Lecture Notes on Network Information Theory

These lecture notes have been converted to a book titled Network Information Theory published recently by Cambridge University Press. This book provides a significantly expanded exposition of the material in the lecture notes as well as problems and bibliographic notes at the end of each chapter. The authors are currently preparing a set of slides based on the book that will be posted in the second half of 2012. More information about the book can be found at http://www.cambridge.org/9781107008731/. The previous (and obsolete) version of the lecture notes can be found at http://arxiv.org/abs/1001.3404v4/

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Doctor of Philosophy

dissertationWireless communication has become an essential part of everyday life. The hunger for more data, more phone calls, more video, and more access in more places, including vehicles, is growing massively. Communication in vehicles is particularly challenging because of their extremely high multipath environment. In addition, there is significant interest in reducing the number of wires in vehicles to reduce weight, complexity, maintenance, etc. and replace them with wireless systems. Preliminary research shows that MIMO systems take advantage of the extreme multipath environment found in aircraft and other vehicles and also provides more consistent channel capacity than SISO systems. The purpose of this research was to quantify complex channels (including the aircraft/vehicle environment) and their relation to other environments, evaluate MIMO in aircraft, provide design constraints for accurately modeling complex channels, and provide information to predict optimum antenna type and location to enable communication in aircraft/cars/buses/ships/trains/etc. and other extreme channels. The ability to evaluate and design MIMO technologies from the guidelines in this paper is potentially transformative for aircraft safety - enabling a new generation of location specific monitoring and maintenance. Average measured capacity was found to be between 18 and 21 bits/s/Hz using a 4x4 array of antennas, and had no direct relation to the size of the channel. Site-specific capacity showed a multipath rich channel, varying between 15 to 23 bits/s/Hz. The capacity decreased for increasing measurement distance, with exceptions near reflective objects that increase multipath. Due to these special circumstances for site-specific locations within complex channels, it is recommended that 3D ray tracing be used for modeling as it is more accurate than commonly used statistical models, within 1.1 bits/s/Hz. This showed that our 3D ray tracing is adaptable to various environments and gives a more accurate depiction than statistical models that average channel variations. This comes at the cost of greater model complexity. If increased complexity is not desirable, Nakagami 1.4 could be used as the next most accurate model. Design requirements for modeling different complex channels involve a detailed depiction of channel geometry, including height, width, length, shape (square, cylindrical, slanted walls, etc.), large windows, and reflective objects inside the channel space, especially those near the transmitter. Overall, the multipath rich channel found in vehicles is an excellent environment for MIMO systems. These complex channels can be simulated accurately without measurement and before they are even built using our sitespecific 3D ray tracing software combined with a detailed signal model to incorporate antenna effects

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Differential Signalling in Free-Space Optical Communication Systems

In this paper, we review the differential signalling technique and investigate its implementation of in free-space optical (FSO) communication systems. The paper is an extended version of our previous works, where the effects of background noise, weak turbulence and pointing errors (PEs) were investigated separately. Here, for the first time, we present a thorough description of the differential signalling scheme including for combined effects. At first, we present an extension of the analysis of differential signalling to the case of moderate to strong atmospheric turbulence. Next, we investigate a more general case where both channel turbulence and PEs are taken into consideration. We provide closed-form expressions for the optimal detection threshold and the average bit-error-rate, and present a set of numerical results to illustrate the performance improvement offered by the proposed differential signalling under various turbulence and PEs conditions