Paper Info
Title
Robust receiver for OFDM-DCSK modulation via rank-1 modeling and ℓ p-minimization
Abstract
AbstractHighlights •Rank-1 property of the signal transmission matrix in OFDM-DCSK system is exploited to achieve noise reduction and lower BER.• ℓ p-norm based minimization algorithm is devised to realize the proposed low-rank matrix approximation procedure in the receiver even in the presence of impulsive noise.•The BER expression of the proposed rank-1 property based receiver is derived. AbstractIn this paper, the problem of receiver design for orthogonal frequency division multiplexing differential chaos shift keying (OFDM-DCSK) communication systems is addressed. By exploiting the rank-1 property of the symbol matrix, we propose to apply dimensionality reduction on the time-domain data symbols received from the OFDM-DCSK transmitter for noise reduction, followed by chaotic demodulation on the resultant symbols to decode the information bits. In the presence of additive white Gaussian noise (AWGN), the rank-1 matrix approximation can be simply achieved by the truncated singular value decomposition, corresponding to the solution of ℓ 2-norm minimization. While for impulsive noise environments such as in power line communication systems, we develop an alternating optimization algorithm for ℓ p-based matrix factorization, where 0 < p < 2. The bit error rate (BER) of our approach in AWGN is also analyzed and verified. Simulation results demonstrate that the devised receiver is superior to the conventional OFDM-DCSK method in terms of BER and root mean square error performance for AWGN as well as impulsive noise including the Middleton class A distribution and α-stable process.
Year
DOI
Venue
2021
10.1016/j.sigpro.2021.108219
Periodicals
Keywords
DocType
Volume
Bit error&nbsp, rate&nbsp, Differential chaos shift&nbsp, keying, tp-minimization, Implusive&nbsp, noise
Journal
188
Issue
ISSN
Citations 
C
0165-1684
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zhaofeng Liu162.11
Hing Cheung So201.01
Lin Zhang33822.81
Xiao Peng Li400.34