This article presents an iterative minimum mean square error-(MMSE-) based method for the joint estimation of signal-to-noise ratio (SNR) and frequency-selective channel in an orthogonal frequency division multiplexing (OFDM) context. We estimate the SNR thanks to the MMSE criterion and the channel frequency response by means of the linear MMSE (LMMSE). As each estimation requires the other one to be performed, the proposed algorithm is iterative. In this article, a realistic case is considered; i.e., the channel covariance matrix used in LMMSE is supposed to be totally unknown at the receiver and must be estimated. We will theoretically prove that the algorithm converges for a relevantly chosen initialization value. Furthermore simulations show that the algorithm quickly converges to a solution that is close to the one in which the covariance matrix is perfectly known. Compared to existing SNR estimation methods, the algorithm improves the trade-off between the number of required pilots and the SNR estimation quality.

International audienceLinear minimum mean square error (LMMSE) is by definition the optimal channel estimator in the sense of meansquare error criterion, but its practical application is limited by its high complexity. Furthermore, the LMMSE estimation methodrequires the knowledge of both the channel and the noise statistics, which are a priori unknown at the receiver. A wide range oftechniques are proposed in the literature in order to overcome these two drawbacks. In this study, the authors give an overviewof the LMMSE-based channel estimation in an orthogonal frequency division multiplexing (OFDM) context. A didactic reminderconcerning the basics of LMMSE estimation and its performance is provided, and a survey of techniques of the literature, whichenable the practical application of LMMSE and the reduction of its complexity, is presented in both single-input single-output andmultiple-input multiple-output contexts. Finally, some perspectives are provided, in particular the application of the LMMSEestimator to flexible waveforms beyond OFDM

The performance of cellular system significantly depends on its network topology, where the spatial deployment of base stations (BSs) plays a key role in the downlink scenario. Moreover, cellular networks are undergoing a heterogeneous evolution, which introduces unplanned deployment of smaller BSs, thus complicating the performance evaluation even further. In this paper, based on large amount of real BS locations data, we present a comprehensive analysis on the spatial modeling of cellular network structure. Unlike the related works, we divide the BSs into different subsets according to geographical factor (e.g. urban or rural) and functional type (e.g. macrocells or microcells), and perform detailed spatial analysis to each subset. After examining the accuracy of Poisson point process (PPP) in BS locations modeling, we take into account the Gibbs point processes as well as Neyman-Scott point processes and compare their accuracy in view of large-scale modeling test. Finally, we declare the inaccuracy of the PPP model, and reveal the general clustering nature of BSs deployment, which distinctly violates the traditional assumption. This paper carries out a first largescale identification regarding available literatures, and provides more realistic and more general results to contribute to the performance analysis for the forthcoming heterogeneous cellular networks.

International audienceThis paper presents a classification of methods that have been proposed to address non-linear power amplification of highly fluctuating signals in telecommunications. The classification proposed uses a tree-like representation wherein each branch refers to a group of methods that all have a common characteristic. By virtue of this representation, each node corresponds to a test used to discriminate between different methods. From top to bottom, these tests are: what is the target of the method, is the method downward-compatible, is the bit error rate degraded, is there a useful data rate loss, does the method require changes in the amplification function? By collating all these requirements, an original classification is proposed that is open enough to allow new methods to be added. It only concerns methods located either only at the transmitter or at both transmitter and receiver. The context of this study generally concerns multicarrier signals (especially OFDM) but can be applied to any multiplex of modulated signals

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