Digital Twin (DT) technology has emerged as a promising link between the physical and virtual worlds, enabling simulation, prediction, and real-time performance optimization in different domains. In this work we develop a high-fidelity digital twin framework, focusing on synchronization and accuracy between physical and digital systems to enhance data-driven decision making. To achieve this, we deploy several stationary UAVs in optimized locations to collect data from IoT devices, which were used to monitor multiple physical entities and perform computations to evaluate their status. We formulate a mixed-integer non-convex program to maximize the total amount of data collected from all IoT devices while ensuring a constrained age of digital twin threshold and solve it using successive convex approximation (SCA). To cope with realistic scenarios involving unpredictable environments and large network sizes, we model our problem as a Markov Decision Process (MDP), and propose a deep reinforcement learning-based approach using a Twin Delayed Deep Deterministic Policy Gradient (TD3) to optimize the unmanned aerial vehicle positions and the sum rate. Finally, we present different simulation results of the SCA and TD3 based solutions together with two baseline approaches and evaluated the sum rate in terms of IoT device count, AoDT threshold, task arrival rate and UAVs’ computational capacity. In all simulation results, the proposed TD3-based approach consistently proved to be superior as compared to the baseline solutions.

Beyond diagonal reconfigurable intelligent surface (BD-RIS) has emerged as an innovative and generalized RIS framework that provides greater flexibility in wave manipulation and enhanced coverage. In comparison to conventional RIS, optimization of BD-RIS is more challenging due to the large number of optimization variables associated with it. Typically, optimization of large-scale optimization problems utilizing traditional optimization methods results in high complexity. To tackle this issue, we propose a gradient-based meta learning algorithm which works without pre-training and is able to solve largescale optimization problems. With the objective to maximize the sum rate of the system, to the best of our knowledge, this is the first work considering joint optimization of receiving beamforming vectors at the base station (BS), scattering matrix of BD-RIS and transmission power of users equipment (UEs) in uplink rate-splitting multiple access (RSMA) communication. Numerical results demonstrate that our proposed scheme can outperform the conventional RIS RSMA framework by 22.5 %.

This paper analyzes a downlink C-NOMA scenario where a base station (BS) is deployed to serve a pair of users equipped with movable antenna (MA) technology. The user with better channel conditions with the BS will be able to transmit the signal to the other user providing an extra transmission resource and enhancing performance. Both users are equipped with a receiving MA each and a transmitting MA for the relaying user. In this regard, we formulate an optimization problem with the objective of maximizing the achievable sum rate by jointly determining the beamforming vector at the BS, the transmit power at the device and the positions of the MAs while meeting the quality of service (QoS) constraints. Due to the non-convex structure of the formulated problem and the randomness in the channels we adopt a deep deterministic policy gradient (DDPG) approach, a reinforcement learning (RL) algorithm capable of dealing with continuous state and action spaces. Numerical results demonstrate the superiority of the presented model compared to the other benchmark schemes showing gains reaching 45% compared to the NOMA enabled MA scheme and 60% compared to C-NOMA model with fixed antennas. The solution approach showed 93% accuracy compared to the optimal solution.

This paper investigates joint user pairing, power, and time slot duration allocation in the uplink multiple-input single-output (MISO) multi-user cooperative rate-splitting multiple access (C-RSMA) networks in half-duplex (HD) mode. We assume two types of users: cell-center users (CCU) and cell-edge users (CEU); first, we propose a user pairing scheme utilizing a semi-orthogonal user selection (SUS) and a matching-game (MG)-based approach where the SUS algorithm is used to select CCU in each pair. Afterward, the CEU in each pair is selected by considering the highest channel gain between CCU and CEU. After pairing is performed, the communication occurs in two phases: in the first phase, in a given pair, CEUs broadcast their signal, which is received by the base station (BS) and CCUs. In the second phase, in a given pair, the CCU decodes the signal from its paired CEU, superimposes its own signal, and transmits it to the BS. Moreover, utilizing uplink RSMA principle, only the CCUs split their messages into two sub-messages. Meanwhile, the messages of CEUs are kept without splitting. We formulate a joint optimization problem in order to maximize the sum rate subject to the power budget constraints of the user equipment (UE) and minimum data rate requirements at each UE. Since the formulated optimization problem is non-convex, we adopt a bi-level optimization to make the problem tractable. We decompose the original problem into two sub-problems: the user pairing sub-problem and the resource allocation sub-problem, where the user pairing sub-problem is independent of the resource allocation sub-problem, and once pairs are identified, the resource allocation sub-problem is solved for a given pair. The resource allocation sub-problem is solved by invoking a low-complexity pre-training free gradient-based meta-learning (GML) algorithm. Simulation results demonstrate that our proposed C-RSMA scheme can achieve around 100%, 51%, 53%, and 215% improvement over C-NOMA with fixed time slot allocation, RSMA, NOMA, and C-RSMA random pairing, respectively at CEU power budget of 17 dBm.

We explore in this paper a scenario involving UAV-assisted NOMA, where the UAV serves a dual purpose by providing communication and sensing capabilities, thus supporting ISAC technology. To this end, we formulate an optimization problem aimed at minimizing the Cramér-Rao Bound (CRB) for target localization, with the goal of jointly determining the beamforming vectors at both the base station (BS) and the UAV, as well as the UAV’s position, while maintaining the communication quality of service (QoS) for the users. Given the complex interdependencies between variables and the stochastic nature of the environment due to channel variations, we adopt a deep deterministic policy gradient (DDPG) algorithm, a reinforcement learning (RL) approach suited for continuous state and action spaces. Our numerical results demonstrate the system’s advantages over the conventional NOMA approach and underscore the algorithm’s accuracy in achieving near-optimal solutions.

This paper examines the downlink scenario where a transmitting base station (BS) provides communication services to a set of users by utilizing the rate-splitting multiple access (RSMA), while concurrently providing sensing functionalities. Owing to the available transmit power of the cellular users and their capabilities of decoding the RSMA common stream, we propose to leverage the users in the network to assist the sensing process by collectively forming a probing beam towards the target(s). Using this proposed system and to evaluate its potential gains, we formulate an optimization problem to jointly determine the beamforming design at the transmitting BS, the common stream split, and the distributed beamforming design at the users as well as at the receiving BS aiming to maximize the minimum rate of the users. Due to the non-convexity posed by the formulated problem, we perform rigorous mathematical operations and leverage the semi-definite relaxation (SDR) method to solve it using a successive convex approximation (SCA) algorithm. Our numerical results demonstrate the advantage of exploiting users' resources to assist in the sensing process which is reflected in an enhancement in the achieved rate by the users. Moreover, we present the advantage of our model in comparison to Spatial Division Multiple Access (SDMA) scheme.

This paper introduces a 2-layer cooperative rate-splitting multiple access (C-RSMA) framework designed for multiple groups of two users. Within each user group, the message is divided into three components: an inter-group common message, an inner-group common message, and a private message. Our framework incorporates a novel user-pairing policy, leveraging a combination of semi-orthogonal user selection (SUS) and a matching-game (MG)-based algorithm to identify user pairs, which allows for selecting the cell-center-users (CCUs) and cell-edge-users (CEUs) for each pair. To enhance signal quality at the CEUs, we employ cooperative communication, where each CCU relays the inner-group common message to its paired CEU. This framework is formulated as an optimization problem by jointly optimizing user pairing, beamforming vectors at the base station (BS), common stream split, time slot duration, and transmit power of CCUs to maximize the network sum rate. The formulated problem is highly non-convex and difficult to solve, and hence, we adopt bi-level optimization which breaks the original problem into outer and inner problems. The outer problem is considered as the user pairing problem and we solve it using the SUS-MG algorithm. Once the users are paired, we solve the inner optimization problem for each pair using a successive convex approximation (SCA) approach. Finally, numerical results demonstrate that our proposed approach can outperform baseline schemes.

Although multi-access edge computing (MEC) has allowed for computation offloading at the network edge, weak wireless signals in the radio access network caused by obstacles and high network load are still preventing efficient edge computation offloading, especially for user requests with stringent latency and reliability requirements. Intelligent reflective surfaces (IRS) have recently emerged as a technology capable of enhancing the quality of the signals in the radio access network, where passive reflecting elements can be tuned to improve the uplink or downlink signals. Harnessing the IRS’s potential in enhancing the performance of edge computation offloading, in this paper, we study the optimized use of a system of multi-IRS along with the design of the offloading (to an edge with multi MECs) and resource allocation parameters for the purpose of minimizing the devices’ energy consumption considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a suboptimal solution based on alternating optimization where we divide the problem into sub-problems which are then solved separately. Specifically, the offloading decision is solved through a matching game algorithm, and then the IRS phase shifts and resource allocation optimizations are solved in an alternating fashion using the Difference of Convex approach. The obtained results demonstrate the gains both in energy and network resources and highlight the IRS’s influence on the design of the MEC parameters.

This paper examines the integrated sensing and communication technology (ISAC) in the downlink scenario where a base station exploits cooperative non-orthogonal multiple access (CNOMA) to jointly offer communication functions to users and sensing functions to targets. CNOMA allows the user with good channel conditions to assist another user with a weak channel using the decode and forward strategy in full duplex mode while forming a beam-pattern that is capable of sensing the targets. The main objective in this work is to maximize the sum rate of the users by jointly optimizing the communication beamformers and the power allocation of the near user subject to the quality of service requirements for sensing and communication functions. The formulated problem is non-convex and hard to solve using traditional solvers. For that reason, a penalty-based approach is adopted to provide an efficient solution. Numerical results demonstrated the advantage of C-NOMA in ISAC, showing gains reaching up to 38% compared to the traditional NOMA, and 65% compared to the spatial division multiple access (SDMA).

This paper studies the downlink scenario of an unmanned aerial vehicle (UAV)-assisted rate-splitting multiple access (RSMA). The UAV serves as a full duplex (FD) amplify-and-forward relay to assist the base station (BS) in its communication with a set of user equipments (UEs). In this framework, we formulate an optimization problem with the goal of maximizing the minimum achievable rate by jointly optimizing the BS precoding vectors, the common-stream split, UAV transmit power, and the UAV location subject to the power budget constraints of the BS and UAV. Due to the non-convex nature of the problem, we propose an alternating optimization algorithm that decomposes the main problem into a power allocation subproblem and a UAV location subproblem, which are solved in an alternative way. Both subproblems are solved using a successive convex approximation approach. Our numerical results show that the proposed model outperforms traditional RSMA, non-orthogonal multiple access (NOMA), and UAV-assisted NOMA, demonstrating the efficacy of our approach in achieving higher minimum achievable rates.

This paper proposes a reconfigurable intelligent surface (RIS)-assisted cooperative rate-splitting multiple access (C-RSMA) framework with simultaneous wireless information and power transfer (SWIPT). In the proposed framework, the user with good channel gain can act as a full-duplex (FD) relay to forward the common stream to the user with poor channel gain. Moreover, by leveraging SWIPT technology, the user with good channel gain can simultaneously receive information and harvest energy from the base station (BS). This framework is formulated as an optimization problem by jointly optimizing beamforming vectors at the BS, common stream split, power splitting factor, and phase shift configuration at the RIS with the objective of maximizing the sum rate of both users. To tackle this challenging problem, an alternating optimization algorithm based on the successive convex approximation and difference-of-convex approach is proposed. Numerical results demonstrate that our proposed approach can outperform the baseline schemes.

This paper presents a downlink reconfigurable intelligent surface (RIS)-assisted half-duplex (HD) cooperative rate-splitting multiple access (C-RSMA) networks. The proposed system model is built up considering one base station (BS), one RIS, and two users. With the goal of minimizing the network energy consumption, a joint framework to optimize the precoding vectors at the BS, common stream split, relaying device transmit power, the time slot allocation, and the passive beamforming at the RIS subject to the power budget constraints at both the BS and the relaying node, the quality of service (QoS) constraints at both users, and a common stream rate constraint is proposed. The formulated problem is a non-convex optimization problem due to the high coupling among the optimization variables. To tackle this challenge, an efficient algorithm is presented by invoking the alternating optimization (AO) technique, which decomposes the original problem into two sub-problems; namely, sub-problem-1 and sub-problem-2, which are alternatively solved. Specifically, sub-problem-1 jointly optimizes the precoding vectors, common stream split, and relaying device power. Meanwhile, sub-problem-2 is to optimize the phase shift matrix at the RIS. In order to solve sub-problem-1, an efficient low-complexity solution based on the successive convex approximation (SCA) is proposed. Meanwhile, and with the aid of difference-of-convex (DC) rank-one representation and the SCA approach, an efficient solution for the phase shift matrix at the RIS is obtained. The simulation results demonstrate that the proposed RIS-assisted HD C-RSMA achieves a significant gain in minimizing the total energy consumption compared to the RIS-assisted RSMA scheme, RIS-assisted HD cooperative non-orthogonal multiple access (C-NOMA), RIS-assisted NOMA, HD C-RSMA without RIS, and HD C-NOMA without RIS.

