Task scheduling plays a critical role in enhancing service quality and reducing latency for the growing ecosystem of IoT applications, particularly in edge computing environments. This research addresses the critical challenge of task scheduling in fog computing architecture. We propose a novel hybrid algorithm that combines Q-learning enhanced Nawaz-Enscore-Ham (Q-NEH) for task prioritization with a Cuckoo Search-Grey Wolf (CS-GW) optimization approach. The increasing deployment of IoT devices generates massive amounts of time-sensitive data, necessitating efficient processing strategies that can handle multiple competing objectives, including minimizing makespan, maximizing resource utilization, and ensuring energy efficiency. Our hybrid solution leverages Q-NEH’s adaptive learning capabilities for intelligent task ordering alongside CS-GW’s balanced exploration-exploitation mechanisms for optimization. Experimental evaluation conducted with varying workloads across different fog node configurations demonstrates that the proposed algorithm achieves superior performance compared to traditional approaches including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony (AC) optimization. The results show that our CS-GW implementation significantly reduced makespan compared to conventional methods, while the integration of Q-NEH further enhanced scheduling efficiency across all tested algorithms. The successful combination of reinforcement learning with nature-inspired optimization techniques provides a robust framework for addressing the complex task scheduling.

In the evolving landscape of global telecommunications, this study proposes the integration of Satellite direct-to-device (D2D) communication into a federated multiagent deep reinforcement learning (DRL) framework, enhanced by blockchain technology. This approach aims to optimize network slicing capabilities for Satellite D2D communication through low-carbon sustainable computing techniques. The federated multiagent-DRL framework dynamically manages network slices, ensuring optimal resource allocation and superior Quality of Service (QoS) for direct satellite links within 6G nonterrestrial networks (NTNs). Satellite D2D addresses connectivity challenges in remote areas, reducing latency while improving network reliability and coverage. The blockchain component ensures security and transparency, fostering a decentralized learning environment with strong data integrity. By incorporating low-carbon computing methods, the framework not only reduces energy consumption and carbon emissions but also achieves sustainability goals. Comprehensive simulations show significant improvements in latency, throughput, and network resilience, optimizing resource utilization and paving the way for efficient and robust 6G NTN architectures.

Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.

Sixth generation (6G) wireless networks are a promising technology for meeting the extreme demands of 2030 of high data rates, ultra-low latency, and ultra-reliability. However, supporting the services of 6G networks requires advanced security mechanisms. In addition, 6G has a rapid and simple means of accessing the channel because of its broadcast transmission method, which makes it prone to eavesdropping attacks. Physical-layer security (PLS) is predicted to be a suitable method of achieving network security, in which the characteristics and advantages of wireless networks are exploited. PLS technologies include wiretap code designs, artificial noise injection (jamming), multi-input multi-output (MIMO)(beamforming), and physical-layer authentication. This paper proposes an erasure corrector code, namely a low-density parity check (LDPC), and ultra-massive MIMO (UMMIMO) to fix this issue. The simulation results show that our solution guarantees data secrecy, even against eavesdroppers with many antennas and considerable decoding resources, while reducing power consumption, consequently supporting secure, green transmission.

The space–air–ground-integrated network (SAGIN) will greatly promote the development of the Internet of Things (IoT). Green IoT will be an important part of SAGIN. Ambient backscatter communication (AmBC) is a potential solution for green SAGIN IoT. To improve the achievable sum rate (ASR) of the AmBC system, we propose a reconfigurable intelligent surface (RIS)-assisted AmBC system. In the single-backscatter device (BD) AmBC scenario, we first give the phase shifts that maximize the gain of the reflection link of the AmBC system, and then give the optimal reflection coefficient. The proposed scheme does not need to solve the convex semidefinite program (SDP) problem and has the characteristics of low computational complexity. In the multi-BD AmBC scenario, we first propose a multi-BD phase shifts initialization strategy to ensure the stability of the proposed scheme. Then, we give the optimal reflection coefficient and phase shifts based on the iterative method. Simulations show that the RIS-assisted AmBC scheme is superior to the non-RIS-assisted AmBC scheme.

The design of the Internet of Things (IoT) system over space–air–ground-integrated network (SAGIN) is still in its infancy. It is critical to get 6G technology involved, in order to address the current problems in the combined SAGIN and IoT ecosystem. This work utilizes a reconfigurable intelligent surface (RIS) to enhance the existing SAGIN infrastructure. We highlight channel modeling and estimate as a major component of RIS beam control and present a sparse Bayesian super-resolution estimation approach to achieve a good balance of accuracy and computing complexity. Our numerical results justify that, with our proposed models and algorithms, RIS will become a cost-effective and spectral-efficient solution for SAGIN-based 6G networks.

Voiceprint recognition technology is a high-quality identification technology, which is currently widely used in all walks of life and brings great convenience to our lives. In order to better promote the actual application range and effects of voiceprint recognition technology, the research and realization of its technology has extremely far-reaching significance. This paper mainly analyzes the basic process of voiceprint recognition, technical indicators, fac-tors that affect the effect of voiceprint recognition, and the current research status of voiceprint recognition technology. In addition, it analyzes the ad-vantages of the current mature deep learning technology, as well as the main problems existing in the current voiceprint recognition research work, and gives directions for the next step in the effective combination of deep learning technology for voiceprint recognition research work.

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

With the emergence of industrial Internet of Things (IIoT), intensive computation workload will be imposed to industrial end units (IEUs). By leveraging mobile edge computing (MEC), the local computational tasks can be offloaded to servers deployed in mobile edge networks with low latency. This article proposes the intelligent cost-and-energy-effective task offloading in the 5G and Wi-Fi 6 coexisting heterogeneous IIoT networks. The novel joint task scheduling and resource allocation approach comprises the following two parts: a Lyapunov optimization-based component to decide local task scheduling and computing power and an online multiagent reinforcement learning component together with a game theory-based algorithm to select offloading link and decide transmit power, respectively. Simulation results demonstrate the proposed approach holds obvious advantage over the compared “intuition” and “cost optimal” approaches in the efficiency of making comprehensive decision that improves energy efficiency and cost while controlling task delay in the multi-IEU and multiaccess-node MEC systems.

