Blind diversity combining equalization for short burst wireless communications

Abstract

Previously, we proposed a dual mode blind equalizer based on the constant modulus algorithm (CMA). The blind equalizer is designed for short burst transmission formats used in many current wireless TDMA systems. It overcomes common problems associated with most blind algorithms, i.e., slow convergence and ill convergence. Thus, it eliminates the overhead associated with training sequences which can be significant for short burst transmission formats. We extend the blind equalizer to a two branch diversity combining blind equalizer. A new initialization scheme for fractionally spaced blind equalizers is introduced. This scheme greatly reduces the probability of ill convergence associated with CMA, by improving the symbol timing recovery. Through simulations with time-varying frequency selective wireless channels, the performance of the proposed equalizer is compared to selection diversity, CMA with the conventional initialization and equalizers with short training sequences. The results indicate that its performance is far superior to that of selection diversity alone and comparable to the performance of equalizers with short training sequences. Thus, the training overhead can be removed with no performance degradation for fast time-varying channels, and with slight performance degradation for static channels.