Seeing the poor wireless conditions caused by obstacles and deep fading that often face the access network, intelligent reflective surfaces (IRS) have been recently studied for enhancing the quality of the wireless signals using a set of passive reflecting elements. Due to the channel quality issue severely impacting the performance of edge computation offloading, the IRS technology can be applied to enhance the edge offloading performance, especially for devices with strict requirements. In this paper, we study the optimized use of the IRS along with the design of the offloading and resource allocation parameters for maximizing the UEs’ sum of offloaded bits, considering 5G services with stringent latency and reliability requirements. After presenting our non-convex mathematical problem, we propose a sub-optimal solution based on the alternating optimization technique. The offloading decision is solved through a customized matching game algorithm, and then the IRS phase shifts and resources allocation are optimized through in alternating fashion using the Difference of Convex approach. Finally, numerical results demonstrate the improvement in the offloading performance provided by the optimized use of the IRSs, and highlights on the IRSs’ influence on the design of the MEC parameters.

Non-orthogonal multiple access (NOMA) is one of the promising access technologies to improve spectral efficiency and serve a higher number of users simultaneously. The latter proves important in time-sensitive services when data has to be collected before a set deadline, otherwise, the data is rendered useless. Therefore, in this paper, we utilize a NOMA-aided unmanned aerial vehicle (UAV) for data collection from time-constrained IoT devices. We optimize the trajectory of the UAV, IoT devices scheduling, and power allocation to maximize the number of served devices while considering the constraints of UAV energy and flight duration, and NOMA clustering. Given the complexity of the problem and the incomplete knowledge about the environment, it is divided into two subproblems. In the first subproblem, the UAV trajectory and the selection of the first device in the NOMA cluster at each time slot are modeled as a Markov Decision Process, and Proximal Policy Optimization is used to solve it. For the second device selection, a heuristic algorithm is used based on prioritizing devices with higher bit rate requirements and strict deadlines. The second subproblem considers power allocation inside the NOMA cluster, where it is formulated as an optimization problem for maximizing the sum rates of the two selected users. Finally, we demonstrate the performance gains of our solution in different scenarios while varying the system parameters as compared with alternative approaches. In particular, our proposed solution achieves a 10% to 30% performance gain compared to the traditional orthogonal multiple access scheme.

Fully automated vehicles deployed with high computational/perceptive capabilities will soon become a reality. Such capabilities enable the cooperation among vehicles and the realization of interacting autonomous driving systems. Edge computing has emerged to provide a plethora of computational services to reduce network latency. Applications at the edge that apply analytics on the sensory data are therefore indispensable for self-driving vehicles. We consider in this paper a network that interconnects vehicles to an edge server at a roadside unit. Each vehicle extracts multiple information by sampling multiple processes and sends them to the corresponding edge application. To make timely decisions, “fresh” information needs to be offloaded, processed, and delivered back to vehicles; in this context, we adopt a new metric called Age of Information (AoI) that has been lately used to measure the freshness of information. We seek to jointly schedule vehicles’ transmission of information and schedule information processing at the edge to minimize the AoI of all processes. We mathematically formulate the problem and prove its NP-Hardness. To overcome this hardness, we propose a logic-based Benders decomposition to divide the problem into a master and several subproblems. Then, we present an exact polynomial-time solution for the subproblems, a scalable heuristic for the master, and devise a valid yet efficient Benders cut. We implement the system simulation on the well-known traffic simulator SUMO and compare the decomposition with CPLEX branch-and-cut; Although the problem is highly intricate, our method finds a near-optimal solution (maximum deviation is 7% from optimal solution) with a speedup that reaches 95%. We study the system performance by varying different system parameters.

This article considers unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) networks, where low resource IoT devices periodically sample a stochastic process and need to upload more recent information to a Base Station (BS). Among the myriad of applications, there is a need for timely delivery of data (for example, status-updates) before the data becomes outdated and loses its value. Since transmission capabilities of IoT devices are limited, it may not always be feasible to transmit over one hop transmission to the BS. To address this challenge, UAVs with virtual queues are deployed as middle layer between IoT devices and the BS to relay recent information over unreliable channels. In the absence of channel conditions, the optimal online scheduling policy is investigated as well as dynamic UAV altitude control that maintains a fresh status of information at the BS. The objective of this paper is to minimize the Expected Weighted Sum Age of Information (EWSA) for IoT devices. First, the problem is formulated as an optimization problem that is however generally hard to solve. Second, an online model free Deep Reinforcement Learning (DRL) is proposed, where the deployed UAV obtains instantaneous channel state information (CSI) in real time along with any adjustment to its deployment altitude. Third, we formulate the online problem as a Markov Decision Process (MDP) and Proximal Policy Optimization (PPO) algorithm, which is a highly stable state-of-the-art DRL algorithm, is leveraged to solve the formulated problem. Finally, extensive simulations are conducted to verify findings and comprehensive comparisons with other baseline approaches are provided to demonstrate the effectiveness of the proposed design.

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.

The work presented in this article aims at improving the ground vehicle connectivity in the context of an intermittent vehicle-to-UAV (V2U) communication scenario where vehicles opportunistically establish time-limited connectivity with passing by unmanned aerial vehicles (UAVs) serving as flying base stations responsible for routing incoming vehicle data over backbone networks and/or the Internet. As opposed to existing work in the literature where vehicles are only allowed to establish direct connectivity with in-range UAVs, this work aims at also exploiting the possible formation of vehicular clusters and, hence, the feasibility of intervehicular communications to establish multihop paths connecting source vehicles to destination UAVs. A mathematical model is presented for the purpose of capturing the nodal (i.e., vehicles and UAVs) mobility dynamics and derive an expression for the overall V2U connectivity probability as well as the overall average vehicle connection time. Extensive simulations are conducted in order to adduce the validity and accuracy of the proposed model and provide further insights into the connectivity sensibility to fundamental system parameters.

Modern 5G services with stringent reliability and latency requirements such as smart healthcare and industrial automation have become possible through the advancement of Multi-access Edge Computing (MEC). However, the rigidity of ground MEC and its susceptibility to infrastructure failure would prevent satisfying the resiliency and strict requirements of those services. Unmanned Aerial Vehicles (UAVs) have been proposed for providing flexible edge computing capability through UAV-mounted cloudlets, harnessing their advantages such as mobility, low-cost, and line-of-sight communication. However, UAV-mounted cloudlets may have failure rates that would impact mission-critical applications, necessitating a novel study for the provisioned reliability considering UAV node reliability and task redundancy. In this paper, we investigate the novel problem of UAV-aided ultra-reliable low-latency computation offloading which would enable future IoT services with strict requirements. We aim at maximizing the rate of served requests, by optimizing the UAVs’ positions, the offloading decisions, and the allocated resources while respecting the stringent latency and reliability requirements. To do so, the problem is divided into two phases, the first being a planning problem to optimize the placement of UAVs and the second an operational problem to make optimized offloading and resource allocation decisions with constrained UAVs’ energy. We formulate both problems associated with each phase as non-convex mixed-integer programs, and due to their non-convexity, we propose a two-stage approximate algorithm where the two problems are transformed into approximate convex programs. Further, we approach the problem considering the task partitioning model which will be prevalent in 5G networks. Through numerical analysis, we demonstrate the efficiency of our solution considering various scenarios, and compare it to other baseline approaches.

Unmanned Aerial Vehicles (UAVs) are gaining growing interests due to the paramount roles they play, particularly these days, in enabling new services that help modernize our transportation, supply chain, search and rescue, among others. They are capable of positively influencing wireless systems through enabling and fostering emerging technologies such as autonomous driving, vertical industries, virtual reality and so many others. The Internet of Vehicles is a prime sector benefiting from the services offered by future cellular systems in general and UAVs in particular, and this paper considers the problem of content delivery to vehicles on road segments with either overloaded or no available communication infrastructure. Incoming vehicles demand service from a library of contents that is partially cached at the UAV; the content of the library is also assumed to change as new vehicles carrying more popular contents arrive. Each inbound vehicle makes a request and the UAV decides on its best trajectory to provide service while maximizing a certain operational utility. Given the energy limitation at the UAV, we seek an energy efficient solution. Hence, our problem consists of jointly finding caching decisions, UAV trajectory and radio resource allocation which is formulated mathematically as a Mixed Integer Non-Linear Problem (MINLP). However, owing to uncertainties in the environment (e.g., random arrival of vehicles, their requests for contents and their existing contents), it is often hard and impractical to solve using standard optimization techniques. To this end, we formulate our problem as a Markov Decision Process (MDP) and we resort to tools such as Proximal Policy Optimization (PPO), a very promising Reinforcement Learning method, along with a set of crafted algorithms to solve our problem. Finally, we conduct simulation-based experiments to analyze and demonstrate the superiority of our solution approach compared with four counterparts and baseline schemes.

Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error.

The success in transitioning towards smart cities relies on the availability of information and communication technologies that meet the demands of this transformation. The terrestrial infrastructure presents itself as a preeminent component in this change. Unmanned aerial vehicles (UAVs) empowered with artificial intelligence (AI) are expected to become an integral component of future smart cities that provide seamless coverage for vehicles on highways with poor cellular infrastructure. Motivated by the above, in this paper, we introduce UAVs cell-free network for providing coverage to vehicles entering a highway that is not covered by other infrastructure. However, UAVs have limited energy resources and cannot serve the entire highway all the time. Furthermore, the deployed UAVs have insufficient knowledge about the environment (e.g., the vehicles' instantaneous location). Therefore, it is challenging to control a swarm of UAVs to achieve efficient communication coverage. To address these challenges, we formulate the trajectories decisions making as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we leverage deep reinforcement learning (DRL) to propose an approach for learning the optimal trajectories of the deployed UAVs to efficiently maximize the vehicular coverage, where we adopt Actor-Critic algorithm to learn the vehicular environment and its dynamics to handle the complex continuous action space. Finally, simulations results are provided to verify our findings and demonstrate the effectiveness of the proposed design and show that during the mission time, the deployed UAVs adapt their velocities in order to cover the vehicles.

The Vehicle-as-a-Resource is an emerging concept that allows the exploitation of the vehicles' computational resources for the purpose of executing tasks offloaded by passengers, vehicles, or even an Internet-of-Things devices. This article revolves around a scenario where a roadside unit located at the edge of a hierarchical multitier edge computing subnetwork resorts to the utilization of idle vehicles computational resources through a fog-enabled substructure yielding a cost-effective computational task offloading solution. In this context, scheduling the offload of these tasks to the appropriate vehicles is a challenging problem that is subject to the interaction of major role-playing parameters. Among these parameters are the variability of vehicles availability and their computational power, the individual tasks' weighted priorities and their deadlines, the tasks required computational power as well as the required data to upload/download. This article proposes an infrastructure-assisted task scheduling scheme where the roadside unit receives computational tasks from different sources and schedules these tasks over a computationally capable vehicle residing within the roadside unit's range. The aim is to maximize the weighted number of admitted tasks while considering the constraints mentioned above. Compared to other works, this article broaches a more realistic scenario by considering a more accurate computational task and system model. Our system considers both the latency and throughput of task accomplishments by maximizing the weighted number of admitted tasks while at the same time respecting the tasks accompanied deadlines. Both radio and computational resources are part of the optimization problem. After proving the NP-hardness of the scheduling problem, we formulated the problem as a mixed-integer linear program. A Dantzig-Wolfe decomposition algorithm is proposed which yields to a master program solvable by the Barrier algorithm and subproblems solved optimally with a polynomial-time dynamic programming approach. Thorough numerical analysis and simulations are conducted in order to verify and assert the validity, correctness, and effectiveness of our approach compared to branch and bound and greedy algorithms.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in downlink communication systems. Using C-NOMA, users with more favorable channel conditions can assist communication between the base station (BS) and the users with less favorable channel conditions using either full-duplex (FD) or half-duplex (HD) device-to-device (D2D) relaying and successive interference cancellation (SIC). To maximize the benefits of C-NOMA, we formulate and solve a novel optimization problem that jointly determines the optimal D2D user pairing and the optimal power control scheme in a downlink cellular system consisting of a BS that communicates with a set of spatially dispersed users. The formulated problem is a non-convex mixed-integer non-linear program (MINLP) which is difficult to solve due to the dependency between power control and user pairing. Thus, we decompose the problem into an inner power control problem and an outer pairing problem. For the inner problem, we derive the optimal closed-form expressions for both HD and FD relaying modes, while the outer problem of user pairing can be solved using the well-known Hungarian method. The simulation results show that the proposed framework outperforms a variety of proposed schemes in the literature and that it can obtain the optimal pairing and power control policies for a network with 100 users in negligible computational time.

