Adaptive MIMO Systems with High Spectral Efficiency

Adaptive MIMO Systems with High Spectral Efficiency

Zhendong Zhou (University of Sydney, Australia) and Branka Vucetic (University of Sydney, Australia)
DOI: 10.4018/978-1-59904-988-5.ch014
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This chapter introduces the adaptive modulation and coding (AMC) as a practical means of approaching the high spectral efficiency theoretically promised by multiple-input multiple-output (MIMO) systems. It investigates the AMC MIMO systems in a generic framework and gives a quantitative analysis of the multiplexing gain of these systems. The effects of imperfect channel state information (CSI) on the AMC MIMO systems are pointed out. In the context of imperfect CSI, a design of robust near-capacity AMC MIMO system is proposed and its good performance is verified by simulation results. The proposed adaptive system is compared with the non-adaptive MIMO system, which shows the adaptive system approaches the channel capacity closer.
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MIMO Channel Model

Consider a point-to-point wireless communication channel equipped with transmit and receive antennas. This channel can be modeled by a multiple-input multiple-output (MIMO) system, which can be represented as a baseband channel matrix H of size . The entry of H at the j-th row and i-th column, , represents the complex channel coefficient relating to the i-th transmit antenna and the j-th receive antenna. We assume that the antennas are placed at an enough distance between each other, such that the channel link between each transmit-receive antenna pair experiences independent fading. The fading is modeled as flat (non-frequency selective), since frequency selective fading can be combated by the orthogonal frequency division multiplexing (OFDM) technique.

The fading distribution is assumed to be Rayleigh, or equivalently, complex Gaussian, which represents the worst case fading situation. Proper normalization is assumed such that each entry can be modeled as a complex Gaussian random process with zero mean and unity variance (variance 0.5 for both real and imaginary parts). We denote this as , where denotes complex Gaussian distribution with mean and variance .

The input to the MIMO channel is represented by an vector , where xi denotes the transmitted (modulated) symbol from the i-th transmit antenna. The output from the MIMO channel is represented by an vector , where yj denotes the received symbol from the j-th receive antenna. The additive white Gaussian noise (AWGN) is represented by an vector , where nj denotes the equivalent complex noise at the j-th receive antenna. The channel equation can be written as

. (1)

Graphically, this can be illustrated by Figure 1.

Figure 1.

MIMO channel model

For convenience, we define


We apply a singular value decomposition (SVD) to the matrix H and get

(2) where D is an diagonal matrix. Its diagonal entries are the singular values of H, that is, the non-negative square roots of the eigenvalues of HHH. We denote the eigenvalues of HHH as , thus

and are and unitary matrices with left and right singular vectors of H as their columns, respectively. and are also called the eigenvectors of HHH and HHH associated with , respectively. The eigenvalues and eigenvectors are related as
, .

Substituting Eq. (2) into (1), we obtain

(3) where


Eq. (3) readily shows a parallel channel model. To put it more explicitly, we can rewrite Eq. (3) in a component-wise form

, (4), and . (5)

Eq. (4) and (5) are further illustrated in Figure 2.

Figure 2.

Parallel equivalent subchannel model

This clearly shows that the MIMO channel has been converted into m parallel subchannels. The equivalent channel input and output are and , respectively. The subchannel power gains are .

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