arXiv subjects

Massimo Franceschetti

Publications and source records attributed to Massimo Franceschetti.

51 records · Page 3Linked to original sources

arXiv2017

Event-triggered second-moment stabilization of linear systems under packet drops

This paper deals with the stabilization of linear systems with process noise under packet drops between the sensor and the controller. Our aim is to ensure exponential convergence of the second moment of the plant state to a given bound in finite time. Motivated by considerations about the efficient use of the available resources, we adopt an event-triggering approach to design the transmission policy. In our design, the sensor's decision to transmit or not the state to the controller is based on an online evaluation of the future satisfaction of the control objective. The resulting event-triggering policy is hence specifically tailored to the control objective. We formally establish that the proposed event-triggering policy meets the desired objective and quantify its efficiency by providing an upper bound on the fraction of expected number of transmissions in an infinite time interval. Simulations for scalar and vector systems illustrate the results.

math.OC↗

arXiv2016

Group buying with bundle discounts: computing efficient, stable and fair solutions

We model a market in which nonstrategic vendors sell items of different types and offer bundles at discounted prices triggered by demand volumes. Each buyer acts strategically in order to maximize her utility, given by the difference between product valuation and price paid. Buyers report their valuations in terms of reserve prices on sets of items, and might be willing to pay prices different than the market price in order to subsidize other buyers and to trigger discounts. The resulting price discrimination can be interpreted as a redistribution of the total discount. We consider a notion of stability that looks at unilateral deviations, and show that efficient allocations - the ones maximizing the social welfare - can be stabilized by prices that enjoy desirable properties of rationality and fairness. These dictate that buyers pay higher prices only to subsidize others who contribute to the activation of the desired discounts, and that they pay premiums over the discounted price proportionally to their surplus - the difference between their current utility and the utility of their best alternative. Therefore, the resulting price discrimination appears to be desirable to buyers. Building on this existence result, and letting N, M and c be the numbers of buyers, vendors and product types, we propose a O(N^2+NM^c) algorithm that, given an efficient allocation, computes prices that are rational and fair and that stabilize the market. The algorithm first determines the redistribution of the discount between groups of buyers with an equal product choice, and then computes single buyers' prices. Our results show that if a desirable form of price discrimination is implemented then social efficiency and stability can coexists in a market presenting subtle externalities, and computing individual prices from market prices is tractable.

cs.GT↗

arXiv2015

Limits of Friendship Networks in Predicting Epidemic Risk

The spread of an infection on a real-world social network is determined by the interplay of two processes: the dynamics of the network, whose structure changes over time according to the encounters between individuals, and the dynamics on the network, whose nodes can infect each other after an encounter. Physical encounter is the most common vehicle for the spread of infectious diseases, but detailed information about encounters is often unavailable because expensive, unpractical to collect or privacy sensitive. We asks whether the friendship ties between the individuals in a social network successfully predict who is at risk. Using a dataset from a popular online review service, we build a time-varying network that is a proxy of physical encounter between users and a static network based on reported friendship. Through computer simulations, we compare infection processes on the resulting networks and show that, whereas distance on the friendship network is correlated to epidemic risk, friendship provides a poor identification of the individuals at risk if the infection is driven by physical encounter. Such limit is not due to the randomness of the infection, but to the structural differences of the two networks. In contrast to the macroscopic similarity between processes spreading on different networks, the differences in local connectivity determined by the two definitions of edges result in striking differences between the dynamics at a microscopic level. Despite the limits highlighted, we show that periodical and relatively infrequent monitoring of the real infection on the encounter network allows to correct the predicted infection on the friendship network and to achieve satisfactory prediction accuracy. In addition, the friendship network contains valuable information to effectively contain epidemic outbreaks when a limited budget is available for immunization.

physics.soc-ph↗

arXiv2015

Event-triggered control under time-varying rates and channel blackouts

This paper studies event-triggered stabilization of linear time-invariant systems over time-varying rate-limited communication channels. We explicitly account for the possibility of channel blackouts, i.e., intervals of time when the communication channel is unavailable for feedback. Assuming prior knowledge of the channel evolution, we study the data capacity, which is the maximum total number of bits that could be communicated over a given time interval, and provide an efficient real-time algorithm to lower bound it for a deterministic time-slotted model of channel evolution. Building on these results, we design an event-triggering strategy that guarantees Zeno-free, exponential stabilization at a desired convergence rate even in the presence of intermittent channel blackouts. The contributions are the notion of channel blackouts, the effective event-triggered control despite their occurrence, and the analysis and quantification of the data capacity for a class of time-varying continuous-time channels. Various simulations illustrate the results.