This paper investigates the data-delivery latency in the context of intermittent vehicle-to-UAV (V2U) communications. Precisely, a V2U communication scenario is considered where vehicles opportunistically establish connectivity with passing by UAVs for a limited period of time during which these vehicles transmit data packets to in-range UAVs serving as flying base stations and, in turn, are responsible for delivering these packets to backbone networks and/or routing them over the Internet. A mathematical framework is established with the objective of modeling the vehicles' OnBoard Units' (OBUs') buffers as single-server queueing systems. The established queueing model will allow for the evaluation of the V2U communication system in terms of the average data packet delivery delay. Extensive simulations are conducted with the objective of asserting the validity and accuracy of the proposed queueing model as well as providing further insights into the delay sensibility to various system parameters.

The global evolution of wireless technologies and intelligent sensing devices are transforming the realization of smart cities. Among the myriad of use cases, there is a need to support applications whereby low-resource IoT devices need to upload their sensor data to a remote control centre by target hard deadlines; otherwise, the data becomes outdated and loses its value, for example, in emergency or industrial control scenarios. In addition, the IoT devices can be either located in remote areas with limited wireless coverage or in dense areas with relatively low quality of service. This motivates the utilization of UAVs to offload traffic from existing wireless networks by collecting data from time-constrained IoT devices with performance guarantees. To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline. The formulated optimization problem is shown to be mixed integer non-convex and generally NP-hard. To solve it, we first propose the high-complexity branch, reduce and bound (BRB) algorithm to find the global optimal solution for relatively small scale scenarios. Then, we develop an effective sub-optimal algorithm based on successive convex approximation in order to obtain results for larger networks. Next, we propose an extension algorithm to further minimize the UAV's flight distance for cases where the initial and final UAV locations are known a priori. We demonstrate the favourable characteristics of the algorithms via extensive simulations and analysis as a function of various system parameters, with benchmarking against two greedy algorithms based on distance and deadline metrics.

This paper investigates the performance of cooperative non-orthogonal multiple access (C-NOMA) in cellular downlink systems. The system model consists of a base station (BS) that needs to serve multiple users within a region of service. A subset of the users, especially those located close to the cell edge, undergo severe fading and suffer from poor channel quality and low achievable rates. To overcome this problem, CNOMA is proposed as the system design methodology, in which users that have the capability of full-duplex (FD) communication can assist the transmissions between the BS and users with poor channel quality through device-to-device (D2D) communications. To harness both the multiplexing gain from NOMA and the diversity gain from FD-D2D communications, we formulate and solve a novel optimization problem that jointly determines D2D user pairing and power allocation. The formulated problem is a mixed-integer non-linear program (MINLP) with prohibitively high complexity. To overcome this issue, a two-step policy is proposed to solve the problem in polynomial time. Our simulation results show that with reasonable assumptions, the proposed scheme always outperforms some existing schemes in the literature, and that, under undesirable conditions, e.g., poor D2D channel conditions or imperfect self-interference (SI) cancellation, the proposed scheme is reduced to conventional NOMA.

The vision towards 5G and beyond is to provide remarkable performance enhancements that enable the launch of new services and markets in different industry verticals. Example scenarios of those services include indoor hotspot, broadband access in a crowd, and dense urban; many of which require very high data rates. Traffic offloading and device-to-device cooperation have been leveraged to expand the system capacity and coverage through using heterogeneous network technologies. In this work, we shed the light on the significant gains incurred when predicted short term network performance is factored into network splitting decisions over multiple wireless interfaces, while guaranteeing a desired quality of experience to end users. Accordingly, we allow dynamic use of multiple network interfaces taking into consideration their energy requirement and price models to deliver premium services and minimize the overall energy consumption and total cost. We develop a novel and efficient real-time traffic splitting approach that makes use of predicted bit rate of each network interface in addition to device-to-device cooperation to decide on the amount of video traffic to be delivered on each interface at every time slot. The proposed approach is validated and evaluated under realistic network conditions. Simulation results demonstrate substantial gains in terms of energy consumption, data cost and quality of user experience as compared to multiple alternative solutions.

Unmanned aerial vehicles (UAVs) have recently emerged as enablers for multitude use cases in 5G networks, one of which utilizes them as aerial base stations to intermittently serve mobile users in emergency situations or hard-to-reach areas. In this work, we address the problem of deploying multiple UAVs optimally in 3D space while autonomously adapting their positions as users move around within the network. We propose a novel approach with the objective of deploying the least number of UAVs to maintain target quality of service requirements. The problem of positioning UAVs in a 3D space is formulated as a mixed integer programming problem (MIP). To obtain an efficient solution, we propose and evaluate an autonomous positioning algorithm that can easily adapt as the users move within a specific area in the network. We present performance results for the algorithm as a function of various system parameters assuming a random walk mobility model. The simulation results demonstrate the effectiveness of the proposed algorithm compared to the optimal solution and related work in the literature for various network scenarios with user mobility.

One of the core novel features of 5G networks is the support of ultra-reliable low latency communications (URLLC). This will pave the way for a plethora of mission critical applications in diverse sectors including health, security, gaming, and manufacturing, among others, through guaranteed premium grade reliability and delay; yet, this requires the development of innovative mechanisms that can address the existing challenges. In this work, we focus on the simultaneous utilization of multiple paths via different wireless interfaces and networks in order to deliver data from source to destination in a timely manner and with high reliability. We formulate the problem as a mixed integer program and generate an optimized strategy to efficiently utilize the resources among the different paths. Simulation results are presented for various scenarios to quantify performance gains with respect to other alternative approaches.

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets. Among the emerging markets, Internet of Things (IoT) use cases are standing out with the proliferation of a wide range of sensors that can be configured to continuously monitor and transmit data for intelligent processing and decision making. Devices in such scenarios are normally extremely energy-constrained and often exist in large numbers and can be located in hard-to-reach areas; the fact that necessitates the design and implementation of effective energy-aware data collection mechanisms. To this end, we propose the utilization of unmanned aerial vehicles (UAVs) to collect data in dense wireless sensor networks using projection-based compressive data gathering (CDG) as a novel solution methodology. CDG is utilized to aggregate data en-route from a large set of sensor nodes to selected projection nodes acting as cluster heads (CHs) in order to reduce the number of needed transmissions leading to notable energy savings and extended network lifetime. The UAV transfers the gathered data from the CHs to a remote sink node, e.g., a 5G cellular base station, which avoids the need for long range transmissions or multihop communications among the sensors. Our problem definition aims at clustering the sensors, constructing an optimized forwarding tree per cluster, and gathering the data from selected CH nodes based on projection-based CDG with minimized UAV trajectory distance. We formulate a joint optimization problem and divide it into four complementary subproblems to generate close-to-optimal results with lower complexity. Moreover, we propose a set of effective algorithms to generate solutions for relatively large-scale network scenarios. We demonstrate the superiority of the proposed approach and the designed algorithms via detailed performance results with analysis, comparisons, and insights.

