Artificial intelligence of things (AIoT) is an emerging paradigm integrating artificial intelligence (AI) technologies within the Internet of Things (IoT) paradigm. However, deploying deep-learning models on IoT devices is challenging due to their inherent computational, communications, and security constraints. To address these challenges, we propose Flexible Neuron Selection (FlexNS), a computation- and communication-efficient personalised multi-task transfer learning framework for AIoT.FlexNSenables IoT devices to train their private task-specific shallow models by leveraging a multi-task, deep-learning model pre-trained by a cloud server.FlexNSsignificantly reduces IoT devices’ computational and communications resource demands by selecting a subset of neurons in an early layer of the server’s public model to be connected to the private models of multiple IoT devices. The neurons need to be carefully selected to ensure effective and efficient knowledge transfer to the fine-tuned private models tailored to each IoT device’s specific task. Experimental results show thatFlexNS-based private models achieve 104.3% and 98.4% model accuracy compared to the public model for two datasets on network intrusion detection and image classification tasks, with 99.5% and 98.0% reduction in training and inference time.

By enabling spectrum sharing between radar and communication operations, the cell-free dual-functional radar–communication (CF-DFRC) system is a promising candidate to significantly improve spectrum efficiency in future sixth-generation (6G) wireless networks. However, in wideband scenarios, synchronization errors caused by carrier frequency offset (CFO) can severely reduce both communication capacity and sensing accuracy, especially when multiple geographically distributed full-duplex (FD) access points (APs) are jointly coordinated. In this paper, we consider a wideband FD CF-DFRC system where each AP is equipped with movable antennas (MAs). This setting is fundamentally different from existing DFRC or MA-aided designs that typically assume fixed-position antennas, half-duplex operation, or perfect synchronization. First, we develop a field-response-based channel model and derive a worst-case weighted communication–sensing rate (WCSR) that explicitly captures the impact of inter-AP CFO on both the uplink communication signal-to-interference-plus-noise ratio (SINR) and the radar echo SINR. Our analysis reveals that CFO increases the Cramér–Rao lower bound (CRLB) of target position estimation, thereby degrading sensing accuracy. Based on this characterization, we formulate a robust worst-case WCSR maximization problem that jointly optimizes MA positions, transmit beamforming vectors, receive filters, and CFO-related parameters under transmit power and MA-position constraints. To tackle the resulting highly non-convex problem, we propose a two-stage robust optimization framework. In the first stage, we employ fractional programming together with manifold optimization (MO) and penalty dual decomposition (PDD) to solve the worst-case CFO subproblem on the complex unit-modulus manifold, thus obtaining a CFO-robust closed-form structure for the WCSR. In the second stage, we design a meta–reinforcement learning (MRL) based resource allocation scheme that jointly optimizes the MA positions and beamforming vectors in a data-driven manner for dynamic wireless environments. Unlike conventional deep reinforcement learning (DRL) methods, the proposed MRL framework learns a meta-policy that can rapidly adapt to varying channel and CFO realizations, substantially improving convergence speed and scalability. Simulation results show that the proposed robust MO–PDD–MRL framework significantly outperforms existing DRL-based and non-robust CF-DFRC schemes in terms of both communication and sensing performance under CFO impairments. Furthermore, compared to fixed-position antenna (FPA) architectures, the MA-aided CF-DFRC system exhibits markedly enhanced robustness and adaptability to CFO effects and target mobility.

In this paper, we propose a hierarchical optimization approach that guarantees the maximum age of information (AoI) for uncrewed aerial vehicle (UAV) assisted Internet-of-Things (IoT) data collection. Our model is based on an energy-efficient simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) beamforming model. We formulate the optimization to minimize the UAV flight energy consumption subject to a maximum average AoI threshold by optimizing the UAV trajectory, IoT device scheduling, and STAR-RIS beamforming. To solve this, we develop an optimization-based hierarchical deep reinforcement learning (OH-DRL) algorithm that decomposes the formulated problem into an inter-cluster UAV visiting policy and STAR-RIS-based intra-cluster IoT scheduling policy. In OH-DRL, we jointly optimize the two policies in a high-level loop and a low-level loop, respectively. In the high-level loop, we design an AoI-guided DRL algorithm to determine the AoI-guaranteed UAV hovering position with minimal flight distance. In the low-level loop, a semidefinite relaxation (SDR)-based optimization algorithm further reduces the UAV’s flying time by minimizing the average AoI. Simulation results validate that OH-DRL achieves better convergence performance and energy-saving efficiency across different network scales. Compared to the state-of-the-art DRL algorithm, OH-DRL reduces the UAV flight energy consumption by 14.4% and decreases the number of training episodes required for convergence by 66%

Network slicing (NS) is a promising technology that supports diverse requirements for next-generation low-latency wireless communication networks. However, the tampering attack is a rising issue of jeopardizing NS service-provisioning. To resist tampering attacks in NS networks, we propose a novel optimization framework for reliable NS resource allocation in a blockchain-secured low-latency wireless network, where trusted base stations (BSs) with high reputations are selected for blockchain management and NS service-provisioning. For such a blockchain-secured network, we consider that the latency is measured by the summation of blockchain management and NS service-provisioning, whilst the NS reliability is evaluated by the BS denial-of-service (DoS) probability. To satisfy the requirements of both the latency and reliability, we formulate a constrained computing resource allocation optimization problem to minimize the total processing latency subject to the BS DoS probability. To efficiently solve the optimization, we design a constrained deep reinforcement learning (DRL) algorithm, which satisfies both latency and DoS probability requirements by introducing an additional critic neural network. The proposed constrained DRL further solves the issue of high input dimension by incorporating feature engineering technology. Simulation results validate the effectiveness of our approach in achieving reliable and low-latency NS service-provisioning in the considered blockchain-secured wireless network.

Whilst blockchain technology holds promise for secure Internet of Things (IoT) data management, its deployment in the massive Internet of Unmanned Aerial Vehicles (IoUAV) still faces significant challenges to satisfy strict requirements for low-latency query services and cost-effective resource consumption. To address these challenges, we present a lightweight multi-type data (MTD) blockchain architecture called LMChain with cost-effective MTD block scheduling. Specifically, LMChain incorporates cross-layer MTD blocks, wherein resource-constrained UAVs retain only lightweight block headers. Block bodies with high query probability are stored in fog nodes, while others are offloaded to cloud storage. Based on the MTD block structure, we develop a cost-effective block scheduling scheme to minimize the overall cost associated with LMChain storage and querying. A cooperative deep reinforcement learning (CDRL) algorithm is designed to efficiently schedule MTD blocks between the fog and cloud layers. Simulation results show that our LMChain significantly reduces the IoUAV blockchain system’s storage resource requirements and overall cost while supporting low-latency query services, making it well-suited for massive IoUAV applications.

Federated learning (FL) is a promising solution to overcome data island and privacy issues in intrusion detection systems (IDSs) for the Industrial Internet of Things (IIoT). However, the heterogeneity of various IIoT devices poses formidable challenges to FL-based intrusion detection, especially the training cost relating to delay and energy consumption. In this article, we propose a delay and energy-efficient asynchronous FL (AFL) framework for intrusion detection (DEAFL-ID) in heterogeneous IIoT. Specifically, we address the shortcomings of low efficiency and high energy consumption in existing FL-based solutions involving all idle IIoT devices. To do so, we formulate an AFL-based optimal device selection problem which aims to select high-quality training devices in advance by exploring the device advantages in detection accuracy, delay reduction, and energy saving. Subsequently, a deep Q-network (DQN)-based learning algorithm is developed to quickly solve the above high-dimensional problem. In addition, to further improve the detection performance, we build a hybrid sampling-assisted convolutional neural network (CNN)-based IDS model, which can eliminate the imbalance of IIoT data and enable the selected devices to fully extract data features. Through simulations, we demonstrate that DEAFL-ID achieves a significant improvement in training cost and detection performance compared with existing IDS schemes.

This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for data management and resource allocation in decentralized wireless mobile edge computing (MEC) networks. In our framework, we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks. We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability. We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and MEC resource allocation algorithms.

In this paper, we develop a secure multi-layer mobile edge computing (MEC) system where an unmanned aerial vehicle (UAV) equipped with a reconfigurable intelligent surface (RIS) acts as an aerial edge server and assists the offloading from multiple ground users to a base station (BS), in the presence of a full-duplex active eavesdropper (AE). To enhance the computing performance, we consider a partially offloading scheme where the computational task at each user can be executed at itself and offloaded to the UAV edge server and the BS via the UAV-enabled RIS, respectively. To maximize the total number of secure computing tasks among all users, we design a low complexity iterative algorithm by jointly optimizing the RIS phase shift, UAV deployment, power and computing resource allocation subject to certain power constraints. Numerical results show that compared to benchmark offloading schemes, our proposed UAV-RIS aided multi-layer MEC design improves the computing performance by at least 12.91%. Numerical results also demonstrate the impact of the full-duplex AE and validate the robustness of our proposed solution.

In this paper, we develop a deep learning-based bandwidth allocation policy that is: 1) scalable with the number of users and 2) transferable to different communication scenarios, such as non-stationary wireless channels, different quality-of-service (QoS) requirements, and dynamically available resources. To support scalability, the bandwidth allocation policy is represented by a graph neural network (GNN), with which the number of training parameters does not change with the number of users. To enable the generalization of the GNN, we develop a hybrid-task meta-learning (HML) algorithm that trains the initial parameters of the GNN with different communication scenarios during meta-training. Next, during meta-testing, a few samples are used to fine-tune the GNN with unseen communication scenarios. Simulation results demonstrate that our HML approach can improve the initial performance by 8.79%, and sample efficiency by 73%, compared with existing benchmarks. After fine-tuning, our near-optimal GNN-based policy can achieve close to the same reward with much lower inference complexity compared to the optimal policy obtained using iterative optimization. Numerical results validate that our HML can reduce the computation time by approximately 200 to 2000 times than the optimal iterative algorithm.

In this paper, we present a novel unmanned aerial vehicle (UAV) aided physical layer authentication (PLA) frame-work to detect the origin of the received signal between a legitimate transmitter and a malicious adversary, based on the physical properties of channel characteristics and geographical locations. First, we model the authentication hypothesis test at the UAV based on the signal-to-noise ratio (SNR) of each transmission and analyze the probability density functions (PDFs) of SNR differences. Then, we derive the explicit expressions of false alarm probability (FAP) and miss detection probability (MDP), both of which depict the occurrence of detection error. Next, with the aim of minimizing the MDP subject to a given FAP constraint, the detection threshold and UAV deployment are jointly optimized. Numerical results verify the accuracy of our derived expressions and demonstrate the impact of distribution rate and adversary’s location on the detection performance. Moreover, numerical results also highlight the superiority of our proposed solution using SNR differences over benchmark strategy in high-rise urban environment.

This paper considers computation offloading for mobile applications with task-dependency requirements in mobile edge computing (MEC) systems. Based on the online arrival patterns and various delay constraints of practical applications, we focus on minimizing the system deadline violation ratio (DVR) to improve the overall reliability performance. Specifically, we propose a DVR minimization computation offloading scheme with task migration and merging, in which the task migration and merging model is designed to construct an overall directed acyclic graph (DAG) for all currently dependent tasks. We consider a multi-slot MEC system where applications arrive slot-by-slot without prior knowledge of future arrivals. Then given the number of application arrivals at each time slot, we equivalently transform the DVR minimization problem into a problem that maximizes the number of completed applications in a finite time horizon. The above problem is challenging to determine the optimal task execution order for different applications with various task dependencies and delay constraints. To address this, we develop a migration-enabled multi-priority task sequencing algorithm, which creatively introduces several task priority metrics and determines the optimal task execution order. Then, a deep deterministic policy gradient (DDPG)-based learning algorithm is developed to find the optimal offloading policy. Experimental results demonstrate that the proposed scheme can reduce the system DVR by 60.34%~70.3% compared with existing benchmark schemes under various network scenarios.

