An optical-see-through near-eye display (NED) for augmented reality (AR) allows the user to perceive virtual and real imagery simultaneously. Existing technologies for optical-see-through AR NEDs involve trade-offs between key metrics such as field of view (FOV), eyebox size, form factor, etc. We have enhanced an existing compact wide-FOV pinlight AR NED design with real-time 3D pupil localization in order to dynamically steer and thus effectively enlarge the usable eyebox. This is achieved with a dual-camera rig that captures stereoscopic views of the pupils. The 3D pupil location is used to dynamically calculate a display pattern that spatio-temporally modulates the light entering the wearer's eyes. We have built a demonstrable compact prototype and have conducted a user study that indicates the effectiveness of our eyebox steering method (e.g., without eyebox steering, in 10.5% of our tests, users were unable to perceive the test pattern correctly before experiment timeout; with eyebox steering, that fraction decreased dramatically to 1.25%). This is a small yet crucial step in making simple wide-FOV pinlight NEDs usable for human users and not just as demonstration prototypes filmed with a precisely positioned camera standing in for the user's eye. Further contributions of this paper include a detailed description of display design, calibration technique, and user study design, all of which may benefit other NED research.

In this paper, we present the impact of self-blockage on Handover (HO) Probability for a user with mobility in a 5G mm-wave cellular network. Self-blockage is defined as a blockage that could occur for a Mobile User (MU) resulting from its own body during its movement. As a result, frequent HOs may occur, which could cause channel fluctuations between different states, namely, Line-of-Sight (LOS) state, Non-Line-of-Sight (NLOS) state and outage state. Since the human body causes a severe attenuation to the mm-wave signal, we assume that the MU’s body blocks any signal from any mm-wave BS that lies in the blocking zone. Consequently, all mm-wave BSs that lie in the blocking zone is considered blocked. In this paper, we present a mathematical analysis for the self-blockage impact on MU where the Self-blockage zone is modelled as a sector of a disc, centred at the origin and has a radius of R. The sector is making an angle of, $\theta$, towards the MU’s body. The results show that self-blockage has an impact on HO probability, especially when the MU is moving in a radial direction, as the MU direction becomes closer to the tagged mm-wave BS as the HO probability gets closer to the case where no self-blockage incorporated into the model. Since the mm-wave signal is very susceptible to blockages interruption, this paper’s work provides us insights on understanding the impact of self-blockage on the HO probability. Therefore, the effect of self-blockage on the HO probability needs to be considered carefully in designing efficient HO management schemes and planning the 5G mm-wave cellular system.

Integrating Device-to-Device (D2D) communications and Femtocells in Heterogeneous Networks (HetNets) is a promising technology for future cellular networks to satisfy the exponentially growing mobile traffic requirements, which have attracted a lot of attention since it can significantly improve the capacity, energy efficiency and spectral performance of next-generation wireless networks (5G). D2D communications and femtocell are introduced as underlays to the cellular systems by reusing the cellular channels to maximize the overall throughput in the network. In this paper, the problem is formulated to maximize the network throughput under the QoS constraints for cellular user-equipment (CUE), D2D user-equipment (DUE), and femto user-equipment (FUE). This problem is a mixed-integer non-linear programming problem, which is NP-hard and it is usually difficult to find the optimal solution directly. To solve this problem, we propose a joint channel selection, power control, and resource allocation scheme to maximize the sum rate of the cellular network system. The simulation results show that the proposed scheme can effectively reduce the computational complexity and improve the overall system throughput compared with existing well-known methods.

The fifth-generation (5G), now being developed for use in the millimeter-wave (mm-wave) frequency bands, would enhance the Quality of Service (QoS) for the Mobile User (MU) in the near future. Despite the advantages of using mm-wave communications that are attained through the large bandwidth available in this band, it suffers from high penetration loss and low diffraction, which causes very high attenuation of the signal in free space. The attenuation of the signal can also be due to the blockages, such as buildings, human presence, vehicles, etc. As a result, the signal propagation path could be interrupted by those blockages causing significant fluctuation in the handover (HO) rate. Thus, the foreseen mm-wave communication gains are achieved at the expense of varying HO rates. Therefore, the impact of blockages on the HO rate for user mobility is a crucial performance factor that needs to be analyzed and addressed in mm-wave cellular communication systems. This paper analyses the impact of static blockage on the HO rate for a MU in the 5G mm-wave cellular network. We began by obtaining the impact of blockage with a fixed location in the 2-D plane on the HO rate for a MU moving radially away from the mm-wave base station (BS) at a certain speed. Afterward, we considered the MU moving in a uniform angle with a certain speed in the mm-wave cellular network. The mm-wave BSs, in this study, have been assumed to be distributed according to a homogeneous Poisson Point Process (PPP). The results show that the blockage has a remarkable impact on the HO rate, depending significantly on its location, and MU's direction and speed.

