reliability of smart grid communication infrastructure. Because the data in the ICT framework contains much privacy of customers and important data for automated controlling, the security of data transmission must be ensured. In order to secure the communications over the Internet in the system, identity‐based schemes are proposed especially because of their advantage in key management. Specifically, an identity‐based signcryption (IBSC) scheme is proposed to provide confidentiality, non‐repudiation, and data integrity. For practical purposes, an identity‐based signature scheme is relaxed from the proposed IBSC to provide non‐repudiation only. Moreover, identity‐based schemes are also proposed to achieve signature delegation within the ICT framework. Security of the proposed IBSC scheme is rigorously analyzed in this work. Efficiency of the proposed IBSC scheme is demonstrated with an implementation using modified Weil pairing over an elliptic curve. Copyright © 2016 John Wiley & Sons, Ltd.

The staggering mobile growth is shaping to be the biggest shift in technology since the advent of the Internet, paving the way for unprecedented and yet to be thought of video services and applications. The proliferation of these mobile devices and applications, however, requires new paradigms to satisfy the increasing demands for capacity and energy, and achieve a good video quality. In this paper, we propose a video quality-based framework for spectrum and energy efficient mobile association and resource allocation in heterogeneous wireless networks. The basic tenets of the framework are (1) two novel performance metrics, namely QSE and QEE, to capture spectrum usage and energy consumption from video quality's perspective; and (2) a computationally efficient optimization model to derive mobile association and resource allocation for video connections in heterogeneous wireless networks. To this end, we first study the fundamental tradeoff between QSE and QEE in a PtP Rayleigh fading wireless channel. We then study QSE and QEE at the system level and develop a mobile association and resource allocation scheme that aims to jointly optimize system level QSE and QEE. The problem is formulated as a mixed-integer nonlinear optimization problem. Nonlinear fractional programming approach and dual decomposition method are applied to search the optimal solutions in a computationally efficient way. The simulation results evaluate the performance tradeoff between QSE and QEE, and show that the system performance, including PSNR distribution and maximum QSE/QEE values, greatly depends on bandwidth and power decaying factors.

The United States is deploying the broadband wireless network for public safety and emergency response. While this will provide a foundational network when disasters or other emergencies appear, the network capacity could still be insufficient when large scale emergency events, such as earthquakes or flooding, happen. On-demand broadcast is a possible solution to address this situation, but its effectiveness remains unknown. In this paper, we aim to uncover the effectiveness of on-demand broadcast at different situations. Specifically, we study the scaling of average response time on the information requests with regard to the indicators of the content diversity - the number of distinct content files and the content popularity distribution. To achieve this goal, we first derive the lower bound of the average response time. We further investigate broadcast for streaming videos and uncover that parallel broadcast could have the same lower bound as the sequential broadcast under certain conditions. Based on the lower bound we derived, we evaluate the scaling of response time with regard to the number of files under different distributions. We further provide numerical results to demonstrate the accuracy of the approximation and the value of the lower bound on evaluating the optimality of a basic heuristic on-demand broadcast scheme.

In this paper, we study a real-time information based demand-side management (DSM) system with advanced communication networks in smart grid. DSM can smooth peak-to-average ratio (PAR) of power usage in the grid, which in turn reduces the waste of fuel and the emission of greenhouse gas. We first target to minimize PAR with a centralized scheme. To motivate power suppliers, we further propose another centralized scheme targeting minimum power generation cost. However, customers may not be motivated by a centralized scheme since such a scheme requires total control and privacy from them. A centralized scheme also requires too much real-time data exchange for frequent DSM deployment. To tackle these issues, we propose game theoretical approaches so that most of the computation is performed locally. In the proposed game, all the customers are motivated by extra savings if participating. Moreover, we prove that all parties benefit from the DSM system to the same level because both the centralized schemes and the game theoretical approach minimize global PAR. Such an analysis is further demonstrated by the simulation results and discussions. Additionally, we evaluate the performance of several (partially) distributed approaches in order to find the best way to deploy DSM system.

Abstract In this paper, we consider switch‐and‐stay combining (SSC) in two‐way relay systems with two amplify‐and‐forward relays, one of which is activated to assist the information exchange between the two sources. The system operates in either analog network coding (ANC) protocol where the communication is only achieved with the help of the active relay or time‐division broadcast (TDBC) protocol where the direct link between two sources can be utilized to exploit more diversity gain. In both cases, we study the outage probability and bit error rate (BER) for Rayleigh fading channels. In particular, we derive closed‐form lower bounds for the outage probability and the average BER, which remain tight for different fading conditions. We also present asymptotic analysis for both the outage probability and the average BER at high signal‐to‐noise ratio. It is shown that SSC can achieve the full diversity order in two‐way relay systems for both ANC and TDBC protocols with proper switching thresholds. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract We consider a multi‐source two‐way relay network, in which one source communicates with N other sources (n = 1,2,…,N) with the help of a single amplify‐and‐forward relay. We propose two opportunistic source scheduling schemes in such a network. According to the proposed schemes, in each transmission interval, only a single out of the N sources is selected, and this selected node acts as either transmitter or receiver depending on the channel conditions. For both schemes, tight closed‐form lower bounds of outage probability and bit error rate (BER) are derived. Asymptotic outage probability and BER that are valid for high signal‐to‐noise ratio regime are also analyzed, which can provide important insights on the impact of system parameters. The analytical results show that the full diversity order N + 1 can be achieved by both proposed schemes. Simulation results are also presented to corroborate the analysis. Copyright © 2014 John Wiley & Sons, Ltd.

Effective inter-cell interference mitigation has been extensively studied because of its outstanding cell-edge signal quality improvement capability. Conventional static inter-cell interference coordination strategies, including fractional frequency reuse and soft frequency reuse, have received much attention owing to their effectiveness in mitigating interference and low complexity in implementation. However, they are less effective when dealing with dense uneven traffic distributions and dynamic traffic demands and thus incur low spectrum utilization in some cells and spectrum shortage in others. This paper proposes a universal frequency reuse scheme in a two-layer Long Term Evolution-Advanced heterogeneous network to ensure good throughput for all user equipment (UE), especially UEs at cell edge. The proposed scheme allows each cell to use all the spectrum resources, limited by an orderly regulation of all sub-bands. This scheme minimizes the potential occurrence probability of inter-cell co-sub-band interference through an intra-cell sub-band resource management. Furthermore, a graph-theoretic based sub-band allocation algorithm is developed to optimize UE throughput performance, especially for the cell-edge low signal to interference noise ratio UEs. A comprehensive performance comparison among different frequency reuse schemes is conducted by considering performance metrics, including cell-edge throughput, average throughput, and signal to interference noise ratio cumulative distribution function. Simulation result shows that the universal frequency reuse scheme outperforms other two schemes significantly. Copyright © 2016 John Wiley & Sons, Ltd.

In this work, we consider the device-to-device (D2D) communications underlaying cellular networks to support local communication needs. In particular, we focus our attention on the design of an optimal resource allocation and mode selection algorithm for both cellular and D2D users. In the design, communication mode selection for D2D users is also taken into account so that a D2D source-destination pair has an option to either directly communicate or indirectly communicate through the base station (BS). On the other hand, it is necessary and important to provide a certain level of Quality of Service (QoS) to users. The users can be further differentiated by assigning different weighting factors and QoS requirements. To this end, we formulate a problem of maximizing the weighed sum rate of all the users constrained by their power and QoS requirements. The tool that we use to handle this problem is the primal-dual technique which transforms the original problem into the equivalent problem showing lower computational complexity. The simulation results verify that our proposed scheme outperforms the previous heuristic scheme by jointly optimizing the power and resource allocation along with the communication mode selection for the D2D communication pairs.

Future heterogeneous networks (HetNets) will have to face a great challenge of overwhelming demand of spectrum resource, due to the exponential increase in mobile internet traffic driven by a new generation of wireless devices. In this paper, we propose a spectrum sensing and scheduling scheme for circular array, in order to make better use of the spectrum resource and improve the performance of multi-user MIMO (MU-MIMO) in HetNets. The proposed scheme can effectively detect the users and frequency use based on angles, and schedule the users with optimized codebook. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and users' data rate, with significantly reduced system complexity and increased efficiency.

In order to address the ever increasing high capacity demands, next generation wireless networks are required to revolutionize the infrastructure design and air interface technologies. In this paper, we introduce a cooperative non-orthogonal multiple access (NOMA) technique with successive interference cancellation (SIC) in wireless heterogeneous networks. Aiming to improve the system capacity, the cooperative NOMA scheme exploits both NOMA and dirty paper coding (DPC), based on which a resource scheduling optimization problem is formulated. The optimization problem is a combinatorial mixed-integer non-linear problem. A genetic algorithm is used to solve the problem with a low computational complexity. Simulation results show that the proposed cooperative NOMA scheme can significantly improve the network capacity.