With the rapid growth of Industrial Internet of Things (IIoT) devices, managing an extensive volume of IIoT data becomes a significant challenge. While the conventional cloud storage approaches with centralized data centers suffer from high latency for large-scale IIoT data storage due to increased communication and latency overheads, distributed storage frameworks, such as blockchains, have become promising solutions. In this article, we design and analyze a dual-blockchain framework for secure and scalable distributed data management in large-scale IIoT networks. The proposed framework, namedMapChain-D, consists of a data chain that is mapped to an index chain to provide efficient data storage and lookup.MapChain-Dis designed for practical IIoT applications with storage, latency, and communication constraints. Detailed data exchange protocols are presented for data insertion and retrieval operations inMapChain-D. Based on these, theoretical analyses are provided on the space, time, and communication complexities ofMapChain-Dcompared with conventional single-chain frameworks with local and distributed data storage. We implement ourMapChain-Dprototype using open-source LoRaWAN communications with multiple Raspberry Pi and Arduino devices, Kademlia-based distributed hash table, and Ethereum-based blockchain with proof-of-authority consensus. Experimental results from our prototype show thatMapChain-Dis more suitable to be deployed on resource-constrained IIoT devices. We also highlight the scalability and flexibility ofMapChain-Dwith different number of edge nodes in the system.

With the development of MEC services, MEC brokers will emerge to facilitate the purchase and management of resources for individual MEC users. Both data communication and computing resources offered by MEC service providers can be purchased by pay-as-you-go (PAYG) or reserved plans. Besides data and computing plans for each type of resource, we also consider combo plans specifically designed for MEC services covering both resources. In this paper, we propose a smart online aggregated reservation (SOAR) framework for MEC brokers to minimize their cost of reserving resources for multiple users without the knowledge of future demands. In our framework, a task aggregation algorithm is designed to aggregate the users’ demands in each PAYG billing cycle to improve the plan utilization, and plan reservation algorithms are proposed to decide when to reserve which plans. The performance gap (competitive ratio) between SOAR and optimal solution which knows all future demands in advance, is analyzed and derived in closed-form. The performance gap is proved to be the minimum among all deterministic online algorithms. Trace-driven simulations verify the cost advantage of our SOAR framework, which can save nearly 40 percent of cost for users through the brokerage service.

This paper investigates how to build an active attack detection framework that is driven by fundamental channel modeling and practical wireless datasets. Firstly, we propose the concept of interpretable channel fingerprints (ICFs), which correspond to the spatial-temporal parameters in real physical wireless signal propagation channels. Based on this, we design an adversarial autoencoder (AAE) with a semi-supervised learning network, which takes as inputs the power spectrum of quantized ICFs and enables small sample learning multiclassification tasks for different types of wireless channel active attacks. We have experimentally verified the performance of our AAE network using the Wireless InSite ray tracing software. Our results show that the proposed semi-supervised network outperforms the fully-supervised network especially in small sample conditions. We highlight the need for careful selection of the hyperparameters for learning rate and mini-batch size, and the system parameters for the ICF power spectrum resolution. We show that the detection accuracy of the proposed AAE model can reach more than 98% with only a small number of input samples.

Information leakage to a guessing adversary in index coding is studied, where some messages in the system are sensitive and others are not. The non-sensitive messages can be used by the server like secret keys to mitigate leakage of the sensitive messages to the adversary. We construct a deterministic linear coding scheme, developed from the rank minimization method based on fitting matrices (Bar-Yossef et al. 2011). The linear scheme leads to a novel upper bound on the optimal information leakage rate, which is proved to be tight over all deterministic scalar linear codes. We also derive a converse result from a graph-theoretic perspective, which holds in general over all deterministic and stochastic coding schemes.

In this article, we present practical stochastic modeling and detailed performance analysis of our double blockchain (DBC) from Haoet al.(2021) for secure information and reputation data management in large-scale wireless Internet of Things (IoT) networks. Specifically, the DBC is a private blockchain deployed on a cloud-fog communication network which is composed of an information blockchain (IBC) storing large amounts of IoT data in the cloud layer and a reputation blockchain (RBC) storing reputation data of the IoT devices in the near-terminal fog layer. The locations of the fog layer nodes are modeled according to a random Poisson point process (PPP) over a given 2-D area to approximate the stochastic property of real-world wireless node deployments. Furthermore, we assume that the number of IoT devices transmitting to the fog nodes also follow a random Poisson distribution. Based on these models, we derive novel closed-form expressions for the storage size, transmission latency, and tampering time of the IoT fog nodes in our DBC architecture. Numerical simulations highlight high storage scalability, low latency, and superior security of the DBC design, and provide insights into the performance gains for different fog node and IoT device densities.

This article proposes a wireless key generation solution for secure low-latency communications with active jamming attack prevention in wireless networked control systems (WNCSs) of Industrial Internet of Things (IIoT) applications. We first identify a new vulnerability in physical-layer key generation schemes using wireless channel and random pilots (RPs) in static environments. We derive a closed-form expression for the probability that the RP-based key is successfully attacked by a long-term eavesdropper at a fixed location. To prevent such attacks, we propose a one-time pad (OTP) encrypted transmission solution assisted by one-way self-interference (SI), which has low-latency, high-security benefits, and active attack detection capability. The performance of the proposed scheme is analytically compared with two benchmark RP-based schemes, and its advantages are verified in a ray-tracing-based simulation environment. We further investigate the impact of critical design parameters, which reveal fundamental insights for the deployment and implementation of our proposed secure communications scheme.

Wireless secret key generation has emerged as a promising technique for Internet-of-Things (IoT) systems to establish shared encryption keys between the server and legitimate mobile user. This article focuses on the use of multidomain joint information to achieve a high key generation rate (KGR) and the implementation of a reliable, low-complexity secret key generation mechanism for distributed antenna systems (DAS) with orthogonal-frequency division multiplexing (OFDM). We present a space-time-frequency channel state information (CSI)-based key generation scheme based on a two-step approach of adaptive link selection and stepwise decorrelation algorithms. The performance is evaluated in terms of KGR, key disagreement rate (KDR), randomness, and computational complexity by using both a standardized channel model and real-world measurements. Numerical results show that our proposed low-complexity algorithms effectively utilize the space-time-frequency CSI to multiply the KGR in both indoor and outdoor environments. Through adaptive link selection in DAS, the KDR is maintained within a correctable range, thereby ensuring the validity of generated keys in dynamic environments. Further applying stepwise decorrelation reduces the computational complexity by more than half while satisfying all eight key generation randomness tests in the NIST test suite.

Double auctions play a pivotal role in stimulating active participation of a large number of users comprising both task requesters and workers in mobile crowdsourcing. However, most existing studies have concentrated on designing offline two-sided auction mechanisms and supporting single-type tasks and fixed auction service models. Such works ignore the need of dynamic services and are unsuitable for large-scale crowdsourcing markets with extremely diverse demands (i.e., types and urgency degrees of tasks required by different requesters) and supplies (i.e., task skills and online durations of different workers). In this article, we consider a practical crowdsourcing application with an on-demand service strategy. Especially, we innovatively design three online service models, namely, online single-bid single-task (OSS), online single-bid multiple-task (OSM), and online multiple-bid multiple-task (OMM) models to accommodate diversified tasks and bidding demands for different users. Furthermore, to effectively allocate tasks and facilitate bidding, we propose a truthful online double auction mechanism for each service model based on the McAfee double auction. By doing so, each user can flexibly select auction service models and corresponding auction mechanisms according to their current interested tasks and online duration. To illustrate this, we present a three-demand example to explain the effectiveness of our on-demand service strategy in realistic crowdsourcing applications. Moreover, we theoretically prove that our mechanisms satisfy truthfulness, individual rationality, budget balance, and consumer sovereignty. Through extensive simulations, we show that our mechanisms can accommodate the various demands of different users and improve social utility, including platform utility and average user utility.

Two main privacy models for sanitising datasets are differential privacy (DP) and syntactic privacy. The former restricts individual values’ impact on the output based on the dataset while the latter restructures the dataset before publication to link any record to multiple sensitive data values. Besides both providing mechanisms to sanitise data, these models are often applied independently of each other and very little is known regarding how they relate. Knowing how privacy models are related can help us develop a deeper understanding of privacy and can inform how a single privacy mechanism can fulfil multiple privacy models. In this paper, we introduce a framework that determines if the privacy mechanisms of one privacy model can also guarantee privacy for another privacy model. We apply our framework to understand the relationship between DP and a form of syntactic privacy called t-closeness. We demonstrate, for the first time, how DP and t-closeness can be interpreted in terms of each other by introducing generalisations and extensions of both models to explain the transition from one model to the other. Finally, we show how applying one mechanism to guarantee multiple privacy models increases data utility compared to applying separate mechanisms for each privacy model.

In this paper, a multiple cluster-based transmission diversity scheme is proposed for asynchronous joint transmissions (JT) in private networks. The use of multiple clusters or small cells is adopted to reduce the transmission distance to users thereby increasing data-rates and reducing latency. To further increase the spectral efficiency and achieve flexible spatial degrees of freedom, we consider that a distributed remote radio unit system (dRRUS) is installed in each of the clusters. A key characteristic of deploying the dRRUS in private networks is the associated multipath-rich and asynchronous delay propagation environment. Therefore, we consider asynchronous multiple signal reception at the remote radio units and propose an intersymbol interference free distributed cyclic delay diversity (dCDD) scheme for JT to achieve the full transmit diversity gain without requiring full channel state information of the private network. The spectral efficiency of the proposed dCDD-based JT is analyzed by deriving a new closed-form expression, and then compared with link-level simulations for non-identically distributed frequency selective fading over the entire network. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server and cluster master (CM), which is the main backhaul connection, and between the CM to remote radio units, which are the secondary backhaul connections. Thus, it is important for us to investigate the impact of reliability of main and secondary backhaul connections on the system. Our results show that the resulting composite backhaul connections can be accurately modeled by our proposed product of independent Bernoulli processes.

In this paper, we consider a mobile edge computing (MEC)-based secure computation offloading system, and design a practical multi-eavesdropper model including two specific scenarios of non-colluding and colluding eavesdropping. Furthermore, we design a requirement satisfaction model by exploring practical variations in user request patterns for security provisioning, delay reduction and energy saving. Based on these, we propose a satisfaction-maximized secure computation offloading (SMax-SCO) scheme, and then formulate an optimization problem aiming at maximizing users’ requirement satisfactions subject to secrecy offloading rate, tolerable delay, task workload and maximum power constraints. Since the optimization problem is nonconvex, we present an efficient successive convex approximation (SCA)-based algorithm to obtain suboptimal solutions. We demonstrate that the proposed SMax-SCO scheme achieves a significant improvement in security performance and requirement satisfaction compared with existing schemes. Moreover, we conclude that SMax-SCO can resist eavesdropping attacks of multiple eavesdroppers and even colluding eavesdroppers.

We study the information leakage to a guessing adversary in zero-error source coding. The source coding problem is defined by a confusion graph capturing the distinguishability between source symbols. The information leakage is measured by the ratio of the adversary's successful guessing probability after and before eavesdropping the codeword, maximized over all possible source distributions. Such measurement under the basic adversarial model where the adversary makes a single guess and the guess is regarded successful if and only if the estimator sequence equals to the true source sequence is known as the maximum min-entropy leakage or the maximal leakage in the literature. We develop a single-letter characterization of the optimal normalized leakage under the basic adversarial model, together with an optimum-achieving memoryless stochastic mapping scheme. An interesting observation is that the optimal normalized leakage is equal to the optimal compression rate with fixed-length source codes, both of which can be simultaneously achieved by some deterministic coding schemes. We then extend the leakage measurement to generalized adversarial models where the adversary makes multiple guesses and allows a certain level of distortion, for which we derive single-letter lower and upper bounds.