Device-to-Device (D2D) communications have attracted great attention in the underlaying cellular networks since it a promising way to improve the capacity, energy efficiency, spectral and latency performance of next-generation wireless networks. In order to alleviate the interference caused by D2D user equipment (DUE) and cellular user-equipment (CUE) in uplink multi-cell networks, the intra-cell and inter-cell interference are considered in this work to improve performance for D2D communication underlaying cellular network. This paper proposes a resource allocation scheme to formulate the resource allocation problem through optimization of the utility function, which eventually reflects the system performance concerning network throughput. The formulated optimization problem of maximizing network throughput while guaranteeing predefined service levels to cellular users is non-convex and hence intractable. Thus, the original problem is broken down into two stages. The first stage is the admission control of D2D users while the second one is the power control for each admissible D2D pair and its reuse partner. Our simulation results have indicated that the proposed scheme can offer near-optimal performance and outperforms the algorithms in the literature, in achievable throughput.

Recently, the integration of Device-to-Device (D2D) communication to cellular networks became a vitality task with the growth of mobile devices, as well as requirements of enhanced network performance in terms of spectral efficiency, energy efficiency, and latency. In this paper, we propose a spectrum allocation framework based on Reinforcement Learning (RL) for joint mode selection, channel assignment, and power control in D2D communication. The objective is to maximize the overall throughput of the network while ensuring the quality of transmission and guaranteeing low latency requirements of D2D communications. The proposed algorithm uses reinforcement learning (RL) based on Markov Decision Process (MDP) with a proposed new reward function to learn the policy by interacting with the D2D environment. An Actor-Critic Reinforcement Learning (AC-RL) approach is then used to solve the resource management problem. The simulation results show that our learning method performs well, can greatly improve the sum rate of D2D links, and converges quickly, compared with the algorithms in the literature.

Big data processing is rapidly growing in recent years due to the immediate demanding of many applications. This growth compels industries to leverage scheduling in order to optimally allocate the resources to the big data streams which requires data-driven big data analysis. Moreover, optimal scheduling of big data stream process should guarantee the QoS requirements of computing tasks. Execution deadlines of tasks within the streams is specified as one of the most significant QoS factors. In this paper, we study the scheduling and execution of big data stream processes. First, a queueing theory approach to the modeling of the streams as a collection of sequential and parallel tasks is proposed. It is assumed that heterogeneous threads are required to handle various big data tasks such as processing, storing and searching which may have quite general service time distributions. Then, with the proposed model, an optimization problem is defined to minimize the total number of resources required to serve the big data streams while guaranteeing the QoS requirements of their tasks. An algorithm is also proposed to mitigate the complexity order of the optimization problem. The objective of this research is to minimize the stream processing resources in terms of threads with constraints over the task waiting time of the application tasks. We apply the proposed scheduling algorithm to Apache Storm, a distributed real-time computation platform, to optimize the cloud resource requirements. The experiment results validate our analysis.

Peer-to-peer (P2P) video streaming is a scalable and cost-effective technology to stream video content to a large population of users and has attracted a lot of research for over a decade now. Recently, network coding has been introduced to improve the efficiency of these systems and to simplify the protocol design. There are already some successful commercial applications that utilize network coding. However, previous analytical studies of network-coding-based P2P streaming systems mainly focused on fundamental properties of the system and ignored the influence of the protocol details. In this study, a unique stochastic model is developed to reveal how segments of the video stream evolve over their lifetime in the buffer before they go into playback. Different strategies for segment selection have been studied with the model, and their performance has been compared. A new approximation of the probability of linear independence of coded blocks has been proposed to study the redundancy of network coding. Finally, extensive numerical results and simulations have been provided to validate our model. From these results, in-depth insights into how system parameters and segment selection strategies affect the performance of the system have been obtained.

