On Efficient Multiplier-Free Implementation of Channel Estimation and Equalization

In state-of-the-art digital communications systems, channel estimation and/or equalization is an indispensable part of the system. Their performance defines the quality of the achieved data throughput. Due to limited resources, only relatively simple algorithms with low complexity can be applied for channel estimation and equalization in high data-rate systems. In fact, the complexity of these relatively low complexity algorithms can become prohibitive for very high bit-rate applications, for example, several 100Msymbols-per-second. Many reduced complexity techniques have been proposed at the expense of reduced performance. In this paper, we explore the signal constellation structure of practical digital communication systems and describe an efficient computation techniques that not only eliminates multiplications but also minimizes the number of addition operations required to implement channel estimation algorithms such as the Least-Mean-Squates (LMS) estimation and equalization algorithms such as Maximum-Likelihood (ML) sequence detection using the Viterbi algoriothm. This new technique preserves the numerical precision of the algorithms while it reduces their complexity dramatically. For example, our proposed method requires only selection operations (i.e., no adders or multipliers) to implement the multiplication of a complex-valued filter tap coefficient with that of a Quadrature Phase Shift Keying (QPSK) constellation point.