This paper introduces an experimental inter-slice RAN controller to allow resource isolation between slices in 4G/5G networks. The inter-slice RAN controller differs from others in the literature in that it allows radio resources in each transmission time interval (TTI) to be used by different slices, dynamically adjusted according to slice feedback to the controller. The inter-slice controller also allows different scheduling strategies to be used for different slices. The proof-of-concept is then implemented by using Software Defined Radio (SDR) and srsLTE software suite. The experimental results show the effectiveness of the RAN inter-slice controller in effectively managing radio resources for different slices.

We study the information leakage to a guessing adversary in index coding with a general message distribution. Under both vanishing-error and zero-error decoding assumptions, we develop lower and upper bounds on the optimal leakage rate, which are based on the broadcast rate of the subproblem induced by the set of messages the adversary tries to guess. When the messages are independent and uniformly distributed, the lower and upper bounds match, establishing an equivalence between the two rates.

Unmanned aerial vehicle (UAV) will be an essential carrier for future wireless communications due to its flexible deployment and low cost. As such, the information security of UAV communications is of paramount concern. In this paper, a novel physical layer secret key generation scheme is proposed for air-to-ground (A2G) UAV multiple-input-multiple-output (MIMO) communications, which is applicable in frequency division duplex (FDD) systems. In UAV communications, line-of-sight (LoS) propagation is a distinctive feature, which significantly weakens the performance of channel state information (CSI) based keys. Therefore, a novel channel parameter, three-dimension (3D) spatial angle, is employed to combat against a novel active eavesdropping method, which is termed as Environment Reconstruction based Attack for SEcret keys (ERASE). Compared to the existing plane-angle-based method, our scheme can efficiently utilize spatial resources and provide a higher key generation rate (KGR). The advantages of the proposed scheme are shown through both theoretical analysis and simulations.

In this article, we investigate joint information-theoretic secrecy and covert communication in a single-input-multioutput (SIMO) system where a transmitter (Alice) is communicating with two legitimate users (Bob and Carol). We consider that an untrusted user and a warden node are also present in the network attempting to attack the secure and covert communications to Bob and Carol, respectively. Specifically, Bob requires secure communications such that his messages from Alice are not decoded by the untrusted user, while Carol requires covert communications such that her messages from Alice are not detected by the warden. To do so, we consider that Alice transmits Carol's messages during selected time slots to hide them from the warden while also transmitting Bob's messages in each time slot contentiously. We formulate an optimization problem with the aim of maximizing the average rate subject to a covert communication requirement and a secure communications constraint. Since the proposed optimization problem is nonconvex, we utilize successive convex approximation to obtain a tractable solution. Moreover, we extend our proposed system model to multiple antenna Alice scenario and find beamforming vectors so that the average sum rate is maximized. Furthermore, we consider practical assumptions that Alice has imperfect knowledge of the warden's location and imperfect channel state information (CSI) of Bob and Carol. Our numerical examples highlight that the imperfect CSI at Carol has a more detrimental impact on the average rate compared to imperfect CSI at Bob.

In this paper, we propose a low-latency virtual reality (VR) delivery system where an unmanned aerial vehicle (UAV) base station (U-BS) is deployed to deliver VR content from a cloud server to multiple ground VR users. Each VR input data requested by the VR users can be either projected at the U-BS before transmission or processed locally at each user. Popular VR input data is cached at the U-BS to further reduce backhaul latency from the cloud server. For this system, we design a low-complexity iterative algorithm to minimize the maximum communications and computing latency among all VR users subject to the computing, caching and transmit power constraints, which is guaranteed to converge. Numerical results indicate that our proposed algorithm can achieve a lower latency compared to other benchmark schemes. Moreover, we observe that the maximum latency mainly comes from communication latency when the bandwidth resource is limited, while it is dominated by computing latency when computing capacity is low. In addition, we find that caching is helpful to reduce latency.

The combination of pervasive edge computing and blockchain technologies opens up significant possibilities for industrial Internet of Things (IIoT) applications, but there are several critical limitations regarding efficient storage and rapid response for large-scale low-delay IIoT scenarios. To address these limitations, in this article we propose a hierarchical edge-cloud blockchain called LayerChain. Specifically, to promote scalability, we design a layered structure to hierarchically store the blockchain data in multiple distributed clouds and edge nodes. Next, we propose a node classification method to accommodate differences between the edge nodes when deploying the blockchain. Moreover, to mitigate lengthy delays during block propagation, we propose a tree-based clustering algorithm where blocks are propagated through different clusters with a compressed tree depth. Simulation results show that our LayerChain efficiently reduces the system's resource requirements and block propagation time, making it well-suited for large-scale low-delay IIoT applications.

For a multi-cluster-based transmit diversity scheme that supports joint transmissions (JT) in private networks, a distributed remote radio unit system (dRRUS) is deployed in each of the clusters to increase the spectral efficiency and coverage, and to achieve flexible spatial degrees of freedom. Due to its distributed structure, the dRRUS relies on backhaul communications between the private network server (PNS) and cluster master (CM), which is the main backhaul communication, and between the CM to remote radio units (RRUs), which is the secondary backhaul communication. Thus, this paper mainly investigates the reliability of main and secondary backhaul connections for cluster-based transmit diversity schemes in private networks. Employing a Bernoulli process to model each backhaul reliability, a composite backhaul connection is modeled by an independent product of Bernoulli processes. By employing the distributed cyclic delay diversity scheme over the dRRUS and precision time protocol for clock synchronization, the multicluster-based JT can be achieved without full channel state information of the private network environment at the PNS and CMs. Having developed necessary distributions for the signal-tonoise ratio realized at the receiver, the closed-form expressions for the outage probability and spectral efficiency are derived. To verify their accuracy, the analytical performances are compared with link-level simulations.

Analyzing network vulnerability, especially connectivity vulnerability, is vital for network security planning. Traditionally, network vulnerability analysis methods separate the studies of global connectivity vulnerability and critical area vulnerability, and thus ignore joint failure of network connectivity and critical-area integrity that may cause grave damage to a network. To this end, this paper proposes a prioritizing critical area approach for connectivity analysis to identify the corresponding vulnerable elements. Specifically, we consider the worst-case scenario of a network and aim at finding the minimum disruption-cost set of elements whose removal not only severely damages network connectivity but also disrupts the critical-area integrity. Since the above optimization problem is NP-hard, a heuristic algorithm based on spectral partitioning is developed to solve it. Simulation results validate the effectiveness of our proposed scheme in accurately identifying the vulnerable elements in critical areas to prevent significant loss in the overall network connectivity and performance.

In this paper, we consider a multiuser multiple-input multiple-output (MIMO) downlink communication system with simultaneous wireless information and power transfer (SWIPT). In particular, we focus on a realistic and efficient multi-receiver multi-eavesdropper MIMO SWIPT system, in which the channel state information (CSI) of each legitimate receiver and energy receiver (i.e., potential eavesdropper) is partially known to the transmitter. Based on this, we propose a robust artificial noise (AN)-aided secure transmission scheme for the system, where the channel uncertainties are modeled by the worst-case model. In the proposed scheme, we aim to maximize the worst-case achievable secrecy rate under the transmit power constraint and the energy harvesting (EH) constraint, by jointly optimizing the transmit precoding matrix and the AN covariance matrix. We utilize the S-Procedure and Taylor series approximation to transform the non-convex problem. Then, we apply the interior point method to tackle the transformed convex problem, obtaining the approximate optimal matrices and the corresponding maximum worst-case secrecy rate. Simulation results show that our proposed scheme achieves significant performance improvements in terms of convergence and the worst-case achievable secrecy rate.

In molecular communications, the direct detection of signaling molecules may be challenging due to the lack of suitable sensors and interference from co-existing substances in the environment. Motivated by examples in nature, we investigate an indirect detection mechanism using chemical reactions between the signaling molecules and a molecular probe to produce an easy-to-measure product at the receiver. The underlying reaction-diffusion equations that describe the concentrations of the reactant and product molecules in the system are non-linear and coupled, and cannot be solved in closed-form. To analyze these molecule concentrations, we develop an efficient iterative algorithm by discretizing the time variable and solving for the space variables in each time step. We also derive insightful closed-form solutions for a special case. The accuracy of the proposed algorithm is verified by particle-based simulations. Our results show that the concentration of the product molecules has a similar characteristic over time as the concentration of the signaling molecules. We analyze the bit error rate (BER) for a threshold detector and highlight that significant improvements in the BER can be achieved by carefully choosing the molecular probe and optimizing the detection threshold.

Mobile crowdsourcing is a promising strategy for trusted data collection in Internet-of-Things (IoT) applications. In this article, we propose a new fog-blockchain distributed approach for crowdsourcing reputation management to prevent user's privacy leakage, malicious users' participation, and reputation tampering in wireless IoT systems. To protect the user's privacy, we design a cross-layer privacy protection model to separate the user's identity and tasks flexibly by means of a hierarchical structure based on fog computing. Moreover, considering the multiconstraint requirement of crowdsourcing tasks, we present a multifactor reputation evaluation method to accurately identify malicious users. Furthermore, to solve the multi-identity problem of users on multiple fog nodes, we propose an adaptive fog-blockchain reputation storage method, which efficiently reduces the system resource consumption by analyzing the adaptive classification of fog nodes. Exhaustive experimental simulation results validate the security and efficiency of our proposed reputation management system.

In this paper, we address the problem of joint covert communication and secure transmission in untrusted relaying networks when multiple wardens exist in the network. We first consider a system model consisting of one source, one untrusted amplify-and-forward relay, one destination, and one warden. For covert communications, our aim is to prevent the warden from detecting the presence of communications via the source-relay-destination link. For secure transmission, our aim is to prevent the untrusted relay from decoding the source signal. To satisfy these requirements, we propose that the destination and the source inject jamming signals during the source-to-relay and relay-to-destination transmission phases, respectively. For the considered system model, we propose a power allocation strategy to maximize the secrecy rate and satisfy the covert requirements in both of the phases. Given that the proposed optimization problem is non-convex, we employ the successive convex approximation (SCA) approach to derive a tractable solution. To obtain further insights, we generalize our analytical results to consider multiple untrusted relays and multiple wardens. We focus on two scenarios of non-colluding and colluding wardens. Our results highlight that as the number of relays increases, the achievable secrecy rate increases while the average detection error probability decreases. This is a novel observation of a fundamental trade-off between covert requirement and secrecy performance.

In this paper, we propose a secure unmanned aerial vehicle (UAV) mobile edge computing (MEC) system where multiple ground users offload large computing tasks to a nearby legitimate UAV in the presence of multiple eavesdropping UAVs with imperfect locations. To enhance security, jamming signals are transmitted from both the full-duplex legitimate UAV and non-offloading ground users. For this system, we design a low-complexity iterative algorithm to maximize the minimum secrecy capacity subject to latency, minimum offloading and total power constraints. Specifically, we jointly optimize the UAV location, users' transmit power, UAV jamming power, offloading ratio, UAV computing capacity, and offloading user association. Numerical results show that our proposed algorithm significantly outperforms baseline strategies over a wide range of UAV self-interference (SI) efficiencies, locations and packet sizes of ground users. Furthermore, we show that there exists a fundamental tradeoff between the security and latency of UAV-enabled MEC systems which depends on the UAV SI efficiency and total UAV power constraints.

We derive new optimal power allocation solutions for a distributed antenna system with orthogonal frequency division multiplexing (DAS-OFDM), in which K remote radio heads (RRHs) allocate power over N subchannels with both in-dividual RRH and total system power constraints. The individual RRH constraints may be necessary due to hardware limitations, while the total power constraint allows the system to limit the overall energy consumption for cost and/or green environmental factors. In order to obtain some insight on the optimal power allocation, we focus on a simple two-RRH case in this paper. The resulting optimal power allocation solution has an interesting sparse feature: Among N subchannels, at most 1 subchannel can be allocated power for joint transmission by the two RRHs, and the rest of the subchannels must be served by a single RRH. A novel subchannel rearrangement scheme is presented to identify the possible joint-transmission subchannel, as well as which RRH the remaining subchannels are associated with. Numerical results are presented to verify our theoretical results.