In order to realize the reliable and safe operation of the smart railways, and provide high quality information transmission service for passengers, the railway system needs to develop innovative communication network and advanced communication technology to meet the gradually increasing service demand of multi-dimensional comprehensive information resources. The Space-Air-Ground Integrated Network (SAGIN) can provide seamless information services for land, sea, air and space users, and is an effective solution to the challenge posed by the future smart railways to the all-time, all-domain, all-air, high-reliability and high-throughput communication. This paper aims to comprehensively discuss the technical development and application examples of High-Speed Railways (HSRs) based onSAGIN. Firstly, we analysis the development of theSAGINand the mobile communication network of theHSRs, and comprehensively discuss the single network architecture of the space-based, air-based and ground-based networks, as well as the integrated network, and discuss the application scenario and network structure of the combination of the integrated networks. At the same time, the communication services, existing problems and key technologies of the space-based, air-based and ground-based networks are discussed, and the application trend of theSAGINinHSRsis presented. Furthermore, the application scenarios of Artificial Intelligence (AI) technologies in solving the efficient resource utilization of smart railways communication and theSAGINare studied. Based on these technologies, we point out the research direction for the future development of AI technologies inSAGINinHSRscommunications.

Energy consumption is becoming a key issue in the research of future network. In practice, network traffic has a periodic time distribution that occurs most often at a low level. This feature provides the possibility of achieving network energy savings through topology switching. By considering the deficiencies in existing studies, such as the low adaptability between network working topology and traffic load, the abnormal topology switching caused by abnormal and unbalanced traffic, and the low reliability of energy-saving topology, this paper proposes an energy-efficient routing method for software-defined networks based on topology switching and reliability. The method involves two parts: a topology-switching method and a failure recovery method. The former adapts the network working topology to the network traffic demands through dynamic topology switching to decrease the network energy consumption. The latter adopts an active strategy for fast fault recovery to ensure network reliability in the energy-efficient topology. Two network typologies and their traffic data are used to experimentally verify the method. The results show that, compared with the static topology switching method TLS, the energy saving of the proposed method can be improved at most 2.07 times and 4.63 times in two typical typologies, respectively, while ensuring network reliability.

The minimum mean square error (MMSE) algorithm is known to be a near-optimal to uplink the large-scale multiple-input-multiple-output (MIMO) systems, however, there are some complexities involved in the procedure in relation to the matrix inversion. This paper proposes that long recurrence enlarged conjugate gradient (LRE-CG) method could be exploited for iteratively realizing the MMSE algorithm while avoiding the matrix inversion complexities. It has been verified by the simulation results that the proposed method outperforms the conventional methods in the literature including the Neumann series approximation-based approach and Gauss-Siedel iterative (GS) methods. The proposed algorithm succeeds to attain near-optimal performance of a typical MMSE algorithm with minimal level of iterations.

In the past few years, the traditional terrestrial wireless communication has experienced explosive growth in the number of users and the services it supports, in order to meet the growing demand of various services. However, due to the limitation of network capacity and coverage, only relying on the ground communication system cannot provide wireless access services anywhere on the earth. It is necessary to develop a new network architecture to adapt to the communication situation in different scenarios. The advantage of Space-Air-Ground integrated network (SAGIN) is that it not only has the characteristics of large satellite coverage on the earth, but also has the characteristics of small ground network propagation delay. This paper combines Space-Air-Ground integrated network with the internet of things, proposes a new network structure, and describes the characteristics and applications of this network structure in detail.

The uncontrollability of the radio frequency (RF) environment is one of the main obstacles hindering the popularization of ambient backscatter communication devices, because the devices need strong signal to maintain the overhead of backscatter circuits and the reflection of the modulated signals. The intelligent reflector (IRS) can improve the radio frequency environment by adjusting the phase and amplitude of the incident signal, which provides the possibility for the widespread deployment of ambient backscatter communication devices. In this paper, we introduce a novel IRS-assisted ambient backscatter communications system (ABCS), in which the signal of ABCS rides on the signal of the primary system. The two systems share the same receiver, and the signals of the two systems can be demodulated separately based on continuous interference cancellation (SIC) technology. The purpose of this paper is to design the beamforming vector and IRS phase shift jointly to minimize the AP's transmit power while ensuring the quality of service of the ABCS and the primary communication system. Due to the non-convex nature of the problem, the time complexity of solving the problem through exhaustive search will be very high. Therefore, we propose an iterative-based beamforming vector and IRS phase shift joint design method to minimize the AP transmit power. This method can effectively reduce the transmission power of the access point, and the simulation results prove the effectiveness of the method.

The Industrial Internet of Things (IIoT) has attracted tremendous interest from both industry and academia as it can significantly improve production efficiency and system intelligence. However, with the explosive growth of various types of user equipment (UE) and data flow, IIoT experiences spectrum resource scarcity for wireless applications. In this article, we propose a solution for spectrum resource management for the IIoT network, with the objective of facilitating the limited spectrum sharing between different kinds of UEs. To overcome the challenges of unknown dynamic IIoT environments, a modified deep $Q$ -learning network (MDQN) is developed. Considering the cost effectiveness of IIoT devices, the base station (BS) acts as a single agent and centrally manages the spectrum resources, which can be executed without coordination or exchange between UEs. In this article, we first built a realistic IIoT model and design a simple medium access control (MAC) frame structure to facilitate the environment state observation. Then, a new reward function is designed to drive the learning process, which takes into account the different communication requirements of various types of UEs. In addition, to improve the learning efficiency, we compress the action space and propose a priority experience replay strategy based on decreasing temporal difference (TD) error. Finally, simulation results show that the proposed algorithm can successfully achieve dynamic spectrum resource management in the IIoT network. Compared with other algorithms, it can achieve superior network performance with a faster convergence rate.

In the network function virtualization (NFV) environment, network services are usually provided in the form of service function chains (SFCs), which defines the link order of virtual network functions required in service requests and are mapped to the physical network. Although NFV facilitates the flexible provision of network services, service interruptions may occur as a result of software and hardware failures. Current solutions mostly use the backup method to ensure the reliability of SFCs. However, these methods ignore the SFC construction phase that has an impact on reliability. Besides, the resource efficiency still requires improvement. To address these issues, reliability-oriented SFC construction and backup problems are investigated in this work. First, an instance-sharing and reliable construction algorithm (ISRCA) is proposed to aggregate multiple SFCs into a service function graph (SFG), and perform reliability screening for the SFG set. After mapping the SFG to the physical network, a node-ranking algorithm with centrality and reliability (NRCR) is proposed for backup node selection and backup instance deployment to improve the reliability of SFCs that have not met the requirements. Experimental results demonstrate that under the premise of ensuring reliability, the proposed backup method can reduce the consumption of bandwidth resources by about 11.7%, when combined with the proposed construction method, it can further reduce the backup resources by 13.9%.

