Ph. D. Thesis

Pathloss and fading are unique features of wireless propagation, respectively referring to the rapid decay in the received signal envelope with distance and to the random fades present in the received signal power. Multi-hopping and diversity are the corresponding countermeasures entailing the division of a longer link into shorter links and the provision of diversified information bearing signal replicas at the destination. This thesis builds on the fact that by letting users collaborate in relaying packets for each other they can obtain independent propagation paths to reach their intended destinations through a series of shorter hops; thus mitigating both pathloss and fading. In a nutshell, collaboration offers both diversity and multi-hopping benefits at the same time. This thesis consists of two interrelated thrusts which explore the role of user collaboration in multiple access networks as a diversity enabler and the role of multihop routing in counteracting the rapid decrease in average received power. Our results suggest that joint exploitation of multipath and multi-hop links in the context of collaborative networking offers substantial improvement in terms of capacity, coverage, power consumption and error performance. Even though different in the principles they exploit, both thrusts commonly rely on what we purport as a paradigm shift in wireless networks: from competition towards collaboration.

We show that user cooperation in random access networks (RA) yields a significant increase in throughput. Specifically, we prove that for networks with a large number of users, the throughput of a cooperative wireless RA network operating over Rayleigh fading links approaches the throughput of an RA network operating over additive white Gaussian noise links. The message borne out of this result is that user cooperation offers a viable choice for migrating diversity benefits to the wireless RA regime, thus bridging the gap to wireline RA networks, without incurring a bandwidth or energy penalty.

In the context of multi-hop routing, existing graph-theoretic approaches rely on so-called disk models. Albeit valuable for wired networks, these models do not capture adequately the random nature of wireless links. To this end, we introduce a novel framework for stochastic routing in wireless multihop networks, whereby each node selects a neighbor to forward a packet with a certain probability. A plethora of valuable criteria emerge from this framework based on which these routing probabilities are obtained efficiently as solutions of typically convex optimization problems. We further develop distributed self-organizing stochastic routing via primal dual decomposition solvers, and study the associated convergence properties.

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M. Sc. Thesis

At the crossroad of sensing, control and wireless communications, wireless sensor networks (WSNs), whereby large numbers of individual nodes collaborate to monitor and control environments, have emerged in recent years along with the field of distributed signal processing. This thesis studies the intertwining between quantization and estimation that arises due to the distributed nature of WSNs. Given that each sensor has available only part of the measurements parameter estimation requires quantization of the original observations, transforming the problem into one of estimation based on quantized observations certainly different from estimation based on the analog-amplitude observations.

This intertwining is studied in a number of setups with an eye towards realistic scenarios. We start with a simple mean location deterministic parameter estimation problem, in the presence of additive white Gaussian noise which we follow with generalizations to deterministic parameter estimation for pragmatic signal models. Among this class of signal models we consider i) known univariate but generally non-Gaussian noise probability density functions (pdfs); ii) known noise pdfs with a finite number of unknown parameters; iii) completely unknown noise pdfs; and iv) practical generalizations to multivariate and possibly correlated pdfs. Within a different paradigm, we also derive and analyze distributed state estimators of dynamical stochastic processes. Following a Kalman filtering (KF) approach, we develop recursive algorithms for distributed state estimation based on the sign of innovations (SOI).

Surprisingly, in all scenarios considered we reveal two common properties: i) the performance of estimators based on quantization to a few bits per sensor can come very close to the performance of estimators based on the analog-amplitude observations; and ii) the complexity of optimal estimators based on quantized observations is low even though quantization leads to a discontinuous signal model.

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