In this paper, a big data driven, cloud based information and communication technology (ICT) framework for smart grid is proposed. The proposed ICT framework is to provide price forecast to customers and energy forecast to utility company. Cloud computing and big data analytics are introduced to assist local control centers dealing with large amount of data. However, public cloud and transmission over internet may be vulnerable in security, especially privacy preserving and authentication. To secure the proposed framework, we propose an identity-based signcryption (IBSC) security scheme. The proposed IBSC scheme provides confidentiality and non-repudiation since it performs simultaneously the functions of encryption and digital signature. Moreover, data integrity is also provided in the IBSC scheme. Identity-based signature and key distribution are presented as extended applications from the IBSC scheme. The security and performance of the proposed IBSC scheme are analyzed. Efficiency of the proposed IBSC scheme is demonstrated with an implementation using modified Weil pairing over an elliptic curve.

Underlaying Device-to-Device (D2D) communications in next generation (5G) cellular networks is a promising technology because D2D can exploit the proximity of communication pairs and reuse existing cellular frequency resources. Although D2D communication has a great potential to improve the capacity and spectral efficiency of the overall system, the interference between D2D user equipment (DUE) and cellular user equipment (CUE) needs to be properly tackled in order to achieve the target performance gains. Fractional frequency reuse (FFR) has been demonstrated as an effective scheme to mitigate uplink interference. Further, uplink power control in a cellular network is a widely employed mechanism to manage interference across the uplink network while improving spectral efficiency. Existing uplink fractional power control (FPC) in LTE trades off reasonably well the cell-edge UE performance with the overall system performance. In this paper, we study the performance of D2D communications underlaying an uplink LTE network which utilizes both FPC and FFR. FPC in such a network is slightly different than the existing FPC mechanism in that it is only applied within certain geographical areas. In such a system, geometry information is exploited to allocate resources so that significant interference reduction can be attained. Coverage analysis is conducted for both CUEs and DUEs by following a mathematically trackable Poisson Point Process (PPP) model. A spectral efficiency study is also presented along with the impact of various uplink parameters on the system performance.

We consider the problem of resource sharing in Device-to-Device (D2D) communication underlaying cellular networks. The problem is formulated as a non-cooperative game in which mobile users, either legacy cellular users or D2D users, decide their respective transmission power over available resource blocks (RBs) with the goal of maximizing their own utility function. The key factor that distinguishes our work from the existing literature is the design of the utility function; the utility function of each user is defined as the achievable rate normalized by the power consumption, which is in [bits/sec/Joule]. Such a utility function well reflects the users' satisfaction in reality when users are mobile and subject to the availability of energy due to the finite battery capacity and limited recharging facility. The scenario where the cellular and the D2D connections share the same resources is considered, in which the interference management between cellular and D2D communications is of great importance to guarantee the performance of high-priority cellular users. The most energy efficient strategy turns out that a DUE allocates the least amount of power on the channel with the best channel gain-to-interference-plus-noise-ratio (CINR) yet still achieves the highest data rate on that channel while a CUE allocates the optimal power on its assigned channel to reach the maximum energy efficiency.

We discuss mechanisms to hide multiuser data in a given host video stream with imperceptible spread-spectrum embedding. After constructing host vectors from original video stream, the hidden message for each user will be spread out with a signature and embedded to the host vectors. First, for any given total embedding distortion constraint, we give the optimal multi-signature assignment and amplitude allocation pair that maximizes the sum capacity of direct concealment procedure. Furthermore, if linear transformation of host vectors is allowed, we propose the transforming matrix design such that the embedded multiuser data lies in the orthogonal space of transformed host vectors.

In this paper, we propose a multi-objective optimization framework to address the joint mobile association and resource allocation problem in a video transmitted wireless heterogeneous network. We consider user quality of experience (QoE) as one of the design objectives together with two other performance metrics to characterize the design tradeoffs among perceived video quality, power consumption, and network resource consumption. In order to find the Pareto optimal solutions for a multi-objective optimization problem, we first apply weighted Tchebycheff approach to aggregate multiple objectives and minimize the Tchebycheff distance between the optimal solution and utopia solution. Dual decomposition technique is then introduced to decompose the above mentioned problem into a series of similar subproblems. By using linear relaxation and variable transformation, standard convex optimization method is applied to solve these subproblems efficiently and the optimal mobile association and resource allocation are obtained. System level simulation studies numerically demonstrate the tradeoffs among three design objectives and provide an insightful understanding on the performance compromise under multi-objective conditions.

Delay issue is a challenge in multi-hop cognitive radio networking because of the dynamic change of channels available to the cognitive radio nodes. To address this challenge, we propose a channel allocation and rate allocation scheme with low complexity in this paper. Our scheme is flexible so that it can react to the dynamic change of channel availability, and it can minimize the delay by considering the primary user activities. First, we model the primary users' activity, channel availability and the interference among the cognitive radio nodes in a cognitive radio network environment, and show how to implement a channel allocation algorithm and a rate allocation scheme in multi-hop cognitive radio networks. Second, we formulate an optimization problem to minimize the end-to-end delay of the network. We consider the channel availability constraint and the primary user activities jointly, and analyze the end-to-end delay in multi-hop cognitive radio networks. In order to reduce the computing complexity, we propose a graph coloring based channel allocation and rate selection algorithm using gradient descent method. Furthermore, we show the performance of our schemes and compare them with existing schemes for different scenarios of channel availability and number of channels through simulations. Our work brings insights on how to make rate selection and channel allocation in multi-hop cognitive radio networks.

Device-to-device (D2D) communication, which enables a user equipment to communicate with another nearby UE directly over a D2D link without going through the central base station (BS), have attracted great research interests for future applications on high data rates and spectral efficiency. In this work, we investigate multi-pair D2D communications with a multiple antenna relay under three possible schemes: (i) Direct D2D transmission; (ii) Multi-hop D2D with analog network coding where the relay will help multi-pair D2D transmission by amplify-and-forwarding mixed received signals; (iii) Multi-hop D2D with space time analog network coding where the relay will help multi-pair D2D transmissions by amplify-and-forwarding mixed received signals together with space time coding techniques. Specifically, three pairs of D2D communications with a two-antenna relay are modeled and space-time analog network coding based on Alamouti scheme is applied. We compare the D2D sum rate and bit error rate performance of different schemes under different channel scenarios.

In this work, we intend to carry out optimized spread-spectrum concealment of multiuser data under a given digital image. First, the overall image is pre-processed into transform-domain small blocks from which host vectors are obtained via zig-zag scanning vectorization. Multiuser data hiding is performed in the generated host vectors. Under this data hiding system model, we give an orthogonal set of embedding spread-spectrum signatures that achieves maximum sum signal-to-interference-plus-noise ratio at the output of the linear-filter receivers for any fixed embedding amplitude values. Then, for any given total embedding distortion constraint, we present the optimal multi-signature assignment and amplitude allocation that maximizes the sum capacity of the concealment procedure. The practical implication of the results is sum signal-to-interference-plus-noise ratio, sum-capacity optimal multiuser/multi-signature spread-spectrum data hiding in the digital image medium. Numerical results demonstrate the effectiveness of the proposed methods. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract The advanced metering infrastructure (AMI) is the key to demand‐side management system in smart grid. The communication over AMI consists of plenty important data, which have different security requirements. In this paper, we propose a hierarchical identity‐based signature scheme (HIBaSS) to enhance the sender authentication for downlink transmission of AMI. The downlink in AMI mainly distributes control messages such as price and tariff information to the smart meters. Unlike the metering data in the uplink transmission that requires confidentiality, most of the control messages in downlink only require data integrity and sender authentication. Moreover, since most of the control messages are valid for a relatively long time period (e.g., an hour), more complicated but stronger cryptographic schemes can be applied in downlink AMI. In our proposed HIBaSS, each smart meter does not trust a signature from a single data aggregate point (DAP) although it includes the original signature from the authentication server (AS), because a smart meter has no way to verify a message or a DAP directly with the AS. Instead, a smart meter receives a group signature created by all the DAPs with certificates from the AS that prevents the messages from forgery, manipulation and repudiation. The performance evaluation also shows that HIBaSS is efficient enough to be applied in the AMI downlink transmission. Copyright © 2015 John Wiley & Sons, Ltd.

In this work, we propose a dynamic distributed resource sharing scheme which jointly considers mode selection, resource allocation, and power control in a unified framework for general D2D communications. First, we model the joint issue of mode selection and resource allocation as a hedonic coalition formation game, while accounting for the tradeoff between the benefits in terms of available rate and the costs in terms of the mutual interference. Moreover, we develop a coalition formation process based on the switch rule, through which each cellular user makes an individual and distributed decision to form a Nash-stable partition. Second, we view the members of each coalition as a whole, and formulate a power control problem to share the aim of maximizing the sum-rate of cellular links in this coalition. To solve this NP-hard problem with online operation, we present a power control process, which employs the local piecewise-linear approach to take a locally and separately approximate optimal outcome. Finally, we present a dynamic resource sharing algorithm, which iteratively operates the coalition formation and power control processes.

ABSTRACT Because of the inevitable trend of green networking, energy efficiency (EE) is quickly becoming one of the key performance metrics to evaluate wireless communication systems, together with spectrum efficiency (SE) and quality of service (QoS) that have been traditionally used. This paper studies the fundamental tradeoff between EE and SE in the presence of statistical QoS requirements in wireless transmission systems. Earlier studies have shown that the performance with QoS requirements in the wireless transmission can be measured through effective capacity, which can capture the physical layer fading channel characteristics in the link layer QoS requirements, such as delay and data rate. Under this context, SE is defined as effective capacity per unit frequency bandwidth, and EE is defined as energy consumed per effective capacity bit. Both circuit power and transmission power are considered in the energy model, based on which we derive the quasi‐convex generalized EE formulation. To exploit the tradeoff between EE and SE with QoS considerations, we propose a generic close‐form approximation for EE–SE formulation by employing a curve fitting approach. The impacts of QoS and circuit power consumption on EE–SE tradeoff are respectively analyzed. QoS requirement and circuit power consumption affect the EE–SE tradeoff differently. In the low‐SNR regime, circuit power shows more impact on the EE–SE tradeoff, whereas QoS impacts EE–SE tradeoff more in the high‐SNR regime. Copyright © 2014 John Wiley & Sons, Ltd.

Device-to-Device (D2D) communication operating as an underlay to the cellular network has been lately exploited to facilitate proximity-aware services and data traffic offloading. While D2D communication has great potentials to improve wireless network spectral efficiency and energy efficiency due to the proximity of communication parties and a higher spectrum reuse gain, how to guarantee the coverage and capacity for both cellular users and D2D users when dense D2D communications are carried underlay cellar networks still remains as a big challenge. This paper provides the coverage study by deriving the uplink and downlink SINR distributions for both cellular users and dense D2D users based on statistical user distribution and channel information. We model the spacial distribution of the D2D pairs as a homogeneous Poisson Point Process (PPP) and D2D users can either use cellular uplink resources or downlink resources. The simulation results match closely with the analytical studies. The analytical tools can be conveniently extended to evaluate other key D2D wireless network performance metrics including network capacity and outage probability, and provide great insights on the critical network design issues such as power control, interference management, and resource allocations.

This paper proposes a distributed resource allocation and power control scheme based on stackelberg game framework to improve network capacity in Device-to-Device (D2D) communications networks. D2D users are supported in an underlay mode by sharing radio resources with the cellular downlink communications. The system aims to maximize the number of underlay D2D users while guaranteeing Quality of Service (QoS) of the prioritized cellular users. We formulate the problem through joint optimization on the D2D power control and resource allocation. The global optimization problem is a complicated task to tackle mathematically and has a high computational complexity. Instead we formulate the optimization problem with a distributed stackelberg game theoretical model and decompose it into two steps to approach the game equilibrium. The simulation results show that our proposed distributed algorithm converges very fast and the system capacity of the D2D underlay network is significantly improved.

In this paper, we study a downlink multiuser network in which one base station sends its message to one of multiple users, assisted by one of multiple intermediate decode-and-forward (DF) relays. Multiple eavesdroppers exist at the receiver side, and they can overhear the message from the base station, which brings out the issue of information security. We consider moderate shadowing environments so that direct links from the base station to the users and eavesdroppers exist. Maximal ratio combing (MRC) technique is employed at the receivers to combine the signals from the relaying and direct links. We select the best user and relay pair by maximizing the received signal-to-noise ratio (SNR) at the user. We study the effects of the selection on the system secrecy performance by deriving the closed-form expression of secrecy outage probability. We also provide the asymptotic secrecy outage probability with high transmit power and high main-to-eavesdropper ratio (MER). From the asymptotic expression, we can find that the system can achieve the diversity of the total number of users and relays, irrespective of the number of eavesdroppers. Simulation and numerical results are finally demonstrated to verify the proposed studies.

This paper presents a novel scheme of adaptive self-organization network (SON) by integrating cognitive radio (CR) with inter-cell interference coordination (ICIC) for LTE-A heterogeneous networks (HetNets). Particularly, we take advantage of the spectrum sensing function from CR and the radio resource layering function from ICIC. Our work addresses the issues of smart low-power node (SLPN) development, which associates appropriate sectorization with radio resource allocation during the self-organization process. We further develop a Hungary algorithm based self-organization strategy to improve the SLPN adaptive optimization. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and coverage, with extra rewards of high flexibility and low complexity in HetNet SON.

In this paper, we intend to hide multiuser data in a given host video stream with imperceptible spread-spectrum embedding. First, host video frames are picked in the original given video stream according to a frame selection pattern. We partition each host video frame into many small blocks. Based on a two-dimensional transformation of each small block and zigzag scanning, we construct the host video vectors. The embedded message for each user will be spread out with a signature and added to the host vectors. We present the orthonormal signature set of embedding carriers that achieves maximum sum signal-to-interference-plus-noise ratio (sum-SINR) at the linear filter output for any fixed embedding amplitudes. Then, for any given total embedding distortion constraint, we give the optimal multi-signature assignment and amplitude allocation pair that maximizes the sum capacity of the concealment procedure. Numerical results demonstrate the effectiveness of the proposed optimal data embedding methods in video streams.

As video content delivery over wireless networks is expected to grow tremendously in upcoming years, how to support energy and bandwidth consuming video applications with high Quality of Experience (QoE) becomes a challenging issue in the future wireless networks. Clearly, there is an urgency for a new disruptive paradigm to bridge the gap between the increasing capacity plus energy demands and the scarce wireless network resources. In this paper, we propose a spectrum and energy efficient mobile association and resource allocation scheme in wireless heterogeneous networks based on two new performance metrics: video content aware or QoE-aware Energy Efficiency (QEE) and video content aware or QoE-aware Spectral Efficiency (QSE). QEE and QSE evaluate the power consumption and bandwidth consumption from video quality's perspective. First we conduct a fundamental study between QEE and QSE in a point-to-point wireless environment. Then we propose a mobile associations and resource allocation scheme in a heterogeneous wireless network that can optimize QEE and QSE. The system level problem is formulated as a mixed-integer nonlinear optimization problem. Nonlinear fractional programming approach and dual decomposition method are applied to search the optimal solutions in a computationally efficient way.

In this paper, we propose a security protocol for advanced metering infrastructure (AMI) in smart grid. AMI is one of the important components in smart grid and it suffers from various vulnerabilities due to its uniqueness compared with wired networks and traditional wireless mesh networks. Our proposed security protocol for AMI includes initial authentication, secure uplink data aggregation/recovery, and secure downlink data transmission. Compared with existing researches in such area, our proposed security protocol let the customers be treated fairly, the privacy of customers be protected, and the control messages from the service provider be delivered safely and timely.

Neighborhood area network (NAN) is one of the most important sections in smart grid communications. It connects residential customers as part of a two-way communication infrastructure responsible for transmitting power grid sensing and measuring status as well as the control messages. In this paper, we propose a cost-effective, flexible, and sustainable NAN design using wireless technologies such as IEEE 802.11s and IEEE 802.16, as well as renewable energy such as solar power. We provide analysis to select the optimum number of gateways in a NAN. We also discuss the general way to compute real time power usage for a NAN gateway. In addition, we set the boundary of the gateway power usage under two extreme scenarios to ensure the NAN can be self-sustaining while meeting the critical transmission criteria.