This paper analyzes the optimal energy beamforming solution for a multiple-input single-output wireless power transfer (WPT) system over frequency-selective fading channels with joint total and individual antenna power constraints. To maximize the total harvested energy, we derive the optimal co-phasing power allocation rule which reveals that all K antennas will participate in energy beamforming with T<; K antennas transmitting with their maximum individual powers due to the total power constraint. We prove that optimally no more than T+1 subchannels are selected for power allocation. We highlight that the optimal power allocation solution can be efficiently obtained based on the corresponding low- complexity dual problem. Our proposed power allocation algorithm generalizes previous solutions considering only total or individual power constraints. Numerical examples verify our theoretical results and show the impact of the joint total and individual power constraints on the average harvested power.

Nanoparticle drug carriers play an important role in facilitating efficient targeted drug delivery, i.e., improving treatment success and reducing drug costs and side effects. However, the mobility of nanoparticle drug carriers poses a challenge in designing drug delivery systems. Moreover, healing results critically depend on the rate and time duration of drug absorption. Therefore, in this paper, we aim to design a controlled-release drug delivery system with a mobile drug carrier that minimizes the total amount of released drugs while ensuring a desired rate of drug absorption during a prescribed time period. We model the mobile drug carrier as a mobile transmitter, the targeted diseased cells as an absorbing receiver, and the channel between the transceivers as a time-variant channel since the carrier mobility results in a time-variant absorption rate of the drug molecules. Based on this, we develop a molecular communication (MC) framework to design the controlled-release drug delivery system. In particular, we develop new analytical expressions for the mean, variance, probability density function, and cumulative distribution function of the channel impulse response (CIR). Equipped with the statistical analysis of the CIR, we design and evaluate the performance of the controlled-release drug delivery system. Numerical results show significant savings in the amount of released drugs compared to a constant-release rate design and reveal the necessity of accounting for drug carrier mobility for reliable drug delivery.

We propose relay-energy access points (REAPs) as a novel Internet-of-Things (IoT) network architecture that supports wireless energy harvesting of IoT devices and long-range relay communications to data access points (DAPs). In this paper, we formulate a Stackelberg game for energy trading with the REAPs and a single relay selection (SRS) scheme for the REAP to assist communications with the DAP. We consider a harvest-then-transmit protocol in which REAPs transmit energy-bearing signals to multiple (active) IoT devices for energy harvesting. Using the harvested energy, the IoT devices then send information to the DAP via the REAP. Numerical examples highlight the effectiveness of the proposed game-theoretic solution and improvements achieved by relaying data through REAPs.

This paper analyzes the optimal transmission strategy and power allocation for a distributed wireless power transfer (WPT) system operating over frequency-selective fading channels. We consider K coordinated energy transmitters (CETs) coherently transmitting energy to a single user over N > K subchannels with individual power constraints. To maximize the total harvested energy, we derive the optimal co-phasing power allocation rule which has the following properties: 1) For any given subchannel, if the optimal power allocation of one CET is zero, then the power allocated by all the other K - 1 CETs to that subchannel is also zero (i.e., the subchannel is inactive); 2) For the non-zero power subchannels, the optimal power allocation obeys a proportionality principle that establishes a relationship between the powers allocated by all K CETs to all active subchannels. Based on this property, we prove that the optimal distributed WPT strategy is for all CETs to select no more than K subchannels. This is in sharp contrast to wireless information transmission where more than K subchannels may be used for capacity maximization. Numerical examples verify our theoretical results and show the performance gains of our proposed scheme compared to two benchmark schemes.

As cyber attacks to Industrial Internet of Things (IIoT) remain a major challenge, blockchain has emerged as a promising technology for IIoT security due to its decentralization and immutability characteristics. Existing blockchain designs, however, introduce high computational complexity and latency challenges which are unsuitable for IIoT. This paper proposes Xyreum, a new high-performance and scalable blockchain for enhanced IIoT security and privacy. Xyreum uses a Time-based Zero-Knowledge Proof of Knowledge (T-ZKPK) with authenticated encryption to perform Mutual Multi-Factor Authentication (MMFA). T-ZKPK properties are also used to support Key Establishment (KE) for securing transactions. Our approach for reaching consensus, which is a blockchain group decision-making process, is based on lightweight cryptographic algorithms. We evaluate our scheme with respect to security, privacy, and performance, and the results show that, compared with existing relevant blockchain solutions, our scheme is secure, privacy-preserving, and achieves a significant decrease in computation complexity and latency performance with high scalability. Furthermore, we explain how to use our scheme to strengthen the security of the REMME protocol, a blockchain-based security protocol deployed in several application domains.

In this paper, we examine new plan pricing schemes for multimedia applications that offload computing-intensive tasks to computing servers incurring both communication and computing costs. Pricing schemes offered to users include: 1) a pay-as-you-go payment for usage of communication or computing resources, 2) an upfront data (resp. computing) plan for unlimited usage of communication (resp. computing) resources during a period, and 3) an upfront combo plan for unlimited usage of both communication and computing resources during a period. We aim to solve an online plan reservation problem: the amount of resources needed by a task is only known when it arrives, i.e., the future is unknown. However, even if the resource usage of future tasks is known in advance, the plan reservation problem is NP-hard and thus challenging. To tackle this problem, we propose a randomized online reservation (ROR) scheme to reserve plans probabilistically, where the probability is determined by the recent usage of resources. The performance gap (competitive ratio) between our proposed scheme and the optimal solution is analyzed and derived in closed-form, and this gap is proved to be the minimum among all online algorithms which do not know the usage of future tasks. Trace-driven simulations verify the cost advantage of ROR and characterize how different prices of plans influence users' plan reservation strategies.

This paper derives the optimal power allocation for a coordinated orthogonal frequency-division multiplexing (OFDM) transmission system in which K coordinated transmission points (CTPs) coherently transmit and allocate power across N subchannels under both total and individual power constraints. In maximizing the system capacity, previous works showed that, under a total power constraint, the optimal transmission strategy is a maximum-ratio transmission (MRT) for CTPs with a waterfilling type of power allocation solution for the subchannels. For CTPs with both total and individual power constraints, we derive a new optimal co-phasing power allocation with the following property: For any given subchannel, if the optimal power allocation of one CTP is zero, then the power allocation of all the other K - 1 CTPs on that subchannel must also be zero; otherwise, the non-zero power allocation on all CTPs must follow a proportional principle which establishes the relationship between the optimal power allocation for all subchannels and all CTPs. This property highlights that the optimal power allocation for CTPs with individual power constraints is different from waterfilling and MRT, as more power is not necessarily allocated to the subchannels with better channel conditions. Numerical results are presented to verify our theoretical findings.

A promising solution to address the spectrum shortage in 5G cellular systems is the deployment of millimeter wave (mmWave) heterogeneous networks (HetNets). However, a key challenge for mmWave HetNets is to manage the user mobility and handovers among mmWave small cells exploiting highly directional antennas and conventional microwave macro cells. In this paper, we propose a new efficient handover decision algorithm based on a Markov Decision Process (MDP) to optimize the overall service experience of users in mmWave HetNets. By utilizing user's mobility information (velocity and location), the proposed algorithm avoids excessive handovers and effectively tackles beamforming misalignments, and signal blockages in mmWave small cells. To improve the computational efficiency of the MDP, we apply the action elimination method by exploiting unique handover properties of mmWave HetNets. While maintaining optimality, theoretical analysis shows that our proposed handover decision algorithm can reduce the computational complexity by 0.25M|Bs| + 0.25M/(M - 1) times, where M is the number of base stations and |Bs| is the number of beamwidth options for mmWave beamforming. Mobility-trace driven numerical results demonstrate the optimality of our proposed algorithm compared with other benchmark schemes and its low computational complexity over the traditional approach.

We consider a molecular communication system comprised of a transmitter, an absorbing receiver, and an interference source. Assuming amplitude modulation, we analyze the dependence of the bit error rate (BER) on the duration of the detection interval, which is the time within one transmission symbol interval during which the receiver is active to absorb and count molecules. We then propose algorithms to obtain the optimal detection interval that minimizes the BER of the considered molecular communication system. Simulation and numerical evaluations are provided to highlight further insights into the optimal results. For example, we demonstrate that the optimal detection interval can be very small compared to the transmission symbol interval. Moreover, our numerical results show that significant BER improvements are achieved by using the optimal detection interval.

Recent findings show that many mission critical Internet of Things (IoT) applications are exposed to increasing security risks. The complex and dynamic nature of the IoT and its applications also bring new types of security threats. To achieve end-to-end secure communication, IoT applications need key establishment schemes with integrated security fundamentals such as identification and authentication of IoT components, as well as integrity, confidentiality, availability and authenticity of data, to prevent security attacks from weakening and disrupting the communication. In this context, we present a new lightweight key establishment scheme that comprises a novel Identity-Based Credentials (IBC) mechanism and key establishment protocol. The IBC mechanism enables an IoT component to securely disclose a single identity for security support and privacy enhancement for key establishment. In this paper, we model IoT application attributes to develop our lightweight security and privacy enhancing key establishment scheme. The formal verification and analysis show that, compared to the existing schemes, our proposed scheme is resilient against more types of security attacks, and incurs lower computational and communication costs in IoT applications.

This paper investigates the secrecy performance of a finite-sized in-band selective relaying system with M transmitters connected via unreliable backhaul links, N decode-and-forward relays, and K collaborative eavesdroppers. To send the source message to the destination, a transmitter-relay pair that achieves the highest end-to-end signal-to-noise ratio is selected for transmissions, while the K eavesdroppers combine all the received signals from the selected transmitter and relay using maximal ratio combining. The proposed model introduces backhaul reliability and eavesdropping probability parameters to investigate practical constraints on the transmitter-relay cooperation and eavesdropper collaboration, respectively. Closed-form expressions are derived for the secrecy outage probability, probability of non-zero achievable secrecy rate, and ergodic secrecy rate for non-identical frequency-selective fading channels with robust cyclic-prefixed single carrier transmissions. These results show that the asymptotic secrecy outage probability and probability of non-zero achievable secrecy rate are exclusively determined by the number of transmitters M and their corresponding set of backhaul reliability levels. Under unreliable backhaul connections, it is found that the secrecy diversity gain is determined by M, N, and the number of multipath components in the frequency selective fading channels. Link-level simulations are conducted to verify the derived impacts of backhaul reliability and collaborative eavesdropping on the secrecy performance.

In this paper, we examine the secrecy performance of two-way relaying between a large-scale multiple antenna base station (BS) and a single antenna mobile user (MU) in the presence of a multiple antenna external jammer. We consider the untrusted relaying scenario, where an amplify-and-forward relay is both a necessary helper and a potential eavesdropper to the BS and MU transmissions. With the aim of maximizing the instantaneous secrecy sum rate, we derive new closed-form solutions for the optimal power allocation (OPA) under the following three scenarios: 1) without jamming (WoJ) where the jammer is not activated; 2) friendly jamming (FJ) where the jamming signal is known a priori at the BS and MU; and 3) Gaussian noise jamming (GNJ) where the jamming signal is unknown at the BS and MU. Based on our OPA solutions, new closed-form expressions are derived for the ergodic secrecy sum rate (ESSR) with Rayleigh fading channels. Furthermore, we characterize the high signal-to-noise ratio slope and power offset of the ESSR to provide a fundamental comparison of the WoJ, FJ, and GNJ scenarios. Based on our analysis, we propose a simple relay selection criterion for the scenario when the BS and MU are assisted by multiple untrusted relays. Numerical examples are presented to demonstrate the impact of the jammer's location, number of antennas and the advantage of the proposed relay selection criterion on the secrecy performance. Our findings highlight that similar to the trusted relaying scenario, the ESSR of all three secure transmission scenarios improve as the number of untrusted relays increases. Moreover, we highlight an interesting insight that the OPA for the GNJ scenario results in the same ESSR performance as the OPA for the WoJ scenario.

