Spammer annotators in Quality of Experience (QoE) assessments provide unreliable ratings, often scoring randomly or assigning extreme ratings, which introduces noise and compromises data reliability. Modern methods to mitigate the effects of this noise rely on parametric approaches like maximum likelihood estimation or Bayesian techniques. These methods are sensitive to model assumptions and might therefore suffer from a lack of robustness. This paper proposes a non-parametric approach to measure annotator reliability and introduces the Non-Parametric subjective Quality Recovery (NPQR) algorithm, which is shown to compare favorably to state-of-the-art methods in terms of robustness against spammers.

This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling errors (e.g., model uncertainty). First, we formally introduce the notion of unreliability in regression, i.e., when the output of the regressor exceeds a specified discrepancy (or error). Then, using powerful tools for probabilistic modeling, we estimate the discrepancy density, and we measure its statistical diversity using our proposed metric for statistical dissimilarity. In turn, this allows us to derive a data-driven score that expresses the uncertainty of the regression outcome. We show empirical improvements in error detection for multiple regression tasks, consistently outperforming popular baseline approaches, and contributing to the broader field of uncertainty quantification and safe machine learning systems.

This paper analyzes the correlation matrix between the a priori error and measurement noise vectors for standard and augmented affine projection algorithms using a unified approach. This correlation stems from the dependence between the filter tap estimates and the noise samples, and has a strong influence on the mean square behavior of the algorithm. We show that the correlation matrix is upper triangular, and compute the diagonal elements in closed form, showing that they are independent of the input process statistics. Also, for white inputs we show that the matrix is fully diagonal. These results are valid in the transient and steady states, considering a possibly variable step-size. Our only assumption is that the filter order is large compared to its projection order and that the input signal is stationary. Using these results, we perform a steady-state analysis for small step size and provide a new simple closed-form expression for the mean-square error, which has comparable or better accuracy to many preexisting expressions, and is much simpler to compute. Finally, we also obtain expressions for the steady-state energy of the other components of the error vector.

This paper focuses on the challenge of accurately estimating the subjective quality of multimedia content from noisy opinion scores gathered from end-users. State-of-the-art methods rely on parametric statistical models to capture the subject's scoring behavior and recover quality estimates. However, these approaches have limitations, as they often require restrictive assumptions to achieve numerical stability during parameter estimation, leading to a lack of robustness when the modeling hypotheses do not fit the data. To overcome these limitations, we propose a paradigm shift towards non-parametric statistical methods. Specifically, we introduce a threefold contribution: i) in contrast to the prevailing approach in subjective quality recovery assuming a parametric score distribution, we propose a non parametric approach that guarantees greater accuracy by measuring reliability per subject and per stimulus, overcoming the limits of existing approaches that measure only per subject reliability; ii) we propose ESQR, a non-parametric algorithm for subjective quality recovery, demonstrating experimentally that it has higher robustness to noise compared to numerous state-of-the-art algorithms, thanks to the weaker assumptions made on data compared to parametric approaches; iii) the proposed approach is theoretically grounded, i.e., we define a non-parametric statistic and prove mathematically that it provides a measure of score reliability. The code to run ESQR and reproduce the results in this paper is made freely available at:http://media.polito.it/ESQR.

This paper studies the gains, in terms of served requests, attainable through out-of-band device-to-device (D2D) video exchanges in large cellular networks. A stochastic framework, in which users are clustered to exchange videos, is introduced, considering several aspects of this problem, i.e., the video caching policy, user matching for exchanges, and aspects regarding scheduling and transmissions. A family of admissible protocols is introduced: in each protocol the users are clustered by means of a hard-core point process and, within the clusters, video exchanges take place. Two metrics, quantifying the “local” and “global” fractions of video requests served through D2D are defined, and relevant trade-off regions involving these metrics, as well as quality-of-service constraints, are identified. A simple communication strategy is proposed and analyzed to obtain inner bounds to the trade-off regions and to draw conclusions on the performance attainable through D2D. To this end, the analysis of the time-varying interference that the nodes experience and the tight approximations of its Laplace transform are derived.