In this paper, we investigate a multiuser cognitive relay network with direct source-destination links and multiple primary destinations. In this network, multiple secondary users compete to communicate with a secondary destination assisted by an amplify-and-forward (AF) relay. We take into account the availability of direct links from the secondary users to the primary and secondary destinations. For the considered system, we select one best secondary user to maximize the received signal-to-noise ratio (SNR) at the secondary destination. We first derive an accurate lower bound of the outage probability, and then provide an asymptotic expression of outage probability in high SNR region. From the lower bound and the asymptotic expressions, we obtain several insights into the system design. Numerical and simulation results are finally demonstrated to verify the proposed studies.

Long Term Evolution uses a new location update (LU) scheme, called a tracking area list (TAL)-based scheme, to overcome the defects of the LU scheme used in 2G and 3G cellular networks. Under the TAL-based LU scheme, each time a user equipment (UE) performs an LU, it is allocated a group of tracking areas, referred to as a TAL, within which the UE can move freely without any LU. The UE performs an LU when moving out of the TAL. The performance of the TAL-based LU scheme depends on the allocated TAL. In this paper we develop a mathematical model to analyze the signaling overhead of the TAL-based LU scheme for local UEs whose mobility exhibits strong regularity. We derive formulas for the LU cost and the paging cost of a TAL allocation strategy. With these formulas we can find an optimal TAL allocation strategy to minimize the signaling cost of the TAL-based LU scheme.

Device-to-device (D2D) communication underlaying a cellular network is a promising technology in the future wireless networks to improve network capacity and user experience. In current works, D2D communications are considered as two user equipments (UEs) communicating directly without going through the central base station (BS). In fact, D2D communications can be further broadened to multi-hop D2D communications in which a UE may help other UEs communicate with each other, or assist other UEs to communicate with BS. In this paper, we investigate a scenario of multi-hop D2D communications where one UE may help other two UEs to exchange information, by utilizing analog network coding technique. We analyze the energy-efficiency of this multi-hop D2D communication, derive the average energy-efficiency under Rayleigh fading channels, and compare with direct one-hop D2D communication and traditional cellular communication through BSs.

We consider the problem of embedding multiuser data in digital media (such as images, video sequences, audio) with minimum perceived distortion. In this direction, we find the orthonormal set of embedding carriers that achieves maximum sum signal-to-interference-plus-noise ratio (sum-SINR) at the output of the receiver linear filters for any fixed embedding amplitude values. Then, for any given total embedding distortion constraint, we calculate the optimal multicarrier assignment and embedding amplitude values that maximize the sum capacity of the embedding process. Experimental results presented herein for multiuser data embedding in images demonstrate the effectiveness of the proposed methods.

In network planning and design, security usually conflicts with other design objectives, such as usability, performance, and even functionality. Meanwhile, security goals have their own inherent contradictions, as confidentiality, integrity, privacy, accountability, availability, and recovery from safety attacks often conflict fundamentally. For a good network design, balancing these design objectives and cost is the prerequisite. However, it is quite difficult to satisfy all the security requirements simultaneously, let alone the multiple interacting and possibly conflicting targets. Ultimately, security comes down to using the information available best to balance the trade-offs among the competing goals of multiple factors. Considered as one of the grand challenges of the field, study the objective metric to evaluate the trade-offs of network security has draw extensive research attentions recently. In this call for papers, we would like to invite novel ideas; theoretical analysis for evaluating, modeling, and optimizing the trade-offs among network security; and other design objectives. Submissions of applications-oriented papers describing case studies and simulations are encouraged as well. High-quality original papers are solicited. Papers must be unpublished and must not be submitted for publication elsewhere. If a paper is extended from a conference paper, then the extension has to have at least 40% new materials. All papers will be peer reviewed according to the guidelines of Wiley's Security and Communication Networks Journal to ensure high quality and relevance to the Special Issue. All submissions should be prepared by following the guidelines given in Section “For Authors” at http://www.interscience.wiley.com/security. Prospective authors should submit their paper online at http://mc.manuscriptcentral.com/scn.

In this paper, we propose a multiuser cognitive relay network, where multiple secondary sources communicate with a secondary destination through the assistance of a secondary relay in the presence of secondary direct links and multiple primary receivers. We consider the two relaying protocols of amplify-and-forward (AF) and decode-and-forward (DF), and take into account the availability of direct links from the secondary sources to the secondary destination. With this in mind, we propose an optimal solution for cognitive multiuser scheduling by selecting the optimal secondary source, which maximizes the received signal-to-noise ratio (SNR) at the secondary destination using maximal ratio combining. This is done by taking into account both the direct link and the relay link in the multiuser selection criterion. For both AF and DF relaying protocols, we first derive closed-form expressions for the outage probability and then provide the asymptotic outage probability, which determines the diversity behavior of the multiuser cognitive relay network. Finally, this paper is corroborated by representative numerical examples.

ABSTRACT In this paper, we address the energy‐efficient connectivity problem of awireless sensor network(WSN) that consists of (1) staticsensor nodesthat have a short communication range and limited energy level, and (2)relay nodesthat have a long communication range and unlimited power supply, and that can be added or relocated arbitrarily. For such a WSN, existing studies have been focused on the design of efficient approximation algorithms to minimize the number of relay nodes. By contrast, we propose a unified backbone construction framework that can be performed in a centralized manner with two objectives: (1) to minimize the number of nodes in the backbone and (2) to maximize the lifetime of the network. To solve such a challenging problem, we formulate three subproblems: (1)partial dominating set with energy threshold(PDSET); (2)partial dominating set with largest residual energy(PDSLE); and (3)minimum relay node placement(MRNP). For these three subproblems, we develop polynomial‐time algorithms. We also prove that our algorithm for PDSLE is optimal, and our algorithm for the PDSET and MRNP problems have small approximation ratios. Numerical results show that the proposed framework can significantly improve energy efficiency and reduce backbone size. Copyright © 2012 John Wiley & Sons, Ltd.

This paper considers a two-phase two-way relay network (TWRN) with two sources and multiple amplify-and-forward (AF) relays, where the direct link between the sources exists and one best relay is chosen for data communication to maximize the minimum signal-to-noise ratio (SNR) of bidirectional communication. To efficiently exploit the direct link within two phases, a switch-and-stay combining (SSC) protocol is employed. In SSC, one branch out of the relay and direct branches is activated for data communication, and the branch switching occurs when the end-to-end SNRs fall below the given thresholds. We analyze the system performances over the independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels, by deriving lower bounds and asymptotic expressions with high SNR for the outage probability and bit error rate (BER). It is shown that SSC can preserve the same spectral efficiency as analog network coding (ANC), while can concurrently achieve the full diversity order as the optimal selection (OS) with less implementation complexity. Numerical and simulation results verify the proposed studies.

Asynchronous system provides great performance improvement for wireless ad hoc networks, such as anti-jamming, collision reduction and device simplification. Nevertheless, new media access and routing protocols are required to assist the asynchronous system, e.g., a neighbor discovery algorithm, which is the first step in the initialization of wireless ad hoc networks. In the past few years, a number of algorithms have been proposed for neighbor discovery. However, most of them only consider synchronous system and cannot work efficiently in asynchronous system. In this paper, firstly, we propose an analytical model for an 1-way asynchronous system in wireless ad hoc networks with directional antennas. Then, we compare the time-slot consumption in asynchronous system to complete the neighbor discovery process with that in synchronous system. Finally, in order to improve the performance of the neighbor discovery process, we extend the 1-way asynchronous discovery algorithms to a 2-way asynchronous discovery algorithm. To the best of our knowledge, this is the first practical analytical model of 2-way asynchronous neighbor discovery algorithm with directional antennas.

Wide deployment of communication devices on vehicles is on the horizon due to the development of intelligent transportation systems. Although these communication devices are originally designed for highway efficiency and safety applications, the mobile communication ability on vehicles also enables a lot of other emerging applications. In this paper we study an application scenario on utilizing vehicles to carry messages for a set of sensor collectors, which are deployed at places without other communication infrastructure. A core problem that we study is on how to schedule messages so that the message delay is minimized. To achieve this goal, we propose three different strategies: the first feasible vehicle strategy, the optimal bound strategy, and the multiple attempt strategy. We further provide mathematical analysis on these strategies that are also verified by simulations, and compare the performance of these three strategies in simulations.We draw the conclusion that the optimal bound strategy is an effective strategy as it achieves low delay of the messages. At the same time it avoids the disadvantages such as high energy consumption and large storage size as in the multiple attempt strategy.

