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A Lyapunov Approach to Stochastic Interaction Dynamics Over Large-Scale Networks

We study stochastic interaction network models whereby a finite population of agents, identified with the nodes of a graph, update their states in response to pairwise interactions with their neighbors as well as spontaneous mutations. These include the main epidemic models, such as the Susceptible-Infected -Susceptible, the Susceptible-Infected-Recovered, and the Susceptible-Infected-Recovered-Su

Impact of Spatially Consistent Channels on Digital Beamforming for Millimeter-Wave Systems: (Invited Paper)

The premise of massive multiple-input multiple-output (MIMO) is based around coherent transmission and detection. Majority of the vast literature on massive MIMO presents performance evaluations over simplified statistical propagation models. All such models are drop-based and do not ensure continuity of channel parameters. In this paper, we quantify the impact of spatially consistent (SC) models

Real-Time Implementation Aspects of Large Intelligent Surfaces

With the potential to provide a clean break from massive multiple-input multiple-output, large intelligent surfaces (LISs) have recently received a thrust of research interest. Various proposals have been made in the literature to define the exact functionality of LISs, ranging from fully active to largely passive solutions. Nevertheless, almost all studies in the literature investigate the fundam

Interventions for treatment of COVID-19 : A protocol for a living systematic review with network meta-analysis including individual patient data (The LIVING Project)

Background: COVID-19 is a rapidly spreading virus infection that has quickly caused extensive burden to individual, families, countries, and the globe. No intervention has yet been proven effective for the treatment of COVID-19. Some randomized clinical trials assessing the effects of different drugs have been published, and more are currently underway. There is an urgent need for a living, dynami

Amplitude and Phase Estimation for Absolute Calibration of Massive MIMO Front-Ends

Massive multiple-input multiple-output (MIMO) promises significantly higher performance relative to conventional multiuser systems. However, the promised gains of massive MIMO systems rely heavily on the accuracy of the absolute front-end calibration, as well as quality of channel estimates at the base station (BS). In this paper, we analyze user equipment-aided calibration mechanism to estimate t

Using Resolution Proofs to Analyse CDCL Solvers

We propose that CDCL SAT solver heuristics such as restarts and clause database management can be analysed by studying the resolution proofs produced by the solvers, and by trimming these proofs to extract the clauses actually used to reach the final conclusion. We find that for non-adaptive Luby restarts higher frequency makes both untrimmed and trimmed proofs smaller, while adaptive restarts bas

Theoretical and Experimental Results for Planning with Learned Binarized Neural Network Transition Models

We study planning problems where the transition function is described by a learned binarized neural network (BNN). Theoretically, we show that feasible planning with a learned BNN model is NP-complete, and present two new constraint programming models of this task as a mathematical optimization problem. Experimentally, we run solvers for constraint programming, weighted partial maximum satisfiabil

Certifying Solvers for Clique and Maximum Common (Connected) Subgraph Problems

An algorithm is said to be certifying if it outputs, together with a solution to the problem it solves, a proof that this solution is correct. We explain how state of the art maximum clique, maximum weighted clique, maximal clique enumeration and maximum common (connected) induced subgraph algorithms can be turned into certifying solvers by using pseudo-Boolean models and cutting planes proofs, an

Generalized LDPC Codes with Convolutional Code Constraints

Braided convolutional codes (BCCs) are a class of spatially coupled turbo-like codes that can be described by a (2), (3)-regular compact graph. In this paper, we introduce a family of (d v , d c )-regular GLDPC codes with convolutional code constraints (CC-GLDPC codes), which form an extension of classical BCCs to arbitrary regular graphs. In order to characterize the performance in the waterfall

Standardization of Propagation Models for Terrestrial Cellular Systems: A Historical Perspective

Propagation models constitute a fundamental building block of wireless communications research. Before we build and operate real systems, we must understand the science of radio propagation, and develop channel models that both reflect the important propagation processes and allow a fair comparison of different systems. In the past five decades, wireless systems have gone through five generations,

Target Tracking using Signal Strength Differences for Long-Range IoT Networks

Radio based positioning or tracking solutions typically require wideband signals or phase coherent antennas. In this paper, we present a target tracking method based on received non-coherent signal strength differences (RSSDs) between antennas for outdoor Internet-of-things (IoT) scenarios. We introduce an RSSD model based on classical path-loss models. With known antenna patterns and antenna arra

Decoder Error Propagation Mitigation for Spatially Coupled LDPC Codes

In this paper, we introduce two new methods of mit- igating decoder error propagation for low-latency sliding window decoding (SWD) of spatially coupled low-density parity-check (SC- LDPC) codes. Building on the recently introduced idea of check node (CN) doping of regular SC-LDPC codes, here we employ variable node (VN) doping to fix (set to a known value) a subset of variable nodes in the coupli

Tinted, Detached, and Lazy CNF-XOR Solving and Its Applications to Counting and Sampling

Given a Boolean formula, the problem of counting seeks to estimate the number of solutions of F while the problem of uniform sampling seeks to sample solutions uniformly at random. Counting and uniform sampling are fundamental problems in computer science with a wide range of applications ranging from constrained random simulation, probabilistic inference to network reliability and beyond. The pas

Interference-free OFDM embedding of wake-up signals for low-power wake-up receivers

The use of ultra-low power wake-up receivers (WuRx) can significantly reduce idle listening energy cost. To tailor a WuRx scheme to orthogonal frequency division multiplexing (OFDM) based systems, such as LTE-MTC or IEEE 802.11, the wake-up signal (WUS) also needs to follow OFDM principles to avoid interfering with other transmissions in the same shared bandwidth. Here, we address this particular

SLAM using LTE Multipath Component Delays

Cellular radio based localization can be an important complement or alternative to other localization technologies, as base stations continuously transmit signals of opportunity with beneficial positioning properties. In this paper, we use the long term evolution (LTE) cell-specific reference signal for this purpose. The multipath component delays are estimated by the ESPRIT algorithm, and the est

Sparse Steiner triple systems of order 21

A (Formula presented.) -configuration is a set of (Formula presented.) blocks on (Formula presented.) points. For Steiner triple systems, (Formula presented.) -configurations are of particular interest. The smallest nontrivial such configuration is the Pasch configuration, which is a (Formula presented.) -configuration. A Steiner triple system of order (Formula presented.), an STS (Formula present

Exponential resolution lower bounds for weak pigeonhole principle and perfect matching formulas over sparse graphs

We show exponential lower bounds on resolution proof length for pigeonhole principle (PHP) formulas and perfect matching formulas over highly unbalanced, sparse expander graphs, thus answering the challenge to establish strong lower bounds in the regime between balanced constant-degree expanders as in [Ben-Sasson and Wigderson'01] and highly unbalanced, dense graphs as in [Raz'04] and [Razborov'03