Adaptive Multichannel Sequential Lattice Prediction Filtering Method For Cognitive Radio Spectrum Sensing in Subbands

Abstract

A multichannel characterization for autoregressive moving average(ARMA) spectrum estimation in subbands with applications to cognitive radio spectrum sensing is considered in this presentation. The fullband ARMA spectrum estimation can be realized in two-channels as a special form of this characterization. A complete or thogonalization of input multichannel data is accomplished using a modified form of sequential processing multichannel lattice stages. Matrix operations are avoided, only scalar operations are used, and a multichannel ARMA prediction filter with a highly modular and suitable structure for VLSI implementations is achieved. The filter is therefore considered as a good candidate for FPGA and DSP chip based spectrum sensing functions in software defined radio implementations. Lattice reflection coefficients for autoregressive and moving average parts are simultaneously computed. These coefficients are then converted to process parameters using a newly developed Levinson-Durbin type multichannel conversion algorithm, so that they can be utilized in the classification of the spectrum. Coarse and fine spectrum sensing can be accomplished using different prediction filter configurations. The computational complexity is given in terms of model order parameters. The performance of the proposed method is compared visually and statistically to that of the multitaper method.