As radio interface approaches its practical capacity limit given limited spectrum resource, network architecture innovation/revolution would play a key role in boosting wireless communication system capacity and improving user experience. Deploying dense small cell networks overlaying the conventional macro cell networks is widely regarded as a key step towards network architecture revolution for improved spectrum and energy efficiency. In this paper, we analytically investigate the spectrum efficiency of densely deployed small cell networks in the downlink using tools from stochastic geometry. Unlike the conventional user-centric link-level spectrum efficiency analysis, we analyze network spectrum efficiency, defined as the average aggregate spectrum efficiency per cell. As the portion of cell-edge users, who would consume more spectrum resource in achieving a target rate, usually outweighs that of the cell-center users, network spectrum efficiency evaluation should count user distribution to portray the network performance as a whole. Based on the network spectrum efficiency results, we are able to find the optimal cell density and the corresponding optimal base station transmit power for achieving a high spectrum efficiency and/or energy efficiency.

This paper investigates the energy efficiency in a wireless channel with delay requirement. We address the energy efficiency transmission by considering both transmission power and circuit power consumptions based on the effective capacity approach. Effective capacity has been widely used to incorporate QoS requirements in the wireless communication and is an effective approach to modeling the physical layer wireless fading channel with the link layer parameters. We first derive the quasi-convex generalized form of energy efficiency formulation based on effective capacity under delay constraints. The general expression can be applied to various fading scenarios. We further develop a two-step binary search algorithm to find the best energy efficiency and the corresponding optimal transmission signal-to-noise ratio (SNR). The numerical results show that both the circuit power and queuing delay requirement have a big impact on the overall wireless channel energy efficiency.

In this paper, we consider a two-way relay network (TWRN) consisting of two sources and N amplify-and-forward (AF) relays (N ≥ 1), among which the best relay is chosen to assist the data communication. In a time-varying fading channel, the relay selection may be based on the outdated channel state information (CSI). We study the impact of outdated relay selection on the system performance by deriving a tight lower bound for the outage probability in Rayleigh block-fading channels. We further provide an asymptotic analysis in high signal-to-noise ratio (SNR) region. From the asymptotic results, we can find that the system diversity order degenerates into unity as long as the CSI is outdated. Numerical results demonstrate the tightness of the performance bounds as well as the effects of outdated relay selection on the system performance. Simulation results are also provided to corroborate the theoretical analysis.

In this paper, switch-and-stay combining (SSC) is considered in two-way relay systems with amplify-and-forward (AF) relays, among which one is activated to assist the information exchange between two sources. We assume that the two-way relay system operates in the analog network coding (ANC) protocol where the communication can only be achieved through the active relay without the direct link. The outage probability and the average bit error rate (BER) for Rayleigh fading channels are studied. In particular, we derive closed-form lower bounds for the outage probability and the average BER, which is tight for different fading conditions. Moreover, we present asymptotic analysis for both the outage probability and the average BER at high signal-to-noise ratio (SNR) region. It is shown that SSC can achieve the full diversity order in two-way relay systems with proper switching thresholds. Numerical and simulation results are also provided to verify the theoretical study.

Wireless heterogeneous networks have emerged as a new paradigm to meet the fast increasing wireless capacity and coverage demands. Coordinated Multipoint Processing (CoMP) and Precoding are two promising techniques to further improve the network capacity and spectral efficiency. This paper presents an optimal intra-cell CoMP resource allocation scheme in a wireless heterogeneous network and explores the Tomlinson-Harashima Precoding (THP) in the physical layer to reduce the inter-user interference. The objective is to maximize the aggregate proportional rates in the system. We derive an asymptotically optimal solution for resource allocation by using a gradient descent based scheduling and KKT conditions for optimality. Simulation results demonstrate the system proportional fairness capacity gain of proposed resource allocation scheme and this resource management framework provides a guideline for future radio resource management in wireless heterogeneous networks.

In this paper we investigate the scalability of communication architectures to provide traffic information services in a vehicular network based on the peer-to-peer (P2P) network technology. We study a general scenario in a metropolitan area where there are a huge number of data sources disseminating traffic data accessible to the vehicles through a multitude of roadside units. The large quantities of both data sources and customers post paramount challenges to the network scalability design. The existing work in P2P multicast, including the clustering based architecture, can only solve the scalability problem for applications with a limited number of data sources. We study the scenario that has a large number of data sources and prove that the clustering based architecture does not scale well with the traffic load at each node. Therefore, we propose a proxy based scalable architecture, in which data download traffic on each node is proved to keep constant even when the network size grows. We further verify the accuracy of the analysis using simulations.

In this work, we investigate the properties of energy-efficiency (EE) and spectrum-efficiency (SE) for video streaming over mobile ad hoc networks by developing an energy-spectrum-aware scheduling (ESAS) scheme. To describe a practical mobile scenario, we use a random walk mobility model, in which each node can choose its mobility direction and velocity randomly and independently. Through rigorous analysis and extensive simulations, we demonstrate that the node mobility is beneficial to EE but not to SE. The contributions of this work are twofold: 1) We propose an ESAS scheme with a dynamic transmission range, which significantly outperforms the previous minimum-distortion video scheduling in terms of joint EE and SE performance; 2) We derive an achievable EE-SE tradeoff range and a tight upper/lower bound with respect to energy-spectrum efficiency index for various node velocities. We believe that this work helps to shed insights on the fundamental design guidelines on building an energy and spectrum efficient mobile video transmission system.

A type of disruption/delay tolerant networks, known as pigeon networks, utilizes controllable special purpose vehicles called pigeons to convey messages among segregated areas. This paper aims to provide optimization methods for the challenging problem that how a pigeon should design its route to minimize the average delay of messages. For small scale networks, we construct an exact optimization algorithm that shows significant reduction in delay over the existing heuristic algorithms. For large scale networks, we present both the lower bound and upper bound of the average delay, and devise partitioning-based optimization algorithms that perform close to the lower bound. Both theoretical analysis and numerical results show that the delay could be reduced by as much as 50% compared to the existing algorithm.

Heterogeneous wireless networks have emerged as a new paradigm to meet the fast growing wireless network capacity and coverage demands. Due to the co-deployment of high power and low power nodes in the same network using the same spectrum, more advanced interference coordination and radio resource management schemes are required than in the traditional cellular network in order to achieve a high network capacity and good user experience. In this paper, we propose an optimal fractional frequency reuse and power control scheme that can effectively coordinate the interference among high power and low power nodes. The scheme can be optimized to maximize the sum of the long term log-scale throughput among all the user equipments (UEs). Towards that end, the Lagrange dual function is first derived for the proposed optimization problem. Gradient descent method is then used to search the optimal solution for the convex dual problem. Due to the strong duality condition, the optimal solution for the dual problem is also the optimal solution for the primal problem. Simulation results show that the proposed scheme can greatly improve the wireless heterogeneous network performance on system capacity and user experience.

ABSTRACT In this paper, we consider the applications of delay‐tolerant networks (DTNs), where the nodes in a network are located in separated areas, and in each separated area, there exists (at least) an anchor node that provides regional network coverage for the nearby nodes. The anchor nodes are responsible for collecting and distributing messages for the nodes in the vicinity. This work proposes to use a set of messengers (named pigeons) that move around the network to deliver messages among multiple anchor nodes. Each source node (anchor node or Internet access point) owns multiple dedicated pigeons, and each pigeon takes a round trip starting from its home (i.e., the source) through the destination anchor nodes and then returns home, disseminating the messages on its way. We named this as a homing‐pigeon‐based messaging (HoPM) scheme. The HoPM scheme is different from the prior schemes in that each messenger is completely dedicated to its home node for providing messaging service. We obtained the average message delay of HoPM scheme in DTN through theoretical analysis with three different pigeon scheduling schemes. The analytical model was validated by simulations. We also studied the effects of several key parameters on the system performance and compared the results with previous solutions. The results allowed us to better understand the impacts of different scheduling schemes on the system performance of HoPM and demonstrated that our proposed scheme outperforms the previous ones. Copyright © 2011 John Wiley & Sons, Ltd.

Many technologies ranging from advanced physical layer antenna transmission schemes to efficient air-interface resource management schemes have been developed for spectrum efficiency improvement for the future wireless networks. However, most of these technologies quickly reach their theoretical limits without much room for further improvement. In order to meet capacity demands from the explosive data traffic growth, heterogeneous networks with base stations of diverse coverage and transmission powers are expected to achieve a high spectral and energy efficiency. Compared to the traditional homogeneous networks, issues such as mobile association, load balancing and interference coordination need to be studied carefully in order to realize the performance gain in a wireless heterogeneous network. In this paper, we propose a new mobile association scheme that jointly maximizes downlink system capacity and minimizes the mobile station (MS) uplink transmitting power. Simulation results show that a significant performance gain on the defined objective function is achieved.

