Low-latency computational-task execution can be achieved by leveraging device-to-device offloading and parallel processing over nearby extreme edge devices (EEDs), a paradigm known as extreme edge computing (EEC). However, EEC performance is challenged by device spatial randomness with intermittent wireless connectivity, limited device computing power, time-varying availability, and device failures. This paper introduces a novel spatiotemporal analytical framework for EEC by integrating stochastic geometry with an absorbing continuous-time Markov chain (ACTMC) to capture the interplay between communication and computation. Modeling a large-scale millimeter-wave network, we derive tractable expressions for the average task response delay and the task completion probability under both random and location-aware EED selection. Numerical results quantify the impact of location-awareness and unveil the existence of an optimal task segmentation that minimizes delay, which depends on network parameters and EED capabilities. We also demonstrate that device failures and EED scarcity exacerbate delay, which can be mitigated through a collaborative load-balancing approach between EEC and Multi-Access Edge Computing (MEC) schemes. Simulations and sensitivity analyses validate the proposed framework and offer design insights for optimizing system performance.

Mobile edge computing (MEC) is a promising paradigm for Internet of Things applications requiring synchronized user experiences. However, sustaining scalable and reliable MEC services is challenging when computational resources are overloaded, especially as MEC service providers (SPs) must minimize operational costs to maximize profits while offering competitively priced services. This article proposes the cooperative multiprovider market (CMPM) scheme, the first to cooperatively enhance service scalability and reliability while addressing the profit-pricing dilemma in a multiprovider market. CMPM enables overloaded home SPs (HSPs) to leverage underutilized computational resources from reliable foreign SPs (FSPs) via reputation-based service replication, meeting the stringent Quality of Service (QoS) requirements for real-time applications involving user groups. CMPM resolves the pricing dilemma by applying a game-theoretic approach, allowing FSPs to dynamically optimize revenue and adjust prices when HSPs cannot meet user demand. We formulate the resource allocation and pricing problem as a Stackelberg game, establish the existence of the equilibrium, and develop a distributed algorithm to reach it. Extensive evaluations show that CMPM significantly reduces unit prices, attracts more HSPs, and better manages high-density user loads compared to state-of-the-art schemes that overlook SP reputation and social welfare. CMPM also achieves up to 84% higher FSP revenue, a 67% improvement in scalability, and a 70% higher task success rate compared to baseline schemes.

Mobile edge computing (MEC) has emanated as a propitious computing paradigm that can foster delay-sensitive and/or data-intensive applications. However, it can be challenging to maintain a scalable MEC service when computational resources are overloaded. In this article, we propose the service replication between multiple service providers (SRMSPs) scheme. SRMSP is the first scheme that fosters service scalability in a cost-efficient manner, while considering the stringent QoS requirements of real-time applications involving groups of users. SRMSP enables SRMSPs to minimize the average response delay and the operational cost incurred by service providers, while satisfying the delay requirements of all user groups. We formulate the resource allocation problem as an integer linear program (ILP) and derive an analytical solution using the Karush–Kuhn–Tucker (KKT) conditions and Lagrangian analysis. In addition, we propose the SRMSP-distributed allocation (SRMSP-DA) scheme to provide a time-efficient solution in distributed scenarios. In SRMSP-DA, we use a game-theoretic strategy that formulates the resource allocation problem as a potential game. Extensive simulations show that SRMSP renders a 50% operational cost reduction compared to a baseline scheme that does not consider the operational cost. In addition, SRMSP-DA exhibits a relatively marginal difference of up to 20% and 4% in terms of the total operational cost and average response delay, respectively, compared to the optimal solution provided by SRMSP.

This paper introduces the new paradigm of Mobile Edge Learning "MEL" that enables the implementation of realistic distributed machine learning (DML) tasks on wireless edge nodes while taking into consideration the heterogeneous computing and networking environments. Therefore, a heterogeneity aware (HA) scheme is designed to solve the problem of dynamic task allocation for MEL in a way that maximizes the DML accuracy over wireless heterogeneous nodes or 'learners' while respecting the time constraints. The problem is first formulated as a quadratically-constrained integer linear program (QCILP). Being NP-hard, it is relaxed into a non-convex problem over real variables which can be solved using commercially available numerical solvers. The relaxation also allows us to propose a solution based on deriving the analytical upper bounds of the optimal solution using Lagrangian analysis and Karush-Kuhn-Tucker (KKT) conditions. The merits of the proposed analytical solution are demonstrated by comparing its performance to the numerical approaches and comparing the validation accuracy of the proposed HA scheme to the baseline heterogeneity unaware (HU) equal task allocation approach. Simulation results show that the HA schemes decrease convergence time up-to 56% and increase the final validation accuracy up-to 8%.

Multi-access Edge Computing (MEC) is a revolutionary computing paradigm that facilitates delay-sensitive and/or data-intensive applications associated with the Internet of Things (IoT). Harvesting copious yet underutilized computational resources of the Extreme Edge Devices (EEDs) is foreseen as a promising endeavor. Such EEDs offer a unique opportunity to bring the computing service closer to IoT devices to curtail delay. However, the efficacy of extreme-edge parallel computing paradigm is profoundly impacted by i) wireless device-to-device communication performance, that is required for task offloading; and ii) computing capabilities of the EEDs, that governs the execution time of each task. In this context, we propose a novel spatiotemporal framework that employs stochastic geometry and continuous time Markov chains to jointly analyze the interwoven communication and computation performance of extreme edge computing systems. Based on the incorporated framework, we study the influence of various system parameters on the task response delay. Our findings reveal the existence of an optimal number of EEDs that need to be recruited in order to minimize the task response delay. Moreover, we show that in some cases, our model can outperform the normal MEC offloading systems.

Edge Computing (EC) is a promising computing paradigm that can foster a wide spectrum of delay-sensitive and/or data-intensive applications. As opposed to cloud computing, which relies on remote cloud servers, EC brings the computing service closer to the end-users, which can significantly reduce the delay. The concept of EC has recently expanded to include harvesting the computation resources of the Extreme Edge Devices (EEDs), such as smartphones, autonomous vehicles, tablets, etc. However, the cost of recruiting EEDs for resource allocation in such EC environments is mostly overlooked. In this paper, we propose the Price-based Compute Clusters Recruitment (PCCR) scheme. In PCCR, we minimize the cost of recruiting the EEDs required to perform a given set of tasks, where each task is satisfied by the collaborative effort of a group of EEDs forming a compute cluster. PCCR strives to minimize the total recruitment cost while keeping the delay below a certain threshold by forming the optimal set of compute clusters from a pool of heterogeneous EEDs available in a given geographical area. We formulate the optimization problem as a Mixed Integer Quadratically Constrained Quadratic Program (MIQCQP). We then derive an analytical solution using the KKT conditions and Lagrangian analysis. Extensive simulations show that PCCR significantly outperforms a prominent baseline approach in terms of recruitment cost.

Cooperative driving is a promising technology in the future Connected Autonomous Vehicles (CAV) because of its benefits to safety and fuel efficiency. However, since CAV will be relying heavily on wireless communication to cooperatively coordinate road maneuvering, latency and reliability of communication still pose a challenge. In this paper, we propose a novel scheme based on deep learning prediction to enhance the uplink resource allocation process in 5G C-V2X. The proposed scheme enables the base station to predict vehicle maneuvers, subsequently, assign it the required resource in advance without the need for scheduling request and granting process. This scheme improved the ability of 5G NR to support cooperative driving requirements. Moreover, we compare both traditional and proposed schemes discussing issues that arise from the introduction of prediction models and possible approaches for further enhancements in the future.

The advent of the Internet-of-Things (IoT), which streams a wide range of computation-intensive applications with strict Quality of Service (QoS) requirements, has caused a paradigm shift from cloud computing to edge computing. Edge computing can drastically reduce latency and improve QoS. However, various dynamic changes can affect service continuity, thus requiring service migration. The dynamic computation load is one of the changes that are typically overlooked in service migration. In this paper, we propose the Dynamic Load-based Proactive Migration (DLPM) scheme. DLPM adopts a finite-state machine (FSM) that models the dynamic computation load, and proactively migrates computation tasks based on the associated transition probabilities. We formulate the service migration problem as an integer linear programming (ILP) optimization problem that aims to minimize the delay. We provide an analytical solution to the optimization problem using the KKT conditions and Lagrangian analysis. Performance evaluation shows that DLPM yields significant improvements in terms of delay and number of migrations compared to the reactive migration approach.

Multi-access Edge Computing (MEC), also known as Mobile Edge Computing, has gained significant momentum as a key facilitator of the stringent Quality of Service (QoS) requirements associated with delay-sensitive and data-intensive applications. Recently, the advantageous nature of MEC has been further enriched by leveraging the latent yet underused computational resources of Extreme Edge Devices (EEDs), such as smartphones, tablets, and autonomous vehicles. However, EEDs are typically user-owned devices, and thus have dynamic resource usage behavior since users dynamically navigate through various applications on their devices. This, along with the heterogeneity of EEDs, makes it harder to accurately estimate their computational capabilities, drastically affecting task allocation and resource utilization, thus increasing the delay. In this paper, we propose the Usage-based WOrker Resource Characterization (U-WORC) scheme to alleviate this problem and address the issues related to device heterogeneity, resource contention, and network communication delay. U-WORC presents a prediction-based approach to characterize the resources of EEDs (i.e., workers) by clustering the resource usage information and the corresponding execution time while running a benchmark task. Performance evaluation shows that U-WORC yields significant improvements that reach 91.42 % and 38.8 % in terms of characterization accuracy and task execution time, respectively, compared to a prominent scheme that does not consider resource contention and network communication delay.

Cloud radio access networks (C-RANs) are promising paradigms for the fifth-generation (5G) networks due to their interference management capabilities. In a C-RAN, a central processor (CP) is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resource blocks (RRBs). In this paper, we develop a novelcross-layer network coding (CLNC)approach that proposes to optimize RRH’s transmit powers and user’s rates in making the coding decisions. As such, cross-layer throughput of the network is maximized. The joint user scheduling, file encoding, and power adaptation problem is solved by designing a subgraph for each RRB, in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and power levels (PLs) for one RRB. It is then shown that the C-RAN throughput maximization problem is equivalent to a maximum-weight clique problem over the union of all such subgraphs, called herein the CRAN-CLNC graph. Numerical results revealed that the proposed joint and iterative schemes offer improved throughput performances as compared to the existing algorithms in the literature. Compared to our proposed joint scheme, our proposed iterative scheme has a certain degradation, roughly in the range of 9%–14%. This small degradation in the throughput performance of the iterative scheme comes at the achieved low computational complexity as compared to the high complexity of the joint scheme.

This paper proposes to maximize the accuracy of a distributed machine learning (ML) model trained on learners connected via the resource-constrained wireless edge. We jointly optimize the number of local/global updates and the task size allocation to minimize the loss while taking into account heterogeneous communication and computation capabilities of each learner. By leveraging existing bounds on the difference between the optimal and actual training loss, we derive an expression for the objective function in terms of the local updates. The resulting convex program is solved to obtain the optimal number of local updates which is used to obtain the total updates and batch sizes for each learner. The merits of the proposed solution, which is heterogeneity aware (HA), are exhibited by comparing its performance to the heterogeneity unaware (HU) approach.

Mobile Edge Learning (MEL) is a decentralized learning paradigm that enables resource-constrained IoT devices to either learn a shared model without sharing the data, or to distribute the learning task with the data to other IoT devices and utilize their available resources. In the former case, IoT devices (a.k.a learners) need to be assigned an orchestrator to facilitate the learning and models' aggregation from different learners. Whereas in the latter case, IoT devices act as orchestrators and look for learners with available resources to distribute the learning task to. However, the coexistence of multiple learning problems in an environment with limited resources poses the learners-orchestrator assignment problem. To this end, we aim to develop an energy-efficient learner assignment and task allocation scheme, in which each orchestrator gets assigned a group of learners based on their communication channel qualities and computational resources. We formulate and solve a multi-objective optimization problem to minimize the total energy consumption and maximize the learning accuracy. To reduce the solution complexity, we also propose a lightweight heuristic algorithm that can achieve near-optimal performance. The conducted simulations show that our proposed approaches can execute multiple learning tasks efficiently and significantly reduce energy consumption compared to current state-of-art methods.

Connected Autonomous Vehicles (CAV) utilize vehicular communication to collect information about the surrounding environment to make informed decisions about speed and maneuvering. This enables safe driving and decreases the number of accidents and thereby the associated fatalities. However, vehicular communication may suffer from high latency and low reliability, especially in dense vehicle environments, which may negatively affect the safety of CAVs. Therefore, it is crucial to study the impact of these metrics on the safety application performance while taking into account realistic CAV kinematics and dynamics. In this paper, we address this problem by comparing the performance of the Short Range Communication (DSRC) to that of the Fifth-Generation New Radio (5G-NR) and their impacts on the safety applications in the CAV environment under different settings. We develop a full-fledged simulation framework that can realistically model both vehicular mobility and communication and can capture the impact of communication on safety applications. Within this framework, we implement an important CAV's safety application, namely, the forward collision avoidance system, in which following vehicles use vehicular communications to gather information from leading vehicles to compute the safe speed and avoid collisions. We then use this framework to study and compare the performance safety of the forward collision avoidance system using both DSRC and 5G-NR communications. The results show that the packet delays and drops in communication networks can adversely affect CAV safety. The results also demonstrate that 5G is more capable of supporting the safety requirements under higher packet traffic loads and vehicle densities.

Federated Learning (FL) is a novel distributed learning paradigm in which local learning models are simultaneously trained using the stored data on multiple devices, then ultimately aggregated into a global model. A promising use case of FL is the training of a global model using the data collected by unmanned aerial vehicles (UAVs) during their flight, which is invaluable in scenarios in which an infrastructure cannot be accessed (e.g., disaster). However, this is challenging as limited resources are to be distributed between flight time, sensing, processing, and communication. In this paper, we address the resource problem for a set of heterogeneous UAVs with different computation and communication capabilities from distributed point of view. We propose the usage of Device-to-Device (D2D) communication to fairly distribute the data so-far collected by UAVs with different capabilities by posing it as an optimal transport problem. Our contribution is two-fold: (1) We obtain the fairest distribution of data given the UAVs’ computational capabilities such that global learning time is minimal; (2) We devise a scheme using Optimal Transport (OT) to achieve such a fair distribution between UAVs. The performance of the proposed techniques is demonstrated in an FL setting with different UAV topologies with the FL training done using the MNIST dataset.

Recently, there had been several proposals to use deep-learning based prediction models in estimating channel state information (CSI). However, all of these proposals were investigated under a fixed indoor-outdoor environment. In this paper, we propose two models to perform uplink CSI prediction in dynamic vehicular environments. One of these models is a tailoring of an existing state-of-the-art deep learning model, based on a combination of convolutional and recurrent neural networks (CNN-RNN), so as to suit the mobility factor in vehicular environments. The other model is a proposed simpler artificial neural network (ANN) model, again tailored to cope with the vehicular settings. We perform a comparative sensitivity analysis of the two models, in which we investigate the effect of changing vehicle speed, prediction horizon, and history horizons on the performance of both models. Interestingly, we have show that the simpler ANN model performs much better than the more sophisticated CNN-RNN model at broad range of vehicular driving speeds as long as the prediction horizon is smaller than the history horizon in the prediction process. The CNN-RNN becomes naturally more efficient in the opposite scenarios.

Vehicular networks are critical pieces in support of advanced intelligent transportation systems (ITS). These networks are formed by vehicles that can be connected to one another as well as to the infrastructure, and are subject to constant topology changes, disconnections, and data congestion. Each ITS application could have a different set of communication requirements, such as delay, bandwidth, and packet delivery ratio. Meeting these heterogeneous requirements in the complex dynamic environment of vehicular networks is a challenge. This paper develops a new framework for application-driven vehicular networks using 5G network slicing. We present the architecture of the proposed solution and design algorithms for heterogeneous traffic in a dynamic vehicular environment. Our simulations on realistic vehicular scenarios show significant improvements in network performance compared to the state-of-the-art approaches.

The exploding increase in the number of connected devices and growing sizes of their generated data gave more opportunities for distributed learning to dominate fast data analytics in mobile edge environments. In this work, we aim to jointly optimize the allocation of learning tasks and wireless resources in such environments with the aim of maximizing the number of local training cycles each device executes within a given time constraint, which was shown to achieve a faster convergence to the desired learning accuracy. This joint problem is formulated as a non-linear constrained integer-linear problem, which is proven to be NP-hard. The problem is then simplified into a simpler form by deducing the optimal solution for some parameters. We then employ numerical solvers to efficiently solve this simplified problem. Simulation results show gains up to 166% and 250% compared to the task allocation only and the resource allocation only techniques, respectively.

A rapid increase has been lately noticed in the number of individual and groups of users offloading independent and inter-related computational tasks to mobile edge computing (MEC) servers, thus overloading them and increasing risks of service interruptions. In response to this issue, reactive service replication has been suggested to enable individual and groups of users to access services on remote edge servers, thus guaranteeing system scalability. In this paper, we propose a task offloading and service replication scheme on local and remote MEC servers, which minimizes the response time of all users while satisfying the delay requirements of user groups involved in same traffic-heavy and/or multimedia-intense applications (e.g., online gaming, multimedia conferencing, augmenting reality). We formulate the problem as an integer non-linear problem, and solve it using numerical solvers. We then compare the performance of our optimized solution with distance-based and resource-based greedy approaches. Simulation results show that our optimized solution can achieve up to 14% and 13% performance gains in comparison to these two greedy approaches, respectively.

We consider a setting in which a rotary-wing unmanned aerial vehicle (UAV) acts as an aerial base station to provide emergency communication service to an area of unknown and inhomogeneous user distribution. The UAV has communication with a ground node deployed to the area, which acts as a charging station. We are interested in two important problems in this setting, namely the path planning and coverage mapping problems. In the path planning problem, the UAV must plan its path starting and ending at the charging station, visiting a series of waypoints over which it hovers to provide coverage to surrounding users. On the other hand, the coverage mapping problem focuses on learning the distribution of user coverage over the area. We highlight the importance of learning this distribution to collect valuable data in an emergency situation. We then propose an online algorithm that simultaneously solves the path planning and coverage mapping problems using a deep learning model. We highlight the interplay and conflicting goals of path planning and coverage mapping, but show through Monte Carlo simulation that, under the correct parameters, the algorithm is able to achieve success on both problems.

Autonomous electric mobility on demand (AEMoD) services are anticipated to be the future of private transportation, serving tens-of-thousands of requests per minute in large cities. To cope with this massive demand, a decentralized (i.e., zone-based) and multi-class management framework of AEMoD fleets was recently introduced. Yet, the inter-zone management of such approach has not been investigated. This paper thus fills this gap by studying the fleet re-balancing problem, with possible in-route charging, in decentralized multiclass AEMoD systems. A queuing model for multi-class re-balancing and possible in-route charging is developed on top of the system's decentralized fleet management. The stability conditions of this model are first derived, then the optimal inter-zone multi-class re-balancing and in-route charging decisions are derived so as to minimize the maximum response time in each deficient zone. Closed-form solutions are derived using Lagrangian analysis and simulations in a realistic setting in the city of Seattle are employed to illustrate the merits of our proposed re-balancing scheme as opposed to different baseline rebalancing approaches.

Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AEMoD) services in big cities are still threatened by two major bottlenecks, namely, the communication/computation and charging delays. In order to target the communication/computation delays, the paper suggests the exploitation of fog-based architectures for localized AEMoD system operations. These emerging architectures are soon to become widely used, allowing for all localized operational decisions to be made with very low latency by fog controllers located close to the end applications (e.g., each city zone for AEMoD systems). As for the charging delays, an optimized multi-class charging and dispatching queuing model, with partial charging option for AEMoD vehicles is developed for each of these zones. The stability conditions of this model and the optimal number of classes are then derived. The decisions on the proportions of each class vehicles to partially/fully charge or directly serve customers are optimized to minimize the maximum and average system response times using convex optimization and Lagrangian analysis. The results show the merits of our proposed model and optimized decision scheme compared to both the always-charge and the equal-split scheme. Furthermore, the comparison of the maximum and average response time minimization results shows a very low variance in performance, which suggests by using the linear programming solution for lower complexity.

To fully realize the potential of vehicular networks, several obstacles and challenges need to be addressed. Chief among the obstacles are strict QoS requirements of applications and differentiated service requirements in different situations. Although DSRC and WAVE have been adopted as the de facto standards, they do not address all the problems and there is room for improvements. In this study, we propose a generic prioritization and resource management algorithm that can be used to prioritize processing of received packets in vehicular networks. We formulate the generic severity-based prioritized packet processing problem as Penalized Multiple Knapsack Problem (PMKP) and prove that it is an NP-Hard problem. We thus develop a real-time heuristic that utilizes a relaxed version of the formulation. The relaxed formulation executes in polynomial time and guarantees a minimum delay per severity-level while respecting the processing rate constraint. To measure the performance of the proposed heuristic, real traffic data is used in a small-scale experiment. The proposed heuristic is tested against the PMKP solution and results show a small degradation of up to 4 percent in profit for the heuristic compared to the PMKP solution. Also, the proposed heuristic is tested against a non-prioritized processing algorithm that works using first come first served policy. Results show that the proposed heuristic gains 9 to 67 percent more profit than the non-prioritized processing algorithm in moderate and high congestion scenarios.

This paper considers a device-based Fog Radio Access Network (F-RAN), where users’ smart devices (denoted by F-UEs) can be exploited to cache popular files so as to communicate them when requested by other F-UEs among them. This architecture restricts the involvement of the central baseband processing unit (BBU) to serving those F-UEs not immediately served by their peers, thus offloading the BBU's fronthaul spectrum and increasing the overall capacity of the network. This paper aims to further maximize fronthaul offloading in this architecture using network coding (NC). The latter exploits both the cached and previously received files by different F-UEs as side information to serve more requesting F-UEs in each transmission from their peers or the BBU so as to maximize fronthaul offloading. Being half-duplex devices (they can only either send or receive in any given time), the problem of BBU fronthaul offloading in this setting is formulated over two transmission phases on an NC graph. After showing that this problem is NP-hard, the paper proposes two novel heuristic algorithms to solve it in real-time. In addition, a lower-bound on the offloading gain of these algorithms is derived for a special file placement case, their asymptotic optimality is proven, and their complexities are analyzed. Simulation results both show that these proposed algorithms perform closely to the optimal solution and quantify the significant offloading gains achieved by them.

Wireless communication became an essential tool for information exchange between modern Implantable Medical Devices (IMDs) and hospital servers. In spite of the many advantages of wireless technology, it puts the patients' health and data privacy in serious danger if no proper security mechanism is imployed. We aim to secure these devices while taking into consideration the limitations of these small devices. The IMDs have resources that are relatively simple and sometimes, once implemented in the body, require surgery to be altered. Consequently, common security mechanisms cannot be simply implemented in fear of consuming all the resources dedicated to healthcare needs. A certain balance between security and efficiency must thus be sought in each IMD architecture. In this work, we propose an encryption scheme for IMDs that stores its monitored data for future use. For privacy issues, not all the stored data should be accessed by any device that has access to the IMD. Certain privileges need to be allocated to different people to protect the privacy of the patient. Hence, we propose a new role-based encryption scheme, that both guarantees hierarchical access to personal data based on their role and still satisfies the computational limitations of IMDs. This scheme employs the Chinese Remainder properties to achieve the desired encryption hierarchy. The IMD uses keys form the same key pool for any encryption, and depending on the access rights of the users, the latter will only be able to decrypt the data he is allowed to. This work resulted in a secure scheme that we have proven it can formally protect the stored data. This scheme performs well under statistical analysis and is characterized by a relatively low complexity. Also, this work led to encrypted data with a lossless compression rate that saves on the communication cost.

We propose a real time data analysis solution for in-flight object detection. The presented solution is able to perform typical post-flight processing in real time, with minimal computational and power requirements, which allows its implementation on light-weight Unmanned Aircraft Systems (UAS). It utilizes adaptive segmentation and 3D convolutions that take advantage of the structure of the LiDAR point cloud, to identify vehicles and their respective positions within 3D point cloud segments that may include background clutter.

Fog radio access networks (F-RANs) have recently been postulated as an innovative solution to improve the fronthaul capacities of cloud base stations (CBSs). This architecture extends the CBS service by involving enhanced remote radio heads (eRRHs), which can pre-store and transmit popular files at the network edge (i.e., close to the end users). This is referred to as caching, and it allows the offloading of CBS resources, e.g., time and frequency. Recent works have been proposed to use rate-aware network coding in order to exploit the previously downloaded popular files at the users’ devices. As such, the CBS offloading is maximized. However, the users’ achieved Quality of Service (QoS), and the standard F-RANs physical-layer resource optimization have not received any attention to date. This paper proposes use of an innovative cross-layer network coding (CLNC) to address the above-mentioned issues. The proposed CLNC scheme is not only aware of different users’ rates but also controls the rates by jointly optimizing coding combinations, users-eRRHs/power zones (PZs) assignments, and transmission power in the PZs. Using a graph theoretical representation, we formulate the joint cross-layer CBS offloading and QoS guarantee problem and show its NP-hardness. Joint and iterative heuristic approaches are then developed to solve this problem using greedy vertex search and coloring techniques. The proposed approaches are finally validated and tested against the existing algorithms in the literature.

Mobility-on-Demand (MoD) systems using electric vehicles (EVs) are expected to play a significantly increasing role with urban transportation systems in the near future, to both cope with the massive increases in urban population and reduce carbon emissions. One inconvenience in MoD-EV systems is the need for some customers to perform in-routing charging for almost-out-of-charge EVs. In this paper, we propose a routing scheme that aims to reduce this inconvenience by minimizing the relative excess time spent by MoD-EV systems customers for in-route charging compared to the on-road trip time. By modeling the routing problem between multiple MoD-EV stations with in-route charging as a multi-server queuing system, we formulate our objective as a stochastic convex optimization problem that minimizes the average overall trip time for all customers relatively to their actual trip time without in-route charging. Both single and multiple charging units per charging station are considered in this paper and modeled as M/M/1 and M/M/c queues, respectively. For both types of queues, the optimal routing proportions are derived analytically using the Lagrangian analysis and the Karush-Kuhn-Tucker conditions. Simulation results show the merits of our proposed solution in both cases as compared to the shortest time and the random routing decisions. Finally, the proposed method is tested on a real-world scenario, and the computation times are calculated for different settings.

Cloud radio access networks (C-RAN) gained much attention thanks to their abilities in mitigating interference and providing high data rates by coordinating multiple Remote Radio Heads (RRHs). This paper considers the use of rate aware instantly decodable network coding (RA-IDNC) as a mean to accelerate the broadcast of a set of messages to a set of users in a C-RAN setting. While previous works focus either on rate-unaware IDNC or rate adaptation for traditional single transmitter systems, this paper extends the results to C-RANs. The various ergodic capacities of the different users to the different RRHs bring a new trade-off between the number of scheduled users and the transmission rates. The proposed framework incorporates such information in the network coding decisions, so as the scheduled users, coded messages, and transmission rates reduce the overall completion time. Given the intractability of the problem, the paper proposes relaxing the optimization by an online approach involving an anticipated version of the completion time which allows mapping the possible associations between users, RRHs, coded packets, and transmission rates to vertices in a newly designed graph. Afterward, the online completion time reduction problem is shown to be equivalent to a maximum weight independent set problem over the proposed graph. Simulation results reveal that the proposed scheme achieves substantial performance gain over uncoded and NC rate-unaware algorithms.

In this paper, we consider the use of cross- layer network coding and fog radio access networks (F- RANs) as a means to jointly optimize users quality-of-service and offload the cloud servers and cellular macro base- stations. Multiple edge nodes called enhanced remote radio heads (eRRHs) are connected to a central unit known as cloud base station (CBS). The transmit frame of each eRRH consists of multiple resources blocks called power zones (PZs), each fixed at a pre-assigned power level. The various ergodic capacities of different users at different PZs/eRRHs and the CBS bring a new trade-off between the number of multiplexed users and the transmission rates of each PZ and each CBS allocated channel. The proposed framework incorporates such information in the network coding decisions. As such, the multiplexed users, encoded files, and transmission rates of each PZ/eRRH and each CBS allocated channel maximizes the throughput which is defined as the number of correctly received bits and actual CBS physical-resource offloading, respectively. The problem is first formulated using graph theory techniques, and its intractability is shown. Given the difficulty of the problem, the paper proposes a heuristic approach by dividing it into two sequential subproblems and solving each subproblem efficiently. Presented simulation results reveal that the proposed solution achieves small offloading performance degradation compared to the cross-layer QoS unaware scheme but largely maximizes the received throughput compared to the state-of- art algorithms.

In this paper, we present a novel approach to track recognized 3D vehicle point clouds from LIDAR scans. This technique is based on tracing the 3D anchor boxes of these vehicles, recognized though convolutional neural networks (CNNs). Exploiting the 3D CNNs detection of vehicles and persons, and the Extended Kalman Filters (EKF) two-steps process for prediction and update, the proposed scheme guarantees the awareness of moving detected objects and improves the perception of Autonomous Vehicles (AV). The proposed scheme reduces the usage of the expensive detection process of feeding the point cloud to CNN by tracking the 3D rectangular coordinates containing already detected objects from early Velodyne scans of the driving sequences. The testing of the proposed method on the well-known KITTI dataset, featuring LIDAR scans of realistic vehicular environments. Results show the merits of the proposed scheme in achieving high tracking accuracy.