By taking a variety of realistic hardware imperfections into consideration, we propose an optimal power allocation (OPA) strategy to maximize the instantaneous secrecy rate of a cooperative wireless network comprised of a source, a destination, and an untrusted amplify-and-forward relay. We assume that either the source or the destination is equipped with a large-scale multiple antennas' system, while the rest are equipped with a single antenna. To prevent the untrusted relay from intercepting the source message, the destination sends an intended jamming noise to the relay, which is referred to as destination-based cooperative jamming. Given this system model, novel closed-form expressions are presented in the high signal-to-noise ratio regime for the ergodic secrecy rate and the secrecy outage probability. We further improve the secrecy performance of the system by optimizing the associated hardware design. The results reveal that by beneficially distributing the tolerable hardware imperfections across the transmission and reception radio-frequency front ends of each node, the system's secrecy rate may be improved. The engineering insight is that equally sharing the total imperfections at the relay between the transmitter and the receiver provides the best secrecy performance. Numerical results illustrate that the proposed OPA together with the most appropriate hardware design significantly increases the secrecy rate.

In this paper, we examine the secrecy performance of two-way relaying between a multiple antenna base station (BS) and a single antenna mobile user (MU) in the presence of a multiple antenna friendly jammer (FJ). We consider the untrusted relaying scenario where an amplify-and-forward relay is both a necessary helper and a potential eavesdropper. To maximize the instantaneous secrecy sum rate, we derive new closed-form solutions for the optimal power allocation (OPA) between the BS and MU under the scenario of relaying with friendly jamming (WFJ). Based on the OPA solution, new closed-form expressions are derived for the ergodic secrecy sum rate (ESSR) with Rayleigh fading channel. Furthermore, we explicitly determine the high signal-to-noise ratio slope and power offset of the ESSR to highlight the benefits of friendly jamming. Numerical examples are provided to demonstrate the impact of the FJ's location and number of antennas on the secrecy performance.

In this paper, we propose a self-dependent two-way secure communication where two sources exchange confidential messages via a wireless powered untrusted amplify-and-forward (AF) relay and friendly jammer (FJ). By adopting the time switching (TS) architecture at the relay, the data transmission is accomplished in three phases: Phase I) Energy harvesting by the untrusted relay and the FJ through non-information transmissions from the sources, Phase II) Information transmission by the sources and jamming transmissions from the FJ to reduce information leakage to the untrusted relay; and Phase III) Forwarding the scaled version of the received signal from the untrusted relay to the sources. For the proposed system, we derive a new closed-form lower bound expression for the ergodic secrecy sum rate (ESSR). Numerical examples are provided to demonstrate the impacts of different system parameters such as energy harvesting time, transmit signal-to-noise ratio (SNR) and the relay/FJ location on the secrecy performance. The numerical results illustrate that the proposed network with friendly jamming (WFJ) outperforms traditional one-way communication and the two-way without friendly jamming (WoFJ) policy.

This paper considers an orthogonal frequency division multiplexing (OFDM) coordinated transmission system in which K coordinated transmission points (CTPs) coherently transmit and allocate power across N subchannels under individual power constraints. In maximizing the system capacity, we derive an optimal co-phasing power allocation solution with the following property: For any subchannel, if the power allocation of one CTP is zero, then the power allocation of all the other CTPs must be zero. Otherwise, the non-zero power allocation of all CTPs follows a proportional rule establishing a relationship between the power allocation for all the subchannels and CTPs. This highlights that the optimal power allocation is different from classical waterfilling and maximum ratio transmission (MRT), as more power is not necessarily allocated to the subchannels with better channel conditions. Based on this property and our derived solution, we successfully reduce the constrained optimization problem with NK variables into an unconstrained one with only K variables, which simplifies computation significantly. Numerical results are presented to verify our theoretical findings.

In this paper, we analyze the outage and diversity performance of a low-complexity relay selection routing algorithm which applies to large-scale distributed decode-and-forward relay networks with two source-destination user pairs. We analyze a suboptimal decentralized relay selection (DRS) strategy that only utilizes local channel state information of the relays within a given hop, to select distinct multihop paths for each user pair. Specifically, we derive exact closed-form expressions for the outage probability and diversity order of the DRS algorithm which prove that the full diversity order is achieved with complexity that is quadratic with the number of relays in each hop. Illustrative analytical results are accurately validated by numerical simulations.

This paper proposes and analyzes a new full-duplex (FD) cooperative cognitive radio network with wireless energy harvesting (EH). We consider that the secondary receiver is equipped with a FD radio and acts as a FD hybrid access point (HAP), which aims to collect information from its associated EH secondary transmitter (ST) and relay the signals. The ST is assumed to be equipped with an EH unit and a rechargeable battery such that it can harvest and accumulate energy from radio frequency (RF) signals transmitted by the primary transmitter (PT) and the HAP. We develop a novel cooperative spectrum sharing (CSS) protocol for the considered system. In the proposed protocol, thanks to its FD capability, the HAP can receive the PT's signals and transmit energy-bearing signals to charge the ST simultaneously, or forward the PT's signals and receive the ST's signals at the same time. We derive analytical expressions for the achievable throughput of both primary and secondary links by characterizing the dynamic charging/discharging behaviors of the ST battery as a finite-state Markov chain. We present numerical results to validate our theoretical analysis and demonstrate the merits of the proposed protocol over its non-cooperative counterpart.

This paper considers a user association (UA) design for the base station (BS) and subcarrier (SC) allocation where a BS allocates different number of SCs to different users associated to it in a downlink heterogeneous cellular network. In jointly optimising the UA and SC allocation, we propose to use the Sharpe Ratio as the utility function for the optimisation objective. The Sharpe ratio is defined as the ratio between the mean of user achievable rates to its standard deviation. With this objective, the achieved user rates will be closer to each other, leading to a fair network access. To reduce the computational complexity of the solution, a simplified method based on binary Belief Propagation (BP) algorithm is proposed. Simulation results show that the achievable user rates are doubled in comparison with other schemes. The low computational complexity of the proposed method is achieved through BP solver by reducing the edges of the factor graph.

In this paper, we propose a new optimization framework that supports energy harvesting user fairness in a wireless information and power transfer OFDMA network. We consider the downlink of a multiuser network, where non-overlapping subchannels are allocated to the users with power splitting receivers for information transfer and energy harvesting. We propose a two-stage optimization algorithm to maximize the minimum energy harvested by the users while satisfying a minimum required user rate. We apply the proposed algorithm to optimize the subchannel allocation, power allocation, and power splitting ratio for each user. Numerical examples are provided to highlight the fairness and efficiency of our two-stage algorithm.

This paper analyzes the symbol error probability (SEP) of cluster-based cooperative networks where a finite cluster of base stations jointly detect multiple in-cluster users in the presence of out- of-cluster interference. For such a network, we derive new accurate upper and lower bounds on the SEP of the in-cluster users with Rayleigh fading, arbitrary path loss, and M-ary phase-shift keying modulation. We further derive new asymptotic expressions to accurately characterize the SEP saturation limit arising from out-of-cluster interference. To obtain deeper insights, we extend our analysis to consider Rician fading with line- of-sight paths from the in-cluster users to their nearest base stations. Numerical examples illustrate the accuracy of our results and highlight novel aspects of fading channels, path loss, cluster configurations, and user locations on the SEP of cluster-based cooperative cellular networks.

In this paper, the secrecy outage probability of cooperative cyclic prefixed single carrier (CP-SC) systems with multiple transmitters and unreliable backhaul connections is derived. The transmitters communicate with the destination in the presence of an eavesdropper over two-hop relay channels with non-identical frequency-selective fading. The existence of asymptotic limits on the secrecy outage probability is verified for various backhaul scenarios. For a fixed eavesdropper signal-to-noise ratio (SNR), the limit is found to be exclusively determined by the backhaul reliability. This shows that the diversity gain promised by cooperative CP-SC systems cannot be achieved in the high SNR region. Simulations are presented to verify the derived impact of backhaul reliability on the secrecy performance.

We investigate the construction of weakly-secure index codes for a sender to send messages to multiple receivers with side information in the presence of an eavesdropper. We derive a sufficient and necessary condition for the existence of index codes that are secure against an eavesdropper with access to any subset of messages of cardinality t, for any fixed t. In contrast to the benefits of using random keys in secure network coding, we prove that random keys do not promote security in three classes of index-coding instances.

We consider cellular networks with co-operative clusters of neighboring base stations detecting multiple in-cluster users subject to interference from out-of-cluster users. We assume that the base stations, equipped with multiple antennas, are connected to a central processor in each cluster. For such a network, we first consider centralized processing where all the in-cluster user signals are sent to the central processor for linear minimum mean squared error (LMMSE) estimation. Next, we consider partially decentralized processing where the in-cluster user signals are locally estimated at each base station, and the local estimates are combined at the central processor. For both processing architectures, we derive new expressions for the achievable rate of an in-cluster user when the channels between the users and base stations are subject to independent Rayleigh fading and distance-dependent path loss. The solutions are based on accurate approximations we derive for the characteristic function (CF) and the probability density function (PDF) of each user's signal-to-interference-plus-noise ratios (SINRs). Numerical examples highlight the accuracy of the analysis and compare the performance of centralized and partially decentralized processing under different cluster scenarios.

We analyze the secrecy outage probability of selective relaying wiretap channels with K decode- and-forward (DF) relays and N collaborative eavesdroppers. In the main channel, we consider a two-hop relay network where the best relay is selected to transmit and the relay link is combined with the direct source-to-destination link at the destination. In the eavesdropper channel, we consider that the eavesdroppers can collaborate to exchange the information obtained from the source and relays. Different from previous works, we introduce an eavesdropping probability measure to model different intercepting capabilities of the malicious nodes. For this network, we derive new closed-form expressions for the secrecy outage probability in Rayleigh fading channels. The impact of the number of eavesdroppers and the eavesdropping probabilities are accurately reflected in the array gain of the asymptotic secrecy outage probability.

We examine the sum capacity of a cluster-based cooperative cellular network where a linear minimum-mean squared error (LMMSE) estimator is deployed across a cluster of base stations to estimate multiple in-cluster users. Different from previous works, we examine the impact of interference from out-of-cluster users whose transmit power scales with that of the in-cluster users. For such a network, we derive the sum capacity of the in-cluster users with independent Rayleigh fading and arbitrary path loss. The sum capacity expression is based on accurate approximations we derive for the characteristic function and the probability density function of the in-cluster users' signal-to-interference-plus-noise ratios (SINRs). Numerical examples demonstrate that our new analytical expressions accurately characterize the impact of out-of-cluster interference and cluster size on the sum capacity. We observe that out-ofcluster interference results in a sum capacity saturation regime when the transmit power is large. We also illustrate that the saturation threshold increases with the cluster size. Furthermore, we examine the sum capacity under different path loss exponents which highlights the significance of out-of-cluster interference.

We consider a cooperative cellular network with multiple users transmitting within a cooperative cluster of multiple antenna base stations. Different from global cooperation where all the signal processing is performed at a central processor, we analyze a distributed processing architecture that performs the estimation of user symbols in two steps, namely, 1) Local LMMSE estimation of the user symbols at each base station, and 2) Central combining of all the estimates from the base stations. For such a network, we derive new expressions for the capacity of a given user with independent Rayleigh fading and arbitrary path loss between the users and all the antennas at the base stations. Our capacity expression is based on accurate approximations we derive for the characteristic function and the probability density function of the users' signal-to-interference-plus-noise ratios (SINRs). Numerical examples demonstrate that our analytical solutions accurately approximate the exact capacity. Furthermore, we highlight that the distributed approach introduces less overhead to the network compared with global cooperation. We note that the performance gap is small when the network is lightly loaded.

