Paraunitary oversampled filter bank design for channel coding

Oversampled filter banks (OSFBs) have been considered for channel coding, since their redundancy can be utilised to permit the detection and correction of channel errors. In this paper, we propose an OSFB-based channel coder for a correlated additive Gaussian noise channel, of which the noise covariance matrix is assumed to be known. Based on a suitable factorisation of this matrix, we develop a design for the decoder's synthesis filter bank in order to minimise the noise power in the decoded signal, subject to admitting perfect reconstruction through paraunitarity of the filter bank. We demonstrate that this approach can lead to a significant reduction of the noise interference by exploiting both the correlation of the channel and the redundancy of the filter banks. Simulation results providing some insight into these mechanisms are provided

Broadband GSC beamformer with spatial and temporal decorrelation

Broadband adaptive beamforming algorithms based on the least mean square (LMS) family are known to exhibit slow convergence, if the input is correlated. In this paper, we will utilised a recently proposed broadband eigen value decomposition method to provide strong spatial decorrelation, while at the same time reduces the subspace in which the beamforming algorithm operates. Additional temporal decorrelation is gained by operating the beamformer in oversampled filter banks. Hybrid structures which combine both spatial and temporal decorrelation demonstrate to provide faster convergence speed than the normalised LMS algorithm or either of the decorrelation approach on its own

Channel Coding for Power Line Communication Based on Oversampled Filter Banks

Oversampled filter banks (OSFB) can be applied as channel coding and have shown benefits in correlated and impulsive noise. Therefore, in this paper, we consider such channel coders for a power line communications scenario, where such conditions create a hostile channel environment. The proposed channel coder assumes the knowledge of the noise covariance matrix. The transmission is accomplished over the N weakest eigenmodes of the noise, which are identified by means of a broadband eigenvalue decomposition. We demonstrate by simulations that this approach can lead to an enhancement of the SNR in the receiver and to improved performance over standard channel coding approaches

An EVD algorithm for para-Hermitian polynomial matrices

An algorithm for computing the eigenvalue decomposition of a para-Hermitian polynomial matrix is described. This amounts to diagonalizing the polynomial matrix by means of a paraunitary "similarity" transformation. The algorithm makes use of "elementary paraunitary transformations" and constitutes a generalization of the classical Jacobi algorithm for conventional Hermitian matrix diagonalization. A proof of convergence is presented. The application to signal processing is highlighted in terms of strong decorrelation and multichannel data compaction. Some simulated results are presented to demonstrate the capability of the algorith