Information Theoretic Limits of State-dependent Networks

We investigate the information theoretic limits of two types of state-dependent models in this dissertation. These models capture a wide range of wireless communication scenarios where there are interference cognition among transmitters. Hence, information theoretic studies of these models provide useful guidelines for designing new interference cancellation schemes in practical wireless networks. In particular, we first study the two-user state-dependent Gaussian multiple access channel (MAC) with a helper. The channel is corrupted by an additive Gaussian state sequence known to neither the transmitters nor the receiver, but to a helper noncausally, which assists state cancellation at the receiver. Inner and outer bounds on the capacity region are first derived, which improve the state-of-the-art bounds given in the literature. Further comparison of these bounds yields either segments on the capacity region boundary or the full capacity region by considering various regimes of channel parameters. We then study the two-user Gaussian state-dependent Z-interference channel (Z-IC), in which two receivers are corrupted respectively by two correlated states that are noncausally known to transmitters, but unknown to receivers. Three interference regimes are studied, and the capacity region or the sum capacity boundary is characterized either fully or partially under various channel parameters. The impact of the correlation between the states on the cancellation of state and interference as well as the achievability of the capacity is demonstrated via numerical analysis. Finally, we extend our results on the state-dependent Z-IC to the state-dependent regular IC. As both receivers in the regular IC are interfered, more sophisticated achievable schemes are designed. For the very strong regime, the capacity region is achieved by a scheme where the two transmitters implement a cooperative dirty paper coding. For the strong but not very strong regime, the sum-rate capacity is characterized by rate splitting, layered dirty paper coding and successive cancellation. For the weak regime, the sum-rate capacity is achieved via dirty paper coding individually at two receivers as well as treating interference as noise. Numerical investigation indicates that for the regular IC, the correlation between states impacts the achievability of the channel capacity in a different way from that of the Z-IC

CHARACTERIZATION OF FUNDAMENTAL COMMUNICATION LIMITS OF STATE-DEPENDENT INTERFERENCE NETWORKS

Interference management is one of the key techniques that drive evolution of wireless networks from one generation to another. Techniques in current cellular networks to deal with interference follow the basic principle of orthogonalizing transmissions in time, frequency, code, and space. My PhD work investigate information theoretic models that represent a new perspective/technique for interference management. The idea is to explore the fact that an interferer knows the interference that it causes to other users noncausally and can/should exploit such information for canceling the interference. In this way, users can transmit simultaneously and the throughput of wireless networks can be substantially improved. We refer to the interference treated in such a way as ``dirty interference\u27\u27 or noncausal state . Towards designing a dirty interference cancelation framework, my PhD thesis investigates two classes of information theoretic models and develops dirty interference cancelation schemes that achieve the fundamental communication limits. One class of models (referred to as state-dependent interference channels) capture the scenarios that users help each other to cancel dirty interference. The other class of models (referred to as state-dependent channels with helper) capture the scenarios that one dominate user interferes a number of other users and assists those users to cancel its dirty interference. For both classes of models, we develop dirty interference cancelation schemes and compared the corresponding achievable rate regions (i.e., inner bounds on the capacity region) with the outer bounds on the capacity region. We characterize the channel parameters under which the developed inner bounds meet the outer bounds either partially of fully, and thus establish the capacity regions or partial boundaries of the capacity regions

Optimal Video Streaming in Dense 5G Networks With D2D Communications

© 2017 IEEE. Mobile video traffic and mobile devices have now outpaced other data traffic and fixed devices. Global service providers are attempting to propose new mobile infrastructures and solutions for high performance of video streaming services, i.e., high quality of experience (QoE) at high resource efficiency. Although device-to-device (D2D) communications have been an emerging technique that is anticipated to provide a massive number of mobile users with advanced services in 5G networks, the management of resource and co-channel interference between D2D pairs, i.e., helper-requester pairs, and cellular users (CUs) is challenging. In this paper, we design an optimal rate allocation and description distribution for high performance video streaming, particularly, achieving high QoE at high energy efficiency while limiting co-channel interference over D2D communications in 5G networks. To this end, we allocate optimal encoding rates to different layers of a video segment and then packetize the video segment into multiple descriptions with embedded forward error correction before transmission. Simultaneously, the optimal numbers of descriptions are distributed to D2D helpers and base stations in a cooperative scheme for transmitting to the D2D requesters. The optimal results are efficiently in correspondence with intra-popularity of different segments of a video characterized by requesters' behavior, characteristic of lossy wireless channels, channel state information of D2D requesters, and constraints on remaining energy of D2D helpers and target signal to interference plus noise ratio of CUs. Simulation results demonstrate the benefits of our proposed solution in terms of high performance video streaming

A Mobile Robot System for Ambient Intelligence

Over the last years, Ambient Intelligence (AmI) has been pointed out as an alternative to current practices in home care. AmI supports the concept of Ambient Assisted Living, which aims to allow older people to remain independent at their own homes for longer. The integration of a mobile robot into a database-centric platform for Ambient Assisted Living is described in this thesis. The robot serves as a rst-aid agent to respond to emergencies, such as a fall, detected by the intelligent environment. To accomplish that the robot must 1) be able to receive tasks from intelligent environment; 2) execute the task; 3) report the progress and the result of the task back to the intelligent environment. The system of the robot is built on top of the Robot Operating System, while the existing intelligent environment on a PostgreSQL database. To receive tasks from the intelligent environment, the robot maintains an active connection with the database and subscribes to specic tasks. A task, for example, is to nd a person in the environment, which includes asking if the person is doing well. To nd a person a map-based approach and a face recognition are used. The robot can interact with people in the environment using text-to-speech and speech recognition. The active connection with the database enables the robot to report back about the execution of a task and to receive new or abort tasks. As a conclusion, together with an AAL system, mobile robots can support people living alone. The system has been implemented and successfully tested at Halmstad University on a Turtlebot 2. The code is available on Github

Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation