Integrated sensing and communication (ISAC) with cloud radio access networks (C-RAN) unifies two foundational technologies for the Internet of Things (IoT), enabling both high-data-rate transmission and accurate environmental awareness. This paper investigates an uplink C-RAN system where multiple remote radio units (RRUs) jointly serve user equipment devices (UEs) and sense a target. We demonstrate that only a subset of RRUs is typically needed to meet sensing requirements, while energy is potentially conserved by deactivating redundant sensing RRUs. Motivated by this, we aim to improve the system’s energy efficiency (EE) by jointly optimizing RRU activation, user association, and power allocation, while ensuring localization accuracy through the Cram´er-Rao lower bound (CRLB). To address this problem, we propose a model-based iterative algorithm that integrates optimal user association, decision tree-based selection of sensing RRUs, and fixed-point power allocation. This algorithm is further used to train a graph neural network (GNN) framework, ISAC-GNN, to overcome scalability and computational complexity limitations. Our theoretical analysis ensures performance guarantees, while a semi-supervised training strategy enhances stability and generalization. Simulation results demonstrate that ISAC-GNN reduces inference time by 67% relative to the model-based approach, enabling real-time implementation for ISAC system resource management. In addition, ISAC-GNN allows scalable deployment across various system scenarios without retraining the model.

Automatic modulation classification (AMC) is a critical task in modern communication systems, particularly under diverse signal conditions and limited data scenarios. Existing transformer-based AMC models often rely on single-stream architectures and uniform input formats, which limit their effectiveness in capturing rich signal features. To address these limitations, we propose DTNet, a novel transformer-based dual-stream network designed for efficient and accurate modulation classification. DTNet introduces two key innovations: (1) a scale feature and extension (SFE) block that applies a scaling function to transform signals into a structured output map, followed by an extension module that reconstructs the signal into a square matrix and integrates a response map using adaptive filters; and (2) a convolutional stream that extracts discriminative features from multi-scale signal representations. Furthermore, a modified feature embedding to leverage the transformer architecture is introduced to capture global dependencies and contextual information from the input signal, thereby enhancing the modulation classification accuracy. Experimental results show that DTNet achieves superior performance on benchmark datasets, reaching classification accuracies of 93.4% on RML2016.10A and 94.4% on RML2016.10B, outperforming state-of-the-art deep learning methods while maintaining lower computational complexity. The source code is available at https://github.com/daothanh2011/DTNet .

The integration of the Internet of Things (IoT) with blockchain technology offers a promising solution to tackle interoperability, privacy, and security issues in IoT applications. However, maintaining low latency is a significant challenge in IoT-blockchain systems, as blockchain relies on an underlying communication network to create an overlay network for transmission and synchronization. The random nature of blockchain’s broadcast mechanism results in an overlay network topology that is not optimized for low-latency transmission. This study examines the impact of overlay network topologies on latency performance within an Ethereum-based IoT system. We developed a novel method for generating various overlay topology configurations and implemented Ethereum clients to establish neighboring connections based on these configurations. Our findings reveal that the overlay network significantly influences latency metrics, specifically the delays in transmitting transactions or blocks. We observed that the trade-off between delays caused by network congestion and latency reduction from fewer hops in the overlay network is dependent on the number of connections. To investigate further, we implemented three models for overlay networks. None of these network models consistently exhibited superior latency performance, indicating that latency is affected by network dynamics. In response, we developed an efficient peer selection method for blockchain nodes to reduce latency in dynamic environments. Drawing inspiration from the Perigee algorithm’s success in peer selection for transaction propagation, we propose Dual Perigee, an extension of Perigee that also optimizes block propagation latency. Through experiments in an emulated 50-node IoT-blockchain system, we compared the effectiveness of Dual Perigee against Ethereum’s default peering method and the Perigee algorithm. The results demonstrate that Dual Perigee reduces block latency by 48.5 %. These latency reductions enable more real-time responsiveness in IoT-blockchain systems and support their deployment in latency-sensitive applications.

In real-world applications, Federated Learning (FL) faces two challenges: (1) scalability and (2) heterogeneous data. To address the first problem, we design a novel FL framework named Full-stack FL (F2L). More specifically, F2L provides a hierarchical network architecture, making extending the FL network accessible without reconstructing the whole network system. Moreover, leveraging the advantages of hierarchical network design, we propose a new Label-driven Knowledge Distillation (LKD) technique at the centralized server to address the second problem. Unlike the current knowledge distillation techniques, LKD is capable of training a student model, which consists of good knowledge from all teachers' models. Therefore, our proposed algorithm can effectively extract the knowledge of the regions' data distribution (i.e., the regional aggregated models) to reduce the divergence between clients' models when operating under the FL system with non-independent identically distributed data. Extensive experiment results reveal that: (i) our F2L method can significantly improve the overall FL efficiency in all global distillations (i.e., accuracy is$7-20\%$higher in non-IID settings), and (ii) F2L rapidly achieves convergence as global distillation stages occur instead of increasing on each communication cycle.

In this study, we address the gradient-based domain generalization problem, where predictors aim for consistent gradient directions across different domains. Existing methods have two main challenges. First, minimization of gradient empirical distance or gradient inner products (GIP) leads to gradient fluctuations among domains, thereby hindering straightforward learning. Second, the direct application of gradient learning to the joint loss function can incur high computation overheads due to second-order derivative approximation. To tackle these challenges, we propose a new Pareto Optimality Gradient Matching (POGM) method. In contrast to existing methods that add gradient matching as regularization, we leverage gradient trajectories as collected data and apply independent training at the meta-learner. In the meta-update, we maximize GIP while limiting the learned gradient from deviating too far from the empirical risk minimization gradient trajectory. By doing so, the aggregate gradient can incorporate knowledge from all domains without suffering gradient fluctuation towards any particular domain. Experimental evaluations on datasets from DomainBed demonstrate competitive results yielded by POGM against other baselines while achieving computational efficiency. The code is available at https://github.com/skydvn/POGM.

Domain Generalization (DG) aims to learn from multiple known source domains a model that can generalize well to unknown target domains. One of the key approaches in DG is training an encoder which generates domain-invariant representations. However, this approach is not applicable in Federated Domain Generalization (FDG), where data from various domains are distributed across different clients. In this paper, we introduce a novel approach, dubbed Federated Learning via On-server Matching Gradient (FedOMG), which can efficiently leverage domain information from distributed domains. Specifically, we utilize the local gradients as information about the distributed models to find an invariant gradient direction across all domains through gradient inner product maximization. The advantages are two-fold: 1) FedOMG can aggregate the characteristics of distributed models on the centralized server without incurring any additional communication cost, and 2) FedOMG is orthogonal to many existing FL/FDG methods, allowing for additional performance improvements by being seamlessly integrated with them. Extensive experimental evaluations on various settings demonstrate the robustness of FedOMG compared to other FL/FDG baselines. Our method outperforms recent SOTA baselines on four FL benchmark datasets (MNIST, EMNIST, CIFAR-10, and CIFAR-100), and three FDG benchmark datasets (PACS, VLCS, and OfficeHome). The reproducible code is publicly available~\footnote[1]{\url{https://github.com/skydvn/fedomg}}.

In wireless communication networks, it is difficult to solve many NP-hard problems owing to computational complexity and high cost. Recently, quantum annealing (QA) based on quantum physics was introduced as a key enabler for solving optimization problems quickly. However, only some studies consider quantum-based approaches in wireless communications. Therefore, we investigate the performance of a QA solution to an optimization problem in wireless networks. Specifically, we aim to maximize the sum rate by jointly optimizing clustering, subchannel assignment, and power allocation in a multiautonomous aerial vehicle-aided wireless network. We formulate the sum rate maximization problem as a combinatorial optimization problem. Then, we divide it into two subproblems: 1) a QA-based clustering and 2) subchannel assignment and power allocation for a given clustering configuration. Subsequently, we obtain an optimized solution for the joint optimization problem by solving these two subproblems. For the first subproblem, we convert the problem into a simplified quadratic unconstrained binary optimization (QUBO) model. As for the second subproblem, we introduce a novel QA algorithm with optimal scaling parameters to address it. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of the sum rate and running time.

Recent research efforts on Semantic Communication (SemCom) have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these approaches introduce a paradox: the accuracy of Artificial Intelligence (AI) tasks should naturally emerge through training rather than being dictated by network constraints. Acknowledging this dilemma, this work introduces an innovative approach that leverages the rate distortion theory to analyze distortions induced by communication and compression, thereby analyzing the learning process. Specifically, we examine the distribution shift between the original data and the distorted data, thus assessing its impact on the AI model's performance. Founding upon this analysis, we can preemptively estimate the empirical accuracy of AI tasks, making the goal-oriented SemCom problem feasible. To achieve this objective, we present the theoretical foundation of our approach, accompanied by simulations and experiments that demonstrate its effectiveness. The experimental results indicate that our proposed method enables accurate AI task performance while adhering to network constraints, establishing it as a valuable contribution to the field of signal processing. Furthermore, this work advances research in goal-oriented SemCom and highlights the significance of data-driven approaches in optimizing the performance of intelligent systems.

Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), critically impacts resource allocation. Many existing works often assume all data throughput requirements are satisfied, which is impractical given resource limitations and diverse demands. When requirements cannot be met, the system becomes infeasible, causing congestion and degraded performance. To address this problem, we propose a novel framework to enhance IoT system robustness by solving two problems, comprising maximizing the number of satisfied IoT devices and jointly maximizing both the number of satisfied devices and total network throughput. These objectives often conflict under infeasible circumstances, necessitating a careful balance. We thus propose a modified branch-and-bound (BB)-based method to solve the first problem. An iterative algorithm is proposed for the second problem that gradually increases the number of satisfied IoT devices and improves the total network throughput. We employ a logarithmic approximation for a lower bound on data throughput and design a fixed-point algorithm for power allocation, followed by a coalition game-based method for device association. Numerical results demonstrate the efficiency of the proposed algorithm, serving fewer devices than the BB-based method but with faster running time and higher total throughput.

This study explores the intersection of neuroscience and computer science, focusing on the use of spiking neural networks (SNNs) to simulate the behavior of biological neurons. A neural network model based on the Izhikevich neuron model is proposed to simulate the local auditory network of crickets. The parameters of the neuron model are optimized based on evaluation functions and identified by Particle Swarm Optimization (PSO), aligning its input-output relationships with the observed cricket neuron responses. The results showed that the network successfully simulated the behavior of individual neurons, promising applications in fields like neural prosthetics.

Blockchain holds significant potential in addressing the security, privacy, decentralization, and interoperability challenges prevalent in the Internet of Things (IoT), However, this advancement often comes at the expense of scalability. Therefore, enhancing the scalability of IoT blockchain systems while preserving other essential blockchain attributes is imperative. This paper aims to mitigate network latency in the blockchain network, a factor directly linked to blockchain scalability. Achieving this goal requires implementing an efficient peer selection method, moving beyond the reliance on default selection (i.e., the one in Bitcoin, Ethereum, etc.). In existing literature, Perigee has been introduced as a method that nearly optimizes the delay in the transaction transmission process. However, Perigee has not comprehensively addressed the complete transaction life cycle, which includes a crucial process-block transmission. In response to this limitation, we propose Dual Perigee, a solution that thoroughly considers and optimizes both transaction-oriented latency (TOL) and block-oriented latency (BOL). To show the effectiveness of Dual Perigee, we implemented and evaluated it within an emulated IoT-Blockchain system, comparing its performance with Perigee and the default peering method in Ethereum. The results reveal that Dual Perigee excels in reducing BOL compared to Perigee. Moreover, Dual Perigee exhibited a latency that was 43% and 80% lower than the default peering method and Perigee, respectively.

Federated learning (FL) has found many successes in wireless communications; however, the implementation of FL has been hindered by the energy limitation of mobile devices (MDs) and the availability of training data at MDs. Wireless power transfer (WPT) and mobile crowdsensing (MCS) are promising technologies that can be leveraged to power energy-limited MDs and acquire data for learning tasks. How to integrate WPT and MCS towards sustainable FL solutions is a research topic entirely missing from the open literature. This work for the first time investigates a resource allocation problem in collaborative sensing-assisted sustainable FL (S2FL) networks with the goal of minimizing the total completion time. In particular, we investigate a practical harvesting-sensing-training-transmitting protocol in which energy-limited MDs first harvest energy from RF signals, use it to gain a reward for user participation, sense the training data from the environment, train the local models at MDs, and transmit the model updates to the edge server. The total completion time minimization problem of jointly optimizing power transfer, transmit power allocation, data sensing, bandwidth allocation, local model training, and data transmission is complicated due to the non-convex objective function, highly non-convex constraints, and strongly coupled variables. In order to solve that problem, we apply the decomposition technique and develop a computationally-efficient path-following algorithm to obtain the solution. In particular, inner convex approximations are developed for the resource allocation subproblem, and the subproblems are performed alternatively in an iterative fashion. Simulation results are provided to evaluate the effectiveness of the proposed S2FL algorithm in reducing the completion time up to 21.45% in comparison with other benchmark schemes. Further, we investigate an extension of our work from frequency division multiple access (FDMA) to non-orthogonal multiple access (NOMA) and show that NOMA can speed up the total completion time 8.36% on average of the considered FL system.

Integrated communications and sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to autonomous vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a coherent processing interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this article develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing. Despite numerous advantages, low computing resources at IoT devices and high communication costs for exchanging model parameters make applications of FL in massive IoT networks very limited. In this work, we develop a novel compression scheme for FL, calledhigh-compression federated learning (HCFL), for very large scale IoT networks. HCFL can reduce the data load for FL processes without changing their structure and hyperparameters. In this way, we not only can significantly reduce communication costs, but also make intensive learning processes more adaptable on low-computing resource IoT devices. Furthermore, we investigate a relationship between the number of IoT devices and the convergence level of the FL model and thereby better assess the quality of the FL process. We demonstrate our HCFL scheme in both simulations and mathematical analyses. Our proposed theoretical research can be used as a minimum level of satisfaction, proving that the FL process can achieve good performance when a determined configuration is met. Therefore, we show that HCFL is applicable in any FL-integrated networks with numerous IoT devices.

This work proposes a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, considering limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, the selected MU can propose a contract to the MAP according to its expected local and encrypted data. To find optimal contracts that can maximize utilities while maintaining high learning quality of the system, we develop a multi-principal one-agent contract-based problem considering the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare up to 114% under the privacy cost consideration compared with those of baseline methods.

Federated learning (FL) has found numerous applications in wireless and mobile networks thanks to its distinctive features. However, efficient FL networks require to address the energy limitation of FL users. Exploited the flexible deployment and agile mobility of unmanned aerial vehicles (UAVs), this work proposes to dispatch the UAV with edge computing capabilities as an aerial energy source to wirelessly power FL users and as an aerial server for model aggregation. In this regard, we investigate a resource allocation problem that minimizes the energy consumption of FL users and the aerial server. To resolve the nonconvexity of the formulated problem, we propose to decompose the entire set of variables into three blocks, and then develop an iterative algorithm. Simulations results are presented to show that our proposed algorithm significantly outperforms several benchmarks.

This article proposes a cooperative friendly jamming framework for swarm unmanned aerial vehicle (UAV)-assisted amplify-and-forward (AF) relaying networks with wireless energy harvesting (EH). In particular, we consider a swarm of hovering UAVs that relays information from a terrestrial base station to a distant mobile user and simultaneously generates friendly jamming signals to interfere/obfuscate an eavesdropper. Due to the limited energy of the UAVs, we develop a collaborative time-switching relaying protocol that allows the UAVs to collaborate in harvesting wireless energy, relay information, and jam the eavesdropper. To evaluate the performance, we derive the secrecy outage probability (SOP) for two popular detection techniques at the eavesdropper, i.e., selection combining and maximum-ratio combining. Monte Carlo simulations are then used to validate the theoretical SOP derivation. Using the derived SOP, one can obtain engineering insights to optimize the EH time and the number of UAVs in the swarm to achieve a given secrecy protection level. Furthermore, simulations show the effectiveness of the proposed framework in terms of SOP compared to the conventional AF relaying system. The analytical SOP derived in this work can also be helpful in future UAV secure-communication optimizations (e.g., trajectory and locations of UAVs). As an example, we present a case study to find the optimal corridor to locate the swarm so as to minimize the system SOP. Our proposed framework helps secure communications for various applications that require large coverage, e.g., industrial IoT, smart city, intelligent transportation systems, and critical IoT infrastructures, such as energy and water.

In recent years, blockchain networks have attracted significant attention in many research areas beyond cryptocurrency, one of them being the Edge of Things (EoT) that is enabled by the combination of edge computing and the Internet of Things (IoT). In this context, blockchain networks enabled with unique features, such as decentralization, immutability, and traceability, have the potential to reshape and transform the conventional EoT systems with higher security levels. Particularly, the convergence of blockchain and EoT leads to a new paradigm, calledBEoTthat has been regarded as a promising enabler for future services and applications. In this article, we present a state-of-the-art review of recent developments in the BEoT technology and discover its great opportunities in many application domains. We start our survey by providing an updated introduction to blockchain and EoT along with their recent advances. Subsequently, we discuss the use of BEoT in a wide range of industrial applications, from smart transportation, smart city, smart healthcare to smart home, and smart grid. Security challenges in the BEoT paradigm are also discussed and analyzed, with some key services, such as access authentication, data privacy preservation, attack detection, and trust management. Finally, some key research challenges and future directions are also highlighted to instigate further research in this promising area.

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature, owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, calledaerial computing, via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing (LAC), high-altitude computing (HAC), and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multiaccess techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

With outstanding features, machine learning (ML) has become the backbone of numerous applications in wireless networks. However, the conventional ML approaches face many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, can impede the effectiveness and applicability of ML in wireless networks. To address these problems, transfer learning (TL) has recently emerged to be a promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks and valuable experiences accumulated from the past to facilitate the learning of new problems. By doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods’ robustness to different wireless environments. This article aims to provide a comprehensive survey on the applications of TL in wireless networks. Particularly, we first provide an overview of TL, including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, signal recognition, security, caching, localization, and human activity recognition, which are all important to next-generation networks, such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

The explosive growth of end devices and mobile applications calls for novel schemes that can enable computation‐hungry applications at small end‐devices and meet the massive connectivity requirement. Cognitive radio (CR), non‐orthogonal multiple access (NOMA) and multi‐access edge computing (MEC) are envisioned as the key technologies in fifth‐generation and beyond. In this work, the authors introduce the concept of CR‐NOMA in MEC offloading, where a secondary user (SU) can utilise the spectrum allocated to a primary user (PU) to offload its computation task to the MEC server for remote execution. For the spectrum utilisation, an equation to specify the minimum transmit power that must be allocated to the PU is derived. The authors also develop an algorithm to determine the offloading decision (i.e. offloading or not) and the paired PU (i.e. subcarrier used for computation offloading) for SUs, using one‐to‐one matching game. Moreover, through numerical simulations, the authors demonstrate the superior performance of the proposed algorithm compared with several baseline schemes.

As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLCs) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this article considers a UAV-assisted VLC using nonorthogonal multiple-access. More specifically, we formulate a joint problem of power allocation and UAV's placement to maximize the sum rate of all users, subject to constraints on power allocation, quality of service of users, and UAV's position. Since the problem is nonconvex and NP-hard in general, it is difficult to be solved optimally. Moreover, the problem is not easy to be solved by conventional approaches, e.g., coordinate descent algorithms, due to channel modeling in VLC. Therefore, we propose using the Harris hawks optimization (HHO) algorithm to solve the formulated problem and obtain an efficient solution. We then use the HHO algorithm together with artificial neural networks to propose a design that can be used in real-time applications and avoid falling into the “local minima” trap in conventional trainers. Numerical results are provided to verify the effectiveness of the proposed algorithm and further demonstrate that the proposed algorithm/HHO trainer is superior to several alternative schemes and existing metaheuristic algorithms.

Caching and multicast have surged as effective tools to alleviate the heavy load from the backhaul links while enabling content-centric delivery in communication networks. The main focus of work in this area has been on the cache placements to manage the network delay and backhaul transmission cost. An important issue of optimizing the cost efficiency in content delivery has not been addressed. This paper tackles this issue by proposing collaborative multicast beamforming in cache-enabled ultra-dense networks. The objective is to maximize the cost efficiency, which is defined as the ratio of the content throughput to the sum of power consumption and backhaul cost, in providing quality-of-service for content delivery. Zero-forcing beamforming and generalized zero-forcing beamforming are employed to force the multi-content interference to zero or mitigate it while amplifying the desired signals for users. These problems of collaborative multicast beamforming design are computationally difficult. Path-following algorithms, which invoke a simple convex quadratic program at each iteration, are developed for their solution. Numerical results are provided to demonstrate the computational efficiency of the proposed algorithms and also give insights into the impact of caching on the cost efficiency.

In this study, the performance of energy harvesting systems in the presence of unreliable backhaul links is investigated over independent but not necessarily identically distributed Nakagami‐ m fading channels. In particular, the energy‐constrained relay uses the amount of harvested energy from the best small‐cell transmitter based on time switching‐based relaying protocol to process for the next hop transmission. The authors derived the closed‐form expressions of the outage probability and the effective throughput with two distinct transmission schemes: (i) delay‐limited transmission, and (ii) delay‐tolerance transmission are attained. In order to assess the impacts of unreliable backhaul links, they thus obtain the asymptotic expression of the outage probability in high signal‐to‐noise ratio (SNR) regime. The numerical results are conducted to analyse the effects of energy harvesting fraction time, energy efficiency, backhaul reliability, and the fading parameters on the system performance. The authors' results show that under the unreliable backhaul links the outage probability yields the error‐floor in the high SNR regime which demonstrates the significant impact of the backhaul unreliability.

A non‐orthogonal multiple access (NOMA) system is now considered as a promising radio access technique for next‐generation networks owing to its benefits, e.g. spectral efficiency improvement. Due to the successive interference cancellation order at receivers, fairness among users in NOMA may not be guaranteed. In this study, the authors focus on ‐fair resource allocation in NOMA. The complexity of the considered problem is then analysed. In particular, the problem is shown to be convex when and , non‐deterministic polynomial‐time (NP)‐hard when , and polynomial‐time solvable when . Finally, simulation results are provided to examine the effects of the fairness degree on the system performance and verify the effectiveness of the proposed algorithms.

In this paper, we investigate the impacts of unreliable backhaul connections on cognitive heterogeneous networks with best relay selection. Since spectrum sharing is employed, the transmit powers of the small-cell transmitter and relays are constrained by the peak interference at the primary user, as well as their maximum transmit powers. The closed-form expressions of the outage probability, ergodic capacity and symbol error rate are derived along with the asymptotic performance to get full insights. Our results show that the backhaul reliability is a limiting factor of the system performance.

