Channel state information

Résumé

Dans les communications sans fil telles que le Wi-Fi, les informations d'état du canal (CSI) font référence aux propriétés connues du canal d'une liaison de communication. Ces informations décrivent comment un signal se propage de l'émetteur vers le récepteur et représentent l'effet combiné, par exemple, de la diffusion, de l’affaiblissement et de la diminution de la puissance du signal avec la distance. La méthode est appelée estimation de canal. Le CSI permet d'adapter les transmissions aux conditions instantanées du canal, ce qui est crucial pour obtenir une communication fiable avec des débits de données élevés dans les systèmes à antennes multiples. Le CSI doit être estimé au niveau du récepteur et est généralement quantifié et renvoyé à l'émetteur (bien que l'estimation de la liaison inverse soit possible dans les systèmes TDD). Par conséquent, l'émetteur et le récepteur peuvent avoir des CSI différents. Le CSI de l'émetteur et le CSI du récepteur sont parfois appelés respectiv

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Cours associés (6)

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Séances de cours associées (9)

Séances de cours associées

Chargement

Channel State Feedback Over the MIMO-MAC

In order to exploit the full multiplexing gain of multi-antenna multi-user downlink schemes, accurate channel state information at the transmitter (i.e., at the base station) is required. We consider the design of a closed-loop channel state information feedback scheme, where user terminals feed back their channel state information simultaneously to a multi-antenna base station. The underlying information theoretic problem consists of lossy source-channel coding of multiple independent analog sources (i.e., the users' channel coefficients) over a Gaussian multiple- input multiple-output multi-access channel (MIMO-MAC). Unlike the classical source-channel coding setting, this application requires low latency, otherwise the channel state information would be outdated. Hence, source-channel codewords can span only a single fading state of the uplink (feedback) channel. Furthermore, the transmitters are ignorant of the realization of the uplink channel coefficients. In this scenario, the scaling of the maximum of the estimated downlink channel mean-square errors with the SNR dominates the performance of the multiuser downlink. This scaling is described by the distortion SNR exponent, previously introduced in a single-user MIMO setting. This paper analyzes the max-min distortion SNR exponent of the MIMO-MAC for both separated source-channel coding, and a particular hybrid digital-analog joint source-channel coding scheme. For the case of single-antenna users, we prove that the distortion SNR exponent of separated source-channel coding can be achieved by the concatenation of scalar quantization and uncoded quadrature-amplitude modulation (QAM) transmission, with lattice decoding at the base-station receiver. The resulting scheme has very low encoding latency (only a few symbols of the uplink slot) and generally outperforms currently proposed channel state feedback schemes based on analog unquantized transmission or vector quantization with fixed codebooks.

2011

Chargement

Channel estimation by inference on Gaussian Markov random fields

Thomas Riedl

In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps in time and delay. The sum-product algorithm is finally employed to infer a MAP channel estimate from given observations.

2009

Chargement

Sampling theory has experienced a strong research revival over the past decade, which led to a generalization of Shannon's original theory and development of more advanced formulations with immediate relevance to signal processing and communications. For example, it was recently shown that it is possible to develop exact sampling schemes for a large class of non-bandlimited signals, namely, certain signals with finite rate of innovation. A common feature of such signals is that they have a parametric representation with a finite number of degrees of freedom and can be perfectly reconstructed from a finite number of samples. The goal of this thesis is to advance the sampling theory for signals of finite rate of innovation and consider its possible extensions and applications. In the first part of the thesis, we revisit the sampling problem for certain classes of such signals, including non-uniform splines and piecewise polynomials, and develop improved schemes that allow for stable and precise reconstruction in the presence of noise. Specifically, we develop a subspace approach to signal reconstruction, which converts a nonlinear estimation problem into the simpler problem of estimating the parameters of a linear model. This provides an elegant and robust framework for solving a large class of sampling problems, while offering more flexibility than the traditional scheme for bandlimited signals. In the second part of the thesis, we focus on applications of our results to certain classes of nonlinear estimation problems encountered in wideband communication systems, most notably ultra-wideband (UWB) systems, where the bandwidth used for transmission is much larger than the bandwidth or rate of information being sent. We develop several frequency domain methods for channel estimation and synchronization in UWB systems, which yield high-resolution estimates of all relevant channel parameters by sampling a received signal below the traditional Nyquist rate. We also propose algorithms that are suitable for identification of more realistic UWB channel models, where a received signal is made up of pulses with different pulse shapes. Finally, we extend our results to multidimensional signals, and develop exact sampling schemes for certain classes of parametric non-bandlimited 2-D signals, such as sets of 2-D Diracs, polygons or signals with polynomial boundaries.

EPFL2004

Chargement

Channel State Feedback Over the MIMO-MAC

2011

EPFL2004