We propose a new resource allocation algorithm to support user fairness for downlink coordinated multipoint (CoMP) transmission with joint processing in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The proposed algorithm utilizes successive convex approximation (SCA) to dynamically allocate the transmit powers between multiple CoMP base stations transmitting to multiple co-channel user terminals (UTs). The aim is to minimize the total transmit power subject to per-antenna power and per-UT rate constraints. Simulation results compare our SCA-based algorithm with iterative waterfilling.

In this study, the authors propose a continuous rate and power allocation algorithm for multiuser downlink multiple‐input–multiple‐output orthogonal frequency‐division multiplexing systems with coordinated multi‐point transmission. The optimisation problem is formulated as a weighted sum‐rate maximisation problem subject to per‐antenna power constraints across multiple cooperating base stations (BSs). The practical consideration of the per‐antenna power constraint limits the average transmit antenna power which indirectly controls the inherent issue of high peak powers in OFDM. The proposed algorithm employs a successive convex approximation (SCA) technique to dynamically allocate powers to multiple co‐channel user terminals. They provide a convexity proof of the transformed optimisation problem and they show that the proposed algorithm converges to a unique solution. They compare the proposed SCA algorithm with two alternative approaches: (i) iterative waterfilling (IWF) and (ii) zero‐forcing beamforming (ZFB) with semi‐orthogonal user selection under both per‐antenna and per‐BS power constraint scenarios. Their simulation results highlight that the proposed SCA algorithm outperforms the existing IWF and ZFB in noise‐limited environments under both power constraint scenarios.

In this paper, we propose two efficient and practical resource allocation algorithms to maximize the weighted sum-rate of coordinated multipoint (CoMP) transmission with joint processing in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems subject to per-antenna power constraints. We first propose a continuous-rate algorithm that utilizes successive convex approximation (SCA) to dynamically allocate the transmit powers of multiple CoMP base stations (BSs) transmitting to multiple co-channel user terminals (UTs). Next, we propose a discrete-rate algorithm that takes the continuous-rate result as a starting point and redistributes the transmit powers to obtain a discrete solution. Simulation results are provided to benchmark our continuous-rate algorithm with two alternative approaches: iterative waterfilling (IWF), and zero-forcing beamforming (ZFB). Results show that SCA provides significant sum-rate improvements over IWF in medium to high interference scenarios, and outperforms ZFB in low to medium interference scenarios. Moreover, our proposed discrete rate algorithm produces a higher discrete sum-rate with much lower computational complexity compared to existing algorithms.

We propose transmit antenna selection with receive generalized selection combining (TAS/GSC) in dual-hop cognitive decode-and-forward (DF) relay networks for reliability enhancement and interference relaxation. In this paradigm, a single antenna which maximizes the receive signal-to-noise ratio (SNR) is selected at the secondary transmitter and a subset of receive antennas with the highest SNRs are combined at the secondary receiver. To demonstrate the impact of multiple primary users on the cognitive relay network, we derive new closed-form expressions for the exact and asymptotic outage probability with TAS/GSC in the secondary network. Several important design insights are reached. We corroborate that the full diversity gain is achieved, which is entirely determined by the total number of antennas in the secondary network. The negative impact of the primary network on the secondary network is reflected in the SNR gain.

We consider a multi-cell cooperative network where a cluster of base stations jointly detect the signals from multiple users transmitting within the cluster. Different from previous works, we examine the impact of interference from out-of-cluster users whose transmit power scales with that of the in-cluster users. For such a network, we derive new upper and lower bounds on the uncoded bit error probability (BEP) of the in-cluster users with independent Rayleigh fading and arbitrary path loss. We observe that our lower bound accurately approximates the BEP at low signal-to-noise ratios (SNRs), whereas the upper bound is accurate at high SNRs. Our analytical bounds accurately characterize the impact of out-of-cluster interference and cluster size on the BEP. Specifically, we highlight that out-of-cluster interference results in a BEP saturation regime when the transmit power is large. We also show that the saturation threshold increases with the cluster size.

This paper advocates physical layer security in wiretap channels with two-wave with diffuse power fading. In such a wiretap channel, we consider that confidential messages transmitted from a single antenna transmitter to an M-antenna receiver is overheard by an N-antenna eavesdropper. The receiver adopts maximal-ratio combining (MRC) to enhance transmission security, whereas the eavesdropper adopts MRC to maximize the probability of successful eavesdropping. We develop a new analytical framework to characterize the average secrecy capacity as the principal security performance metric in active eavesdropping. Specifically, we derive new closed-form expressions for the exact and asymptotic average secrecy capacity. Based on these, we determine the high signal-to-noise ratio power offset to explicitly quantify the impact of the main channel and the eavesdropper's channel on the average secrecy capacity.

We examine the impact of primary and secondary interference on opportunistic relaying in cognitive spectrum sharing networks. In particular, new closed-form exact and asymptotic expressions for the outage probability of cognitive opportunistic relaying are derived over Rayleigh and Nakagami-m fading channels. Our analysis presents revealing insights into the diversity and array gains, diversity-multiplexing tradeoff, impact of primary transceivers' positions, and the optimal position of relays. We highlight that cognitive opportunistic relaying achieves the full diversity gain which is a product of the number of relays and the minimum Nakagami-m fading parameter in the secondary network. Furthermore, we confirm that the diversity gain reduces to zero when the peak interference constraint in the secondary network is proportional to the interference power from the primary network.

This paper advocates physical layer security of maximal ratio combining (MRC) in wiretap two-wave with diffuse power fading channels. In such a wiretap channel, we consider that confidential messages transmitted from a single antenna transmitter to an M-antenna receiver are overheard by an N-antenna eavesdropper. The receiver adopts MRC to maximize the probability of secure transmission, whereas the eavesdropper adopts MRC to maximize the probability of successful eavesdropping. We derive the secrecy performance for two practical scenarios: 1) the eavesdropper's channel state information (CSI) is available at the transmitter and 2) the eavesdropper's CSI is not available at the transmitter. For the first scenario, we develop a new analytical framework to characterize the average secrecy capacity as the principal security performance metric. Specifically, we derive new closed-form expressions for the exact and asymptotic average secrecy capacity. Based on these, we determine the high signal-to-noise ratio power offset to explicitly quantify the impacts of the main channel and the eavesdropper's channel on the average secrecy capacity. For the second scenario, the secrecy outage probability is the primary security performance metric. Here, we derive new closed-form expressions for the exact and asymptotic secrecy outage probability. We also derive the probability of nonzero secrecy capacity. The asymptotic secrecy outage probability explicitly indicates that the positive impact of M is reflected in the secrecy diversity order and the negative impact of N is reflected in the secrecy array gain. Motivated by this, we examine the performance gap between N and N+1 antennas based on their respective secrecy array gains.

We propose transmit antenna selection with receive generalized selection combining in dual-hop cognitive decode-and-forward relay networks with spectrum sharing for reliability enhancement and interference relaxation. In this network, a single antenna, which maximizes the receive signal-to-noise ratio (SNR) is selected at the secondary transmitter, and a subset of receive antennas with the highest SNRs is combined at the secondary receiver. To demonstrate the advantages of our proposed framework, we derive new exact closed-form expressions for the outage probability and the symbol error rate of the secondary network in Rayleigh fading. We also derive easy-to-evaluate asymptotic expressions in the high-SNR regime to gain practical insights. Several important design insights are reached. Under the proportional interference power constraint, the full diversity gain is achieved and is entirely determined by the total number of antennas available in the secondary network. This result is independent of the number of receive antennas combined and the number of primary users. The positive impact of the number of receive antennas combined and the negative impact of the number of primary users on the secondary network are showcased in the SNR gain. Under the fixed interference power constraint, error floors are displayed, and the diversity gain is lost.

This paper tackles the important question of how to compensate the inherent spectrum efficiency loss in cognitive relay networks. Particularly, by considering two-way cognitive relaying, we seek to enhance the performance of the secondary network in terms of the reliability due to limited transmit power, and the spectral efficiency of the half-duplex dual-hop relay transmission. We derive new closed-form expressions for the outage probability of a cognitive relay network with two-way communications in the presence of multiple primary users. Our expressions accurately take into account the impact of the maximum allowable interference constraint at the primary users on the secondary network.

We examine the impact of clusters of primary transceivers in cognitive multiple-input multiple-output (MIMO) relay networks with underlay spectrum sharing. In such a network, we propose antenna selection as an interference-aware design to satisfy the power constraints in the primary and secondary networks. To demonstrate this, we consider transmit antenna selection with maximal ratio combining (TAS/MRC) in the primary and secondary networks. With this in mind, we derive new closed-form asymptotic expressions for the outage probability and the symbol error rate (SER) over independent Nakagami-m fading channels. Our results lead to several new fundamental insights. In particular, we highlight that TAS/MRC achieves a full diversity gain when the maximum transmit power in the secondary network is proportional to the peak interference temperature in the primary network.

In this paper, we propose two cooperative jamming protocols in two-hop amplify-and-forward (AF) wiretap channels: 1) jamming signal at the source (JSS) and 2) jamming signal at the relay (JSR). We apply optimal power allocation (OPA) between the useful signal and the jamming signal to maximize the secrecy rate for each of the protocols. A fundamental question to address is “Which cooperative jamming protocol is superior under OPA?” To this end, we evaluate the maximum secrecy rate of JSS and JSR for the general scenario of independent but not necessarily identically distributed fading with distinct average signal-to-noise ratios (SNRs) in the first hop, the second hop, and the wiretap link. We demonstrate that the strong first and second hops equally benefit the secrecy rates of JSS and JSR, when the wiretap link is weak. When the wiretap link is strong, the secrecy rate of JSR is superior to JSS.

We examine the impact of multiple primary receivers on cognitive multiple-input multiple-output (MIMO) relay networks with underlay spectrum sharing. For such a network, we propose transmit antenna selection with receive maximal-ratio combining (TAS/MRC) as an interference-aware design to satisfy the power constraints in the primary and secondary networks. To demonstrate this, we derive new closed-form expressions for the exact and asymptotic outage probability with TAS/MRC and decode-and-forward (DF) relaying over independent Nakagami-m fading channels in the primary and secondary networks. Several important design insights are reached. We find that the TAS/MRC strategy achieves a full diversity gain when the transmit power in the secondary network is proportional to the peak interference power in the primary network. Furthermore, we highlight that the diversity-multiplexing tradeoff (DMT) of TAS/MRC is independent of the primary network and entirely dependent on the secondary network.

Coordinated multipoint (CoMP) is a base station (BS) cooperation technique to boost the signal-to-noise ratio (SNR) of cell-edge users in future generation wireless networks. We propose a fixed weight CoMP downlink scheduler to reduce the packet loss probability (PLP) due to buffer overflow in BSs with finite queues. The CoMP scheduler selects a single BS to serve the associated cell-edge user with the largest weighted SNR. To meet PLP targets, we develop a simple strategy to design the packet transmission time and the scheduling weights of each BS. The network design capitalizes on our new closed-form expression for the PLP that relates three key network parameters: packet arrival rate, packet transmission time, and probability that each BS is scheduled. We compare the proposed fixed weight scheduler with an adaptive weight scheduler that requires instantaneous packet delay information. We show via analysis and simulation that the fixed weight scheduler can achieve a comparable PLP to the adaptive weight scheduler, while reducing communication overheads for the BSs.