In this paper, we study the performance, in terms of the asymptotic error probability, of a user that communicates with a destination with the aid of a full-duplex in-band relay. We consider that the network is interference-limited, and interfering users are distributed as a Poisson point process. In this case, the asymptotic error probability is upper bounded by the outage probability (OP). We investigate the outage behavior for well-known cooperative schemes, namely, decode-and-forward (DF) and compress-and-forward (CF) considering fading and path loss. For DF, we determine the exact OP and develop upper bounds that are tight in typical operating conditions. Also, we find the correlation coefficient between source and relay signals that minimizes the OP when the density of interferers is small. For CF, the achievable rates are determined by the spatial correlation of the interferences, and a straightforward analysis is not possible. To handle this issue, we show that the rate with correlated noises is at most one bit worse than with uncorrelated noises and thus find an upper bound on the performance of CF. These results are useful to evaluate the performance and to optimize relaying schemes in the context of full-duplex wireless networks.

This paper investigates the benefits of cooperation and proposes a relay activation strategy for a large wireless network with multiple transmitters. In this framework, some nodes cooperate with a nearby node that acts as a relay, using the decode-and-forward protocol, and others use direct transmission. The network is modeled as an independently marked Poisson point process, and the source nodes may choose their relays from the set of inactive nodes. Although cooperation can potentially lead to significant improvements in the performance of a communication pair, relaying causes additional interference in the network, increasing the average noise that other nodes see. We investigate how source nodes should balance cooperation versus interference to obtain reliable transmissions, and for this purpose, we study and optimize a relay activation strategy with respect to the outage probability. Surprisingly, in the high reliability regime, the optimized strategy consists on the activation of all the relays or none at all, depending on network parameters. We provide a simple closed-form expression that indicates when the relays should be active, and we introduce closed-form expressions that quantify the performance gains of this scheme with respect to a network that only uses direct transmission.

This paper investigates the potential gains of half-duplex unicast strategies in a large wireless network consisting of clusters in which a source node attempts to transmit a message with the aid of other inactive-potential relays- nodes within the cluster. The network is modeled as an independently marked Poisson point process and a slow-fading scenario is considered. A two-phase transmission protocol is studied and an achievable (upper) bound on the asymptotic error probability of any cluster in the network is derived in terms of the outage probability (OP). The proposed scheme takes advantage of the spatial distribution of the cooperating nodes to create a distributed virtual antenna array, thus improving the OP over direct transmission (DT) through cooperative diversity. Finally, we obtain a converse (lower) bound on the OP of any unicast strategy when restricting all admissible protocols to belong to the same-still quite general- class of half-duplex codes that includes the one studied.

This paper investigates the potential gain of cooperation in large wireless networks with multiple sources and relays, where the nodes form an homogeneous Poisson point process. The source nodes may choose their nearest neighbor from the set of inactive nodes as their relay. Although cooperation can potentially lead to significant improvements on the asymptotic error probability of a communication pair, relaying causes additional interference in the network, increasing the average noise. We address the basic question: how should source nodes optimally balance cooperation vs. interference to guarantee reliability in all communication pairs. Based on the decode-and-forward (DF) scheme at the relays, we derive closed-form approximations to the upper bounds on the error probability, averaging over all node positions. Surprisingly, in the small outage probability regime, there is an almost binary behavior that dictates - depending on network parameters - the activation or not of all relay nodes.

Consider the communication of a single user aided by a nearby relay involved in a large and dense wireless network where the nodes form an homogeneous Poisson point process. We assume that the network is working in the interference-limited regime. In this case the asymptotic error probability is bounded from above by the outage probability experienced by the user. We investigate the outage behavior for the well-known cooperative schemes, namely, decode-and-forward (DF) and compress-and-forward (CF). In this setting, the outage events are induced by both fading and the spatial proximity of neighbor nodes who generate the strongest interference and hence the worst communication case. Upper and lower bounds on the asymptotic error probability which are tight in some cases are derived. It is shown that there exists a clear trade-off between the network density and the benefits of user cooperation. These results are useful to evaluate performance and to optimize relaying schemes in the context of large wireless networks.