Multi-access edge computing (MEC) has recently emerged as a novel paradigm to facilitate access to advanced computing capabilities at the edge of the network, in close proximity to end devices, thereby enabling a rich variety of latency sensitive services demanded by various emerging industry verticals. Internet-of-Things (IoT) devices, being highly ubiquitous and connected, can offload their computational tasks to be processed by applications hosted on the MEC servers due to their limited battery, computing, and storage capacities. Such IoT applications providing services to offloaded tasks of IoT devices are hosted on edge servers with limited computing capabilities. Given the heterogeneity in the requirements of the offloaded tasks (different computing requirements, latency, and so on) and limited MEC capabilities, we jointly decide on the task offloading (tasks to application assignment) and scheduling (order of executing them), which yields a challenging problem of combinatorial nature. Furthermore, we jointly decide on the computing resource allocation for the hosted applications, and we refer this problem as the Dynamic Task Offloading and Scheduling problem, encompassing the three subproblems mentioned earlier. We mathematically formulate this problem, and owing to its complexity, we design a novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition. This technique solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space. Ultimately, both the master and the sub-problem will converge to yield the optimal solution. We show that this technique offers several order of magnitude (more than 140 times) improvements in the run time for the studied instances. One other advantage of this method is its capability of providing solutions with performance guarantees. Finally, we use this method to highlight the insightful performance trends for different vertical industries as a function of multiple system parameters with a focus on the delay-sensitive use cases.

In this paper, we investigate downlink transmissions in a wireless communication system enabled by a swarm of unmanned aerial vehicles (UAVs) which are spatially dispatched to cooperatively deliver requested contents to ground users. First, we propose a communication scheme that exploits the flexible deployment of UAVs as well as their cooperative transmissions to improve in-network user admission. Unlike previous literature, a practical operational constraint of limited storage capacity for UAVs is considered. Then, from the knowledge that cooperation among UAVs depends on the availability of the contents in their limited storage space, we propose a novel joint optimization problem to determine the content placement, location planning, user admission decision and transmit beamforming to maximize the number of users experiencing a minimum required rate, so-called admitted users. Since the formulated problem is a mixed-integer non-linear program which is generally non-deterministic polynomial-time hard, we proposed a framework that is developed on the basis of difference-of-convex (DC) programming to transform the original problem into a series of approximate convex problems which can be iteratively solved until convergence. Our extensive simulation results reveal that the proposed scheme outperforms other schemes that have been introduced in previous work and reflect a notable trend that deploying more cooperative UAVs with fewer resources (power and storage capacity) is more efficient than deploying fewer UAVs with more resources. In particular, in one of our collected results, the total communication power can be reduced by roughly 40 dB when doubling the number of cooperative UAVs.

The proliferation of smart connected Internet of Things (IoT) devices is bringing tremendous challenges in meeting the performance requirement of their supported real-time applications due to their limited resources in terms of computing, storage, and battery life. In addition, the considerable amount of data they generate brings extra burden to the existing wireless network infrastructure. By enabling distributed computing and storage capabilities at the edge of the network, multi-access edge computing (MEC) serves delay sensitive, computationally intensive applications. Managing the heterogeneity of the workload generated by IoT devices, especially in terms of computing and delay requirements, while being cognizant of the cost to network operators, requires an efficient dimensioning of the MEC-enabled network infrastructure. Hence, in this paper, we study and formulate the problem of MEC resource provisioning and workload assignment for IoT services (RPWA) as a mixed integer program to jointly decide on the number and the location of edge servers and applications to deploy, in addition to the workload assignment. Given its complexity, we propose a decomposition approach to solve it which consists of decomposing RPWA into the delay aware load assignment sub-problem and the mobile edge servers dimensioning sub-problem. We analyze the effectiveness of the proposed algorithm through extensive simulations and highlight valuable performance trends and trade-offs as a function of various system parameters.

Along with the dramatic increase in the number of IoT devices, different IoT services with heterogeneous QoS requirements are evolving with the aim of making the current society smarter and more connected. In order to deliver such services to the end users, the network infrastructure has to accommodate the tremendous workload generated by the smart devices and their heterogeneous and stringent latency and reliability requirements. This would only be possible with the emergence of ultra reliable low latency communications (uRLLC) promised by 5G. Mobile Edge Computing (MEC) has emerged as an enabling technology to help with the realization of such services by bringing the remote computing and storage capabilities of the cloud closer to the users. However, integrating uRLLC with MEC would require the network operator to efficiently map the generated workloads to MEC nodes along with resolving the trade-off between the latency and reliability requirements. Thus, we study in this paper the problem of Workload Assignment (WA) and formulate it as a Mixed Integer Program (MIP) to decide on the assignment of the workloads to the available MEC nodes. Due to the complexity of the WA problem, we decompose the problem into two subproblems; Reliability Aware Candidate Selection (RACS) and Latency Aware Workload Assignment (LAWA-MIP). We evaluate the performance of the decomposition approach and propose a more scalable approach; Tabu meta-heuristic (WA-Tabu). Through extensive numerical evaluation, we analyze the performance and show the efficiency of our proposed approach under different system parameters.

Fifth generation wireless networks are expected to provide advanced capabilities and create new markets spanning a wide range of use cases. Among these, massive IoT is standing out with the proliferation of sensors and wearable devices that continuously monitor and transmit data for further processing. This paper proposes a novel data collection technique using Unmanned Aerial Vehicles (UAVs) in dense wireless sensor networks (WSNs) using projection-based Compressive Data Gathering (CDG) as a solution methodology. CDG is utilized to aggregate data en route from sets of sensor nodes to a set of projection nodes (heads) in order to notably reduce the number of transmissions leading to energy savings and extended WSN lifetime. The UAVs forward the gathered data from heads to a remote sink to enhance efficiency by avoiding long range transmissions from heads to the sink or multi-hop communications among sensors to the sink. We formulate a joint optimization problem that captures clustering, heads selection, routing trees construction, and UAV trajectory planning. In order to overcome the complexity of the joint optimization problem, we decompose the problem into separate parts and propose a heuristic to solve each subproblem for large-scale network scenarios.