eess.SY↗

arXiv2015

Information without rolling dice

The deterministic notions of capacity and entropy are studied in the context of communication and storage of information using square-integrable, bandlimited signals subject to perturbation. The $(ε,δ)$-capacity, that extends the Kolmogorov $ε$-capacity to packing sets of overlap at most $δ$, is introduced and compared to the Shannon capacity. The functional form of the results indicates that in both Kolmogorov and Shannon's settings, capacity and entropy grow linearly with the number of degrees of freedom, but only logarithmically with the signal to noise ratio. This basic insight transcends the details of the stochastic or deterministic description of the information-theoretic model. For $δ=0$, the analysis leads to new bounds on the Kolmogorov $ε$-capacity, and to a tight asymptotic expression of the Kolmogorov $ε$-entropy of bandlimited signals. A deterministic notion of error exponent is introduced. Applications of the theory are briefly discussed.

cs.IT↗

arXiv2014

On Landau's eigenvalue theorem and information cut-sets

A variation of Landau's eigenvalue theorem describing the phase transition of the eigenvalues of a time-frequency limiting, self adjoint operator is presented. The total number of degrees of freedom of square-integrable, multi-dimensional, bandlimited functions is defined in terms of Kolmogorov's $n$-width and computed in some limiting regimes where the original theorem cannot be directly applied. Results are used to characterize up to order the total amount of information that can be transported in time and space by multiple-scattered electromagnetic waves, rigorously addressing a question originally posed in the early works of Toraldo di Francia and Gabor. Applications in the context of wireless communication and electromagnetic sensing are discussed.

cs.IT↗

arXiv2012

Distributed team formation in multi-agent systems: stability and approximation

We consider a scenario in which leaders are required to recruit teams of followers. Each leader cannot recruit all followers, but interaction is constrained according to a bipartite network. The objective for each leader is to reach a state of local stability in which it controls a team whose size is equal to a given constraint. We focus on distributed strategies, in which agents have only local information of the network topology and propose a distributed algorithm in which leaders and followers act according to simple local rules. The performance of the algorithm is analyzed with respect to the convergence to a stable solution. Our results are as follows. For any network, the proposed algorithm is shown to converge to an approximate stable solution in polynomial time, namely the leaders quickly form teams in which the total number of additional followers required to satisfy all team size constraints is an arbitrarily small fraction of the entire population. In contrast, for general graphs there can be an exponential time gap between convergence to an approximate solution and to a stable solution.

cs.MA↗

arXiv2011

Linear Codes, Target Function Classes, and Network Computing Capacity

We study the use of linear codes for network computing in single-receiver networks with various classes of target functions of the source messages. Such classes include reducible, injective, semi-injective, and linear target functions over finite fields. Computing capacity bounds and achievability are given with respect to these target function classes for network codes that use routing, linear coding, or nonlinear coding.

cs.IT↗

arXiv2011

Computing linear functions by linear coding over networks

We consider the scenario in which a set of sources generate messages in a network and a receiver node demands an arbitrary linear function of these messages. We formulate an algebraic test to determine whether an arbitrary network can compute linear functions using linear codes. We identify a class of linear functions that can be computed using linear codes in every network that satisfies a natural cut-based condition. Conversely, for another class of linear functions, we show that the cut-based condition does not guarantee the existence of a linear coding solution. For linear functions over the binary field, the two classes are complements of each other.

cs.IT↗

arXiv2011

LQG Control Approach to Gaussian Broadcast Channels with Feedback

A code for communication over the k-receiver additive white Gaussian noise broadcast channel with feedback is presented and analyzed using tools from the theory of linear quadratic Gaussian optimal control. It is shown that the performance of this code depends on the noise correlation at the receivers and it is related to the solution of a discrete algebraic Riccati equation. For the case of independent noises, the sum rate achieved by the proposed code, satisfying average power constraint P, is characterized as 1/2 log (1+P*phi), where the coefficient "phi" in the interval [1,k] quantifies the power gain due to the presence of feedback. When specialized to the case of two receivers, this includes a previous result by Elia and strictly improves upon the code of Ozarow and Leung. When the noises are correlated, the pre-log of the sum-capacity of the broadcast channel with feedback can be strictly greater than one. It is established that for all noise covariance matrices of rank r the pre-log of the sum capacity is at most k-r+1 and, conversely, there exists a noise covariance matrix of rank r for which the proposed code achieves this upper bound. This generalizes a previous result by Gastpar and Wigger for the two-receiver broadcast channel.