We present new analytical expressions for optimal multiuser detection in the uplink of a cellular network with base station cooperation. We consider a cooperative multicell scenario where multiple base stations jointly detect the signals from multiple users distributed throughout the network. For such a network, we derive new upper and lower bounds on the uncoded bit error probability (BEP) with independent Rayleigh fading and arbitrary path loss between the users and the base stations. Our analytical results are further simplified to produce closed-form bounds on the BEP when the path loss from a given user to each base station is distinct. We demonstrate that the lower bound is accurate at low signal-to-noise ratios (SNRs) while the upper bound is accurate at medium to high SNRs.

We propose transmit antenna selection (TAS) with decode-and-forward relaying as an effective approach to reduce interference in cognitive multiple-input multiple-output (MIMO) relay networks. To demonstrate this, we derive new closed-form expressions for the exact and asymptotic outage probability of TAS/MRC with multiple antennas at the primary and secondary users. We consider underlay spectrum sharing where the secondary users (SUs) transmit in the presence of multiple primary users (PUs). We consider independent Nakagami-m fading in both the primary and secondary networks. Several important design insights are revealed. We find that TAS/MRC achieves a full diversity when the transmit power at the SUs is proportional to the peak interference power at the PUs. Furthermore, we highlight that this diversity gain is completely independent of the number of antennas at the PUs.

We propose and analyze transmit antenna selection (TAS) to enhance physical layer security in a wiretap channel with NAantennas at the transmitter, NBantennas at the receiver, and NEantennas at the eavesdropper. We focus on the practical scenario where the transmitter does not have any channel state information (CSI) of the eavesdropper's channel. The transmitter selects a single antenna that maximizes the instantaneous signal-to-noise ratio (SNR) at the receiver. The receiver and the eavesdropper employ either maximal-ratio combining (MRC) or selection combining (SC) to combine the received signals. For the proposed protocols, we derive new closed-form expressions for the probability of non-zero secrecy capacity. We consider Nakagami-m fading with non-identical fading parameters of the main channel, mB, and of the eavesdropper's channel, mE. Next, we derive new closed-form expressions for the exact secrecy outage probability, based on which the ε-outage secrecy capacity is characterized. Based on the exact expressions, we derive the asymptotic secrecy outage probability which accurately reveals the secrecy diversity order and the secrecy array gain. We confirm that the proposed protocols achieve identical secrecy diversity orders of NANBmB. An interesting conclusion is reached that this diversity order is independent of NEand mE. Furthermore, we prove that under the proposed protocols, the secrecy outage probability and the ε-outage secrecy capacity improve with increasing NA.

We propose a new scheduler for wireless multimedia relay networks (WMRNs). Our scheduler is designed to account for delay, symbol error probability (SEP), and packet loss probability (PLP) due to buffer overflow. We develop a cross-layer scheduling approach for the downlink to balance these system metrics. Our scheduler is based on a new metric which is referred to as the delay in packet scheduling (DPS). The user with the largest weighted signal-to-noise ratio is scheduled, where the weight is a function of the DPS. We then derive analytical expressions for the probability mass function (PMF) of the DPS, and the SEP of the scheduled user in Rayleigh fading. We derive an analytical approximation for the PMF of the buffer state. An analytical expression is then derived for the PLP due to buffer overflow. Our analysis is verified via simulations. We show the probability that a target DPS is met is 30% higher for our new scheme compared to the standard opportunistic equal weight scheduler, with negligible degradation in the SEP of the scheduled user. This can lead to a 85% improvement in the PLP.

In this paper, maximal-ratio combining (MRC) and selection combining (SC) are proposed in spectrum sharing single-carrier networks with multiple primary user receivers (PU-Rxs). Taking into account the peak interference power at the PU-Rx's and the maximum transmit power at the secondary user (SU), the impact of multiple PU-Rx's on the secondary network is characterized when the secondary user receiver (SU-Rx) is equipped with multiple antennas. In doing so, exact and asymptotic expressions are derived for the cumulative distribution function, taking into account two realistic scenarios: non-identical frequency selective fading between the secondary user transmitter (SU-Tx) and the PUs, and frequency selective fading between the SU-Tx and the SU-Rx. Based on these, exact and asymptotic expressions for the outage probability and average bit error rate are derived. Furthermore, an exact closed-form expression for the ergodic capacity is derived. It is shown that the asymptotic diversity gain depends only on the number of receive antennas and the number of multipath channels. It is further shown that the number of PU-Rx's and fading severities between the SU-Tx and the PU-Rx's have no impact on the asymptotic diversity gain.

We propose and analyze transmit antenna selection (TAS) to enhance physical layer security in a wiretap channel with multiple antennas at the transmitter, the receiver, and the eavesdropper. We consider the practical scenario of passive eavesdropping, where the transmitter does not have any channel state information (CSI) of the eavesdropper's channel. In the main channel between the transmitter and the receiver, we select a single antenna at the transmitter that maximizes the instantaneous signal-to-noise ratio (SNR) at the receiver. At the receiver and the eavesdropper, we consider two combining techniques: 1) maximal-ratio combining (MRC) and 2) selection combining (SC). For non-identical Rayleigh fading between the main channel and the eavesdropper's channel, we first derive new closed-form expressions for the exact and asymptotic secrecy outage probabilities. The asymptotic results accurately reveal the secrecy diversity order and the secrecy array gain. Next, we derive new closed-form expressions for the probability of positive secrecy and characterize the ε-outage secrecy capacity. We show that, under TAS/MRC and TAS/SC protocols, the secrecy outage probability approaches zero and the ε-outage secrecy capacity increases with increasing number of transmitter antennas.

This paper investigates the outage performance of dual-hop decode-and-forward (DF) cooperative spectrum sharing systems in the presence of multiple primary user (PU) receivers and multiple secondary user (SU) destinations. Our analysis allows for a general Nakagami-m fading environment where distinct fading parameters as well as unequal average fading powers between the interference and relaying links are assumed. Focusing on the cooperation process among the SU nodes and making use of the underlay cognitive approach, an exact closed-form expression for the outage probability is derived. Our analysis employs an opportunistic scheduling algorithm for selecting one out of L SU destinations available. Additional interference constraints are also considered due to the presence of multiple PU receivers. The effects of fading severity, SU relay placement, and number of PU receivers, and SU destinations on the end-to-end system performance are examined through some representative numerical plots. Monte Carlo simulation results are presented to corroborate the proposed analysis.

Cooperative multiplexing has the potential to double the achievable throughput by allowing the base station (BS) and the relay station (RS) to transmit to different users at the same time in the second time slot of the half time division duplexed (TDD) relay transmission. This throughput improvement comes at a cost of performance degradation due to inter-user interference between the BS and the RS. To overcome this degradation, we propose cooperative multiplexing optimum combining (CMOC) for the relay-link users. The proposed CMOC receiver combines the signals in the first and second time slot of the half TDD transmission such that the output signal-to-interference-plus-noise ratio (SINR) is maximized. New insights are drawn from our exact closed-form expressions of SINR distributions. Based on these, we present new analytical expressions for the outage probability, symbol error rate, and achievable throughput. Our results show a 3.5 times improvement in the achievable throughput relative to the standard single-channel receiver in the high interference regime.

We examine transmit antenna selection with receiver maximal-ratio combining (TAS/MRC) and transmit antenna selection with receiver selection combining (TAS/SC) in multiple-input multiple-output (MIMO) relay networks. Amongst L two-hop relay links, a single relay offering the highest end-to-end signal-to-noise ratio (SNR) is activated. Assuming independent non-identically distributed Weibull fading between the hops, new closed-form asymptotic expressions for the outage probability and the symbol error rate are derived considering NS, NR, and NDantennas at the source, the relays, and the destination, respectively. Based on such expressions, the diversity order and the array gain for M-ary phase shift keying and M-ary quadrature amplitude modulation are analyzed. We highlight that the diversity order of TAS/MRC is the same as TAS/SC. As such, we explicitly characterize the SNR gap between TAS/MRC and TAS/SC as the ratio of their respective array gains. An interesting observation is reached that for equal per-hop SNRs, the SNR gap between the two protocols is independent of L.

We propose and analyze two MIMO protocols with analog network coding (ANC) in two-way amplify-and-forward (AF) relaying where multi-antenna nodes communicate via a single antenna relay. Specifically, we present a new framework for the comparative analysis of beamforming and antenna selection in two-way relaying with non-identical Rayleigh fading between the hops. To facilitate the comparison, we derive new closed-form expressions for the exact and asymptotic sum symbol error rate (SSER). We show that beamforming and antenna selection offer the same diversity order of min{NA, NB}, where NA and NB are the number of antennas at the two nodes. We proceed to characterize the fundamental difference between the two protocols in terms of their array gains. A pivotal conclusion is reached that when either of the two nodes is equipped with a single antenna, antenna selection provides identical performance to beamforming at medium and high signal-to-noise ratios without the added hardware and signaling overhead.

We examine the impact of multiple primary transmitters and receivers (PU-TxRx) on the outage performance of cognitive decode-and-forward relay networks. In such a joint relaying/spectrum-sharing arrangement, we address fundamental questions concerning three key power constraints: 1) maximum transmit power at the secondary transmitter (SU-Tx), 2) peak interference power at the primary receivers (PU-Rx), and 3) interference power at SU-Rx caused by the primary transmitter (PU-Tx). Our answers to these are given in new analytical expressions for the exact and asymptotic outage probability of the secondary relay network. Based on our asymptotic expressions, important design insights into the impact of primary transceivers on the performance of cognitive relay networks is reached. We have shown that zero diversity order is attained when the peak interference power at the PU-Rx is independent of the maximum transmit power at the SU-Tx.

This paper proposes a low complexity protocol that preserves full diversity in multiuser amplify-and-forward relay networks with NSantennas at the source, NRantennas at the relay, and NDantennas at each of the K destinations. In the proposed protocol, a two-fold diversity is guaranteed: 1) multi-antenna diversity via transmit antenna selection with maximal-ratio combining (TAS/MRC), and 2) multiuser diversity via opportunistic scheduling. Under perfect feedback with precise channel state information (CSI), we derive new exact and asymptotic symbol error rate (SER) expressions in closed-form for the general case of Nakagami-m fading. We prove that the full diversity order of NSNDKmX+ min{NSNRmY, NRNDKmZ} is guaranteed, where mX, mY, and mZdenote the fading parameters of the source-destination, source-relay, and relay-destination links, respectively. To examine the impact of delayed feedback, we next derive new exact and asymptotic SER expressions in closed-form. We prove that in the presence of delayed feedback, outdated CSI degrades the diversity order to NDmX+ min{NRmY, NDmZ}. In addition, based on our asymptotic expressions, we determine the optimal power allocation between the source and the relay such that the SER is minimized. We show that optimal power allocation offers superior performance over uniform power allocation; highlighting a pivotal design choice for maximizing network performance without investing additional resources.

We propose cascaded transmit antenna selection with maximal-ratio combining (TAS/MRC) for use in multiuser relay networks (MRN) with NS, NR, and NDantennas at the source, the relay, and each of the K destinations, respectively. We consider opportunistic scheduling where the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is scheduled for transmission. In cascaded TAS/MRC, a single transmit antenna that maximizes the instantaneous received SNR in each hop is selected, and all the receive antennas are MRC combined. We derive new exact closed-form statistics of the end-to-end SNR, from which we derive the exact and the approximate symbol error rate (SER) for M-ary quadrature amplitude modulation (M-QAM) and M-ary phase-shift keying (M-PSK). New concise expressions are derived to characterize the diversity order and the array gain. We highlight that our proposed scheme attains the maximum diversity order of NR× min{NS, NDK}. Furthermore, we determine the optimal power assignment at the source and the relay that minimizes the SER.