To achieve efficient and reliable data transmission in a communication system in time varying conditions, a downlink non-orthogonal multiple access system based on rateless codes (NOMA-RC) is proposed in this paper. The NOMA-RC system can continuously send superimposed signals to the users according to the decoding results within a limited time. After a user decodes the signals successfully, it will send an acknowledgement to the transmitter. Then the system may adjust the message to be transmitted to improve the decoding probability for the remaining users. The performance of the NOMA-RC system with delay constrains is analyzed over Nakagami-m fading channels. The simulation results show that the NOMA-RC system is capable of reducing the transmission time and improving system efficiency compared to orthogonal multiple access system based on rateless codes.

Mobility load balancing (MLB) in small cell networks transfers the load from an overloaded small cell to under-loaded neighbouring small cells by adjusting the mobility parameters. Consequently, a greater number of handovers is required. This is one of the costs for the MLB operation that may affect the network performance severely. Furthermore, frequent handovers may occur if the handed over user equipment (UE) is moving fast across the small cell network. The traditional MLB considers only the edge-UEs (Edge-UEs) for the offloading process. In this paper, we presented a Utility-based Mobility Load Balancing algorithm with Handover minimization (UMLB-HO) by considering not only the edge-UEs but also the non-edge-UEs for overloaded small cells. The overloaded cells determine the fast-moving non-edge-UEs and transfer them to the underloaded Macrocell. Moreover, the small cells determine the fast-, slow or very slow-moving edge-UEs during the MLB and transfer them to either under loaded neighbour small cell or Macrocell. Four terms are defined to determine whether the UE is fast-, slow or very slow-moving to determine the best handover decisions for each UE. Simulation results show that the proposed UMLB-HO algorithm has the minimum number of handovers for a minimum standard deviation with an enhanced level of throughput.

In this paper, the performance of a mobile receiver in an Internet-of-Things (IoT) network is analyzed. In particular, a multi-antenna base-station sends information signals to a mobile receiver. The receiver employs the maximal-ratio transmission technique to improve the quality of the received signal. The mobility of the receiver is characterized by the well-known random waypoint mobility model. The wireless fading channels between the base-station and the receiver is described by the composite F fading model. Closed-form analytical expressions for several important metrics such as outage probability, average bit error rate, and average channel capacity are derived. The analytical expressions are validate by numerical and Monte-Carlo simulations.

Massive Multiple-Input Multiple-Output (m-MIMO) is a promising technology for improving the capacity of fifth generation (5G) wireless networks. However, m-MiMo suffers from the cost and complexity of the radio frequency (RF) chain. One solution to solve this problem is the method of antenna selection. However, this method requires information on the channel state (CSI) in order to select the most efficient subset, which is impossible in the presence of the pilot contamination. In addition, the exhaustive search method, used in conventional MiMo for selecting a subset of antennas, is ineffective for the massive MIMO system because it adds complexity to the processing and requires high energy consumption. For this purpose, a model using a water-filling algorithm and Low-Density Parity-Check (LDPC) decoded symbols is proposed in this article. This method exploits the characteristic of the Physical Layer to address the problem. It does not require supplementary chain, and it does not use pilot symbols to estimate the channel, which makes this proposed model frugal in term of energy consumption and processing resources comparing to the exhaustive method. Simulation results show that the proposed solution attains the same optimal values when the Exhaustive search method is used.

The unmanned aerial vehicle (UAV) cellular communications is a promising technique to boost coverage and realize intelligent applications. The communication power consumption is a major challenge faced by the UAV cellular networks. In this work, we consider a single UAV user equipment (UE) connected with a terrestrial base station (BS), and focus on the uplink (UL) performance. Through theoretical analysis, we find an optimal power control factor in terms of the communication energy efficiency, when the distance between the UAV and the BS is small.

Wireless Sensor Networks (WSNs) are attracted great attention in the past decade due to the unlimited number of applications they support. However, security has always been a serious concern for these networks due to the insecure communication links they exploit. In order to mitigate the possible security threats, sophisticated key management schemes must be employed to ensure the generating, distributing and revocation of the cryptographic keys that are needed to implement variety of security measures. In this paper, we propose a novel decentralized key management scheme for hierarchical grid organized WSNs. The main goal of our scheme is to reduce the total number of cryptographic keys stored in sensor nodes while maintaining the desired network connectivity. The performance analysis shows the efficiency of the proposed protocol in terms of communication overhead, storage cost and network connectivity.

Technological advances have enabled the concept of the Internet of things to be put into practice in many fields from industry to agriculture. A large number of devices connected to the Internet of things pose a huge challenge to data processing and transmission. Terahertz band is a promising solution because of its huge spectrum resource and high transmission rate, but its high space loss limits the communication distance. In this paper, a MIMO channel model for terahertz communication is proposed, which can increase the communication distance by beam shaping and improve the communication capacity by spatial multiplexing. According to the results of simulation analysis, MIMO channel precoding technology can improve the signal-to-noise ratio of communication system.

In this paper, a cumulative distribution function (CDF)-based scheduling scheme for uplink non-orthogonal multiple access (NOMA) network is investigated. With considering imperfect successive interference cancellation (SIC) and SIC power constraint, closed-form expressions for the outage probability of two scheduled users are derived in cognitive-radio-inspired power allocation (CPA) scenario. To get more insights, high SNR approximations of the outage probabilities are given, and the results reveal that the two users can achieve a diversity order linear with the number of users. Simulation results validate the accuracy of the analytical expressions.

In this paper, we address the problem of placing the unmanned aerial vehicles (UAVs) in an area covered by a congested or damaged base station (BS). We aim to cover the affected users while maximizing the operator's profit. We proposed to adopt the k-means method associated with the pattern search technique to find the optimal UAVs 3D locations. We show in the simulations, that our approach achieves higher profit and a higher load balancing index among UAVs.

Internet of Things (IoT) has attracted tremendous interest since it can improve production efficiency and system intelligence significantly. However, with the explosive growth of various types of device and data flow, IoT suffers spectrum resource scarcity for wireless applications. In this paper, we propose a solution for spectrum resource sharing in the IoT network, with the objective to facilitate the limited spectrum sharing between different kinds of sensors. To overcome the challenges of unknown dynamic IoT environment, the deep Q-learning network (DQN) is adopted. BS acts as the single agent and centrally manages all spectrum resources. First, a new reward function is designed to drive the learning process, which takes into account the different communication requirements of various sensors. In addition, to improve the learning efficiency of DQN, we compress the action space. Finally, simulation results show that compared with other algorithms, the proposed algorithm can achieve good network performance.