As a key technology in 4G-LTE, heterogeneous networks can effectively extend the coverage and capacity of wireless networks by deploying multiple micro-nodes on top of the conventional macro base stations (BS). The deployed micro-nodes differ in transmission power and processing capabilities, leading to new challenges in interference management, mobile association, and radio resource management (RRM). In this paper, we consider RRM for heterogeneous networks with relays (RN) where the RNs have full RRM capabilities and can be viewed as micro BSs. A radio resource allocation framework is proposed with the objective to ensure proportional fairness among the UEs. An asymptotically optimal solution is derived by applying the gradient-based scheduling scheme and the Karush-Kuhn-Tucker (KKT) conditions for optimality. To implement RRM in networks with RNs, the resource consumption in the backhaul links, which depends on the demand of the UEs associated with the RN, should be counted at both the BS and the RN. The derived resource allocation scheme gives insight on the optimal radio resource allocation for heterogeneous networks with RNs.

Node clustering has been considered as a promising approach in ad hoc networks, where the inter cluster routing is limited to some special nodes called cluster head (CH). In practice, the performance of data transmission often degrades with the increase of distance between neighboring CHs. This paper proposes a new scheme that employs intermediate nodes as relays between neighboring CHs. Particularly, we develop both feedback and non-feedback fountain coded cooperative communications to improve reliability and robustness. It is observed that the number of relays plays a significant role in our scheme. Accordingly, we design a rateless coded relay selection (RCRS) algorithm to guarantee the required data rate with a minimum cost. Simulation results show that feedback based protocol outperforms its non-feedback counterpart in terms of end to end rate and throughput.

Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high speed data transmission over multipath fading channels. In an MIMO-OFDM system, cyclic delay diversity (CDD) serves as a simple and elegant solution to exploit transmit diversity. This paper investigates the uneven comb pilots based channel estimation for CDD-OFDM system with periodical frequency selective channel. Our study reveals that uneven comb pilots outperform their even counterpart in complicated channel conditions, such as adaptive CDD, where cyclic delay parameters may change from time to time. Furthermore, we identify the interpolation as a key problem in uneven-pilot based OFDM system and study several scattered data interpolation algorithms. Simulation results show that radial basis function (RBF) interpolation has the best tradeoff in terms of accuracy and computational complexity.

Heterogeneous wireless networks have emerged as a new paradigm to meet the fast increasing wireless capacity and coverage demands. Cooperative communication is a promising technique to further improve the cell edge performance for future communication systems by allowing nodes in a communication network to collaborate with each other in information transmission. This paper presents an optimal intra-cell cooperative transmission in a relay based heterogeneous networks to best address both capacity and coverage demands. We first formulate the optimization problem to maximize the time averaged log-scale throughput. The Lagrange dual function is then derived for the proposed optimization problem, from which we use gradient descent method to search the optimal intra-cell cooperative transmission solution. Simulation results show that the optimal cooperative transmission can greatly improve the log-scale throughput in the heterogeneous networks with relays.

In this paper, we study the communication architecture of smart grid for rural areas that employ cognitive radio technique. Since the communication in a cognitive radio network is normally unreliable, it is a great challenge to support applications which have stringent delay requirements, for instance, the transmission line monitoring application. To this end, we propose to reroute the data traffic with stringent delay requirement through the neighboring cells that work properly. This leads to a challenging scheduling problem, with the goal of maximizing the total throughput of the network while preserving the priority of real-time traffic in the local cell. To solve this problem, we present a scheduling algorithm that outperforms the earliest deadline scheduling and priority queue scheduling. The simulation results verify the effectiveness of our algorithm in achieving the design goals.

Heterogeneous networks improve the spectrum efficiency and coverage of wireless communication networks by deploying low-power nodes on top of the conventional network nodes. In this paper, we consider a heterogeneous cellular network with multiple low-power relay nodes (RN) deployed in each cell using in-band wireless backhaul between the RN and the base stations (BS). The RNs work cooperatively with the BSs on the downlink communication towards the user equipments (UE). Due to the disparity between the transmit powers of the BSs and the RNs, mobile association schemes developed for the conventional homogeneous networks may lead to a highly unbalanced traffic loading with most of the traffic being concentrated on the BSs. In this paper, we propose a new mobile association framework for the heterogeneous wireless networks with intra-cell node cooperations. The proposed framework aims to maximize the network capacity and balance the traffic load among the network nodes. Furthermore, we introduce a pricing mechanism to enable the distributed implementation of the proposed scheme. Numerical results show that a prominent improvement in network capacity can be achieved by the proposed pricing-based mobile association scheme.

Smart grid uses machine-to-machine (M2M) communication infrastructures and advanced control techniques for improved power distributions and management. Different types of communication schemes could possibly be used for smart grid applications, but need to be further explored in smart grid application scenarios. In this paper, we present the utilization of a zero correlation zone (ZCZ) CDMA based scheme in M2M communications for the advanced metering infrastructure (AMI) in smart grid. We design the ZCZ code generation and distribution through mutual authentication in the initialization procedure before data transmission. We examine the security and efficiency aspects of the proposed ZCZ CDMA based scheme. The paper concludes with the comparison of the proposed scheme and the legacy approach for the system security and performance in smart grid M2M communications.

In a disruption/delay tolerant network, message carriers are often used to act as relays between segregated nodes and gateways. In this paper, we study a special routing problem for message carriers in delay tolerant networks, the visiting frequency problem. Specifically, we present a detailed analysis on a near-optimal visiting frequency on the nodes in a subregion of the network, to ensure low average message delay, using a partitioning-based optimization approach. Then we propose a multiple visit algorithm and apply the above near-optimal visiting frequency to our multiple visit algorithm, to guarantee the low average message delay for the network. Simulation results demonstrate the superiority of our new algorithm comparing with the existing single visit algorithms.

This paper considers a cellular network with multiple low-power relay nodes (RN) deployed in each cell, helping the downlink transmission between the base stations (BS) and the user equipments (UE). The difference in transmission power between the BSs and the RNs makes this network heterogeneous. With conventional mobile association schemes, traffic load may concentrate on the BSs due to their high transmit power, leading to a highly unbalanced traffic load distribution and inefficient utilization of the RNs. To improve the capacity and the overall spectrum efficiency of the network, it is beneficial to let the RNs share a larger amount of the traffic burden. Towards this end, in associating the UEs to the RNs, the spectrum consumption on both UE to RN and RN to BS links should be cautiously considered. Also the interference from the high power BSs to the low power RNs need to be carefully mitigated. A systematic mobile association scheme considering load-balancing and overall spectrum efficiency is essential to ensure a fair and efficient usage of network resources. In this paper, we propose a load-balancing based mobile association framework under both full frequency reuse and partial frequency reuse and find the pseudo-optimal solutions using gradient descent method. By comparing with other mobile association schemes, a significant improvement in overall network capacity and RN utilization can be observed for the proposed mobile association framework. To enable dynamic and timely mobile association for the incoming UEs, we also develop an online mobile association algorithm based on the gradient descent method. Simulation results show that the proposed online algorithm achieves a good tradeoff between association delay and network capacity.

In this paper, we investigate the scalability of three communication architectures for advanced metering infrastructure (AMI) in smart grid. AMI in smart grid is a typical cyber-physical system (CPS) example, in which large amount of data from hundreds of thousands of smart meters are collected and processed through an AMI communication infrastructure. Scalability is one of the most important issues for the AMI deployment in smart grid. In this study, we introduce a new performance metric, accumulated bandwidth-distance product (ABDP), to represent the total communication resource usages. For each distributed communication architecture, we formulate an optimization problem and obtain the solutions for minimizing the total cost of the system that considers both the ABDP and the deployment cost of the meter data management system (MDMS). The simulation results indicate the significant benefits of the distributed communication architectures over the traditional centralized one. More importantly, we analyze the scalability of the total cost of the communication system (including MDMS) with regard to the traffic load on the smart meters for both the centralized and the distributed communication architectures. Through the closed form expressions obtained in our analysis, we demonstrate that the total cost for the centralized architecture scales linearly as O(\lambda N), with N being the number of smart meters, and \lambda being the average traffic rate on a smart meter. In contrast, the total cost for the fully distributed communication architecture is O(\lambda^{2\over 3} N^{2\over 3} ), which is significantly lower.

