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 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.

MZM Optimization of PAM-4 Transmission in Data Center Interconnect

An analog optimization of 4-level pulse amplitude modulation (PAM-4) signal is proposed, together with maximum likelihood sequence estimation digital signal processing. The proposed optimizations are verified by experimental demonstration at 53 Gbaud, indicating an improvement of 4–5 dB in the optical signal to noise sensitivity.