Convergence performance of subband arrays for spatio-temporal equalization

Subband arrays have been proposed as useful means to realize joint spatio-temporal domain equalization in digital mobile communications. They are used to mitigate channel impairment problems caused by the inter-symbol interference (ISI) and co-channel interference (CCI). In this paper, we propose the normalized subband array and locally orthogonalized subband array techniques for channel equalizations. The least square mean (LMS) algorithm is used for adaptation. The convergence performance of the proposed techniques is analyzed and compared with that of conventional space-time adaptive processing (STAP) techniques. It is shown that subband decompositions provide great flexibility in implementing spatio-temporal equalizations. Both analytical and numerical simulation results demonstrate that the proposed subband array techniques substantially improve the convergence performance without significant additional computations.