TV white space (TVWS) has been considered a favorable spectrum for sharing with other spectrum deficient technologies. There are three popular TV standards in China: the Digital Terrestrial Multimedia Broadcast (DTMB), China Multimedia Mobile Broadcasting (CMMB) and PAL-D/K (Phase Alternating Line -D/K). In this paper we present a prototype platform for experimental spectrum sensing, which is developed in-house to test TVWS spectrum sharing for all the three popular TV standards. We discuss the details on the prototype architecture, the spectrum sensing algorithms and the testing setup in this paper. The performance results show that the developed prototype platform can detect the signals from all the three TV standards robustly and efficiently.

Heterogeneous cellular networks have emerged as a new paradigm in the wireless network to increase cellular capacity and coverage. Future heterogeneous networks will have a mixed deployment of high power and low power nodes. This creates complicated interference scenarios that require more tight resource coordination among neighbouring nodes than in the traditional cellular networks. In this paper we consider a heterogeneous LTE relay network and investigate two downlink resource coordination schemes in the time/frequency/power domains in such a network. We perform detailed simulation study on the two proposed schemes, and compare them with a third scheme that does not have frequency or power coordination. Our study shows that by allowing tight time/frequency/power coordination, the system can achieve much higher system capacity and better received downlink signal quality.

In this paper, we investigate secrecy capacity of a cognitive radio network based on stochastic geometry distributions. We consider the Poisson process of both the secondary users and the eavesdroppers, and analyze how the stochastic interference from the secondary users can influence the secrecy capacity of the primary users. First, we describe a network model with primary users, secondary users and eavesdroppers in a cognitive radio communication network environment, and obtain the expression of secrecy capacity in an additive white Gaussian noise channel. Then, we study the outage probability of secrecy capacity of a primary node from a secure communication graph point of view. Furthermore, we present numerical results of the cumulative distribution function (c.d.f.) of the secrecy capacity between a primary transmitter and a primary receiver. Our analysis brings the insights on secure communications in terms of spatially Poisson distributions of primary users, secondary users and eavesdroppers.

Cognitive Radio has emerged as a new technology to improve spectrum utilization and to alleviate spectrum scarcity by allowing unlicensed users to sense and opportunistically access the under-utilized spectrum. This paper proposes a system framework for the Cognitive Radio enabled TD-LTE system that opportunistically accesses and utilizes the TV White Space spectrum. The system performance of both TV broadcasting network and TD-LTE network is evaluated and analyzed. The simulation studies show that the proposed framework can significantly improve the performance of both TV and TD-LTE systems when they are co-operating within the same TV spectrum with TV as the primary user and TD-LTE as the secondary user. Both TV spectrum utilization and TD-LTE coverage are greatly enhanced.

With the proliferation of wireless networks for practical deployments in recent years, secure fast handoff has become significantly important to provide secured access while reduce the latency caused by handoff procedure. Channel probing/scanning delay has been a major contribution to the overall latency of handoff in IEEE 802.11 based wireless networks. In this paper, we present a novel secure fast handoff scheme that adopts network-assisted radio statement (NACS) to eliminate lengthy probing/scanning delay by taking advantage of the knowledge of network topology of neighboring nodes in the network. We apply an opportunistic mechanism to retrieve the channel condition from neighboring nodes close enough to reduce the scanning/probing delay while providing secure wireless access for the handoff candidate node. In this manner, it can reduce the channel probing/scanning delay and achieve secure handoff. We show analysis and simulation results to verify the performance of the proposed secure fast handoff scheme.

Heterogeneous networks utilizing relay nodes with low transmission power becomes an important deployment scenario in 4G cellular standards. It can offer high data rate delivery as well as ubiquitous end-user experience. For such a network, an efficient mobile association and load balancing scheme is critical to achieve high system capacity and satisfactory user experience. In this paper, the main challenges for mobile association and load balancing in a heterogeneous network with relay nodes are discussed and addressed. An optimal framework that aims to maximize system capacity is presented. It considers both the relay backhaul resource usage and wireless access link resource availability. A heuristic algorithm that enables the practical implementation is proposed and evaluated. Simulation results show that the proposed mobile association and load balancing scheme outperforms the conventional schemes in a heterogeneous environment.

Fitting noisy measurements to a circle is a classic statistical estimation problem. In this paper, we make two contributions to the study of this problem. First, we propose a novel formulation of the maximum likelihood (ML) estimator for identifying the center and radius of the circle from noisy measurements. This new estimator uses the unknown true values of the measurement points as the nuisance parameter to obtain an exact ML formulation. We then examine the Karush-Kuhn-Tucker (KKT) conditions for the optimum solution to the ML estimator. We show analytically that this new estimator is in fact equivalent to the well-known least squares (LS) form of the circle fitting problem. Second, from the insights gained in deriving the optimum solution, a computationally simple circle fitting algorithm based on greedy search is proposed. Performance results are given to illustrate the performance of the proposed algorithm.

In the future heterogeneous wireless networks with a mixed deployment of macro BS and micro relay stations, different nodes will compete for the same radio resources for providing mobile services. This poses great challenges to radio resource management and coordination. One critical step to achieve the optimal radio resource usage is for the mobiles to connect to the access nodes that provide the best radio conditions to them. This paper proposes a new mobile association mechanism in such a heterogeneous environment that aims to provide the best service to the mobiles on both uplink and downlink while minimizing the interference level. The simulation results show that with the proposed scheme the mobile performance shall be improved compared with the traditional mobile association schemes.

In the past few years, wireless mesh networks (WMNs) have drawn significant attention from academia and industry as a fast, easy, and inexpensive solution for broadband wireless access. In WMNs, it is important to support video communications in an efficient way. To address this issue, this paper studies the multipath routing for multiple description (MD) video delivery over IEEE 802.11 based WMN. Specifically, we first design a framework to transmit MD video over WMNs through multiple paths; we then investigate the technical challenges encountered. In our proposed framework, multipath routing relies on the maximally disjoint paths to achieve good traffic engineering performance. However, video applications usually have strict delay requirements, which make it difficult to find multiple qualified paths with the least joints. To overcome this problem, we develop an enhanced version of Guaranteed-Rate (GR) packet scheduling algorithm, namely virtual reserved rate GR (VRR-GR), to shorten the packet delay of video communications in multiservice network environment. Simulation study shows that our proposed approach can reduce the latency of video delivery and achieve desirable traffic engineering performance in multipath routing environment.

In this paper, gains of the codebook based CL-MIMO for down link WiMax system has been studied using system level simulations. The system level simulation is based on the IEEE 802.16 m evaluation methodology. The 2 times 2 and 4 times 2 antenna configurations, and 3 bits and 6 bits preferred matrix index (PMI) feedback codebooks are considered. Both the sub-band and wide band PMI feedback are studied. The results show that the CL-MIMO provides substantial gains in cell edge and average sector throughput over OL-MIMO system.

Wireless mesh network (WMN) has recently emerged as a promising technology for next-generation wireless networking. In WMNs, many important applications, such as mobile TV and video/audio conferencing, require the support of multicast communication with quality-of-service (QoS) guarantee. In this paper, we address the QoS multicast routing issue in WMNs. Specifically, we propose a novel network graph preprocessing approach to enable traffic engineering and enhance the performance of QoS multicast routing algorithms. In this approach, we employ prioritized admission control scheme and develop a utility-constrained optimal priority gain policy. Extensive simulation results show that our approach can significantly improve the performance of QoS multicast routing in WMNs.

The current trends in service consolidation over Internet Protocol (IP) also stimulates the integration of the industrial automation system with the information technology (IT) infrastructure for more efficient information access and more cost-effective production and management. Field buses have been the de facto communication standard in industrial automation, but mostly based on manufacture-specific protocols. Thus, the interoperability between the manufacturer-specific field bus systems and the external operating environment is the critical factor in enabling the networked industrial automation systems. However, most of the existing field bus integration solutions lack either flexibility or scalability. In this paper, we propose a service-oriented architecture (SOA) based field bus integration architecture (SOAFBIA), where each field bus system is encapsulated with optional interface, manageability interface, and semantic descriptions in a standard format to facilitate interoperability. Moreover, a resource agent is proposed as an enhanced service broker, which implements not only the standard service registry functionality in SOA, but also the resource management functions including admission control, service scheduling, and load balancing.

For deploying wireless group-oriented applications in an adversarial environment such as battlefield or disaster rescue cases, it is necessary to provide support for secure multicast communication. In this paper, pyramidal security model is proposed to safeguard a special multicast scenario of multi-security- level information broadcast in an information sharing domain of mobile ad hoc network. In order to give an efficient key management solution to the pyramidal security model, we propose an integrated tree key graph scheme. Performance comparison proves that this scheme possesses many advantages over its counterparts.