Device‐to‐device (D2D) offloading has been shown to be a highly effective technique to enhance the performance of wireless networks. Yet, for any two mobile users to share data efficiently and reliably via D2D links, they should be in close proximity for long enough period of time, share similar content interests, and have some level of incentive and trust to cooperate. In this work, we focus on the practical implementation aspects of D2D data sharing taking into account realistic operational conditions. To this end, we design and conduct an experimental study to collect location and neighbor discovery data from 38 mobile users in a university campus over several weeks using our own customized crowdsourcing Android mobile application. The collected data is then processed and utilized to empirically model mobility‐related parameters that include contact frequency, contact duration, and inter‐contact duration. The participating users did also fill a user interest survey in order to correlate mobility and connectivity patterns with content interests and social network relations. The obtained insights are then used to develop a practical implementation framework for designing effective D2D data sharing strategies. To test the proposed ideas under realistic operational constraints, we design and implement a social‐aware D2D data sharing Android mobile application and demonstrate its functionality and effectiveness using an example case study scenario.

Ultra dense networks and device-to-device communications are expected to play a major role in 5G networks to meet tremendous traffic requirements. In our work, we address traffic offloading in dense device-to-device cooperative heterogeneous networks with focus on use cases where a very large number of users request simultaneously common streaming content from a remote server with quality of service guarantees. We formulate an optimization problem to maximize the number of users served and reduce the number of access points deployed while satisfying a set of system constraints. The solution determines the best strategy for downloading the content either over long range connectivity from the access points or short range connectivity from peer mobile devices. Results are presented for various scenarios in a stadium setting to demonstrate the significant gains of optimized traffic offloading in ultra dense wireless networks.

The planning procedure of 802.11 WLAN networks is specific to each type of venue. In the case of large venues with high number and dense Wi-Fi devices, the network planning requires a deep investigation on: available channels, number of devices to associate, throughput per channels, etc. In addition, the WLAN planning of dense Wi-Fi devices becomes more complicated when adding collaboration between devices. The collaboration between devices is achieved by forwarding and sharing data through neighboring devices, named relays. In this paper, we provide guidelines for WLAN planning in high-dense collaborative environments. In addition, we study the network performance gains when content is distributed in cooperative manner through both unicast and multicast mode. Results show that using cooperative content distribution in dense environments leads to notable gain in network performance.

The Wi-Fi Direct technology was proposed by Wi-Fi Alliance in order to facilitate device-to-device communications in Wi-Fi. The Wi-Fi Direct technology offers new possibilities to deploy opportunistic and cooperative networks. The actual state of the Wi-Fi Direct specification lacks optimization for P2P grouping and creating opportunistic networks. In this paper, we present a new P2P group formation method for opportunistic networks and we introduce the concept of nominating a backup group owner that can replace the group owner of a broken P2P group. In addition, we investigate required times to form a P2P group of variable number of nodes and we evaluate the efficiency of the backup group owner. Results show that our proposed P2P group formation method, in addition to the introduction of the backup group owner, can facilitate and accelerate opportunistic P2P grouping.

Node failures in distributed storage systems are becoming a critical issue, and many erasure codes are designed to handle such failures. The purpose of this paper is to evaluate fractional repetition (FR) codes, a class of regenerating codes for distributed storage systems, as a practical solution. FR codes consist of a concatenation of an outer maximum distance separable (MDS) code and an inner fractional repetition code that splits the data into several blocks and stores multiple replicas of each on different nodes in the system. We model the problem as an integer linear programming problem that uses modified versions of the fractional repetition code by allowing different block sizes, and minimizes the recovery cost of all single node failure scenarios. The contribution of this work is three fold: We generate an optimized block distribution schema that minimizes the total system repair cost in a data center and we present a full recovery plan for the system. In addition, we account for new-comer blocks and allocate them to nodes with minimal computations and without changing the original optimal schema. This makes our work practical to apply. Hence, a practical solution for node failures is presented by using a self-designed genetic algorithm that searches within the feasible solution space. We show that our results are close to optimal.

As multiple node failures are becoming so frequent in distributed storage systems, many erasure coding techniques are emerging to handle such failures. In this paper we use the fractional repetition code to apply as a redundancy scheme for multiple failure recovery with optimized system cost. The fractional repetition (FR) code is a class of regenerating codes that consists of a concatenation of an outer maximum distance separable (MDS) code and an inner fractional repetition code that splits the data into several blocks and stores multiple replicas of each on different nodes in the system. We model the problem as an integer linear programming problem that uses modified versions of the fractional repetition code by allowing different block sizes, and minimizes the recovery cost of all dependent and independent multiple node failure scenarios. First, we generate an optimized block distribution scheme that minimizes the total system repair cost together with a full recovery plan with a node repair order for the system. Moreover, we account for the common scenario of having newcomer blocks. We allocate newcomers to nodes with minimal computations and without changing the original optimized plan. The problem is solved using genetic algorithms that search within the feasible solution space. Fast convergence validates the efficacy of our algorithms for different system parameters. Simulation results are shown to be close to optimal for the case of newly arriving blocks.

We present a scalable mobile multimedia streaming system with device-to-device cooperation that enables common content distribution in dense wireless networking environments. This is particularly applicable to use cases such as delivering real-time multimedia content to fans watching a soccer game in a stadium or to participants attending a major conference in a large auditorium. The key novel characteristics of our system include seamless neighbor discovery and link quality estimation, intelligent clustering and channel allocation algorithms based on constrained minimum spanning trees, robustness against device mobility, and device centric operation with no changes to existing wireless systems. We demonstrate the functionality of the proposed system on Android devices using heterogeneous networks (cellular/WiFi/WiFi-Direct) and show the formation of multiple clusters to allow for scalable operation. The gained insights will help bridge the gap between theoretical and simulation based research conducted in this area and practical operation taking into account the capabilities and limitations of existing wireless technologies and smartphones/tablets.

Heterogeneous networks are expected to play a major role towards meeting the exploding traffic demand over cellular systems. Particularly, existing WiFi hotspots will be dynamically utilized to offload the traffic of cellular mobile subscribers. This will be further facilitated by forthcoming advances in mobile device capabilities that will include the ability to operate multiple wireless interfaces simultaneously. To this end, we focus in this work on cellular/WiFi heterogeneous networks with traffic splitting where a mobile device can utilize existing cellular and WiFi links simultaneously to achieve various performance gains. We propose a multi-objective approach for traffic splitting that captures the tradeoffs between throughput maximization on one hand and battery energy minimization on the other hand. We evaluate the proposed approach using parameters determined via experimental measurements using Samsung Galaxy SIII mobile devices. Results are presented for various scenarios in order to quantify and analyze the throughput-energy tradeoffs of traffic splitting in cellular/WiFi heterogeneous networks.

