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Yayın Maximum likelihood blind channel estimation for space-time coding systems(Hindawi Publishing Corporation, 2002-05) Çırpan, Hakan Ali; Panayırcı, Erdal; Çekli, ErdinçSophisticated signal processing techniques have to be developed for capacity enhancement of future wireless communication systems, In recent years, space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. Space-time codes are obtained by combining channel coding, modulation, transmit diversity, and optional receive diversity in order to provide diversity at the receiver and coding gain without sacrificing the bandwidth. In this paper, we consider the problem of blind estimation of space-time coded signals along with the channel parameters. Both conditional and unconditional maximum likelihood approaches are developed and iterative solutions are proposed. The conditional maximum likelihood algorithm is based on iterative least squares with projection whereas the unconditional maximum likelihood approach is developed by means of finite state Markov process modelling. The performance analysis issues of the proposed methods are studied. Finally, some simulation results are presented.Yayın Sequence estimation with transmit diversity for wireless communications(Urban & Fischer Verlag, 2003) Panayırcı, Erdal; Aygölü, Hasan Ümit; Pusane, Ali EmreIn this paper, an optimum sequence estimation algorithm for wireless systems with Alamouti's two transmitter diversity in the presence of multipath fading is proposed. The algorithm is based on a jointly iterative channel and sequence estimation according to the maximum likelihood (ML) criterion, using the Expectation-Maximization (EM) algorithm employing an M-level phase-shift keying (M-PSK) modulation scheme with additive Gaussian noise. The discrete multipath channel is represented in terms of the channel gains from each transmit antenna to the receive antenna. EM algorithm estimates jointly the complex channel parameters of each channel and the data sequence transmitted, iteratively, which converges to the true ML solution. The channel estimation is achieved in a simple way through the iterative equations by decoupling of the signals transmitted from different antennas. The algorithm is applied to the trellis coded modulation systems and the efficiency of the algorithm proposed has been shown with computer simulations. The simulation results show that the EM algorithm converges quickly for fast fading channels. The performance of the EM-based decoder approaches that of the ML receiver which has perfect knowledge of the channel.Yayın Blind channel estimation for space-time coding systems with Baum-Welch algorithm(IEEE, 2002) Çırpan, Hakan Ali; Panayırcı, ErdalIn recent years, space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. In this paper, we consider the problem of blind estimation of the channel parameters along with space-time coded signals. Our proposed approach exploits the finite alphabet property of the space-time coded signals and is based on the unconditional signal model by treating the information sequence as stochastic I.I.D. sequences. The iterative Baum-Welch algorithm is then adapted to solve resulting unconditional ML optimization cost function. Finally, some simulation results are presented.Yayın EM-Based sequence estimation for wireless systems with orthogonal transmit diversity(IEEE, 2003) Panayırcı, Erdal; Aygölü, Hasan Ümit; Pusane, Ali EmreIn this paper, an optimum sequence estimation algorithm for wireless systems with Alamouti's two transmitter diversity in the presence of multipath fading is proposed. The algorithm is based on a jointly iterative channel and sequence estimation according to the maximum likelihood (ML) criterion, using the Expectation-Maximization (EM) algorithm employing M-PSK modulation scheme with additive Gaussian noise. The discrete multipath channel is represented in terms of the channel gains from each transmit antenna to the receive antenna. EM algorithm derived estimates jointly the complex channel parameters of each channel And the data sequence transmitted, iteratively, which converges to the true ML solution. The channel estimation is achieved in a simple way through the iterative equations by decoupling of the signals transmitted from different antennas. The algorithm is applied to the trellis coded modulation systems and efficiency of the algorithm proposed has been shown by the computer simulations. Simulation results show that the EM algorithm converges quickly for fast fading channels. The performance of the EM-based decoder approaches that of the ML receiver which has perfect knowledge of the channel.
