Reduced complexity turbo equalization using a dynamic Bayesian network

It is proposed that a dynamic Bayesian network (DBN) is used to perform turbo equalization in a system transmitting information over a Rayleigh fading multipath channel. The DBN turbo equalizer (DBN-TE) is modeled on a single directed acyclic graph by relaxing the Markov assumption and allowing weak connections to past and future states. Its complexity is exponential in encoder constraint length and approximately linear in the channel memory length. Results show that the performance of the DBN-TE closely matches that of a traditional turbo equalizer that uses a maximum a posteriori equalizer and decoder pair. The DBN-TE achieves full convergence and near-optimal performance after small number of iterations.Additional file 1: DBN-TE Pseudocode algorithm. (a) DBN-TE function pseudocode. (b) FORWARD MESSAGE function pseudocode. (c) BACKWARD MESSAGE function pseudocode. (d) FORWARD BACKWARD MESSAGE function pseudocode. (e) LLR ESTIMATES function pseudocode.http://www.hindawi.com/journals/asp/am2013ai201

EXIT-charts-aided hybrid multiuser detector for multicarrier interleave-division multiple access

A generically applicable hybrid multiuser detector (MUD) concept is proposed by appropriately activating different MUDs in consecutive turbo iterations based on the mutual information (MI) gain. It is demonstrated that the proposed hybrid MUD is capable of approaching the optimal Bayesian MUD's performance despite its reduced complexity, which is at a modestly increased complexity in comparison with that of the suboptimum soft interference cancellation (SoIC) MU

A Robust Nonlinear Beamforming Assisted Receiver for BPSK Signalling

Nonlinear beamforming designed for wireless communications is investigated. We derive the optimal nonlinear beamforming assisted receiver designed for binary phase shift keying (BPSK) signalling. It is shown that this optimal Bayesian beamformer significantly outperforms the classic linear minimum mean square error (LMMSE) beamformer at the expense of an increased complexity. Hence the achievable user capacity of the wireless system invoking the proposed beamformer is substantially enhanced. In particular, when the angular separation between the desired and interfering signals is below a certain threshold, a linear beamformer will fail while a nonlinear beamformer can still perform adequately. Blockadaptive implementation of the optimal Bayesian beamformer can be realized using a Radial Basis Function network based on the Relevance Vector Machine (RVM) for classification, and a recursive sample-by-sample adaptation is proposed based on an enhanced ?-means clustering aided recursive least squares algorithm