ABSTRACT Quantifying and modeling energy consumption in mobile devices are essential for developing energy‐aware protocols and energy reduction techniques. In this work, we address the energy requirements for secure Web browsing sessions over handheld mobile devices. The contributions of this work are twofold. On one hand, we present a detailed study based on experimental measurements to quantify the energy consumed by the mobile device during secure Web browsing sessions. This includes the energy consumed due to data transmission/reception, encryption/decryption, hashing in addition to browser processing. On the other hand, we derive an empirical energy consumption model for secure Web browsing as a function of various protocol and device parameters. The developed model can be utilized to identify the various components that affect energy consumption during secure Web browsing sessions, to implement application‐layer energy models in network simulation tools, and to develop adaptive energy‐aware Web browsing protocols. The effectiveness of the developed model is demonstrated via experimental testing on several secure websites. Copyright © 2011 John Wiley & Sons, Ltd.

Bluetooth wireless technology is being installed in almost any electronic or information device for ad-hoc connectivity among them. Up to eight Bluetooth devices can form a network called a piconet. Interconnecting multiple piconets through gateway devices forms a scatternet. In the Bluetooth specifications, no scatternet topology has been specified. This paper proposes a scatternet formation topology that, unlike the existing literature, considers the non-uniform distribution of Bluetooth devices. Our proposed approach dictates a ring topology formed in the dense area and extended by trees to the other areas. The proposed approach is denoted as BlueHRT: Hybrid Ring Tree Scatternet Formation in Bluetooth Networks. Results are presented to highlight the performance gains of the proposed approach.

Multimedia applications are migrating to IP networks imposing high challenges on network planners. Challenges arise due to the stringent quality of service (QoS) requirements of multimedia applications that cannot be met over enterprise IP networks unless advanced techniques and strategies are applied. Example techniques are traffic differentiation, capacity evaluation and reservation, and call admission control. In this work, we assume practical call admission control (CAC) schemes and study by simulations the distribution of traffic inside the network. We show how capacity needs of traffic are affected when various CAC schemes are employed. Finally, we identify a procedure to evaluate the link capacity share for realtime traffic and study the resulting tradeoff between capacity needs and QoS parameters such as packet loss and blocking probability.

Third generation CDMA networks strive to deliver high speed data services through a shared radio channel with scarce resources. To efficiently utilize the available radio resources, we propose a new scheduling algorithm based on techniques from micro-economics. Unlike existing literature that mainly focuses on maximizing total system and/or individual user utility, this new algorithm aims at ensuring QoS guarantees from end to end for all active connections. Moreover, it considers the time varying channel conditions in both uplink and downlink directions jointly rather than each direction separately. Simulation results show that the proposed algorithm allows several users to simultaneously transmit while providing end-to-end QoS guarantees in terms of frame success rate and end-to-end delay.

Transport of compressed video is expected to pervade computer networks in the near future. Video is commonly encoded in variable bit rate traffic to improve video quality and reduce encoding delays and yet to make efficient use of the available capacity using statistical multiplexing. Variable bit rate coded video imposes a real challenge to network planners who aim to evaluate its necessary capacity share. In this paper, we present a dimensioning model tailored especially for variable bit rate video transmission over IP networks taking the network delay into account unlike existing dimensioning models. We investigate this new model in terms of different system parameters.

As a basis for planning integrated QoS-enabled IP networks, proper evaluation of link capacities is needed to provide the desired level of quality for all supported classes in the network. As traffic characteristics and requirements of these classes are quite different, various dimensioning schemes are required, each estimating the share of capacity needed for its corresponding class. In S. Sharafeddine et al. (July 2003), a dimensioning method has been proposed to provide almost guaranteed quality of service for real-time traffic classes. While this method is applied for dimensioning single links, we propose in this work an appropriate extension that enables end-to-end capacity allocation. Moreover, we realize the given capacity planning problem in a dimensioning tool that supports different traffic classes belonging to the two broad categories, namely stream and elastic traffic. For this purpose, per-hop mapping of end-to-end QoS constraints is handled and a generic layered architecture is proposed and implemented.

IP network planning based on a given traffic demand becomes obsolete as soon as the actual traffic slightly deviates from the given values. It is highly probable that this be the case in IP networks with no deployed resource control mechanisms. Complex and comprehensive resource control mechanisms are still a matter of research; while the most practical and common scenario is to have control units at the ingress of the IP network as pertained by the differentiated services architecture (DiffServ). Based on this practical model, we study the distribution of traffic demand inside the network and introduce the concept of a capacity margin, which is added to the network links in order to account for a certain level of traffic demand variability. Moreover, we analytically calculate the capacity margin and investigate the tradeoff provided between the degree of network robustness and the consequent additional costs in terms of network resources. Finally, we show how such statistical consideration of traffic variability can offer high degrees of network robustness with rather low costs.

Cellular operators make use of radio network planning tools to determine the number of base stations required to cover an area of interest with a given traffic profile. These tools are based on a link budget analysis that takes into account the pathloss model in addition to various losses and power margins. The power margins compensate for sources of variation in the network in order to guarantee a bounded probability of outage and, thus, help in reducing the complexity of radio network planning tools. In CDMA cellular systems, the mean of the intercell interference is commonly used for coverage calculations and radio network planning. However, the intercell interference is a random variable that depends on the positions of the users in neighboring cells. In this work, we propose and calculate a new power margin that should be used in link budget analysis to compensate for variation in intercell interference. We show that not using the proposed margin leads to a notable increase in outage probability. The proposed margin is of particular interest to cellular operators and for the development of radio network planning tools.

Voice over IP (VoIP) services will play an important role in future IP networks, promising cost savings and new revenue sources to operators and service providers. To provide quality of service guarantees, certain mechanisms need to be implemented which support predictable packet handling, bandwidth allocation, and call admission control. In order to configure these mechanisms, a solid understanding of VoIP traffic characteristics and its respective bandwidth requirements is necessary. In this paper, we characterize traffic traces generated by various VoIP applications. According to the H.323 standard, the characteristics are described by means of token bucket parameters, which are then used to derive the required service rates for individual traffic flows. While in most cases sources send out fairly steady packet streams, there are situations where software based clients emit rather bursty traffic resulting in unreasonably high bandwidth needs. On basis of these traffic flows, we investigate the effects that token bucket parameters have on the bandwidth demand and discuss tradeoff possibilities in order to reduce it.