This paper proposes a new approach, Cooperator Resource Allocation Algorithm (CoopeRA), to improve the radio resources allocation scheme and reduce the impact of congestion in LTE Heterogeneous network (HetNets) and 5G networks by using Device-to-Device (D2D) communications and by borrowing some adjacent Macro-cell resources from the neighboring nodes that are belonging to the same WLAN network but not to same Macro-cell. Our solution is based on cluster construction and D2D transmissions in Local Area Networks (LAN) to relay the traffic of users belonging to congested LTE cells. Our new algorithm increases the number of new calls in the congested Macro-cell, even if the resources are limited. The simulation validates this study when the results shows the performance of our mechanism.

The emergence of various applications is driving the continuous development of 5G mobile networks as infrastructure. To better support real-time wireless services, the low-latency communication is the current research focus. However, the existing radio resource scheduling methods cannot guarantee the strict delay constraint of the low-latency communication. Therefore, a reinforcement learning (RL) approach of radio spectrum resource scheduling strategy is proposed, which can guarantee the low-latency constraint when spectrum resources are insufficient. The Q-learning algorithm is used to approximate the optimal goal of RL. To speed up the learning, the deep neural network (DNN) is used to train the learning parameters. Simulation results show that the strategy converges quickly and has satisfactory results for mobile networks with a high load of spectrum resources.

Network slicing is a widely discussed technology for satisfying the diverse requirements in 5G and beyond scenarios. It enables the operators to create end-to-end virtual slices in network infrastructures. The flexible assignation of multi-dimensional slice resources including radio and spectral resources is essential for 5G communication networks. However, there is a lack of effective solutions to reconfigure the spectral and the radio resources in 5G radio access network (RAN) and core network. In this paper, we realize orchestration functionalities by exploiting a novel two-step slice reconfiguration strategy. Firstly, a slice-monitoring model is proposed to map slices to vectors by representation learning. Secondly, a slice reconfiguration algorithm is introduced to realize flexible slice reconfiguration. The slice reconfiguration strategy can decide when to trigger the reconfiguration strategy and how to reconfigure the slice resources. As for network performance, simulation results show our reconfiguration strategy can further improve slice resource utilization rate by 31% as well as reduce the blocking rate by 40% in 5G RAN and core network.

The Internet of Things (IoT) will significantly change both industrial manufacturing and our daily lives. Data collection and 3-D positioning of IoT devices are two indispensable services of such networks. However, in conventional networks, only terrestrial base stations (BSs) are used to provide these two services. On the one hand, this leads to high energy consumption for devices transmitting at cell edges. On the other hand, terrestrial BSs are relatively close in height, resulting in poor performance of device positioning in elevation. Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be a promising technology to overcome the above shortcomings. In this article, we propose a novel UAV-assisted IoT network, in which a low-altitude UAV platform is employed as both a mobile data collector and an aerial anchor node to assist terrestrial BSs in data collection and device positioning. We aim to minimize the maximum energy consumption of all devices by jointly optimizing the UAV trajectory and devices' transmission schedule over time, while ensuring the reliability of data collection and required 3-D positioning performance. This formulation is a mixed-integer nonconvex optimization problem, and an efficient differential evolution (DE)-based method is proposed for solving it. Numerical results demonstrate that the proposed network and the optimization method achieve significant performance gains in both energy-efficient data collection and 3-D device positioning, as compared with a conventional terrestrial IoT network.

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this study, we have discussed different paradigms for data processing and storage including Cloud, Fog, and Edge computing models and their suitability in integrating with the IoT. Moreover, a detailed discussion on low latency and massive connectivity requirements of future cellular networks in accordance with machine-type communication (MTC) is also presented. Furthermore, the need to bring IoT devices to Internet connectivity and a standardized protocol stack to regulate the data transmission between these devices is also addressed, while keeping in view the resource-constraint nature of IoT devices.

In the future Fifth-Generation networks, the eavesdropping is a critical threat due to their broadcast-based transmission. This problem can be addressed with the cryptographic protocols. However, this method is complex and difficult because of the dynamic topology of wireless networks, which does not allow an efficient management of security keys. As a complement solution, Physical-layer security (PLS) is integrated to enhance secrecy in wireless networks. The PLS exploits the schemes features of this layer, namely the modulation, Massive Multi-Input Multi-Output(m-MiMo) and channel coding. The fountain code is one of those systems where the secrecy is provided when the destination retrieves packets encoded before the intruder. Nevertheless, the secrecy can not be guaranteed when eavesdropper uses large number of the antennas as in the m-MiMo. The feature of m-MiMo should be considered to secure main channel with fountain codes. Therefore, we propose to use Raptor code which is a class of fountain code, aided by an Artificial noise (AN) and the punctuated data to reduce the efficient of intruder channel. This allows the main channel to retrieve the signal before eavesdropper. The numerical results show that using Raptor code in massive MiMo enhances the reliability and the security on the channel of legitimate user, while minimizes the abilities of intruders to spy on data.

In 5G communications, small cells are one of the main approaches to achieve data diversion and improve network capacity. The problem of blind area is partially solved by this way, because the distances between small base stations and users are cut short. However, the intensive deployment of small base stations will bring about complex disturbance and a large amount of energy consumption. To overcome this challenge, we propose a new approach of dual-antenna passive repeater, which consists of a four-element patch antenna array, a feeding network and an improved planar Yagi-Uda antenna with added parasitic patches. It can be used in cooperation with small base stations to replace the function of the small base stations in a certain point, change the beam pointing, and achieve wide-angle scattering to realize the blind area signal coverage. The genetic algorithm which is a branch of machine learning is used to optimize the antenna parameters. Simulation results show that our proposed passive repeater can effectively reduce the path loss and improve the signal power of the receiving end.

With the rapid evolution of smart city all over the world, the appealing services of IoT and big data analytics have prompted the design of more reliable assurance mechanism for network quality. It has been a crucial issue of network operation that once multiple links fail simultaneously, the transmission of real-time services cannot be guaranteed. Therefore, rapid locating of faults is the premise for network to recover quickly. However, current faults location methods can't satisfy the requirement due to the expansion scale of wireless and optical networks and the growing demands of customers. In this paper, we propose an efficient multi-link faults location algorithm based on Hopfield Neural Network (HNN). We make full use of the information of network topology and the services transmitted to model the relationship between fault set and alarm set. HNN is used as an optimization method to analyze the uncertainty of faults and alarms and to find where the faults most likely occur by constructing a proper energy function. It has been proved by experiments that this method can achieve real-time faults location while ensuring positioning accuracy, which provides a good solution for smart city service assurance.

