Mimo System

Topics: Information theory, Bayesian network, MIMO Pages: 78 (16933 words) Published: February 16, 2013

MIMO Wireless Communication

MIMO Wireless Communication

Daniel W. Bliss, Keith W. Forsythe, and Amanda M. Chan
■ Wireless communication using multiple-input multiple-output (MIMO) systems enables increased spectral efficiency for a given total transmit power. Increased capacity is achieved by introducing additional spatial channels that are exploited by using space-time coding. In this article, we survey the environmental factors that affect MIMO capacity. These factors include channel complexity, external interference, and channel estimation error. We discuss examples of space-time codes, including space-time low-density parity-check codes and spacetime turbo codes, and we investigate receiver approaches, including multichannel multiuser detection (MCMUD). The ‘multichannel’ term indicates that the receiver incorporates multiple antennas by using space-time-frequency adaptive processing. The article reports the experimental performance of these codes and receivers.


- multiple-output (MIMO) systems are a natural extension of developments in antenna array communication. While the advantages of multiple receive antennas, such as gain and spatial diversity, have been known and exploited for some time [1, 2, 3], the use of transmit diversity has only been investigated recently [4, 5]. The advantages of MIMO communication, which exploits the physical channel between many transmit and receive antennas, are currently receiving significant attention [6–9]. While the channel can be so nonstationary that it cannot be estimated in any useful sense [10], in this article we assume the channel is quasistatic. MIMO systems provide a number of advantages over single-antenna-to-single-antenna communication. Sensitivity to fading is reduced by the spatial diversity provided by multiple spatial paths. Under certain environmental conditions, the power requirements associated with high spectral-efficiency communication can be significantly reduced by avoiding the compressive region of the information-theoretic capacity bound. Here, spectral efficiency is defined as the total number of information bits per second per Hertz transmitted from one array to the other.

After an introductory section, we describe the concept of MIMO information-theoretic capacity bounds. Because the phenomenology of the channel is important for capacity, we discuss this phenomenology and associated parameterization techniques, followed by examples of space-time codes and their respective receivers and decoders. We performed experiments to investigate channel phenomenology and to test coding and receiver techniques. Capacity We discuss MIMO information-theoretic performance bounds in more detail in the next section. Capacity increases linearly with signal-to-noise ratio (SNR) at low SNR, but increases logarithmically with SNR at high SNR. In a MIMO system, a given total transmit power can be divided among multiple spatial paths (or modes), driving the capacity closer to the linear regime for each mode, thus increasing the aggregate spectral efficiency. As seen in Figure 1, which assumes an optimal high spectral-efficiency MIMO channel (a channel matrix with a flat singular-value distribution), MIMO systems enable high spectral efficiency at much lower required energy per information bit. VOLUME 15, NUMBER 1, 2005 LINCOLN LABORATORY JOURNAL



MIMO Wireless Communication

The information-theoretic bound on the spectral efficiency is a function of the total transmit power and the channel phenomenology. In implementing MIMO systems, we must decide whether channel estimation information will be fed back to the transmitter so that the transmitter can adapt. Most MIMO communication research has focused on systems without feedback. A MIMO system with an uninformed transmitter (without feedback) is simpler to implement, and at high SNR its spectral-efficiency bound approaches...
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