Efficient Cosine Modulated Filter Bank Using Multiplierless Masking Filter and Representation of Prototype Filter Coefficients Using CSD

Abstract

Abstract-This paper presents a design of low complexity multichannel Nearly Perfect Reconstruction (NPR) Cosine Modulated Filter Bank (CMFB). CMFBs are used extensively because of ease realization and the inherent advantage of high stop-band attenuation. But, when the number of channel becomes large, it leads to certain limitations as it would require large number of filter coefficients to be optimized and hence longer CPU time; e.g. 32-band or 64-band CMFB. Large number of filter coefficients would also mean that computational complexity of the prototype filter is extremely increased that tends to slow down the convergence to best possible solution. Here, the prototype filter is designed using modified Interpolated Finite Impulse Response (IFIR) technique where masking filter is replaced by multiplier free cascaded structure and coefficients of model filter are converted to nearest Canonical Signed Digit (CSD). The interpolation factor is chosen in such a way that computational cost of the overall filter and different error parameters are reduced. The proposed approach thus leads to reduction in stop-band energy as well as high Side-Lobe-Fall-off-Rate (SLFOR). Three examples have been included to demonstrate the effectiveness of the proposed technique over the existing design methods and savings in computational complexity is also highlighted.

Waveform Design for 5G and beyond Systems

“…In Reference [4], a survey of multicarrier communications about prototype filters, lattice structures and the implementation aspects is reported by A. Şahin et al, and this work provides four classes of filters according to the design criteria-energy concentration, rapid decay, spectrum nulling and channel/hardware characteristics. In Reference [54], the methods of prototype filter design for cosine modulated filter banks (CMFBs) with nearly perfect reconstruction (NPR) are reviewed by K. Shaeen et al, and these methods are categorized into nonlinear optimization methods [55][56][57][58][59][60][61][62][63], spectral factorization methods [64], linear search methods [65][66][67][68][69][70], interpolated finite impulse response (IFIR) methods [71][72][73], and frequency response masking (FRM) methods [58,[74][75][76][77][78][79], and so forth. The authors of Reference [80] provide the basic introduction of the FIR filter, some relevant works on FIR and various factors which effect the performance of FIR filter in communication systems.…”