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

Abstract

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.

Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes

“…On the other hand, different optimization criteria are distinguished for realizing the SISO equalizer, leading to distinct families of turbo equalizers. The MAP turbo equalization is an optimal equalizer in the sense of maximum a posteriori likelihood [37]. The SISO equalizer is then typically implemented using the MAP algorithm, which creates LLR for each entry bit, u i .…”