Diversity and MIMO Techniques

The purpose of this chapter is to describe various diversity and MIMO techniques capable of improving single-input single-output system performance. Through MIMO concept it is also possible to improve the aggregate data rate. The diversity and MIMO techniques described are applicable to wireless MIMO communications as well as free-space optical and fiber-optics communications with coherent optical detection. Various diversity schemes are described including polarization diversity, spatial diversity, and frequency diversity schemes. The following receiver diversity schemes are described: selection combining, threshold combining, maximum-ratio combining, and equal-gain combining schemes. Various transmit diversity schemes are described, depending on the availability of channel state information on transmitter side. The most of the chapter is devoted to wireless and optical MIMO techniques. After description of various wireless and optical MIMO models, we describe the parallel decomposition of MIMO channels, followed by space-time coding (STC) principles. The maximum likelihood (ML) decoding for STC is described together with various design criteria. The following classes of STC are described: Alamouti code, orthogonal designs, linear space-time block codes, and space-time trellis codes. The corresponding STC decoding algorithms are described as well. After that we move our attention to spatial division multiplexing (SDM) principles and describe various BLAST encoding architectures as well as multigroup space-time coded modulation. The following classes of linear and feedback MIMO receiver for uncoded signals are subsequently described: zero-forcing, linear minimum MSE, and decision-feedback receivers. The next topic in the chapter is related to suboptimum MIMO receivers for coded signals. Within this topic we first describe linear zero-forcing (ZF) and linear MMSE receivers (interfaces) but in the context of STC. Within decision-feedback and BLAST receivers, we describe various horizontal/vertical (H/V) BLAST architecture including ZF and MMSE ones. The topic on suboptimum receivers is concluded with diagonal (D-)-BLAST and iterative receiver interfaces. The section on iterative MIMO receivers starts with brief introduction of concept of factor graphs, following with the description of factor graphs for MIMO channel and channels with memory. We then describe the sum-product algorithm (SPA) operating on factor graphs, followed by description of SPA for channels with memory. The following iterative MIMO receivers for uncoded signals are described: ZF, MMSE, ZF H/V-BLAST, and LMMSE H/V-BLAST receivers. After brief description of factor graphs for linear block and trellis codes, we describe several iterative MIMO receivers for space-time coded signals. The section on broadband MIMO describes how frequency selectivity can be used as an additional degree of freedom, followed by description of MIMO-OFDM and space-frequency block coding principles. The focus is then moved to MIMO channel capacity calculations for various MIMO channel models including deterministic, ergodic, and non-ergodic random channels, as well as correlated channel models. The concepts of ergodic and outage channel capacity are described. The section on MIMO channel capacity ends with MIMO-OFDM channel capacity description. In MIMO channel estimation section, the following MIMO channel estimation techniques are described: ML, least squares (LS), and linear minimum MSE MIMO channel estimation techniques. The last section concludes the chapter.