This paper investigates the problem of blindly and semi-blindly acquiring the channel gains for an underdetermined synchronous multiuser multicarrier system. The special case of a MISO (Multiple Input Single Output) channel is considered where the different users transmit at the same time and in the same bandwidth. In order to separate the different users blindly, techniques exploiting the finite alphabet are used. For such techniques, and for a general underdetermined MIMO system, we study conditions under which the channel and the data for each user are blindly and semi-blindly identifiable. We consider the Stochastic Maximum Likelihood (SML) criterion in which the unknown input symbols are modeled as discrete random variables. We apply the Expectation-Maximization (EM) algorithm in the frequency domain to get blind and semi-blind channel estimates for each user in the MISO case. We also present a recursive EM solution that updates the channel and noise estimates at each time instant. Simulations show that users can be separated even at low SNR. Furthermore, semi-blind estimation allows for a more robust estimation solution since a possible singularity problem is avoided.

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Multiuser transmission methods for digital subscriber line (DSL) systems have become of interest with the potential for increased data rate and loop reach. These methods often assume that the set of crosstalk interferers, called the crosstalk profile, and their associated channel responses are known. For DSL systems, the interferers are often uncoordinated, so that in a dynamic environment where DSL transmitters can energize and deenergize, the crosstalk profile cannot be transmitted to the user of interest. While the crosstalk channel estimation problem in a dynamic environment can be intractable for general transmission systems, channel and crosstalk analysis can make use of the specific DSL environment. Namely, the physical channels in a DSL system do not change rapidly, and hence estimates of the crosstalk channel can be saved for future reference. For this reason, we introduce the concept of a channel profile. We develop several algorithms to detect the crosstalk profile and investigate the asymptotic behavior of the new algorithms. Simulations show that for typical crosstalk interference scenarios, the observation time to determine the correct crosstalk profile at probability of error less than 10 3 can be less than 2 ms.

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