502 research outputs found
Strong Secrecy for Multiple Access Channels
We show strongly secret achievable rate regions for two different wiretap
multiple-access channel coding problems. In the first problem, each encoder has
a private message and both together have a common message to transmit. The
encoders have entropy-limited access to common randomness. If no common
randomness is available, then the achievable region derived here does not allow
for the secret transmission of a common message. The second coding problem
assumes that the encoders do not have a common message nor access to common
randomness. However, they may have a conferencing link over which they may
iteratively exchange rate-limited information. This can be used to form a
common message and common randomness to reduce the second coding problem to the
first one. We give the example of a channel where the achievable region equals
zero without conferencing or common randomness and where conferencing
establishes the possibility of secret message transmission. Both coding
problems describe practically relevant networks which need to be secured
against eavesdropping attacks.Comment: 55 page
In-network Sparsity-regularized Rank Minimization: Algorithms and Applications
Given a limited number of entries from the superposition of a low-rank matrix
plus the product of a known fat compression matrix times a sparse matrix,
recovery of the low-rank and sparse components is a fundamental task subsuming
compressed sensing, matrix completion, and principal components pursuit. This
paper develops algorithms for distributed sparsity-regularized rank
minimization over networks, when the nuclear- and -norm are used as
surrogates to the rank and nonzero entry counts of the sought matrices,
respectively. While nuclear-norm minimization has well-documented merits when
centralized processing is viable, non-separability of the singular-value sum
challenges its distributed minimization. To overcome this limitation, an
alternative characterization of the nuclear norm is adopted which leads to a
separable, yet non-convex cost minimized via the alternating-direction method
of multipliers. The novel distributed iterations entail reduced-complexity
per-node tasks, and affordable message passing among single-hop neighbors.
Interestingly, upon convergence the distributed (non-convex) estimator provably
attains the global optimum of its centralized counterpart, regardless of
initialization. Several application domains are outlined to highlight the
generality and impact of the proposed framework. These include unveiling
traffic anomalies in backbone networks, predicting networkwide path latencies,
and mapping the RF ambiance using wireless cognitive radios. Simulations with
synthetic and real network data corroborate the convergence of the novel
distributed algorithm, and its centralized performance guarantees.Comment: 30 pages, submitted for publication on the IEEE Trans. Signal Proces
Cooperative Wideband Spectrum Sensing Based on Joint Sparsity
COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY
By Ghazaleh Jowkar, Master of Science
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University
Virginia Commonwealth University 2017
Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering
In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically
Foundations of User-Centric Cell-Free Massive MIMO
Imagine a coverage area where each mobile device is communicating with a
preferred set of wireless access points (among many) that are selected based on
its needs and cooperate to jointly serve it, instead of creating autonomous
cells. This effectively leads to a user-centric post-cellular network
architecture, which can resolve many of the interference issues and
service-quality variations that appear in cellular networks. This concept is
called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and
has its roots in the intersection between three technology components: Massive
MIMO, coordinated multipoint processing, and ultra-dense networks. The main
challenge is to achieve the benefits of cell-free operation in a practically
feasible way, with computational complexity and fronthaul requirements that are
scalable to enable massively large networks with many mobile devices. This
monograph covers the foundations of User-centric Cell-free Massive MIMO,
starting from the motivation and mathematical definition. It continues by
describing the state-of-the-art signal processing algorithms for channel
estimation, uplink data reception, and downlink data transmission with either
centralized or distributed implementation. The achievable spectral efficiency
is mathematically derived and evaluated numerically using a running example
that exposes the impact of various system parameters and algorithmic choices.
The fundamental tradeoffs between communication performance, computational
complexity, and fronthaul signaling requirements are thoroughly analyzed.
Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster
formation, and power optimization are provided, while open problems related to
these and other resource allocation problems are reviewed. All the numerical
examples can be reproduced using the accompanying Matlab code.Comment: This is the authors' version of the manuscript: \"Ozlem Tugfe Demir,
Emil Bj\"ornson and Luca Sanguinetti (2021), "Foundations of User-Centric
Cell-Free Massive MIMO", Foundations and Trends in Signal Processing: Vol.
14, No. 3-4, pp 162-47
Contributions of formal language theory to the study of dialogues
For more than 30 years, the problem of providing a formal framework for modeling dialogues has been a topic of great interest for the scientific areas of Linguistics, Philosophy, Cognitive Science, Formal Languages, Software Engineering and Artificial Intelligence. In the beginning the goal was to develop a "conversational computer", an automated system that could engage in a conversation in the same way as humans do. After studies showed the difficulties of achieving this goal Formal Language Theory and Artificial Intelligence have contributed to Dialogue Theory with the study and simulation of machine to machine and human to machine dialogues inspired by Linguistic studies of human interactions. The aim of our thesis is to propose a formal approach for the study of dialogues. Our work is an interdisciplinary one that connects theories and results in Dialogue Theory mainly from Formal Language Theory, but also from another areas like Artificial Intelligence, Linguistics and Multiprogramming. We contribute to Dialogue Theory by introducing a hierarchy of formal frameworks for the definition of protocols for dialogue interaction. Each framework defines a transition system in which dialogue protocols might be uniformly expressed and compared. The frameworks we propose are based on finite state transition systems and Grammar systems from Formal Language Theory and a multi-agent language for the specification of dialogue protocols from Artificial Intelligence. Grammar System Theory is a subfield of Formal Language Theory that studies how several (a finite number) of language defining devices (language processors or grammars) jointly develop a common symbolic environment (a string or a finite set of strings) by the application of language operations (for instance rewriting rules). For the frameworks we propose we study some of their formal properties, we compare their expressiveness, we investigate their practical application in Dialogue Theory and we analyze their connection with theories of human-like conversation from Linguistics. In addition we contribute to Grammar System Theory by proposing a new approach for the verification and derivation of Grammar systems. We analyze possible advantages of interpreting grammars as multiprograms that are susceptible of verification and derivation using the Owicki-Gries logic, a Hoare-based logic from the Multiprogramming field
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