Nowadays, IoT video applications are in a sharp rise, various real-time video streaming of video surveillance systems transmitted via Internet are widely investigated. The real-time video surveillance can actively monitor and detect the abnormal events in time. In 5G HetNets, we specifically develop a compressed sensing based linear predictor to predict the traffic load at the next moment. The results justify that our proposed method can forecast the traffic load and improve system performance.

Ambient backscatter employs existing radio frequency (RF) signals in the environment to support sustainable and independent communications, thereby providing a new set of applications that promote the Internet of Things (IoT). However, nondirectional forms of communication are prone to information leakage. In order to ensure the security of the IoT communication system, in this paper, we propose a machine learning based antenna design scheme, which achieves directional communication from the relay tag to the receiving reader by combining patch antenna with log-periodic dual-dipole antenna (LPDA). A multiobjective genetic algorithm optimizes the antenna side lobe, gain, standing wave ratio, and return loss, with a goal of limiting the number of large side lobes and reduce the side lobe level (SLL). The simulation results demonstrate that our proposed antenna design is well suited for practical applications in physical layer security communication, where signal-to-noise ratio of the wiretap channel is reduced, communication quality of the main channel is ensured, and information leakage is prevented.

In the next generation satellite networks,millimeter wave (mmWave) communications will play a critical role, as there exists a lot more open bandwidth in high frequencies. The rapid growth of mmWave systems poses a variety of challenges in physical layer(PHY) security. This paper investigates those challenges in the context of several mmWave satellite communication technologies, including multiple-input multiple-output(MIMO), non-orthogonal multiple access (NOMA), and so on. In particular we introduce a ray tracing (RT) based mmWave satellite communication channel model, and reveal that the secrecy capacity in mmWave band widely depends on the richness of radio frequency (RF) environment through numerical experiments.

In this work, the authors study the performance of decode‐and‐forward cognitive multi‐hop networks in an underlay spectrum sharing strategy for Nakagami‐ m fading channels with additive white generalised Gaussian noise. To this end, new exact analytical expressions for the average bit error rate of M ‐ary quadrature amplitude modulation ( M ‐QAM) and M ‐ary phase shift keying are derived and evaluated. In addition, an exact expression for the average symbol error rate of M ‐QAM is provided. Moreover, lower‐ and upper‐bound expressions for the ergodic capacity are obtained. Different scenarios are presented to study the influence of various key system parameters, such as fading severity, noise shaping parameter, and interference temperature, on the system performance. The obtained analytical results are supported with Monte Carlo simulations to confirm the accuracy of the analytical derivations.

This work investigates multicast services, which play a critical role in 5G new radio (NR). In particular, we propose a hybrid unicast/multicast MIMO precoding based on NOMA. Our scheme first categorizes unicast users into NOMA and non‐NOMA types and then utilizes null space and successive interference cancellation to eliminate the signal leakage. To justify the effectiveness and efficiency of our design, we also present simulation results in typical massive MIMO scenarios.

This paper investigates the application of 5G wireless multicast to the big data system. In particular, we propose a hybrid unicast/multicast transmission scheme using 3D massive multi-input multi-output (MIMO) technique. The proposed scheme employs multicast beamforming in the multicast group and multi-user MIMO (MU-MIMO) linear precoding in the unicast group. It also applies the null space based interference cancellation to eliminate the signal leakage generated by other groups. Simulation results show that the proposed system can significantly improve the system throughput with an antenna array configuration, precoding/beamforming schemes and user grouping strategies.

In this paper, we analyze the performance of decode-and-forward (DF) dual-hop relaying networks with wireless energy harvesting at the relay node over the generalized η-μ and κ-μ fading channels. In the analysis, the source and destination nodes are equipped with multiple antennas, whereas the relay node is equipped with a single antenna. Exact analytical expression for the system outage probability (OP) is derived and evaluated. The derived OP expression considers two practical energy harvesting techniques, namely, time switching-based relaying (TSR) and power splitting-based relaying (PSR). Some numerical examples are provided to study the overall system performance under different system parameters, such as fading parameters, energy harvesting time, power splitting factor, and signal-to-noise ratio (SNR). Our numerical results are supported with Monte-Carlo simulations to validate the accuracy of our analysis.

To adapt with the frequent link failures in mobile ad-hoc networks, the routing protocols shall catch more than one route, at least two routes. In case of two routes, the first route can be used as the main route; while the other one can be used as a standby route used upon the detections of link failures. If this is the case, the packet losses are virtually decreased to zero, and seamless multimedia communications can be supported over mobile ad-hoc networks. To ensure the existence of two routes between any randomly chosen two nodes, the node density in terms of the number of neighbors must be above a certain threshold. In this paper, we propose a mathematical model to find such node density, and propose some solutions in case the node density is below the required density in order to increase it to the required node density. Our results show that the probability to have two routes rapidly increases with node density till it reaches the saturation region, where the increase of node density has negligible improvement in terms of network availability. This paper shows that the network nodes must have at least 16 neighbors in order to ensure the existence of two routes between any randomly chosen source and destination pair.

This paper concentrates on the multicast service in the next generation (5G) communications system, and develops an opportunistic massive MIMO-NOMA scheme. The proposed scheme superimposes the signal of Non-orthogonal Multiple Access (NOMA) users on the multicast signal, and employs successive interference cancellation (SIC) to separate the signal of NOMA users from the multicast signal. This scheme also applies null space based interference cancellation to eliminate the signal leakage generated by other groups. And multi-user MIMO (MU-MIMO) linear precoding is employed in the unicast group. Numerical results revealed that opportunistic massive MIMO-NOMA system significantly satisfies the requirement of high peak spectral efficiency in typical massive MIMO scenarios.