Optimistic unchoking plays an important role in BitTorrent peer-to-peer (P2P) file sharing networks. Peers use optimistic unchoking to find upload bandwidth information about their neighbors. However, free-riders can also take advantage of optimistic unchoking and download from the network without uploading anything. In this paper, we propose a novel optimistic unchoking algorithm for BitTorrent. The main purposes of our algorithm are to prevent free-riding and to improve the efficiency of optimistic unchoking. We then propose a stochastic model to analyze the performance of our algorithm. We also verify the results by simulations. Both theoretical and simulation results show that our algorithm can effectively prevent free-riding and significantly improve the download rate of normal peers at the same time.

In Internet Protocol Television (IPTV) systems, Quality-of-Service (QoS) is a critical factor for the user satisfaction. In this paper, we first propose a queueing model for IPTV systems and discuss how to ensure the QoS of IPTV. We then use the model to analyze the effects of IPTV traffic on other applications in home networks. We find that TCP congestion control may not work well under this circumstance and propose a new approach to improve the performance of TCP. We also verify our results throughns2simulations.

In peer-to-peer (P2P) file sharing networks, the average download time is determined by how efficiently the upload bandwidth of each peer can be utilized. In this paper, we will first propose a stochastic model for P2P file sharing networks. By numerically solving the proposed model, we will be able to gain interesting insight on how the performance of a P2P file sharing network is affected by different parameters such as the piece numbers of the file, the number of neighbors of a peer, and the seed departure rate etc. We will also provide some useful guidelines on how to design an efficient P2P system based on our results.

In mobile ad hoc networks (MANETs), packets are forwarded by a series of nodes to the desired destination. Previously, we have studied the network connectivity of MANETs. In this paper, we study the reliability of an established route between a source to a destination. The route is considered reliable only when all links in the route remain connected for packet relaying. We analyze the link reliability under the condition that the forwarding nodes are mobile. With this analysis, we derive the time period that the established route remains reliable for packet forwarding. Simulation is conducted to validate our analysis.

With the rapid development and wide deployment of wireless local area networks (WLANs), WLAN-based positioning system employing signal-strength-based technique has become an attractive solution for location estimation in indoor environment. In recent years, a number of such systems has been presented, and most of the systems use the common nearest neighbor in signal space (NNSS) algorithm. In this paper, we propose an enhancement to the NNSS algorithm. We analyze the enhancement to show its effectiveness. The performance of the enhanced NNSS algorithm is evaluated with different values of the parameters. Based on the performance evaluation and analysis, we recommend some guidelines on optimizing the parameters of our proposed enhanced algorithm.

In this paper, we consider a network with both controllable and uncontrollable flows. Uncontrollable flows are typically generated from applications with stringent QoS requirements and are given high priority. On the other hand, controllable flows are typically generated by elastic applications and can adapt to the available link capacities in the network. We provide a general model of such a system and analyze its queueing behavior. Specially, we obtain a lower bound and an asymptotic upper bound for the tail of the workload distribution at each link in the network. These queueing results provide us with guidelines on how to design a feedback flow control system. Simulation results show that the lower bound and asymptotic upper bound are quite accurate and that our feedback control method can effectively control the queue length in the presence of both controllable and uncontrollable traffic. Finally, we describe a distributed strategy that uses the notion of Active Queue Management (AQM) for implementing our flow control solution.

In this paper, we develop simple models to study the performance of BitTorrent, a second generation peer-to-peer (P2P) application. We first present a simple fluid model and study the scalability, performance and efficiency of such a file-sharing mechanism. We then consider the built-in incentive mechanism of BitTorrent and study its effect on network performance. We also provide numerical results based on both simulations and real traces obtained from the Internet.

In this paper, we develop a new predictive flow control scheme and analyze its performance. This scheme controls the nonreal-time (controllable) traffic based on predicting the real-time (uncontrollable) traffic. The goal of the work is to operate the network in a low congestion, high throughput regime. We provide a rigorous analysis of the performance of our flow control method and show that the algorithm has attractive and useful properties. From our analysis we obtain an explicit condition that gives us design guidelines on how to choose a predictor. We learn that it is especially important to take the queueing effect into account in developing the predictor. We also provide numerical results comparing different predictors that use varying degrees of information from the network.