This paper proposes a hierarchical multiresolution based empirical mode decomposition approach for performing a wireless digital demodulation. The waveform corresponding to each digital symbol is represented as the sum of the intrinsic mode functions. Each obtained intrinsic mode function of each symbol is further decomposed via a discrete cosine transform approach. First, zeros are inserted in each intrinsic mode function in the discrete cosine transform domain. Then, the next level empirical mode decomposition is performed in the time domain. The discrete cosine transform coefficients of the next level intrinsic mode functions where the zeros are added are removed in the discrete cosine transform domain. This step is repeated and the obtained intrinsic mode functions in each level form a dictionary. The received signals corrupted by the additive noises with unknown distributions and distorted by nonlinear channels are represented by both the weight vectors and the residue vectors based on the dictionary at each level of decomposition. The decoding scheme is to find the corresponding symbol such that the sum of the residue vectors in various levels of the decomposition is minimized. Experimental results show that the decoding accuracy is higher than that of the conventional matched filter approach.
|Title of host publication||2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)|
|Publication status||Published - 25 Feb 2016|
|Name||2015 ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA)|