cs.IT↗

arXiv2010

Network Coding for Computing: Cut-Set Bounds

The following \textit{network computing} problem is considered. Source nodes in a directed acyclic network generate independent messages and a single receiver node computes a target function $f$ of the messages. The objective is to maximize the average number of times $f$ can be computed per network usage, i.e., the ``computing capacity''. The \textit{network coding} problem for a single-receiver network is a special case of the network computing problem in which all of the source messages must be reproduced at the receiver. For network coding with a single receiver, routing is known to achieve the capacity by achieving the network \textit{min-cut} upper bound. We extend the definition of min-cut to the network computing problem and show that the min-cut is still an upper bound on the maximum achievable rate and is tight for computing (using coding) any target function in multi-edge tree networks and for computing linear target functions in any network. We also study the bound's tightness for different classes of target functions. In particular, we give a lower bound on the computing capacity in terms of the Steiner tree packing number and a different bound for symmetric functions. We also show that for certain networks and target functions, the computing capacity can be less than an arbitrarily small fraction of the min-cut bound.

cs.IT↗

arXiv2010

Strict inequalities of critical values in continuum percolation

We consider the supercritical finite-range random connection model where the points $x,y$ of a homogeneous planar Poisson process are connected with probability $f(|y-x|)$ for a given $f$. Performing percolation on the resulting graph, we show that the critical probabilities for site and bond percolation satisfy the strict inequality $p_c^{\rm site} > p_c^{\rm bond}$. We also show that reducing the connection function $f$ strictly increases the critical Poisson intensity. Finally, we deduce that performing a spreading transformation on $f$ (thereby allowing connections over greater distances but with lower probabilities, leaving average degrees unchanged) {\em strictly} reduces the critical Poisson intensity. This is of practical relevance, indicating that in many real networks it is in principle possible to exploit the presence of spread-out, long range connections, to achieve connectivity at a strictly lower density value.

math.PR↗

arXiv2010

Control-theoretic Approach to Communication with Feedback: Fundamental Limits and Code Design

Feedback communication is studied from a control-theoretic perspective, mapping the communication problem to a control problem in which the control signal is received through the same noisy channel as in the communication problem, and the (nonlinear and time-varying) dynamics of the system determine a subclass of encoders available at the transmitter. The MMSE capacity is defined to be the supremum exponential decay rate of the mean square decoding error. This is upper bounded by the information-theoretic feedback capacity, which is the supremum of the achievable rates. A sufficient condition is provided under which the upper bound holds with equality. For the special class of stationary Gaussian channels, a simple application of Bode's integral formula shows that the feedback capacity, recently characterized by Kim, is equal to the maximum instability that can be tolerated by the controller under a given power constraint. Finally, the control mapping is generalized to the N-sender AWGN multiple access channel. It is shown that Kramer's code for this channel, which is known to be sum rate optimal in the class of generalized linear feedback codes, can be obtained by solving a linear quadratic Gaussian control problem.

cs.IT↗

arXiv2010

Strict inequalities of critical probabilities on Gilbert's continuum percolation graph

Any infinite graph has site and bond percolation critical probabilities satisfying $p_c^{site}\geq p_c^{bond}$. The strict version of this inequality holds for many, but not all, infinite graphs. In this paper, the class of graphs for which the strict inequality holds is extended to a continuum percolation model. In Gilbert's graph with supercritical density on the Euclidean plane, there is almost surely a unique infinite connected component. We show that on this component $p_c^{site} > p_c^{bond}$. This also holds in higher dimensions.

math.PR↗

arXiv2009

Random Access: An Information-Theoretic Perspective

This paper considers a random access system where each sender can be in two modes of operation, active or not active, and where the set of active users is available to a common receiver only. Active transmitters encode data into independent streams of information, a subset of which are decoded by the receiver, depending on the value of the collective interference. The main contribution is to present an information-theoretic formulation of the problem which allows us to characterize, with a guaranteed gap to optimality, the rates that can be achieved by different data streams. Our results are articulated as follows. First, we exactly characterize the capacity region of a two-user system assuming a binary-expansion deterministic channel model. Second, we extend this result to a two-user additive white Gaussian noise channel, providing an approximate characterization within $\sqrt{3}/2$ bit of the actual capacity. Third, we focus on the symmetric scenario in which users are active with the same probability and subject to the same received power constraint, and study the maximum achievable expected sum-rate, or throughput, for any number of users. In this case, for the symmetric binary expansion deterministic channel (which is related to the packet collision model used in the networking literature), we show that a simple coding scheme which does not employ superposition coding achieves the system throughput. This result also shows that the performance of slotted ALOHA systems can be improved by allowing encoding rate adaptation at the transmitters. For the symmetric additive white Gaussian noise channel, we propose a scheme that is within one bit of the system throughput for any value of the underlying parameters.

cs.IT↗