Exploiting Regional Differences: A Spatially Adaptive Random Access

In this paper, we discuss the potential for improvement of the simple random access scheme by utilizing local information such as the received signal-to-interference-plus-noise-ratio (SINR). We propose a spatially adaptive random access (SARA) scheme in which the transmitters in the network utilize different transmit probabilities depending on the local situation. In our proposed scheme, the transmit probability is adaptively updated by the ratio of the received SINR and the target SINR. We investigate the performance of the spatially adaptive random access scheme. For the comparison, we derive an optimal transmit probability of ALOHA random access scheme in which all transmitters use the same transmit probability. We illustrate the performance of the spatially adaptive random access scheme through simulations. We show that the performance of the proposed scheme surpasses that of the optimal ALOHA random access scheme and is comparable with the CSMA/CA scheme.Comment: 10 pages, 10 figure

Heteroskedasticity and Spatiotemporal Dependence Robust Inference for Linear Panel Models with Fixed Effects

This paper studies robust inference for linear panel models with fixed effects in the presence of heteroskedasticity and spatiotemporal dependence of unknown forms. We propose a bivariate kernel covariance estimator that is flexible to nest existing estimators as special cases with certain choices of bandwidths. For distributional approximations, we embed the level of smoothing and the sample size in two different limiting sequences. In the first case where the level of smoothing increases with the sample size, the proposed covariance estimator is consistent and the associated Wald statistic converges to a chi square distribution. We show that our covariance estimator improves upon existing estimators in terms of robustness and efficiency. In the second case where the level of smoothing is fixed, the covariance estimator has a random limit and we show by asymptotic expansion that the limiting distribution of the Wald statistic depends on the bandwidth parameters, the kernel function, and the number of restrictions being tested. As this distribution is nonstandard, we establish the validity of a convenient F-approximation to this distribution. For bandwidth selection, we employ and optimize a modified asymptotic mean square error criterion. The fl exibility of our estimator and the proposed bandwidth selection procedure make our estimator adaptive to the dependence structure. This adaptiveness effectively automates the selection of covariance estimators. Simulation results show that our proposed testing procedure works reasonably well in finite samples.Adaptiveness, HAC estimator, F-approximation, Fixed-smoothing asymptotics, Increasing-smoothing asymptotics, Panel data, Optimal bandwidth, Robust inference, Spatiotemporal dependence

To Believe in Historical Progress: On Axel Honneth’s Normative Grounding of Critique

One of the most ambitious contributions Axel Honneth has made to critical theory consists in his attempt to ground the normativity of critique beyond communicative reason—the normative ground of critique that had been proposed by Honneth’s predecessor at the Institut für Sozialforschung, Jürgen Habermas. Defending an affirmative stance toward historical progress is critical to Honneth’s project, which attempts to pursue the aspiration of the Frankfurt School to practice a robust form of immanent critique: for preserving the idea of progress allows Honneth to derive the validity of the underlying normative presuppositions of the existing social order, thereby securing the normative grounds of critique without relying on transcendent or transhistorical principles. Through a consideration of an aspect of the relation between universality and particularity that remains undertheorized in Honneth’s account, this essay attempts to question the success of his strategy for grounding the normativity of critique