We propose transmit antenna selection with maximal-ratio combining (TAS/MRC) for use in multiple-input-multiple-output (MIMO) multiuser relay networks (MRN). The network under consideration is equipped with NS, NR, and NDantennas at the source, the relay, and each of the K destinations, respectively. For this network, the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is scheduled for transmission. In each hop, a single transmit antenna that maximizes the post-processing SNR is selected, while all the receive antennas are MRC combined. We first derive new closed-form expressions for the outage probability and the symbol error rate (SER) for amplify-and-forward (AaF) relaying. Next, we present compact and easy-to-compute expressions for the diversity order and the array gain to provide practical insights into the network behavior. We highlight the fact that our proposed scheme attains the maximum diversity order of NR× min{NS,NDK}.

This paper proposes multiple-input multiple-output (MIMO) transmission in fixed gain amplify-and-forward relaying to allow for high data rate coverage in wireless distributed networks. We consider a hierarchical network architecture in which the relay is a multiple antenna static node assisting single antenna mobile nodes. We derive new exact closed-form expressions for the outage probability and the symbol error rate (SER) valid for arbitrary N antennas at the relay under independent but not necessarily identically distributed (i.n.d.) Rayleigh fading. Our solutions apply to general operating scenarios with distinct average received signal-to-noise ratios (SNRs) throughout the network. Based on these, we derive new concise asymptotic expressions which accurately characterizes the outage probability and the SER in the high SNR regime. In our asymptotic solutions, we present an exact expression for the array gain in terms of the average received SNRs and the number of antennas N. We further show that the maximum achievable diversity order is N+1.

We present new statistical properties of the end-to-end signal-to-noise ratio (SNR) in multiple-input multiple-output (MIMO) relaying with distributed transmit antenna selection and receiver maximal-ratio combining (TAS/MRC). In particular, we derive new expressions for the exact and the first order expansions of the cumulative distribution function (cdf). Based on these, new closed-form expressions are derived for the exact and the first order expansions of the moment generating function (mgf). We then present a new concise expression for the symbol error rate (SER) with M-ary phase-shift keying in the high SNR regime. Our asymptotic SER expression explicitly reveals the diversity order and the array gain of the MIMO relay network.

Distributed transmit antenna selection with maximal-ratio combining (TAS/MRC) is proposed for use in multiple-input multiple-output (MIMO) multiuser relay networks (MRN), where NS, NR, and NDantennas are equipped at the source, the relay, and each of the K destinations, respectively. For such networks, the destination with the highest instantaneous end-to-end signal-to-noise ratio (SNR) is scheduled for transmission. In each hop, a single transmit antenna that maximizes the post-processing SNR is selected, while all the receive antennas are MRC combined. New exact closed-form expressions are derived for the cumulative distribution function (CDF), the probability density function (PDF), and the moment generating function (MGF) of the highest instantaneous end-to-end SNR. Based on these, we determine the symbol error rate with M-ary phase-shift keying. Our derived results apply to general operating scenarios with arbitrary number of antennas, arbitrary number of destinations, and distinct average SNRs.

This paper presents a comparison and analysis of selection relaying with transmit beamforming as an effective approach to combat channel impairments in relay-assisted cellular networks. We consider the downlink scenario where the base station equipped with N antennas transmits to the mobile station either directly, or indirectly via a relay station, according to the link with the strongest received signal-to-noise ratio (SNR). We compare two amplify-and-forward protocols: i) fixed gain relaying which requires partial channel state information (CSI), and ii) variable gain relaying which requires full CSI. We present new exact closed-form expressions for the generalized moments of the end-to-end SNR to characterize the higher-order statistical properties of the SNR. We derive new exact closed-form expressions for the symbol error rate (SER), which are valid for a wide variety of modulations. Furthermore, we explicitly characterize the asymptotic behavior of the SER to obtain two key performance parameters: the array gain and the diversity order. Based on these, we reveal that the SNR advantage of variable over fixed gain relaying vanishes in the large N limit.

We develop a unified framework for the symbol error rate (SER) of distributed transmit antenna selection with receiver maximal-ratio combining (TAS/MRC) in multiple-input multiple-output (MIMO) relay networks. We focus on nonregenerative relaying with NS, NR, and NDantennas at the source, relay, and destination, respectively. We consider the general fading scenario of Nakagami-m fading with distinct m fading parameters in the source-to-relay and the relay-to-destination links. We present new analytical expressions for the statistics of the end-to-end signal-to-noise ratio (SNR). Specifically, we derive exact expressions and first order expansions for the cumulative distribution function and moment generating function of the end-to-end SNR. Based on these, we derive new closed-form expressions for the asymptotic SER under M-ary phase-shift keying (M-PSK) and M-ary quadrature amplitude modulation (M-QAM). Our asymptotic solutions accurately identify the diversity order and the array gain as two key design components of the network.

In wireless mesh networks, it is desirable to utilize overlapping coverage of multiple parallel relays to assist the source-destination transmission. In this letter, we consider selection diversity (SD) to select the strongest link amongst the direct and N amplify-and-forward (AF) relay links. We derive new closed-form expressions for the symbol error rate (SER) in independent but not necessarily identically distributed (i.n.d.) Rayleigh fading relay channels. Our results are given as both lower bound and asymptotic expressions based on an accurate upper bound on the signal-to-noise ratio (SNR) of the relay links. Our asymptotic results provide key performance parameters such as the array gain and diversity order, which prove that a full N+1 diversity order is achieved. We show that SD can offer an array gain advantage over maximal-ratio combining which entails all the relays to transmit. Numerical results are shown to validate the analysis.

We propose distributed transmit antenna selection with receiver maximal-ratio combining (TAS/MRC) for use in a two-hop multiple-input multiple-output (MIMO) relay network. The network under consideration is equipped with NS, NR, and NDantennas at the source, relay, and destination, respectively. First, we derive a new closed-form expression for the exact cumulative distribution function (cdf) of the end-to-end SNR. Based on this, we present a new closed-form expression for the exact symbol error rate (SER). Our analytical results are further evaluated in the high SNR regime, leading to practical design insights. Our asymptotic expressions are concise and have the added advantage of explicitly characterizing the diversity order and the array gain of the network. Our exact and asymptotic results are valid for general operating scenarios with distinct average received SNRs in each hop.

Nonregenerative relaying in MIMO multi-relay networks is analyzed under the general operating scenario of independent but not necessarily identically distributed (i.n.d.) Rayleigh fading. Exact closed-form expressions are derived for the moment generating function of the end-to-end signal-to-noise ratio (SNR) with distinct average received SNRs in each link. Our analysis applies to arbitrary L number of relays, each equipped with arbitrary N transmit/receive antennas. Based on the derived results, we determine the symbol error rate with M-ary phase-shift keying. In addition, we carry out a high SNR analysis of the error performance. We explicitly reveal that the diversity order is equal to LN+1.

Cooperative diversity is a promising solution in wireless distributed networks where integrating multiple antennas onto small mobile devices is practically impossible due to size and cost constraints. As such, we consider a cooperative diversity network where the source and the destination user-pair are equipped with single antennas while the relay is a wireless access point equipped with N antennas. For such networks, we focus on cooperative selection diversity (CSD) to select a single link with the highest instantaneous received signal-to-noise ratio (SNR) between the direct link and the multiple-input multiple-output (MIMO) relay link. We present new closed-form expressions for the exact outage probability and the exact symbol error rate (SER) based on the cumulative distribution function (cdf) of the instantaneous received SNR. Our expressions are valid for arbitrary N antennas and apply to general operating scenarios with distinct average received SNRs in each link. Furthermore, we present a high SNR analysis of the outage probability and SER to explicitly characterize the diversity order and array gain. We show that the diversity order increases with the number of antennas according to N + 1.

Multiple amplify-and-forward (AF) relays with receive diversity is analyzed over unbalanced hops with independent but not necessarily identically distributed Rayleigh fading. Exact closed-form expressions are derived for the moment generating function of the end-to-end signal-to-noise ratio (SNR) at the destination. Our analysis apply to arbitrary numbers of relays and arbitrary numbers of antennas at the destination. Based on the derived results, we determine the symbol error rate with M-ary phase-shift keying. In addition, we carry out an asymptotic analysis in the high SNR regime. Our results provide fundamental insights into the impact of the number of antennas on the performance of multiple AF relaying. We explicitly reveal that the diversity order is equal to the sum of the number of relays and the number of antennas. Theoretical analysis is verified by simulation.

This paper analyzes selection diversity as an effective tool to combat channel impairments in relay-assisted cellular systems. We consider the downlink scenario where the base station equipped with N antennas transmits to the mobile station either directly, or indirectly via a relay station, according to the link with the strongest received signal-to-noise ratio (SNR). For this system, we analyze the performance of fixed gain amplify-and-forward relaying that does not require full channel-state-information (CSI) at the relay. We derive new exact closed-form expressions for the outage probability and symbol error rate (SER) based on new statistical properties of the end-to-end SNR. Furthermore, we quantify the asymptotic behavior of the outage probability and SER. We explicitly reveal the impact of multiple antennas with relay selection diversity, on the array gain and the diversity order. Our new asymptotic results prove that the maximum diversity order of N+1 is achieved.

Distributed multi-antenna relaying is analyzed in nonregenerative cooperative networks with unbalanced hops and independent but not necessarily identically distributed Rayleigh fading. Exact closed-form expressions are derived for the moment generating function of the end-to-end signal-to-noise ratio (SNR) at the destination. Our analysis applies to arbitrary N number of antennas at the relay and arbitrary average received SNRs. Based on the derived results, we determine the symbol error rate with M-ary phase-shift keying. In addition, we carry out an asymptotic analysis in the high SNR regime. Our results provide fundamental insights into the impact of the number of antennas on the performance of nonregenerative relaying. We explicitly reveal that the diversity order is equal to N + 1. Theoretical analysis is verified by simulation.

This paper proposes and analyzes selection diversity as an effective tool to combat channel impairments in relay-assisted cellular systems. We consider the uplink scenario where the base station equipped with N antennas receives from the mobile station either directly, or indirectly via a relay station, according to the link with the strongest received signal-to-noise ratio (SNR). For this system, we analyze the performance of fixed gain amplify-and-forward relaying that does not require full channel-state-information (CSI) at the relay. We derive new exact closed-form expressions for the outage probability and symbol error rate (SER) based on new statistical properties of the end-to-end SNR. Furthermore, we quantify the asymptotic behavior of the outage probability and SER. We explicitly reveal the impact of multiple antennas with relay selection diversity, on the array gain and the diversity order. Our new asymptotic results prove that the maximum diversity order of N + 1 is achieved.

Multiple fixed gain amplify-and-forward (AF) relays with receive diversity is analyzed for the practical case of unbalanced hops with independent but non-identically distributed (i.n.d.) Rayleigh fading channels. We derive a new exact closed-form expression for the moment generating function (mgf) of the end-to-end signal-to-noise ratio (SNR). Our mgf is valid for the general case of arbitrary numbers of relays and arbitrary numbers of antennas at the destination. Based on this, we determine the exact and approximate symbol error rate (SER) for M-ary phase-shift keying. Our results provide new important insights into the joint impacts of the number of relays and the number of receive antennas on the system performance. Theoretical analysis is verified by simulation.

We derive new exact closed-form expressions for the symbol error rate (SER) of cooperative selection diversity (CSD) with amplify-and-forward (AF) transmission using channel-state-information (CSI)-based gain relaying. We consider the general fading condition of independent but not necessarily identically distributed (i.n.d.) Nakagami-m fading. We also present a closed-form expression for i.n.d. Rayleigh fading as a special case. We highlight the impact of the m fading parameter on the SER performance in unbalanced fading conditions. Numerical results substantiate the validity of our analysis.

We present new exact closed-form expressions for the symbol error rate (SER) of cooperative selection diversity (CSD) with a single fixed gain amplify-and-forward (AF) relay. We consider the general fading condition of independent but not necessarily identically distributed (i.n.d.) Nakagami-m fading. We also present a closed-form expression for i.n.d. Rayleigh fading as a special case. We highlight the performance improvements provided by CSD with fixed gain relaying in unbalanced fading conditions. Numerical results substantiate the validity of our analysis.