Mobile edge computing (MEC) enables the computation of complex tasks efficiently on end-devices. It has been shown that MEC can potentially reduce the delay and offer energy-efficient operation. So far, the research has been focused on or limited to multi-server optimization for partial offloading or competing to offload to the best server among different sets of mobile device. In this paper, we propose a hierarchical structure where user devices that have available resources can act as servers without the need to offload to the cloud. Results show that this model improves the system utility. Furthermore, there is a tradeoff between the number of users in the network and the number of peers willing to accept tasks. As the number of users increase, the utility of the hierarchical system keeps increasing whereas the simple MEC system utility saturates.

On-Demand electric vehicle (EV) systems are expected to play a significantly increasing role in near future urban transportation systems, to cope with the massive increases in urban population and reduce global carbon emissions. One inconvenience in MoD-EV systems is the need of some customers to perform in-routing charging, which may cause delays in the trip time. Moreover, the customer choice of which station to charge at is an operational issue for the MoD-EV service operator due to the different pricing for the charging at different stations. Given a connected system linking these EVs and charging stations to the operator, we propose a routing scheme that aims to reduce these inconveniences for both the customers and the operator. By modeling the routing problem between multiple MoD-EV stations with in-route charging as a multi-server queuing system, we formulate the joint problem of minimizing the average overall trip time for all customers, relative to their actual trip time without in-route charging, and the average overall cost of charging as a dual-objective stochastic convex optimization problem. Optimal routing decisions are then derived analytically for any arbitrary weighting of the two problem objectives. Simulation results show the significant merits of our proposed solution as compared to shortest time and random routing decisions. They also illustrate the trade-offs between the delay experienced by the customer and the charging cost for the operator.

On-Demand electric vehicle (EV) systems are expected to have a significantly increasing role in the future of transportation systems in urban areas, to cope with the tremendous increases in urban population and decrease global carbon emissions. An inconvenience in Mobility-on-Demand Electric Vehicle (MoD-EV) systems is the need for some customers to charge EVs before reaching their destinations, which may cause delays in the trip time. Local and in-route charging options are available but the system operator needs to manage the charging assignments of the different EVs as charging all EVs locally may result in large delays. Given a connected system, we propose a routing strategy that aims to decrease these charging delays for the customers by sending the customers to different charging stations either at the pick-up location or nearby charging stations to minimize their average total trip time while respecting the charging constraints and avoid roads congestion. The problem is then formulated by modeling the routing between multiple MoD-EV stations as a multi-server queuing system with an objective of minimizing the expected overall trip duration for all customers, relative to their actual trip time without charging as a convex optimization problem. Optimal routing decisions and actual trip times are then derived analytically and simulation results show the significant gains of our proposed model as compared to shortest path and random routing schemes.

This paper considers a device-to-device (D2D) fog-radio access network wherein a set of users are required to store/receive a set of files. The D2D devices are connected to a subset of the cloud data centers and thus possess a subset of the data. This paper is interested in reducing the total time of communication, i.e., the completion time, required to disseminate all files among all devices using instantly decodable network coding (IDNC). Unlike previous studies that assume a fully connected communication network, this paper tackles the more realistic scenario of a partially connected network in which devices are not all in the transmission range of one another. The joint optimization of selecting the transmitting device(s) and the file combination(s) is first formulated, and its intractability is exhibited. The completion time is approximated using the celebrated decoding delay approach by deriving the relationship between the quantities in a partially connected network. The paper introduces the cooperation graph and demonstrates that the problem is equivalent to a maximum weight clique problem over the newly designed graph. Extensive simulations reveal that the proposed solution provides noticeable performance enhancement and outperforms previously proposed IDNC-based schemes.

We study the problem of reducing the download time of multiple files requested by multiple clients from multiple cloud/fog storage servers. Given possible previous file downloads by the clients, network coding can be efficiently exploited to expedite the download process. Since each client can tune to only one server at a time, the sets of clients served by the different servers must be disjoint in order to guarantee a maximum reduction in download time. To accomplish disjoint download mechanisms, a dual conflict network coding graph is proposed. Given the intractability of the long-term optimal solution, we propose an online algorithm using the designed dual conflict graph. For the case of one file request per client, both asymptotic lower and upper bounds of the performance of the proposed conflict-free algorithm are derived. Simulation results show that this proposed algorithm exhibits near optimum performance compared to the optimum solution, and a significant reduction in download time as compared to the per-server network coding scheme. Furthermore, imperfect feedback environment scenarios are investigated. A maximum likelihood approach is employed at the server to estimate the network state, which is then incorporated in our proposed algorithm to reduce the download time in such scenarios.

This paper considers the problem of reducing the broadcast decoding delay of wireless networks using instantly decodable network coding- based device-to-device communications. In contrast with the previous works that assume a fully connected network, this paper investigates a partially connected configuration in which multiple devices are allowed to transmit simultaneously. To that end, different events occurring at each device are identified so as to derive an expression for the probability distribution of the decoding delay. Afterward, the joint optimization problem over the set of transmitting devices and packet combination of each is formulated. The optimal solution of the joint optimization problem is derived using a graph-theoretic approach by introducing the cooperation graph in which each vertex represents a transmitting device with a weight translating its contribution to the network. This paper solves the problem by reformulating it as a maximum weight clique problem which can efficiently be solved. Numerical results suggest that the proposed solution outperforms state-of-the-art schemes and provides significant gain, especially for poorly connected networks.

Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestion at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability in multi-channel cache-enabled 5G networks with both unicast/multicast capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing the opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. An optimization framework for file caching is then developed to maximize the hit probability. To this end, a simple concave approximation for the hit probability is proposed, which highly reduces the optimization complexity and leads to a closed-form solution. The sub-optimal solution is benchmarked against two widely employed caching distribution schemes, namely, uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the caching strategy should be adapted to the network parameters and capabilities. For instance, diversifying file caching according to the Zipf distribution is better in multicast systems with large number of channels. However, when the number of channels is low and/or the network is restricted to unicast transmissions, it is better to confine caching to the most popular files only.

Cache-enabled small base station (SBS) densification is foreseen as a key component of 5G cellular networks. This architecture enables storing popular files at the network edge (i.e., SBS caches), which empowers local communication and alleviates traffic congestions at the core/backhaul network. This paper develops a mathematical framework, based on stochastic geometry, to characterize the hit probability of a cache-enabled multicast 5G network with SBS multi-channel capabilities and opportunistic spectrum access. To this end, we first derive the hit probability by characterizing opportunistic spectrum access success probabilities, service distance distributions, and coverage probabilities. The optimal caching distribution to maximize the hit probability is then computed. The performance and trade-offs of the derived optimal caching distributions are then assessed and compared with two widely employed caching distribution schemes, namely uniform and Zipf caching, through numerical results and extensive simulations. It is shown that the Zipf caching almost optimal only in scenarios with large number of available channels and large cache sizes.

This paper studies the problem of offloading the fronthaul of Fog Radio Access Networks (F-RANs) with smart user equipments (denoted by F-UEs) that can cache files and cooperate with each other to retrieve their requested files. This cooperation between the F-UE devices will reduce the load on the fronthaul of the central baseband processing unit (BBU), thus increasing the overall capacity of the network. By smartly employing network coding (NC), this paper aims to minimize the number of transmissions required from the BBU given a realistic half-duplex transmission scenario. In this setting, F-UE devices can only send or receive at a time, and thus must receive their requested files in maximum two time-slots in order to achieve high quality of experience (QoE). The above problem is first formulated over an NC graph. Being NP-hard, two heuristic algorithms are proposed to solve the problem in real-time. Simulation results show that these proposed heuristics perform closely to the optimal solution. They also demonstrate the significant fronthaul offloading gains achieved by our proposed algorithms.

In this paper, we design a multimodal framework for object detection, recognition and mapping based on the fusion of stereo camera frames, point cloud Velodyne LIDAR scans, and Vehicle-to-Vehicle (V2V) Basic Safety Messages (BSMs) that are exchanged using Dedicated Short Range Communication (DSRC). We merge the key features of rich texture descriptions of objects from 2D images using Convolutional Neural Networks (CNN). In addition, depth and distance between objects are provided by the 3D LIDAR point cloud and the awareness of hidden vehicles is achieved from BSMs' beacons. We present a joint pixel to point cloud and pixel to V2V correspondence of objects in frames of driving sequences in the KITTI Vision Benchmark Suite. We achieve this by using a semi-supervised manifold alignment approach to achieve camera-LIDAR and camera-V2V mapping of their recognized persons and cars that have the same underlying manifold.

This paper considers a device-to-device (D2D) fog-radio access network wherein a set of devices are required to store a set of files. The D2D devices are connected to a subset of the cloud data centers and thus possess a subset of the data. This paper is interested in reducing the total time of communication, i.e., the completion time, needed to disseminate all files among the devices using instantly decodable network coding (IDNC). Unlike previous studies that assume a fully connected communication network, this paper tackles the more realistic scenario of a partially connected network in which devices can only target devices in their transmission range. The joint optimization of selecting the transmitting device(s) and the file combination(s) is first formulated and its intractability exhibited. The completion time is approximated using the celebrated decoding delay approach by deriving the relationship between the quantities in a partially connected network. The paper introduces the cooperation graph and demonstrates that under the collision-free transmissions assumption, the problem is equivalent to a maximum weight clique problem over the newly designed graph. Extensive simulations reveal that the proposed solution provides noticeable performance enhancement and outperforms previously proposed IDNC-based schemes.

Mobility-On-Demand (MoD) systems using electric vehicles (EVs) are expected to play a significantly increasing role in near future urban transportation systems, to cope with the massive increases in urban population and reduce carbon emissions. One inconvenience in MoD- EV systems is the need of some customers to perform in-routing charging. In this paper, we propose a routing scheme that aims to reduce the inconvenience of in-route charging in MoD-EV systems. By modeling the routing problem between multiple MoD-EV stations with in-route charging as a multi-server queuing system, we formulate our objective as a stochastic convex optimization problem that minimizes the average overall trip time for all customers relative to their actual trip time without in-route charging. Optimal routing decisions that achieve this goal are then derived analytically. Simulation results show the significant merits of our proposed solution as compared to shortest and random routing decisions.

Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AEMoD) services are still threatened by two major bottlenecks, namely the computational and charging delays. This paper proposes a solution for these two challenges by suggesting the use of fog computing for AEMoD systems, and developing an optimized multi-class charging and dispatching scheme for its vehicles. A queuing model representing the proposed multi-class charging and dispatching scheme is first introduced. The stability conditions of this model and the number of classes that fit the charging capabilities of any given city zone are then derived. Decisions on the proportions of each class vehicles to partially/fully charge, or directly serve customers are then optimized using a stochastic linear program that minimizes the maximum response time of the system. Results show the merits of our proposed model and optimized decision scheme compared to both the always-charge and the equal split schemes.

This paper studies the cloud offloading gains of using heterogeneous enhanced remote radio heads (eRRHs) and dual-interface clients in fog radio access networks (F-RANs). First, the cloud offloading problem is formulated as a collection of independent sets selection problem over a network coding graph, and its NP-hardness is shown. Therefore, a computationally simple online heuristic algorithm is proposed, that maximizes cloud offloading by finding an efficient schedule of coded file transmissions from the eRRHs and the cloud base station (CBS). Furthermore, a lower bound on the average number of required CBS channels to serve all clients is derived. Simulation results show that our proposed framework that uses both network coding and a heterogeneous F-RAN setting enhances cloud offloading as compared to conventional homogeneous F-RANs with network coding.

Cache-enabled base station (BS) densification, denoted as a fog radio access network (F-RAN), is foreseen as a key component of 5G cellular networks. F-RAN enables storing popular files at the network edge (i.e., BS caches), which empowers local communication and alleviates traffic congestions at the core/backhaul network. The hitting probability, which is the probability of successfully transmitting popular files request from the network edge, is a fundamental key performance indicator (KPI) for F-RAN. This paper develops a scheduling aware mathematical framework, based on stochastic geometry, to characterize the hitting probability of F-RAN in a multi-channel environment. To this end, we assess and compare the performance of two caching distribution schemes, namely, uniform caching and Zipf caching. The numerical results show that the commonly used single channel environment leads to pessimistic assessment for the hitting probability of F-RAN. Furthermore, the numerical results manifest the superiority of the Zipf caching scheme and quantify the hitting probability gains in terms of the number of channels and cache size.

This paper investigates the delay minimization problem for instantly decodable network coding (IDNC) based device-to-device (D2D) communications. In D2D enabled systems, users cooperate to recover all their missing packets. The paper proposes a game theoretic framework as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. The session is modeled by self-interested players in a non-cooperative potential game. The utility functions are designed so as increasing individual payoff results in a collective behavior which achieves both a desirable system performance in a shared network environment and the Nash equilibrium. Three games are developed whose first reduces the completion time, the second the maximum decoding delay and the third the sum decoding delay. The paper, further, improves the formulations by including a punishment policy upon collision occurrence so as to achieve the Nash bargaining solution. Learning algorithms are proposed for systems with complete and incomplete information, and for the imperfect feedback scenario. Numerical results suggest that the proposed game-theoretical formulation provides appreciable performance gain against the conventional point-to-multipoint (PMP), especially for reliable user-to-user channels.

While network densification is considered an important solution to cater the ever-increasing capacity demand, its effect on the handover (HO) rate is overlooked. In dense 5G networks, HO delays may neutralize or even negate the gains offered by network densification. Hence, user mobility imposes a nontrivial challenge to harvest capacity gains via network densification. In this paper, we propose a velocity-aware HO management scheme for two-tier downlink cellular network to mitigate the HO effect on the foreseen densification throughput gains. The proposed HO scheme sacrifices the best base station (BS) connectivity, by skipping HO to some BSs along the user trajectory, to maintain longer connection durations and reduce HO rates. Furthermore, the proposed scheme enables cooperative BS service and strongest interference cancellation to compensate for skipping the best connectivity. To this end, we consider different HO skipping scenarios and develop a velocity-aware mathematical model, via stochastic geometry, to quantify the performance of the proposed HO schemes in terms of the coverage probability and user throughput. The results highlight the HO rate problem in dense cellular environments and show the importance of the proposed HO schemes. Finally, the value of BS cooperation along with handover skipping is quantified for different user mobility profiles.

This paper considers a heterogeneous network architecture wherein devices use two wireless interfaces to receive packets from the base station and to transmit or receive packets from other devices concurrently. For such a network architecture, this paper focuses on time-critical and order-constrained applications that require quick and reliable in-order decoding of the packets. This paper first introduces the dual delivery delay as a measure of degradation compared with the optimal in-order packet delivery to the devices. It then addresses the minimum delivery delay problem using instantly decodable network coding (IDNC). In particular, the dual interface IDNC graph is constructed to represent all feasible coding opportunities and conflict-free transmissions. Subsequently, the minimum delivery delay problem is shown to be equivalent to a maximum weight independent set selection problem over the dual interface IDNC graph. Simulation results demonstrate that the proposed IDNC algorithm effectively reduces the delivery delay as compared with the existing network coding algorithms. Especially, for a layered video transmission, the proposed solution provides a sequential delivering of video layers to individual devices.

Network densification has always been an important factor to cope with the ever increasing capacity demand. Deploying more base stations (BSs) improves the spatial frequency utilization, which increases the network capacity. However, such improvement comes at the expense of shrinking the BSs' footprints, which increases the handover (HO) rate and may diminish the foreseen capacity gains. In this paper, we propose a cooperative HO management scheme to mitigate the HO effect on throughput gains achieved via cellular network densification. The proposed HO scheme relies on skipping HO to the nearest BS at some instances along the user's trajectory while enabling cooperative BS service during HO execution at other instances. To this end, we develop a mathematical model, via stochastic geometry, to quantify the performance of the proposed HO scheme in terms of coverage probability and user throughput. The results show that the proposed cooperative HO scheme outperforms the always best connected based association at high mobility. Also, the value of BS cooperation along with handover skipping is quantified with respect to the HO skipping only that has recently appeared in the literature. Particularly, the proposed cooperative HO scheme shows throughput gains of 12% to 27% and 17% on average, when compared to the always best connected and HO skipping only schemes at user velocity ranging from 80 km/h to 160 Km/h, respectively.

Cellular operators are continuously densifying their networks to cope with the ever-increasing capacity demand. Furthermore, an extreme densification phase for cellular networks is foreseen to fulfill the ambitious fifth generation (5G) performance requirements. Network densification improves spectrum utilization and network capacity by shrinking base stations' (BSs) footprints and reusing the same spectrum more frequently over the spatial domain. However, network densification also increases the handover (HO) rate, which may diminish the capacity gains for mobile users due to HO delays. In highly dense 5G cellular networks, HO delays may neutralize or even negate the gains offered by network densification. In this paper, we present an analytical paradigm, based on stochastic geometry, to quantify the effect of HO delay on the average user rate in cellular networks. To this end, we propose a flexible handover scheme to reduce HO delay in case of highly dense cellular networks. This scheme allows skipping the HO procedure with some BSs along users' trajectories. The performance evaluation and testing of this scheme for only single HO skipping shows considerable gains in many practical scenarios.

In this paper, we consider a fully connected device-to-device communications network, where a group of devices with heterogeneous channel capacities cooperate with each other to recover their missing packets. In such cooperative network, we aim to minimize the completion time required for recovering all missing packets at devices using instantly decodable network coding (IDNC). In particular, we first introduce a new IDNC graph to represent all feasible rate and coding decisions for all potential transmitting devices in one unified framework. We then show that finding the optimal schedule that minimizes the completion time is computationally complex. Nevertheless by using the new graph and the properties of the optimal schedule, we design a completion time reduction heuristic that balances between the transmission rate and the number of targeted devices with a new packet. Simulation results show that our proposed IDNC algorithm provides an appreciable completion time gain compared to the conventional rate oblivious network coding algorithms.

In this paper, we address the problem of reducing the completion time of a radio access network to deliver a frame of messages using Rate Aware Instantly Decodable Network Coding (RA-IDNC). While previous works only considered a single base-station setting, this paper extends the results to a more modern paradigm of networks with multiple coordinated base-stations. The different rates of the base-stations to the various users will be thus incorporated in the network coding decisions, so as to schedule the coded messages and transmission rates jointly in order to reduce the overall completion time. Given the notorious intractability of the completion time reduction problem, the paper uses an online relaxation using an anticipated version of the completion time. This problem is then solved by showing that it is equivalent to a maximum weight independent set problem on a newly designed graph. An efficient multi-layer heuristic is further developed to address this problem in polynomial time. Simulation results suggest that the proposed solution outperforms the uncoded schemes.

Consider a radio access network wherein a basestation is required to deliver a set of order-constrained messages to a set of users over independent erasure channels. This paper studies the delivery time reduction problem using instantly decodable network coding (IDNC). Motivated by time-critical and order-constrained applications, the delivery time is defined, at each transmission, as the number of undelivered messages. The delivery time minimization problem being computationally intractable, most of the existing literature on IDNC propose suboptimal online solutions. This paper suggests a novel method for solving the problem by introducing the delivery delay as a measure of distance to optimality. An expression characterizing the delivery time using the delivery delay is derived, allowing the approximation of the delivery time minimization problem by an optimization problem involving the delivery delay. The problem is, then, formulated as a maximum weight clique selection problem over the IDNC graph wherein the weight of each vertex reflects its corresponding user and message's delay. Simulation results suggest that the proposed solution achieves lower delivery and completion times as compared to the best-known heuristics for delivery time reduction.