This paper proposes a new wavelength retuning scheme in a survivable all optical WDM network. Compared with the existing wavelength retuning schemes developed for all-optical networks, which can alleviate the wavelength continuity constraint but may not avoid service interruption or data loss, the proposed scheme is able to alleviate the wavelength continuity constraint and reduce the connection blocking probability with no service interruption to the on-going traffic. This is achieved by allocating two link-disjoint routes, one for active path and one for backup path, to each incoming connection request and conducting wavelength retuning only on the backup path. An optimal backup path wavelength retuning scheme and a heuristic algorithm are developed and the performance evaluation on the proposed schemes is presented.

This paper proposes a new survivable traffic grooming wavelength retuning (STGWR) scheme in an all-optical wavelength division multiplexing (WDM) network. In a dynamic WDM network, a connection may require bandwidth less than a wavelength capacity. Also, a connection should be protected against any network failures. Survivable traffic grooming (STG) can protect connections at subwavelength granularities. Wavelength retuning is a promising approach in an all-optical WDM network, where a signal must remain on the same wavelength from its source to the destination, to alleviate the wavelength continuity constraint and reduce the connection blocking probability. While both STG and wavelength retuning have attracted extensive research attentions nowadays, no effort has been made to combine these two promising approaches in one network. In this paper, we propose a wavelength retuning scheme with no service interruption in an all-optical network with survivable traffic grooming capability. The scheme allocates two routes, one for active path and one for backup path, in a shared mesh restoration way to each incoming connection request and conducts wavelength retuning only on the backup path. Both wavelength retuning and mesh protection are done at the connection level instead of at the lightpath level. The simulation results of the proposed schemes are also presented

In this paper, we propose an adaptive p-persistent based IEEE 802.11 medium access control (MAC) scheme in a heterogeneous WLAN. Quality of Service in a DCF based heterogeneous WLAN is a challenging task due to the lack of centralized scheduling capability. The proposed scheme can maximize the total channel throughput and provide the service differentiation among different traffic stations. This is achieved by updating the transmission probability for each station in a timely manner based on the real-time network measurements. The simulation results show that the scheme can quickly adapt the station transmission probabilities to the desirable values in order to achieve maximum throughput and QoS provisioning in a dynamic WLAN environment.

With the explosively increasing demand of multimedia applications in wireless local area networks (WLAN), Quality of Service (QoS) provisioning has become an important issue. IEEE 802.11 WLAN is the most popular WLAN technology today. In this paper, we propose an adaptive p-persistent based IEEE 802.11 medium access control (MAC) scheme in WLAN. The proposed scheme can maximize the total channel throughput and also can provide the service differentiation to multiple traffic classes. This is achieved by updating the transmission probability for each station in a timely manner based on the network measurements. The simulation results show the scheme can quickly adapt the station transmission probabilities to the desirable values to achieve maximum throughput and QoS provisioning in a dynamic WLAN environment. The simulation results also match well with the theoretical analysis.

This paper proposes efficient resource allocation techniques for a domain-based wireless/wireline interworking architecture. Resource allocation is driven by the service level agreement (SLA). Each wireless domain can freely choose its internal resource management schemes to guarantee the customer access SLA (CASLA), while the border-crossing traffic is served by a DiffServ/MPLS core network according to the transit domain SLA (TRSLA). Specifically, we propose an engineered priority scheme for a cellular wireless domain, where the CASLA for each service class is met with efficient resource utilization and the interdomain TRSLA bandwidth requirement can be obtained conveniently. In the transit domain, the traffic load fluctuation from upstream access domains is tackled with an inter-TRSLA resource sharing technique, where the spare capacity from underloaded TRSLAs can be exploited by the overloaded TRSLAs to improve resource utilization.

In this paper we present a heuristic scheduling algorithm for core optical routers with heterogeneous traffic. We contrast two versions of the algorithm, the 'frozen algorithm' and the 'nonfrozen algorithm.' The nonfrozen algorithm deals effectively with the hot spot scenarios by allowing more flexibility in the wave slot assignments. Performance evaluation results indicate that the nonfrozen algorithm produces a 'higher valued' schedule than the frozen algorithm at a marginal incremental increase cost in computation time.

This paper addresses end-to-end connectivity planning and call admission control for a high capacity multi-beam satellite network with on-board cross-connectivity. On board satellite switching is a technology designed to offer multimedia services, especially in demographically dispersed areas. Nevertheless, full on-board switching techniques are far from maturity. Their implementations have been proven expensive and difficult. There are also high risks involved in launching satellites for the stationary orbit surrounding the earth. As a substitute, a satellite network with on-board cross-connect is devised in this paper. Connectivity planning and call admission control mechanisms associated with such a network are also presented. Simulation studies are conducted to show the effectiveness of the proposed mechanisms.

In this paper, we propose a new model for the slow-start phase based on the discrete evolutions on the congestion window, and we use this slow-start model together with our improved TCP steady-state model to develop an extensive stochastic model which can more accurately predict the throughput and latency of short-lived TCP connections as functions of loss rate, round-trip time (RTT), and file size. The result from simulation experiments show that our model's performance predictions are up to 20% more accurate than the predictions obtained from the models proposed in [N. Cardwell et al., Mar. 2002] and [B. Sikdar et al., 2001].

There are many system proposals for satellite-based multimedia communications that promise high capacity and ease of access. Many of these proposals require advanced switching technology and signal processing on-board satellites that will directly impact their cost, performance, availability, and time-to-market. Given the amount of commercial and technical risk involved in such complex systems, satellite operators have been looking for solutions that are simpler, yet flexible. One solution is based on a geosynchronous (GEO) satellite system equipped with simple on-board processing and switching. The satellite network is ATM-based and carries heterogeneous traffic. An important feature of this system is allowing for a maximum number of simultaneous users, hence, requiring effective connection admission control (CAC) and bandwidth on demand (BOD) algorithms. Nortel Networks has already developed an innovative CAC and BOD algorithm for the system. We present the BOD performance evaluations of the integrated algorithm. By detailed simulations, we show that the BOD scheme is able to efficiently utilize all available bandwidth and to gain high throughput. We also find that the end-to-end delays for voice traffic in the system falls well within the ITU's QoS specification for GEO-based satellite systems. The application buffer sizes observed in the simulations can serve as a guideline for ground station and satellite on-board memory design.

This paper addresses the design and analysis of an end-to-end rate-based feedback flow control algorithm motivated by the available bit rate (ABR) service in wide-area asynchronous transfer mode (ATM) networks. Recognizing that the explicit feedback rate at time t will not affect the ABR buffer until time t+D for some D/spl ges/0, our approach is to first predict the ABR buffer status at time t+D, then base fuzzy-logic rate control decisions on these predicted values, and finally tune the controller parameters using gradient descent methods. Simulations show that this predictive self-tuning fuzzy-logic (PSTF) control scheme is efficient, stable, and outperforms other proposed ABR rate controllers in a variety of network environments. With delays corresponding to a US coast-to-coast connection, the PSTF controller can maintain high link utilization, avoid buffer overflows, and provide fair allocation of resources.

Broadband satellite networks are a mandatory complement to the terrestrial facility to build up the information infrastructure, especially for unserved and underserved regions of the world. This paper applies the generalized bass model (GBM) to estimate the number of multimedia subscribers for specified services. Because of varying demographics, economics, and interests, countries are grouped into several "regions", and a different model is used for each service, and for each region. The models also include the effect of price elasticity. An analysis of competing emerging broadband access technologies is performed and the market share for satellite-based services is estimated. The subscribers representing the satellite share of the market for each service are then geographically distributed across each region. The resulting subscriber distribution is used to generate hourly point-to-point traffic. In this paper, only methodologies for market forecast and traffic estimation are discussed.

We investigate the impact of uplink multiple access schemes on traffic reshaping for a meshed broadband VSAT network over geostationary (GEO) satellite. The system includes a bent-pipe satellite with a number of broadband VSATs carrying multimedia traffic. An integrated CAC and BOD algorithm is used for the uplink M-TDMA access. An OPNET(TM) simulator is developed to investigate the affects of the CAC/BOD algorithm on the different applications in the satellite networks. Source application traffic is modeled using one of the following ATM transfer capabilities; CBR, rt-VBR, nrt-VBR, ABR, and UBR. We perform detailed traffic studies to compare the source traffic with the traffic out of the uplink multiple access, We show by simulation and analysis that all traffic contracts for the CBR, rt-VBR, and nrt-VBR source traffic are violated after uplink access. The results provide guidelines on the design of a corrective traffic reshaping function that counter-acts the traffic reshaping caused by the CAC/BOD uplink multiple access of the satellite networks.