Decision-Feedback Equalization for Pulse-Position Modulation

• Published in: IEEE transactions on signal processing Vol. 55; no. 11; pp. 5361 - 5369
• Main Authors: Klein, A.G., Duhamel, P.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.11.2007; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Amplitude modulation; Applied sciences; Channels; Computational efficiency; Computer simulation; Decision feedback equalization (DFE); Decision feedback equalizers; Detection, estimation, filtering, equalization, prediction; Equalization; Equalizers; Exact sciences and technology; Finite impulse response filter; Information, signal and communications theory; Intersymbol interference; Maximum likelihood estimation; Modulation; Modulation, demodulation; optical communication; Optical feedback; Optical fiber communication; Optical pulses; Pulse modulation; pulse position modulation (PPM); Signal and communications theory; Signal, noise; Telecommunications and information theory; Ultra wideband technology; Ultrawideband; pulse position modulation (PPM); optical communication; Decision feedback equalization (DFE); ultrawideband; Performance evaluation; High performance; Optical telecommunication; Modulated signal; Decision feedback equalizers; Pulse position modulation; Stochastic method; Ultra wide band; Mean square error; Learning; Simulation; Intersymbol interference; Telecommunication system; Descent method; Gradient method
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2007.899392

In this paper, we propose a minimum mean squared error (MMSE) decision feedback equalizer (DFE) for pulse position modulated (PPM) signals in the presence of intersymbol interference (ISI). While traditional uses of PPM may not have had ISI, PPM is increasingly being considered for use in situations where ISI is an issue, such as high-performance optical communication systems and ultrawideband communications. First, we review previous work on the subject which used the zero-forcing criterion under strict assumptions about the channel and equalizer lengths. Then, we derive a computationally efficient MMSE equalizer which removes these restrictions, and is suitable for use with training-based stochastic gradient-descent algorithms. Finally, we demonstrate the performance of the proposed equalizer with simulations.