The Received Signal Strength (RSS) based fingerprinting approaches for indoor localization pose a need for updating the fingerprint databases due to dynamic nature of the indoor environment. This process is hectic and time-consuming when the size of the indoor area is large. The semi-supervised approaches reduce this workload and achieve good accuracy around 15 percent of the fingerprinting load but the performance is severely degraded if it is reduced below this level. We propose an indoor localization framework that uses unsupervised manifold alignment. It requires only 1 percent of the fingerprinting load, some crowd sourced readings, and plan coordinates of the indoor area. The 1 percent fingerprinting load is used only in perturbing the local geometries of the plan coordinates. The proposed framework achieves less than 5 m mean localization error, which is considerably better than semi-supervised approaches at very small amount of fingerprinting load. In addition, the few location estimations together with few fingerprints help to estimate the complete radio map of the indoor environment. The estimation of radio map does not demand extra workload rather it employs the already available information from the proposed indoor localization framework. The testing results for radio map estimation show almost 50 percent performance improvement by using this information as compared to using only fingerprints.

Next-generation cellular networks are expected to be assisted by femtocaches (FCs), which collectively store the most popular files for the clients. Given any arbitrary non-fragmented placement of such files, a strict no-latency constraint, and clients' prior knowledge, new file download requests could be efficiently handled by both the FCs and the macrocell base station (MBS) using opportunistic network coding (ONC). In this paper, we aim to find the best allocation of coded file downloads to the FCs so as to minimize the MBS involvement in this download process. We first formulate this optimization problem over an ONC graph, and show that it is NP-hard. We then propose a greedy approach that maximizes the number of files downloaded by the FCs, with the goal to reduce the download share of the MBS. This allocation is performed using a dual conflict ONC graph to avoid conflicts among the FC downloads. Simulations show that our proposed scheme almost achieves the optimal performance and significantly saves on the MBS bandwidth.

In this paper, we design a conflict free instantly decodable network coding (IDNC) solution for file download from distributed storage servers. Considering previously downloaded files at the clients from these servers as side information, IDNC can speed up the current download process. However, transmission conflicts can occur since multiple servers can simultaneously send IDNC combinations of files to the same client, which can tune to only one of them at a time. To avoid such conflicts and design more efficient coded download patterns, we propose a dual conflict IDNC graph model, which extends the conventional IDNC graph model in order to guarantee conflict free server transmissions to each of the clients. We then formulate the download time minimization problem as a stochastic shortest path problem whose action space is defined by the independent sets of this new graph. Given the intractability of the solution, we design a channel-aware heuristic algorithm and show that it achieves a considerable reduction in the file download time, compared to applying the conventional IDNC approach separately at each of the servers.

In this paper, as an extension to [1], we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in hybrid (interweave and underlay) cellular cognitive radio networks. This scheme allows the uncoordinated collaboration between the collocated primary and cognitive radio base-stations in order to minimize their own as well as each other's packet recovery overheads, thus by improving their throughput. The proposed scheme ensures that each network's performance is not degraded by its help to the other network. Moreover, it guarantees that the primary network's interference threshold is not violated in the same and adjacent cells. Yet, the scheme allows the reduction of the recovery overhead in the collocated primary and cognitive radio networks. The reduction in the cognitive radio network is further amplified due to the perfect detection of spectrum holes which allows the cognitive radio base station to transmit at higher power without fear of violating the interference threshold of the primary network. For the secondary network, simulation results show reductions of 20% and 34% in the packet recovery overhead, compared to the non-collaborative scheme, for low and high probabilities of primary packet arrivals, respectively. For the primary network, this reduction was found to be 12%.

One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment efforts required to construct the radio maps through fingerprinting. In this paper, we aim to design an indoor localization scheme that can be directly employed without building a full fingerprinted radio map of the indoor environment. By accumulating the information of localized RSSs, this scheme can also simultaneously construct the radio map with limited calibration. To design this scheme, we employ a source data set that possesses the same spatial correlation of the RSSs in the indoor environment of interest. The knowledge of this data set is then transferred to a limited number of calibration fingerprints and one or several RSS observations with unknown locations, in order to perform direct localization of these observations using manifold alignment. We test two different source data sets, namely a simulated radio propagation map and the environment's plan coordinates. For moving users, we exploit the correlation of their observations to improve their localization accuracy. The online testing in two indoor environments shows that the plan coordinates achieve better results than the simulated radio maps, and a negligible degradation with 70-85 percent reduction in the calibration load.

In this paper, we consider the problem of minimizing the completion delay for instantly decodable network coding (IDNC) in wireless multicast and broadcast scenarios. We are interested in this class of network coding due to its numerous benefits, such as low decoding delay, low coding and decoding complexities, and simple receiver requirements. We first extend the IDNC graph, which represents all feasible IDNC coding opportunities, to efficiently operate in both multicast and broadcast scenarios. We then formulate the minimum completion delay problem for IDNC as a stochastic shortest path (SSP) problem. Although finding the optimal policy using SSP is intractable, we use this formulation to draw the theoretical guidelines for the policies that can minimize the completion delay in IDNC. Based on these guidelines, we design a maximum weight clique selection algorithm, which can efficiently reduce the IDNC completion delay in polynomial time. We also design a quadratic-time heuristic clique selection algorithm, which can operate in real-time applications. Simulation results show that our proposed algorithms significantly reduce the IDNC completion delay compared to the random and maximum-rate algorithms, and almost achieve the global optimal completion delay performance over all network codes in broadcast scenarios.

This paper considers the multicast decoding delay reduction problem for generalized instantly decodable network coding (G-IDNC) over persistent erasure channels with feedback imperfections. The feedback scenario discussed is the most general situation in which the sender does not always receive acknowledgments from the receivers after each transmission and the feedback communications are subject to loss. The decoding delay increment expressions are derived and employed to express the decoding delay reduction problem as a maximum weight clique problem in the G-IDNC graph. This paper provides a theoretical analysis of the expected decoding delay increase at each time instant. Problem formulations in simpler channel and feedback models are shown to be special cases of the proposed generalized formulation. Since finding the optimal solution to the problem is known to be NP-hard, a suboptimal greedy algorithm is designed and compared with blind approaches proposed in the literature. Through extensive simulations, the proposed algorithm is shown to outperform the blind methods in all situations and to achieve significant improvement, particularly for high time-correlated channels.

For several years, the completion time and the decoding delay problems in Instantly Decodable Network Coding (IDNC) were considered separately and were thought to completely act against each other. Recently, some works aimed to balance the effects of these two important IDNC metrics but none of them studied a further optimization of one by controlling the other. In this paper, we study the effect of controlling the decoding delay to reduce the completion time below its currently best known solution. We first derive the decoding-delay-dependent expressions of the users' and their overall completion times. Although using such expressions to find the optimal overall completion time is NP-hard, we use a heuristic that minimizes the probability of increasing the maximum of these decoding-delay-dependent completion time expressions after each transmission through a layered control of their decoding delays. Simulation results show that this new algorithm achieves both a lower mean completion time and mean decoding delay compared to the best known heuristic for completion time reduction. The gap in performance becomes significant for harsh erasure scenarios.

In this paper, we introduce a game theoretic framework for studying the problem of minimizing the completion time of instantly decodable network coding (IDNC) for cooperative data exchange (CDE) in decentralized wireless network. In this configuration, clients cooperate with each other to recover the erased packets without a central controller. Game theory is employed herein as a tool for improving the distributed solution by overcoming the need for a central controller or additional signaling in the system. We model the session by self-interested players in a non-cooperative potential game. The utility function is designed such that increasing individual payoff results in a collective behavior achieving both a desirable system performance in a shared network environment and the Pareto optimal solution. We further show that our distributed solution achieves the centralized solution. Through extensive simulations, our approach is compared to the best performance that could be found in the conventional point-to-multipoint (PMP) recovery process. Numerical results show that our formulation largely outperforms the conventional PMP scheme in most practical situations and achieves a lower delay.

In this paper, we consider the problem of minimizing the maximum broadcast decoding delay experienced by all the receivers of generalized instantly decodable network coding (IDNC). Unlike the sum decoding delay, the maximum decoding delay as a definition of delay for IDNC allows a more equitable distribution of the delays between the different receivers and thus a better Quality of Service (QoS). In order to solve this problem, we first derive the expressions for the probability distributions of maximum decoding delay increments. Given these expressions, we formulate the problem as a maximum weight clique problem in the IDNC graph. Although this problem is known to be NP-hard, we design a greedy algorithm to perform effective packet selection. Through extensive simulations, we compare the sum decoding delay and the max decoding delay experienced when applying the policies to minimize the sum decoding delay and our policy to reduce the max decoding delay. Simulations results show that our policy gives a good agreement among all the delay aspects in all situations and outperforms the sum decoding delay policy to effectively minimize the sum decoding delay when the channel conditions become harsher. They also show that our definition of delay significantly improve the number of served receivers when they are subject to strict delay constraints.

The main limitation of deploying/updating Received Signal Strength (RSS) based indoor localization is the construction of fingerprinted radio map, which is quite a hectic and time-consuming process especially when the indoor area is enormous and/or dynamic. Different approaches have been undertaken to reduce such deployment/update efforts, but the performance degrades when the fingerprinting load is reduced below a certain level. In this paper, we propose an indoor localization scheme that requires as low as 1% fingerprinting load. This scheme employs unsupervised manifold alignment that takes crowd sourced RSS readings and localization requests as source data set and the environment's plan coordinates as destination data set. The 1% fingerprinting load is only used to perturb the local geometries in the destination data set. Our proposed algorithm was shown to achieve less than 5 m mean localization error with 1% fingerprinting load and a limited number of crowd sourced readings, when other learning based localization schemes pass the 10 m mean error with the same information.