Abstract As the next generation sequencing and deoxyribonucleic acid (DNA) synthesis technologies rapidly develop, DNA engineering became faster and cheaper with higher accuracy than before. External information such as secret messages or digital archives can be embedded into the DNA sequence by mutating its molecular sequence according to a certain algorithm. This study presents a sector‐based DNA steganography method using non‐coding regions of the DNA sequence as the host for carrying binary data. The aim of the sector‐based embedding is to have a high level of security, data capacity, and error handling while preserving the organism's life information. A sector consists of reference bases, segmented message bases, and parity bases. The parity bases can detect and handle mutation errors in each sector. The sector length controls the data capacity and security. Our in silico experiments verified that the bit‐per‐nucleotide of our method can be [1.2, 2.0] by controlling the sector length, which is 0.2 point higher than that of conventional methods. As many as 10 4 base mutations in the DNA sequence can be handled for perfect message retrieval. Furthermore, the probability for message detection without keys is lower than 1.3949 × 10 −62 , which is very low. Copyright © 2016 John Wiley & Sons, Ltd.

In Qualcomm High Data Rate System, a base station chooses the user with the highest ratio between demand rate and cumulative rate to transmit in each slot time. This scheduling method, namely propositional fairness scheduling, solves a trade-off between fairness and efficiency. This fairness corresponds to a network utility maximization problem with the logarithmic aggregate objective function. However, this approach only considers the instant rates while fairness should be considered for the long term where the cumulative rates are more suitable. This paper proposes a proportional quasi-fairness optimization framework for wireless ad hoc networks to guarantee fairness of the cumulative data rates. The framework can be extended for other schemes such as max-min fairness and throughput maximization.

The dynamic throughput optimal policy that supports all incoming traffic while minimizing outage probability for cooperative ad-hoc network is consider in this paper. The algorithm operates without knowledge of traffic rates or the memory historical channel states. Using the technique of Lyapunov optimization, we present a framework that can tradeoff between the capacity region the total outage probability of network. Distance this minimum outage probability is controlled by a parameter V effecting an explicit tradeoff in average queue length. We also use simulation to compare the performance of our policy with two-hop relay policy as well as non-cooperative policy.

We study flow control and scheduling policy for end-to-end delay-constrained traffic in multi-hop wireless networks. Our paper develops a scheduling and flow control algorithm which guarantees delay constraints and allows distributed implementation. The resulting policy makes decision bases on a delay feedback from destination nodes. The proposed policy guarantees delay and stabilizes network system given that sufficient condition of network stability under specified delay requirement is satisfied.

This paper presents 3D geometric watermarking scheme for general 3D animation model, which it has most of the hierarchical structure with various transform nodes of geometry and interpolator. The proposed scheme selects some transform nodes with geometric property and embeds the watermark into the distance distribution of vertex coordinates in the selected transform node. Experimental results verified that the proposed scheme has the robustness against most of modifiers in public 3D editing tool as well as the invisibility.

Cognitive radio in network core devices such as basestations is being considered as a spectrum management solution for future society's communication demands. Aside from new resource allocation algorithms, efficient inter- and intra-protocol processing should be considered. We propose an opportunistic cross layer architecture called COmmon Layer Architecture (COLA) for information exchange between arbitrary layers for New Generation Networks (NWGN) with network-oriented cognitive radio. Through COLA, information from different protocols, as well as different layers, can be efficiently processed with minimized performance degradation. Emulation and simulation results showed improved aggregate throughput, handoff delay, connectivity and packet drop ratio.

Quality of service (QoS) guarantees grant ways for service providers to establish service differentiation among subscribers. On the other hand, service subscribers are also assured the level of service they paid for. In addition, the efficient level of service quality can be selected according to the subscribers' needs thus ensuring efficient use of available bandwidth. While network utility optimization techniques assure certain QoS metrics, a number of situations exist where some QoS goals are not met. The optimality of the network parameters is not mandatory to guarantee specified QoS levels. This paper proposes a joint data rate and power control scheme that guarantees service contract QoS level to a subscriber using goal programming. In using goal programming, this paper focuses on finding the range of feasible solutions as opposed to solving for the optimal. In addition, in case no feasible solution is found, an acceptable compromised solution is solved.

