Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion

Maximum likelihood sequence estimation (MLSE) and maximum a posteriori probability (MAP) equalizers are optimum receivers for dealing with intersymbol interference (ISI) in time-dispersive channels. However, their high complexity and latency limit their widespread implementation; therefore, research...

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Bibliographic Details
Main Authors: Karel Charles-Darby, Roberto Carrasco-Alvarez, R. Parra-Michel
Format: Article
Language:English
Published: MDPI AG 2019-11-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/8/11/1333
Description
Summary:Maximum likelihood sequence estimation (MLSE) and maximum a posteriori probability (MAP) equalizers are optimum receivers for dealing with intersymbol interference (ISI) in time-dispersive channels. However, their high complexity and latency limit their widespread implementation; therefore, research into reducing their complexity is an open topic. This paper proposes a novel modification to reduce the computational complexity of the aforementioned algorithms, which exploits the representation of the communication channels in a time-delay-domain basis expansion model (BEM). It is shown that an appropriate basis is a set of modified prolate functions, in which the transmitter and receiver filters are considered in the kernel construction. Simulation results show that a reduction in sums and multiplications on the order of 55% can be obtained, maintaining the same bit error rate performance as in the traditional implementation.
ISSN:2079-9292