This paper studies the complicated interplay of the completion time (as a measure of throughput) and the decoding delay performance in instantly decodable network coded (IDNC) systems over wireless broadcast erasure channels with memory. We propose two new algorithms that enable a tradeoff for an improved balance between completion time and decoding delay of broadcasting a block of packets. We first formulate the IDNC packet selection problem that improves the balance between completion time and decoding delay as a statistical shortest path (SSP) problem. However, since finding such packet selection policy using the SSP technique is computationally complex, we employ its geometric structure to find some guidelines and use them to propose two efficient heuristic packet selection algorithms for broadcast erasure channels with a wide range of memory conditions. It is shown that each one of the two proposed algorithms is superior for a specific range of memory conditions. Furthermore, we show that the proposed algorithms achieve an improved fairness in terms of the decoding delay across all receivers.

In this paper, we study the multicast completion and decoding delay minimization problems for instantly decodable network coding (IDNC) in the case of lossy feedback. When feedback loss events occur, the sender falls into uncertainties about packet reception at the different receivers, which forces it to perform partially blind selections of packet combinations in subsequent transmissions. To determine efficient selection policies that reduce the completion and decoding delays of IDNC in such an environment, we first extend the perfect feedback formulation in our previous works to the lossy feedback environment, by incorporating the uncertainties resulting from unheard feedback events in these formulations. For the completion delay problem, we use this formulation to identify the maximum likelihood state of the network in events of unheard feedback and employ it to design a partially blind graph update extension to the multicast IDNC algorithm in our earlier work. For the decoding delay problem, we derive an expression for the expected decoding delay increment for any arbitrary transmission. This expression is then used to find the optimal policy that reduces the decoding delay in such lossy feedback environment. Results show that our proposed solutions both outperform previously proposed approaches and achieve tolerable degradation even at relatively high feedback loss rates.

In this paper, we propose a prioritized multi-layer network coding scheme for collaborative packet recovery in underlay cellular cognitive radio networks. This scheme allows the collocated primary and cognitive radio base-stations to collaborate with each other, in order to minimize their own and each other's packet recovery overheads, and thus improve their throughput, without any coordination between them. This non-coordinated collaboration is done using a novel multi-layer instantly decodable network coding scheme, which guarantees that each network's help to the other network does not result in any degradation in its own performance. It also does not cause any violation to the primary networks interference thresholds in the same and adjacent cells. Yet, our proposed scheme both guarantees the reduction of the recovery overhead in collocated primary and cognitive radio networks, and allows early recovery of their packets compared to non-collaborative schemes. Simulation results show that a recovery overhead reduction of 15% and 40% can be achieved by our proposed scheme in the primary and cognitive radio networks, respectively, compared to the corresponding non-collaborative scheme.

In this paper, we investigate the problem of minimizing the frame completion delay for Instantly Decodable Network Coding (IDNC) in relay-assisted wireless multicast networks. We first propose a packet recovery algorithm in the single relay topology which employs generalized IDNC instead of strict IDNC previously proposed in the literature for the same relay-assisted topology. This use of generalized IDNC is supported by showing that it is a super-set of the strict IDNC scheme, and thus can generate coding combinations that are at least as efficient as strict IDNC in reducing the average completion delay. We then extend our study to the multiple relay topology and propose a joint generalized IDNC and relay selection algorithm. This proposed algorithm benefits from the reception diversity of the multiple relays to further reduce the average completion delay in the network. Simulation results show that our proposed solutions achieve much better performance compared to previous solutions in the literature.

In this paper, we consider the problem of minimizing the decoding delay of generalized instantly decodable network coding (G-IDNC) in persistent erasure channels (PECs). By persistent erasure channels, we mean erasure channels with memory, which are modeled as a Gilbert-Elliott two-state Markov model with good and bad channel states. In this scenario, the channel erasure dependence, represented by the transition probabilities of this channel model, is an important factor that could be exploited to reduce the decoding delay. We first formulate the G-IDNC minimum decoding delay problem in PECs as a maximum weight clique problem over the G-IDNC graph. Since finding the optimal solution of this formulation is NP-hard, we propose two heuristic algorithms to solve it and compare them using extensive simulations. Simulation results show that each of these heuristics outperforms the other in certain ranges of channel memory levels. They also show that the proposed heuristics significantly outperform both the optimal strict IDNC in the literature and the channel-unaware G-IDNC algorithms.

In this paper, we study the effect of lossy intermittent feedback loss events on the multicast decoding delay performance of generalized instantly decodable network coding. These feedback loss events create uncertainty at the sender about the reception statues of different receivers and thus uncertainty to accurately determine subsequent instantly decodable coded packets. To solve this problem, we first identify the different possibilities of uncertain packets at the sender and their probabilities. We then derive the expression of the mean decoding delay. We formulate the Generalized Instantly Decodable Network Coding (G-IDNC) minimum decoding delay problem as a maximum weight clique problem. Since finding the optimal solution is NP-hard, we design a variant of the algorithm employed in [1]. Our algorithm is compared to the two blind graph update proposed in [2] through extensive simulations. Results show that our algorithm outperforms the blind approaches in all the situations and achieves a tolerable degradation, against the perfect feedback, for large feedback loss period.

In this paper, we aim to identify the strategies that maximize and monotonically increase the density of coding opportunities in instantly decodable network coding (IDNC). Using the graph representation of IDNC, we first derive an expression for the exact evolution of the edge set size after the transmission of any arbitrary coded packet. From the derived expression, we show that sending commonly wanted packets for all the receivers can maximize the number of coding opportunities. Since guaranteeing such property in IDNC is usually impossible, this strategy does not guarantee the achievement of our target. Consequently, we further investigate the problem by deriving an expression for the expected edge set size evolution after ignoring the identities of the packets requested by the different receivers and considering only their numbers. This expression was then employed to show that serving the maximum number of receivers with largest numbers of missing packets and erasure probabilities tends to maximize and monotonically increase the expected density of coding opportunities. Simulation results justify our theoretical findings. Finally, we validate the importance of our work through two case studies showing that our\ignore{ identified} strategy outperforms several well-known IDNC solutions in optimizing the IDNC completion delay and receiver goodput.

An indoor tracking and navigation system based on measurements of received signal strength (RSS) in wireless local area network (WLAN) is proposed. In the system, the location determination problem is solved by first applying a proximity constraint to limit the distance between a coarse estimate of the current position and a previous estimate. Then, a Compressive Sensing-based (CS--based) positioning scheme, proposed in our previous work , , is applied to obtain a refined position estimate. The refined estimate is used with a map-adaptive Kalman filter, which assumes a linear motion between intersections on a map that describes the user's path, to obtain a more robust position estimate. Experimental results with the system that is implemented on a PDA with limited resources (HP iPAQ hx2750 PDA) show that the proposed tracking system outperforms the widely used traditional positioning and tracking systems. Meanwhile, the tracking system leads to 12.6 percent reduction in the mean position error compared to the CS-based stationary positioning system when three APs are used. A navigation module that is integrated with the tracking system provides users with instructions to guide them to predefined destinations. Thirty visually impaired subjects from the Canadian National Institute for the Blind (CNIB) were invited to further evaluate the performance of the navigation system. Testing results suggest that the proposed system can be used to guide visually impaired subjects to their desired destinations.

One major bottleneck in the practical implementation of received signal strength (RSS) based indoor localization systems is the extensive deployment load required to construct radio maps through fingerprinting. Several works aimed to employ radio propagation models as alternative to fingerprinting but the different sources of inaccuracies in the generation of these models result in high localization errors. In this paper, we propose an indoor localization scheme that can be directly deployed and employed without building a full radio map of the indoor environment. The proposed scheme employs the information from a radio propagation simulator and limited number of calibration measurements to perform direct localization using manifold alignment. For moving users, we exploit the correlation of their reported observations to improve the localization accuracy. The online performance evaluation shows that our algorithm achieves localization errors in the order of 2.5 to 3 m with as low as 15% - 30 % of the complete fingerprinting load.

In this paper, we propose a coding strategy that maximizes the density of the coding opportunities in instantly decodable network coding (IDNC). Using the graph representation of IDNC, we derive the expression for the expected evolutions of coding opportunities after the transmission of any arbitrary coded packet and show that serving the maximum number of receivers, with the largest numbers of missing packets and erasure probabilities, tends to both maximize the expected number of coding opportunities and increase the expected coding density almost monotonically. Simulation results justify our theoretical findings and demonstrate the importance of maintaining high coding density when optimizing long-term parameters.

M-LWWF/M-LWDF scheduling schemes have attracted much interest due to their ability to both stabilize queues whenever possible and control delay through parameter selection. However, a good implementation of these schedulers would require a mechanism to minimize the required fraction of the bandwidth while satisfying its stability and delay requirements. To the best of our knowledge, previous works on these scheduling policies did not address the problem of minimizing the bandwidth utilization while satisfying delay constraints. In this paper, we explore the solution of this problem using a joint bandwidth and weight adaptation approach. We characterize the problem solution space for M-LWWF and M-LWDF scheduling, assuming time-varying traffic. We also show that, starting from any point in the solution space, simple dynamic bandwidth and weight updates can surely lead to the convergence to the optimal operation point in this space. Based on these characteristics, we propose a dynamic parameter adaptation algorithm that is able to track the time-varying optimal operation points for dynamic traffic and channel conditions. Simulation results show the efficiency of our proposed algorithm in tracking the optimal operation points in dynamic traffic and channel settings.

In this paper, we consider the problem of minimizing the broadcast completion delay for instantly decodable network coding with limited feedback. We first extend the stochastic shortest path formulation of the full feedback scenario in to the limited feedback scenario. We then show that the resulting formulation is more complicated to solve than the original one but has its same properties and structure. Based on this result, we design four variants of the algorithm employed in with four different approaches to deal with un-acknowledged transmissions. We finally compare these four algorithms through extensive simulations and show that the algorithm that temporarily avoids all un-acknowledged transmissions in subsequent coding decisions can result in tolerable degradation against the full feedback performance while using much lower feedback.

