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Abstract - COMM7 |
 |
COMM7.1 |
A Semi-Blind Approach to Structured Channel Equalization B. Ng, D. Gesbert, A. Paulraj (Stanford University, USA) This paper describes a direct equalization approach for channels with some underlying structure. A semi-blind approach is taken here where a small amount of training symbols is available. A family of MMSE equalizers is obtained that includes some prior information about the channel structure. The channel structure assumed in this paper is that the channel vector lies approximately in the subspace of a matrix associated with the samples of the transmit pulse shape. Blind identifiability issues of the structured equalizer are also addressed. Numerical results using experimental indoor channel data indicate that these structured equalizers can achieve bit error rates that are significantly lower than traditional non-blind MMSE equalizers. |
| COMM7.2 |
Semi-Blind Channel Identification Used in GSM System G. Li, Z. Ding (Auburn University, USA) In this paper, we present a semi-blind channel identification scheme for GSM system. Even though the GMSK signal has almost zero excess bandwidth (oversample will give no more information), two diversity channels for each GMSK signal can be generated using a de-rotation scheme without additional antennas. Based on this single input and two output system, the semi-blind algorithm is applied to GSM signals successfully. Simulation results are presented. |
| COMM7.3 |
On the Use of Kernel Structure for Blind Equalization J. Gunther, A. Swindlehurst (Brigham Young University, USA) The mathematical theory of kernel (null space) structure of Hankel and Hankel-like matrices is applied to the problem of blind equalization of co-channel signals. This work builds on recently introduced ideas in blind equalization where the symbols are treated as deterministic parameters and estimated directly without estimating the channel first. The main contribution of the new approach is that it allows the simultaneous exploitation of shift structure in the data model and the finite alphabet property of the signals. |
| COMM7.4 |
Performance Analysis of Blind Channel Estimators Based on Non-Redundant Periodic Modulation Precoders A. Chevreuil (Telecom Paris, France); E. Serpedin (University of Virginia, USA); P. Loubaton (Universite de Marne-la-Valle, France); G. Giannakis (University of Virginia, USA) Periodic modulation precoders allow blind identifiability of SISO channels from the output second-order cyclic statistics, irrespective of the location of channel zeros, color of additive stationary noise, or channel order overestimation errors. In the present paper the performance of blind channel estimators is investigated. Some criteria for optimally designing the periodic modulation precoders are also presented. |
| COMM7.5 |
Second Order Blind Equalization: The Band Limited Case P. Ciblat, P. Loubaton (University of Marne-la-Vallee, France) Most of the second order based fractionnally sampled blind equalizers are known to perform poorly in the context of band limited signals. In this paper, we analyse the behaviour of the subspace method in the particular context of band limited signals. As it is well known, the subspace channel estimate is obtained as the eigenvector associated to the eigenvalue 0 of a certain positive quadratic form Q. We show that apart 0, Q has quite small eigenvalues, and that this induces poor statistical performance. More importantly, we characterize the numerical kernel of Q, and show that it contains vectors constructed from certain spheroidal wave sequences. From this, we deduce that the subspace method does not allow to estimate accurately the transfer function of the channel on a certain frequency interval. |
| COMM7.6 |
Fast Blind Identification of FIR Communications Channels C. Becchetti, G. Scarano (University Roma "La Sapienza", Italy) This contribution describes a fast frequency domain approach for blind channel identification which does not rely on the statistic of the symbols. The proposed approach is based on the so--called "intraspectral relations" of DFT's of PAM fractionally sampled signals. The use of DFT's is allowed under certain conditions commonly encountered in data communication systems. From the intraspectral relations, asymptotically efficient solutions are derived which turn out to be either more accurate or less expensive in term of complexity w.r.t. the time domain counterparts. Simulation results are provided to assess the validity of the proposed approach in comparison with the Rao Cramer bound and with other approaches from the literature. |
| COMM7.7 |
Blind Equalization of I.I.R. Single-Input Multiple-Output Channels with Common Zeros Using Second-Order Statistics B. Huang, J. Tugnait (Auburn University, USA) The problem of blind equalization of SIMO (single-input multiple-output) communications channels is considered using only the second-order statistics of the data. Such models arise when a single receiver data is fractionally sampled (assuming that there is excess bandwidth), or when an antenna array is used with or without fractional sampling. We focus on direct design of finite-length MMSE (minimum mean-square error) blind equalizers. Unlike the past work on this problem, we allow infinite impulse response (IIR) channels. Our approaches also work when the ``subchannel'' transfer functions have common zeros so long as the common zeros are minimum-phase zeros. Illustrative simulation examples are provided. |
| COMM7.8 |
Blind Linearization, and Identification of Nonlinear Systems - A Least Squares P-th Order Inverse Approach G. Raz, B. Van Veen (University of Wisconsin-Madison, USA) A deterministic approach to blind nonlinear channel equalization and identification is presented. This approach applies to nonlinear channels that can be approximately linearized by finite memory, finite order Volterra filters. Both the Volterra equalizers and the linearized channels are identified. This method also applies to blind identification of linear IIR channels. General conditions for existence and uniqueness are discussed and numerical examples are given. |
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