Adaptive channel equalization: A simplified approach using the quantized-LMF algorithm

In digital communication, adaptive channel equalization techniques underpin the successful provision of high speed and reliable data transmission over severely-dispersive channels, e.g. wireless and mobile ones. In a real world that is largely dominated by non-Gaussian interference signals, adaptive equalizers relying heavily on the LMS are bound to yield suboptimal performances. This work addresses this sub-optimality issue by proposing a new adaptive equalizer which judiciously combines the power of the least-mean fourth (LMF) algorithm to better tackle non-Gaussian environments, and the capability of the power-of-two quantizer (PTQ) to greatly reduce the LMF's high computational load. This combination endows the proposed algorithm with a capability of tracking fast-changing channels. A performance analysis of the proposed adaptive channel equalizer, based on a new linear approximation of the PTQ, is also presented. Extensive simulation testing of the proposed adaptive equalizer corroborates very well the theoretical findings predicted by the analysis of the linearized LMF-PTQ algorithm.