Mobile innovative services are being introduced continuously to enhance people's life through facilitating the interaction between human and the rest of the world. The world of “Internet of Things” (IoT) is expanding everyday to include more things to be connected. Considering that more and more innovative mobile services are being introduced, the long-term evolution (LTE) telecom systems/networks will be more complicated, require more resources, and demand more challenging requirements. The LTE evolved packet core (EPC) network internal design is inadequate with regard to the resources reservation techniques used to carry out the guaranteed dedicated services. In fact, EPC does not have the capabilities to utilize properly the unused bandwidth of the guaranteed bearer when the reserved bandwidth is not fully used by the mobile service, the unused guaranteed bandwidth is considered as wasted resources and consequently the whole LTE/EPC network efficiency gets affected. In this paper, we propose an adaptive technique which enhances the resource reservation for the LTE Mobile guaranteed services, our solution provides techniques to: analyze the ongoing mobile guaranteed traffic usage, provide time-series models that mathematically represent the conducted data, forecast the mobile service guaranteed resource consumption, identify the wasted/unused resources, and utilize these resources by other services. Our experiments were conducted on a dataset captured on an LTE network, the experimental results show that our approach is feasible and beneficial as it enhances the resource allocation for the LTE mobile services and increases the overall throughput of the LTE/EPC networks.

Machine-type communications (MTC) also known as machine-to-machine (M2M) communications over long-term evolution-advanced (LTE-A) networks with ubiquitous coverage and global Internet working is expected to constitute a significant part of the Internet of Things (IoT) paradigm. However, there are paramount challenges and active research activities to accommodate M2M communications over LTE-A network without sacrificing the quality of human-based services. Among them the performance of M2M communications in LTE advanced relay network has not been fully investigated yet. In particular, LTE/LTE-A uses turbo codes as the forward error correction (FEC) mechanism in the physical layer. Since turbo codes are fixed rate codes, an automatic repeat request (ARQ) is sent to the sender when the receiver fails to recover complete packet that results in packet failure and waste of radio resources. In this paper, to combat the detrimental effects of wireless fading channel, we propose a set of joint network and fountain coding (JNFC) schemes for reliable uplink transmission between M2M devices and evolved NodeB (eNB). Specially, we consider clustered heterogeneous M2M networks, where cluster heads (CHs) transmit to eNB directly or by the help of relays. Numerical results are presented to demonstrate the performance of the proposed schemes over type I and type II relay-enhanced LTE-A networks. It shows that in type I relay enhanced networks, JNFC with space time block codes (JNFC-STBC) outperforms the direct transmission with relays (DTRs) and in type II enhanced networks, JNFC with robust soliton distribution (JNFC-RSD) outperforms JNFC with random linear network coding (JNFC-RLNC) in a variety of metrics.

This article deals with the Synchronous Optical NETwork (SONET) and Resilient Packet Ring (RPR). The two protocols used the same layer (OSI Model) and often are used separately. In our paper, we will demonstrate that SONET and RPR could be integrated together; we will explain and detail the use of SONET over RPR (SONEToRPR) with a simulation that shows the possibility to create a New Large Layer 2 Network and to point the benefits of it with a better performance.

Cellular relaying architecture is in an early stage for development and deployment. A restricted number of deployment scenarios are addressed in the LTE-A relay standard, though different design options in alternative deployment scenarios do potentially exist. Presently, the LTE-A relay standard is restricted to a single-hop relaying. The principal reason for opting for this architecture is to minimize system complexity. Nonetheless, multi-hop relay architecture could potentially provide greater capacity and coverage, in the future, particularly for urban sparse area deployment scenarios. However, many problems involving the complexity of multi-hop relaying paradigm need to be resolved, specifically resource management. In this paper, we focus on the resource management problem, and we propose a new resource allocation framework to overcome the additional challenges introduced by the multi-hop relay stations. Numerical results are presented to demonstrate the validity of the proposed framework.

This paper develops a novel scheme of game theory based spectrum sharing to mitigate the inter-cell interference in LTE-A heterogeneous networks (HetNets). In particular, we first assume that the macro-cell protects itself by pricing the bandwidth allocated to small-cells. We then employ the Stackelberg game to jointly consider the utility maximization of macro-cell and small-cells. During game formulation, our scheme takes the advantage of LTE-A standards, such as almost blank sub-frames (ABS) for enhanced inter-cell interference coordination (eICIC) and cell range expansion (CRE) for small cell. Simulation results justify that our approach can significantly improve the throughput of multi-tier cellular network as well as leverage the spectral efficiency of the macro-users in the vicinity of small-cells.

The next generation cellular networks target significant capacity improvements and require dense frequency reuse of the scarce radio spectrum, resulting in a danger of severe interference. Fractional frequency reuse (FFR) techniques have been proposed for macro-cell networks to mitigate interference. However, these static schemes are not adaptive to meet the traffic demand changes from mobile users, and thus inefficient with respect to power consumption in many scenarios. This paper starts with the basic soft fractional frequency reuse (SFFR) framework and proposes a traffic demand orientated green power control scheme that can make power allocation by considering different traffic demands. Afterwards, this green concept is extended to heterogeneous network (HetNet) for the power control strategies in low-power nodes (LPNs). Numerical results show that our proposed schemes can achieve considerable improvement in terms of throughput and power consumption, as well as extra rewards of high flexibility and low complexity.

To overcome the challenge of large data demanding in future 5G cellular networks, heterogeneous networks (HetNets) take advantage of low power nodes (LPNs) to enhance capacity and coverage. This paper aims at 5G HetNets and presents a novel scheme of adaptive self-organization network (SON) by integrating cognitive radio (CR) with inter-cell interference coordination (ICIC). Particularly, we combine the spectrum sensing function from CR and the radio resource layering function from ICIC. Our work addresses the issues of smart low-power node (SLPN) development, which associates appropriate sectorization with radio resource allocation during the self-organization process. We further develop a Hungarian algorithm based self-organization strategy to improve the SLPN adaptive optimization. Simulation results show that our proposed scheme can achieve considerable gain in terms of throughput and coverage, with extra rewards of high flexibility and low complexity in HetNet SON.

The performance of dual-hop wireless communication systems with semi-blind amplify-and-forward (AF) relays over independent, but not necessarily identically distributed, η-μ fading channels is investigated. This performance is studied based on the evaluation of the outage probability, average bit-error probability (ABEP), and average end-to-end received signal-to-noise ratio (SNR). To this end, novel expressions for the outage probability and average end-to-end received SNR are derived. Moreover, an expression for end-to-end SNR of the probability density function (PDF) is derived. Based on the derived PDF, we then obtain a novel expression for the generalized moments of the received SNR. We conduct extensive simulation in order to study the effect of the fading parameters (i.e., η and μ) on the system performance. Our findings show that, in contrast to the fading parameter η, μ has a significant impact on the system performance. In addition, simulation results demonstrate that, the derived expressions match very well with the numerical simulations.