Due to the reduction of equipment cost and the demand of higher capacity, Wireless Mesh network (WMN) router devices usually have several interfaces and work on multichannels. Jointing channel allocation, interface assignment and routing (JCIAR) can expressively enhance the network capacity. In this paper, regarding the characteristic of network node and network traffic, we formulate the JCIAR problem as a two-stage optimization problem. In the first stage, we will obtain the optimal throughput of network regarding radios and channel constraints over set of predetermined disjoint paths. In the second stage, basing on the flows value obtained from the first stage, channel allocation and interface assignment are solved distributedly to obtain the optimal network throughput. The argument and evaluation show the advantage of our method in comparison with other existing method in terms of implementation scheme and network performance.

Digitalization of broadcast advances rapidly in Japan since the broadcast satellite (BS) digital broadcast services began in December 2000. It is a requirement that the receiver has a built-in HDD, for storing interesting programs, which may be connected with appliances over the home network. However, it is difficult to guarantee QoS on the home network composed of heterogeneous sub-networks. Therefore, for the purpose of an efficient and appropriate transmission of a video stream such as MPEG over the home network, it is required to develop a new scalable MPEG transmission method. For this reason, we propose a scalability technique considering impairments propagation (SIP), which takes into account the impairments propagated to other frames due to the loss of a macroblock in a given frame. We introduce the SIP technique, evaluate its performance and give a system model using SIP.

We believe that existing wire solutions such as Home-PNA2.0 and HomePlug and wireless solution such as HomeRF are the most promising solutions, because of its cost-effectiveness. However, MAC protocols of these solutions provide only class of service (CoS) using priority mechanism like HomePNA and HomePlug or consider only voice among real-time traffics like HomeRF For these reasons, we perceive the needs of the new MAC protocol which is no new wire solution and provides guaranteed quality of service (QoS) for not only voice but also video and audio. In light of this, we present the design and software implementation of a new MAC protocol for the home network called the HomeMAC. Our evaluation results of software implementation verify that HomeMAC can provide low delay and low jitter to the real-time traffic by reservation of the bandwidth.

In 1999, HomePNA2.0 using phone lines was proposed, and we believe it is one of the most promising solutions, because of its cost-effectiveness. However, owing to the adaptation of the mature IEEE802.3 CSMA/CD technology used for Ethernet, the QoS cannot be guaranteed. In light of this, we present the design, simulation evaluation, software implementation and empirical evaluation of a new MAC protocol for the home network called the HomeMAC. In this paper, the software-based HomeMAC is implemented by programming the kernel space of FreeBSD. The HomeMAC features a hybrid CSMA/CD-timed token protocol, which combines the CSMA/CD for non-real-time traffic with a timed token protocol for real-time traffic. In addition, by providing flexible bandwidth allocation based on a QoS level table (QLT), the HomeMAC can serve high QoS covering the whole offered load. From the results of the evaluation of the software implementation, we verified that HomeMAC can provide low delay, low loss, and low jitter to real-time traffic by reserving bandwidth.

The home network is one of the most important information infrastructures of the future. Among the home network solutions available, one promising technology is a HomePNA utilizing the phone line. However, it is not able to guarantee the QoS due to the adaptation of the mature IEEE802.3 CSMA/CD technology which is used for Ethernet. In light of this, we propose and evaluate a new MAC protocol for the home network called the HomeMAC that provides guaranteed QoS for appliances and PCs. HomeMAC features a hybrid CSMA/CD-timed token protocol which combines the CSMA/CD with timed token protocol and transmits real-tune traffic based on the QoS level table (QLT) for guaranteeing QoS. In the HomeMAC, there are two different transmission modes, namely, the CSMA/CD mode when there is no real-time traffic, and the timed token mode when there is real-time traffic taking place. By dynamically switching the transmission mode between CSMA/CD mode and timed token mode in accordance with the different kinds of traffic, the hybrid protocol provides low delay, low jitter, and low loss rate to multimedia appliances such as TVs, DVDs, and PCs. Moreover, by providing flexible bandwidth allocation based on QLT, the HomeMAC can serve high QoS whole covering entire offered load.