In this paper, we study the effect of probabilistic and prolonged packet feedback loss events on the broadcast completion time of instantly decodable network coding (IDNC). These feedback loss events result in a lack of knowledge about the reception status at different subsets of receivers, which creates a challenge in selecting efficient IDNC packet combinations in subsequent transmissions. To solve this problem for both probabilistic and prolonged feedback loss, we first identify the different possibilities of feedback loss events at the sender and determine their probabilities in both cases. Given these probabilities and the nature of the IDNC completion time problem, we design three blind instantly decodable network coding approaches that perform coding decisions similar to the algorithms proposed in, but on blindly updated graphs to account for feedback events. These three approaches are then compared through extensive simulations. Results show that the full consideration and the full negligence of all the attempted packet requests with probabilistic and prolonged feedback loss events, respectively, in subsequent coding decisions can achieve a tolerable degradation against the perfect feedback performance for relatively high feedback loss probabilities and periods.

In this paper, we study the effect of packet feedback loss events on the broadcast completion delay performance of instantly decodable network coding. These feedback loss events result in a continuous lack of knowledge about the reception status at different subsets of receivers. This lack of knowledge creates a challenge in selecting efficient packet combinations in subsequent transmissions. To solve this problem, we first identify the different possibilities of unheard feedback events at the sender and determine their probabilities. Given these probabilities and the nature of the problem, we design three partially blind instantly decodable network coding approaches that perform coding decisions similar to the algorithms proposed in [1], [2], but on blindly updated graphs to account for unheard feedback events. These three approaches are then compared through extensive simulations. Results show that re-considering all the attempted packet requests, with unheard feedback, in subsequent coding decisions can achieve a tolerable degradation against the perfect feedback performance for relatively high feedback loss probabilities.

Network coding has recently attracted attention as a substantial improvement to packet retransmission schemes in wireless multicast broadcast services (MBS). Since the problem of finding the optimal network code maximizing the bandwidth efficiency is hard to solve and hard to approximate, two main network coding heuristic schemes, namely opportunistic and full network coding, were suggested in the literature to improve the MBS bandwidth efficiency. However, each of these two schemes usually outperforms the other in different receiver, demand, and feedback settings. The continuous and rapid change of these settings in wireless networks limits the bandwidth efficiency gains if only one scheme is always employed. In this paper, we propose an adaptive scheme that maintains the highest bandwidth efficiency obtainable by both opportunistic and full network coding schemes in wireless MBS. The proposed scheme adaptively selects, between these two schemes, the one that is expected to achieve the better bandwidth efficiency performance. The core contribution in this adaptive selection scheme lies in our derivation of performance metrics for opportunistic network coding, using random graph theory, which achieves efficient selection when compared to appropriate full network coding parameters. To compare between different complexity levels, we present three approaches to compute the performance metric for opportunistic coding using different levels of knowledge about the opportunistic coding graph. For the three considered approaches, simulation results show that our proposed scheme almost achieves the bandwidth efficiency performance that could be obtained by the optimal selection between the opportunistic and full coding schemes.

In this paper, we introduce the concept of generalized instantly decodable network coding (G-IDNC) to further minimize decoding delay in wireless broadcast, compared to strict instantly decodable network coding (S-IDNC), studied in. G-IDNC loosens the strict instant decodability constraint in order to target more receivers while preserving the attractive properties of S-IDNC. We show that the minimum decoding delay problem for G-IDNC can be formulated as a maximum weight clique problem over a well structured graph. Since finding the maximum weight clique of a graph is NP-hard, we design a simple heuristic G-IDNC algorithm with sub-optimal performance. However, simulation results show that both proposed optimal and heuristic G-IDNC algorithms considerably outperform several other S-IDNC and G-IDNC optimal and heuristic approaches.

In this paper, we consider the problem of minimizing the mean completion delay in wireless broadcast for instantly decodable network coding. We first formulate the problem as a stochastic shortest path (SSP) problem. Although finding the packet selection policy using SSP is intractable, we use this formulation to draw the theoretical properties of efficient selection algorithms. Based on these properties, we propose a simple online selection algorithm that efficiently minimizes the mean completion delay of a frame of broadcast packets, compared to the random and greedy selection algorithms with a similar computational complexity. Simulation results show that our proposed algorithm indeed outperforms these random and greedy selection algorithms.

In this paper, we study the possibility of throughput improvement through precoding in OFDMA based wireless systems with limited channel feedback. Precoding can increase the overall system throughput by selecting the use of higher modulation and coding schemes (if there are any) in each physical resource unit (PRU) under a maximum bit error rate constraint. For a specific form of precoding that we proposed in, we analytically derive a formula for the maximum PRU throughput. Sufficient conditions guaranteeing that our proposed technique outperforms the conventional technique are derived. Finally, numerical results show the amount of throughput gain achieved by our proposed technique.

Peak to average power ratio (PAPR) and symbol loss rate (SLR) are two challenges of multicarrier based communications that have recently drawn much attention. High SLR renders the system unreliable and high PAPR is associated with power inefficiency and nonlinearity of the system. There are rich literatures studying these two issues separately but, unfortunately, only a few works have studied simultaneous reductions of PAPR and SLR. This paper studies the problem of reducing the PAPR while keeping the SLR at minimum. In, we derived the conditions for the minimum SLR in On-Off channels. The algorithm proposed in this paper simultaneously satisfies the conditions derived in and reduces the PAPR substantially. This paper differs from previous techniques in the sense that none of the previously proposed techniques are capable of reducing PAPR substantially while achieving the minimum symbol loss rate. We compare our algorithm with the optimum selected mapping PAPR reduction method, which is well known in the literature for having a strong reduction capability. The comparison is done in terms of Complementary Cumulative Distribution Function (CCDF) of the PAPR of the multicarrier signal. The simulation results show that our algorithm can achieve a stronger PAPR reduction while maintaining the minimum SLR.

MAC layer random network coding (MRNC) was proposed as an alternative to HARQ for reliable data transmission in WiMAX unicast. It has been shown that MRNC achieves a higher transmission efficiency than HARQ as it avoids the problem of ACK/NAK packet overhead and the additional redundancy resulting from their loss. However, it did not address the problem of restricting the number of transmissions to an upper bound which is important for delay sensitive applications. In this paper, we investigate a more structured MAC layer coding scheme that achieves the optimum performance in the delay sensitive traffic context while achieving the same overhead level as MRNC. We first formulate the delay sensitive traffic satisfaction, in such an environment, as a minimax optimization problem over all possible coding schemes. We then show that the MAC layer systematic network coding (MSNC), which transmits the packets once uncoded and employs random network coding for retransmissions, achieves the optimum performance for delay sensitive applications while achieving the same overhead level as MRNC.

In wireless multicast, the receivers are interested in obtaining only a subset of the packets transmitted by the access node. Consequently, it is intuitively assumed that random network coded packet retransmissions will result in a lower bandwidth efficiency compared to opportunistic network coded retransmissions as the former involves the delivery of unwanted packets. In the first part of this paper, we show, through simulations, that the random network coded retransmission (RNCR) scheme outperforms the opportunistic network coded retransmission (ONCR) scheme in terms of bandwidth efficiency in a wide range of multicast settings. Motivated by this result, we propose an adaptive algorithm that can dynamically select, from the RNCR and ONCR schemes, the one that is expected to achieve a better performance for each multicast frame. Simulation results show that the proposed algorithm almost achieves the optimal performance that can be obtained by combining these two retransmission schemes.

Due to the high round trip delay in satellite systems, the retransmission of lost packets using conventional ARQ schemes is performed in a very rigid manner and after a very long time of the initial packet transmission. This results in a high average packet delay and packet drop rate in broadcast streaming applications. Moreover, conventional ARQ schemes are generally inefficient in broadcast scenarios. In this paper, we propose a network coded ARQ protocol that performs both proactive and reactive packet retransmissions in hybrid satellite systems. The proposed protocol employs a network coding approach to generate efficient proactive retransmission packets without the knowledge of lost packets. This not only allows the transmission of these coded retransmissions before the arrival of the initial packets to their destinations but also achieves more efficient packet recovery compared to conventional ARQ. Reactive retransmissions in response to packet acknowledgments are then employed if one or more packets are still lost. Simulation results show considerable gains for our proposed protocol over the selective repeat ARQ protocol in terms of average packet delay, packet drop rate and goodput.

Two-hop network architectures, with wireline and 802.16a backhauls, respectively, and 802.11 repeaters/relays, were proposed for high-speed rail. The resulting infrastructure cost in the former, and complexity of dual mode wireless relays in the latter, urge the need for more technically and economically efficient solutions. In this paper, we first propose a two-hop wireless network architecture for high-speed rail employing 802.16j. Due to its backward compatibility with 802.16e, the use of 802.16j not only mitigates the restrictions of the previous two-hop heterogeneous solutions but also allows a third direct communication link from the base-station to the trains, thus providing opportunities for throughput improvements. We then propose a network coded downlink transmission scheme over the proposed network architecture to both eliminate the undesirable ARQ overhead in high-speed rail communications and better exploit relay diversity. We refer to our proposed scheme as network coded information raining. Simulation results show the merits of our proposed solutions.

This paper studies the problem of minimizing symbol loss probability while keeping the system throughput above a certain threshold in downlink transmission of future OFDMA based wireless networks that rely on imperfect one-bit channel state feedback. To solve this problem, we study different preceding classes and propose a new class of preceding matrices that can gain a better result. This work is different from previous OFDM preceding literature in two main aspects. First, it addresses a more practical case where one-bit channel state feedback is available at the base station. Second, it compares precoding classes and proposes a new one. We prove analytically that our proposed precoding class has a lower symbol loss probability than the existing classes. Numerical evaluations show that a large gain in symbol loss probability is achieved by our class in comparison with the other classes.