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Convexity properties of error rates of a class of decoders, including the ML/min-distance one as a special case, are studied for arbitrary constellations, bit mapping and coding. Earlier results obtained for the AWGN channel are extended to…

Information Theory · Computer Science 2016-11-15 Sergey Loyka , Victoria Kostina , Francois Gagnon

In this paper, a lower bound on the capacity of wireless ad hoc erasure networks is derived in closed form in the canonical case where $n$ nodes are uniformly and independently distributed in the unit area square. The bound holds almost…

Information Theory · Computer Science 2009-01-15 Rayyan G. Jaber , Jeffrey G. Andrews

Motivated by a wide-spread use of convex optimization techniques, convexity properties of bit error rate of the maximum likelihood detector operating in the AWGN channel are studied for arbitrary constellations and bit mappings, which also…

Information Theory · Computer Science 2010-04-16 Sergey Loyka , Francois Gagnon , Victoria Kostina

We consider the problem of modulating the value of a parameter onto a band-limited signal to be transmitted over a continuous-time, additive white Gaussian noise (AWGN) channel, and estimating this parameter at the receiver. The performance…

Information Theory · Computer Science 2016-06-22 Nir Weinberger , Neri Merhav

This paper presents a framework to evaluate the probability that a decision error event occurs in wireless sensor networks, including sensing and communication errors. We consider a scenario where sensors need to identify whether a given…

Information Theory · Computer Science 2016-02-17 Pedro H. J. Nardelli , Iran Ramezanipour , Hirley Alves , Carlos H. M. de Lima , Matti Latva-aho

In addition to serve as platforms for dynamic spectrum access, cognitive radios can also serve as a method for improving the performance of wireless communication systems by smartly adjusting their operating parameters according to the…

Information Theory · Computer Science 2016-11-18 Hector Reyes , Sriram Subramaniam , Naima Kaabouch

In this paper, we present a comprehensive study of the monotonicity and log-concavity of the generalized Marcum and Nuttall Q-functions. More precisely, a simple probabilistic method is firstly given to prove the monotonicity of these two…

Information Theory · Computer Science 2015-03-13 Yin Sun , Arpad Baricz , Shidong Zhou

Motivated by a wide-spread use of convex optimization techniques, convexity properties of bit error rate of the maximum likelihood detector operating in the AWGN channel are studied for arbitrary constellations and bit mappings, which may…

Information Theory · Computer Science 2009-12-31 Sergey Loyka , Victoria Kostina , Francois Gagnon

We use techniques for lower bounds on communication to derive necessary conditions in terms of detector efficiency or amount of super-luminal communication for being able to reproduce with classical local hidden-variable theories the…

Quantum Physics · Physics 2016-12-30 H. Buhrman , P. Hoyer , S. Massar , H. Roehrig

In this paper, we study classification and regression error bounds for inhomogenous data that are independent but not necessarily identically distributed. First, we consider classification of data in the presence of non-stationary noise and…

Information Theory · Computer Science 2024-04-04 Ghurumuruhan Ganesan

We provide a closed form upper bound formulation for the average pairwise-error probability (PEP) of selective decode and forward (SDF) cooperation protocol for a keyhole (pinhole) channel condition. We have employed orthogonal space-time…

Networking and Internet Architecture · Computer Science 2018-09-11 Ravi Shankar , Yamini Chandrakar , Radhika Sinha , Ritesh Kumar Mishra

In the context of signal detection in the presence of an unknown time-varying channel parameter, receivers based on the Expectation Propagation (EP) framework appear to be very promising. EP is a message-passing algorithm based on factor…

Signal Processing · Electrical Eng. & Systems 2024-04-09 Elisa Conti , Armando Vannucci , Amina Piemontese , Giulio Colavolpe

Score-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for SGMs in the W2-distance rely on stringent assumptions about…

Machine Learning · Statistics 2025-12-12 Marta Gentiloni-Silveri , Antonio Ocello

Convexity/concavity properties of symbol error rates (SER) of the maximum likelihood detector operating in the AWGN channel (non-fading and fading) are studied. Generic conditions are identified under which the SER is a convex/concave…

Information Theory · Computer Science 2016-11-17 Sergey Loyka , Victoria Kostina , Francois Gagnon

Expectation Propagation is a very popular algorithm for variational inference, but comes with few theoretical guarantees. In this article, we prove that the approximation errors made by EP can be bounded. Our bounds have an asymptotic…

Computation · Statistics 2016-01-12 Guillaume P Dehaene , Simon Barthelmé

This work studies the location estimation problem for a mixture of two rotation invariant log-concave densities. We demonstrate that Least Squares EM, a variant of the EM algorithm, converges to the true location parameter from a randomly…

Machine Learning · Statistics 2019-06-21 Wei Qian , Yuqian Zhang , Yudong Chen

This paper analyzes the localization outage probability (LOP), the probability that the position error exceeds a given threshold, in randomly deployed wireless networks. Two typical cases are considered: a mobile agent uses all the…

Networking and Internet Architecture · Computer Science 2017-03-29 Fengyu Zhou , Yuan Shen

Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically…

Networking and Internet Architecture · Computer Science 2024-11-19 Gabriele Formis , Stefano Scanzio , Lukasz Wisniewski , Gianluca Cena

We consider the problem of stochastic convex optimization with exp-concave losses using Empirical Risk Minimization in a convex class. Answering a question raised in several prior works, we provide a $O( d / n + \log( 1 / \delta) / n )$…

Machine Learning · Computer Science 2023-07-06 Nikita Puchkin , Nikita Zhivotovskiy

Equilibrium Propagation (EP) is a biologically inspired local learning rule first proposed for convergent recurrent neural networks (CRNNs), in which synaptic updates depend only on neuron states from two distinct phases. EP estimates…

Machine Learning · Computer Science 2026-05-11 Jiaqi Lin , Malyaban Bal , Abhronil Sengupta
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