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

This paper addresses the problem of video multicast for heterogeneous destinations in mobile ad hoc networks. Multiple Description Coding (MDC) is used for video coding. MDC generates multiple independent bit-streams, where the multiple bit-streams are referred to as multiple descriptions (MD). Furthermore, MDC enables a useful reproduction of the video when any description is correctly received. Specifically, we propose Sequential MDMTR (Multiple Disjoint Multicast Trees Routing) protocol for video multicast. It builds multiple disjoint multicast trees and assigns MD video in a centralized fashion. Sequential MDMTR protocol aims at increasing the number of assigned MD video to each destination. We extensively evaluate our proposed protocol by simulations and show that it outperforms the existing work.

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

In this paper, we investigate how Multiple Description Coding (MDC) can improve the user's satisfaction for a group of heterogeneous destinations for video multicast over wireless ad hoc networks. Specifically, we explore the independent-description property of MDC with multiple paths to improve the user's satisfaction. In our approach, the assignment of multiple description (MD) video is randomly done by a multicast source node. Based on the assignment of MD video, the multicast source constructs multiple multicast trees accordingly. Simulation results demonstrate that our approach can achieve higher user's satisfaction regardless of the multicast group size.

In this paper, we address the issue of video multicast for heterogeneous destinations in mobile ad hoc networks (MANETs). By exploiting multiple node-disjoint trees and multiple description coding (MDC), we propose an efficient video multicast routing protocol for heterogeneous destinations (EVMR-HD). EVMR-HD deploys, for the first time, the independent-description property of MDC to increase number of video descriptions requested by each destination and therefore improve the quality of the received video. Our results show that EVMR-HD outperforms Serial MDTMR in terms of several metrics.

Recent technological advances in code division multiple access (CDMA) with multiuser detection (MUD) allow to consider this technology for future wireless Ad Hoc networks. Due to the fundamentally different physical layer architecture, application of MUD in Ad Hoc networks requires novel approaches for medium access control (MAC) and scheduling mechanisms in order to take advantage of the new features. This paper proposes a new MAC and scheduling paradigm which addresses three design issues: distributed dynamic code assignment that avoids code collision, distributed scheduling scheme that provides fairness among contending nodes, and organization of the data transmission based on multiuser detection. Simulation model is used to verify the performance gains from the increased spectrum reuse due to multiuser reception and from the reduced interference due to multiuser detection. This is done by comparisons with existing MAC paradigms, such as CSMA/CA and parallel CDMA.

In wireless mesh network (WMN), it is important to provide an efficient handoff scheme, due to the frequent user mobility. To address this issue, we propose a mobile agent (MA) based handoff approach, where each mesh client has a MA residing on its registered mesh router. To guarantee quality of service (QoS) and achieve differentiated priorities during the handoff, we develop a proportional threshold structured optimal effective bandwidth policy for call admission control (CAC) on the mesh router. Simulation study shows that our proposed CAC scheme can obtain satisfying tradeoff between differentiated priorities and statistical effective bandwidth in WMN handoff environment.

This paper presents a novel approach for the construction of Multiple Shortest Path Trees (MSPT) using multiple description (MD) video coding to satisfy the destination requirements (i.e., each destination has a preference number, which depends on its capacity, of video layers). Specifically, the independent-descriptions property of multiple description (MD) video coding along with multiple trees are exploited to increase user satisfaction. This approach is suitable for heterogeneous wireless ad hoc networks. Simulation results demonstrate that our proposed scheme (MSPT-MDC) outperforms MSPT-LC (multiple trees with layered coding) in terms of user satisfaction. In addition, simulation results show that user satisfaction, of the proposed scheme, is insensitive to the change of the number of destinations.

In this paper, we address the issue of multilayered multicast routing in wireless ad hoc networks (WAHNs). Existing multilayered multicast protocols assume homogeneous ad hoc wireless networks; in which all nodes are of the same type (they have the same processing, data rate and communication capabilities and characteristics). A more realistic assumption is a heterogeneous network; in which nodes have different processing, communication capabilities and other characteristics. In this paper, we assume heterogeneous network; in which nodes have different capabilities. Two multilayered multicast routing algorithms are proposed, namely, multiple trees based on shortest path tree (MSPT) and multiple trees based on Steiner minimum tree (MSMT). We assume that each destination has a preference number of video layers; which is equal to its capacity. Moreover, we do not consider only the capacities of nodes in the network but also the bandwidth of each link. Simulations show that the proposed schemes greatly improve the QoS requirements (improve user satisfaction ratio (USR)) for a set of destinations. In addition, simulations show that multiple trees schemes achieve substantially higher satisfaction ratio than the single tree scheme.

Recent technological developments in code division multiple access (CDMA) with multiuser detection (MUD) make multiple packets reception a more appropriate model for the physical layer of future wireless networks. To take advantage of the new features, a shift of responsibility from transmitters to

With the migration of real-time applications such as voice onto IP-based platforms, the existing IP network capabilities become inadequate to provide the quality-of- service (QoS) levels that the end-users are accustomed to. While new protocols such as DiffServ and MPLS allow some amount of traffic prioritization, guaranteed QoS requires admission control and dynamic resource management. Over-reservation has the advantage of infrequent variations but leads to wastage of resources. Under- reservation, on the other hand, does not meet the QoS expectations of the user flows. In this paper, we consider the architecture based on SIP (Session Initiation Protocol) over MPLS to provide control admission. In this architecture, we propose a new method to reserve optimally the bandwidth of an LSP (Label Switched Path), avoiding an excess of bandwidth renegotiations on the basis of prediction of future traffic.

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

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

The main concerns of network performance are throughput, end-to-end packet delay and packet loss. A high-performance network is characterized by high throughput, small delay, and low packet loss. In this paper, a theoretical model to analyse the one hop throughput with regular structure operating under the CSMA/CA access protocol in wireless ad hoc networks is proposed. Numerical results show that we can find an optimal transmission range to achieve the highest throughput. An optimal transmission range is essential to limit the energy dissipation on the mobile devices

The rapid growth of multimedia group-based applications motivates research into QoS guaranteed multicast network. In the multicast network carrying traffics of different QoS requirements, bandwidth fragmentation is considered as a bottleneck for traffic admission fairness and efficient usage of bandwidth resource. When the bandwidth fragmentation happens, the chance of accepting high-bandwidth flows is greatly decreased. To prevent bandwidth fragmentation in multicast network, we propose a new approach of bandwidth fragmentation avoided QoS multicast routing in this paper. In our new approach, we integrate active admission control into traditional QoS multicast routing algorithms to gain bandwidth fragmentation immunity. Moreover, an algorithm named dynamic bandwidth allocation with adaptive constraint is proposed and investigated as an example of active admission control algorithm in this paper. Because this algorithm is nonlinear and unsolvable by analytical approach, we employ OPNET simulation to study its performance.

The next generation communication system will provide high quality multimedia service in a more flexible and intelligent manner. In this paper, we propose a new SIP over MPLS network architecture to achieve this goal. To integrate SIP protocol with the traffic engineering (TE) function of MPLS network seamlessly and facilitate SIP call setup, the SIP-MPLS traffic aggregation server (TA server) is highlighted in this new architecture. Furthermore, the call admission and bandwidth renegotiation algorithms running on the TA server are investigated carefully. We rely on OPNET simulation to assess the performance of our network architecture and the capability of different call admission and bandwidth renegotiation algorithms. The simulation results show that the algorithm of TBCA-BP is a suitable candidate for accomplishing the call admission and bandwidth renegotiation task due to its satisfying performance, and our network architecture is able to shorten the SIP call setup delay greatly by using the TA server.

In order to provide high quality multimedia service in a flexible and intelligent manner, future wired and wireless networks will employ SIP as the call control protocol and MPLS as the core network packet switching technology. In this paper, we propose a traffic aggregation based SIP over MPLS network architecture to integrate SIP protocol with the traffic engineering (TE) function of MPLS network seamlessly. Furthermore, the call admission and bandwidth prediction/re-negotiation algorithms running in this new network architecture are investigated intensively. We rely on OPNET simulation to assess the performance of different call admission and bandwidth prediction/re-negotiation algorithms. The simulation results show that the algorithm of FCFS-LMS-BPR is an eligible candidate for achieving the designated goals.

To make the best use of network resources across a QoS guaranteed multicast network, it is urgent to add a traffic engineering mechanism to the existing QoS multicast routing protocols. We propose a new approach of integrating QoS multicast routing with TE-oriented admission control to offer a traffic engineering function. Moreover, a TE-oriented admission control algorithm, namely dynamic bandwidth allocation with adaptive constraint (DBA-AC), is suggested to make the best use of resources across the entire multicast network by distributing traffic over different paths. Because this admission control algorithm is nonlinear and unsolvable by an analytical approach, we employ OPNET simulation to study its performance.

In this paper we propose a new maintenance procedure for dynamic multicasting. With this procedure, we can guarantee that we add/delete a node to the existing tree without the need to rebuild the entire tree. Our approach finds the best route for a new member taking into consideration the time duration of each member in the group. Simulations results show that tree cost is very close to other algorithm that does not consider the dynamic of members in the group

The expected number of retransmissions is a very important parameter to evaluate the multicast performance. It is often used to estimate other performance parameters such as bandwidth consumption and delay. A simple and efficient evaluation to these parameters is helpful for design of multicast protocols. In this work, we will derive a good analytical evaluation to the number of retransmissions. We also obtain the total bandwidth consumption for hierarchical reliable multicast where some special receivers or routers are assigned for retransmission handling of each domain. The optimal partitioning of the tree also depends on topology of networks for each subgroup. The effects of topology on optimal placements are analyzed using bandwidth consumption as criteria for hierarchical reliable multicast.

The paper proposes an alternative for modeling managed resources using Java and telecommunication network management standards. It emphasizes functions related to fault management, namely: diagnostic testing and performance monitoring. Based on Java management extension (JMX/sup TM/), specific extensions are proposed to facilitate diagnostic testing and performance measurements implementation. The new API also called Java fault management extension (JFMX) consists of managed objects that model real resources being tested or monitored and support objects defined for the need of diagnostic testing and performance measurements. The paper discusses four Java implementations of a 3-tier client/server scenario focusing on the "SystemUnderTest" package of the new API to instrument a minimalist managed system scenario. These implementations are respectively built on top of the following Java based communication infrastructures: JMX/JFMX, RMI, CORBA/Java, and Voyager/sup TM/. The paper extends the Voyager implementation with JMX/JFMX and uses their dynamic and advanced features to provide a highly efficient solution. The later implementation also uses the mobile agent paradigm to overcome well-known limitations of the RPC based implementations.

The selective repeat (SR) transport user end-to-end throughput and delay characteristics are analyzed in a hybrid asynchronous transfer mode/time division multiple access (ATM/TDMA) network. The SR transport user is assumed with an underlying cell-level global congestion control in the ATM multiplexer node. The analysis is based on the interaction of packet level control with the queue management at the ATM cell level. The end-to-end transport users are assumed over an M-node virtual circuit (VC) to derive the throughput and delay using Norton equivalent queueing model. The transport service characteristic of the model is obtained from the end-to-end protocol efficiency. The ATM layer is assumed with a leaky bucket (LB), virtual leaky bucket (VLB), modified LB (mLB), or modified VLB (mVLB) congestion control. A global congestion control scheme prioritizes transit traffic over local traffic and ensures the quality of service (QOS) to several classes of traffic. Based on global congestion status, the transport users modulate their end-to-end flow control parameters, i.e., packet size for video and voice users and window size for data users. The probability of cell-loss at the ATM layer is reflected at the transport layer to derive the effective throughput and delay characteristics. The mVLB scheme consistently provided better end-to-end throughput and delay performance for SR transport user.

We pursue a performance analysis for computing the various performance criteria in a hybrid time division/asynchronous transfer mode/code division multiple access network, i.e. TDMA/ATM/CDMA network. Users accessing this TDMA/ATM/CDMA uplink frame are assumed to belong to one of 4 service classes, namely video, voice, file and interactive data. Each user accesses only a portion of the subframe slots assigned to its class. A variable frame boundary strategy is used to adjust the subframe boundaries depending on the call load. To alleviate congestion in the assumed hubless signalling free satellite network, the satellite measures the uplink traffic of each class and issues pilot congestion control indicators to on-going calls of each class. These will be subsequently used by ground users to control their activities and police their calls using modified versions of leaky bucket and virtual leaky bucket congestion control techniques. The new techniques alleviate many of difficulties of specific slot assignment, onboard call management, superframe counting and management involved in the state of the art TDMA based systems, and yield a call establishment free yet dynamic and very well controlled access technique.