The impact of MIMO Communication on Non-Frequency Selective Channels Performance

The impact of MIMO Communication on Non-Frequency Selective Channels Performance

Andreas Ahrens (Hochschule Wismar, Germany) and César Benavente-Peces (Universidad Politécnica de Madrid, Spain)
DOI: 10.4018/978-1-60960-042-6.ch007
OnDemand PDF Download:
List Price: $37.50


This chapter reviews the basic concepts of multiple-input multiple-output (MIMO) communication systems and analyses their performance within non-frequency selective channels. The MIMO system model is established and by applying the singular value decomposition (SVD) to the channel matrix, the whole MIMO system can be transformed into multiple single-input single-output (SISO) channels having unequal gains. In order to analyze the system performance, the quality criteria needed to calculate the error probability of M-ary QAM (Quadrature Amplitude Modulation) are briefly reviewed and used as reference to measure the improvements when applying different signal processing techniques. Bit and power allocation is a well-known technique that allows improvement in the bit-error rate (BER) by managing appropriately the different properties of the multiple SISO channels. It can be used to balance the BER’s in the multiple SISO channels when minimizing the overall BER. In order to compare the various results, the efficiency of fixed transmission modes is studied in this work regardless of the channel quality. It is demonstrated that only an appropriate number of MIMO layers should be activated when minimizing the overall BER under the constraints of a given fixed date rate.
Chapter Preview


There is no doubt about the key role that communication systems have in the information society. The various available technologies allow users to share, store and transmit information to others.

The rising demands of new services especially broadband ones like video require the appropriate technologies to transmit and receive information with the expected quality. The demand for higher network capacity and for higher performance of wireless networks is enormous.

There are two major challenges in the design of future wireless communication systems (i.e. in LTE, Long Term Evolution): increasing the spectral efficiency (channel capacity) and improving the link reliability (BER). MIMO Systems are able to improve the spectral efficiency significantly, and consequently MIMO plays a key role in many future wireless communication systems.

MIMO technology has attracted a lot of attention in wireless communications, since it offers significant increases in data throughput and link range without additional bandwidth or transmit power. It achieves this by higher spectral efficiency (more bits per second per hertz of bandwidth) and link reliability or diversity (reduced fading). Because of these properties, MIMO is a hot topic in international wireless research.

Multiple antennas techniques can be used for different objectives and the most common are beamforming and transmit/receive diversity. Diversity techniques provide some protection against channel fading and increase the system range. MIMO techniques are a different way in which multiple antennas are used in a communication system.

The signal propagating through the radio channel is disturbed by different effects. It suffers from fading, multipath effects, noise and interference from other users and systems. Diversity and coding are two well known techniques for combating fading. Diversity is a technique that provides the receiver with several replicas of the transmitted signal which can be processed appropriately in order to combat against fading and interference and are able to improve the link quality.

There are different ways in which diversity techniques are applied (time diversity and frequency diversity). In the last years, the use of spatial diversity has become widely used due to the advantages this technique offers. One of the main properties is that it can be applied without losing spectral efficiency.

Key Terms in this Chapter

MIMO (Multiple-Input Multiple-Output): It’s a technique used to increase channel capacity (spectral efficiency) by adapting the spatial dimension of the transmission channel. This technology incorporates at least two antennas at the transmitter side and at least two antennas at the receiver side. MIMO takes advantage from multipath to improve the system performance.

BLAST (Bell Laboratories Layered Space-Time): BLAST is a spectrally efficient algorithm applied to wireless communication which uses the spatial dimension (SDM) to transmit and receive different data streams using multiple antennas. There are various improvements of this algorithm called V-BLAST (vertical BLAST) and D-BLAST (diagonal BLAST).

PA (Power Allocation): It’s a technique used to distribute the total available power at the transmitter along the various antennas (or layers).

SDM (Space Division Multiplexing): It’s a multiplexing technique in which physical separation of transmitting (antennas) is used to deliver simultaneously different data streams. SDM technique is an approach to MIMO systems and it improves capacity by increasing the number of antennas in the fading channel. The most popular algorithm is the BLAST.

Beamforming: It is a signal processing technique applied to multiple antennas system in order to obtain the required radiation pattern for each case in order to transmit most of the radiated power in a concrete direction (or directions).

AM (Adaptive Modulation): Adaptive Modulation refers to the capability to perform a modulation and power adjustment in the communication (wireless) system to prevent channel conditions and disturbances changing along time (known as fading). This technique increases the spectral efficiency of wireless transmission systems by adapting the signal parameters appropriately.

Complete Chapter List

Search this Book: