Multiple Input And Multiple Output Technology Computer Science Essay

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In this assignment we have discussed about the MIMO Multiple-input and multiple-output and OMI OFDM MIMO Intercrossed. MIMO technology has fascinated and has drawn attention in of academic and corporate world of wireless communications on account of its unique characteristics. On one hand it offers considerable enhancement in data throughput and on other the link range in addition to no extra baggage of extra transmit power or bandwidth. This unique character of the MIMO technology is attained by higher spectral efficiency (producing higher number of bits per second per hertz of bandwidth)

That is the reason why MIMO technology today is the major part of the mainstream research of the [update]international wireless. If we further investigate the subject topic we will examine that the technology can be defined in way that it is further divided in three main categories, pre-coding, spatial multiplexing(SM), and diversity coding.OMI technique is a combination of Multiple Input and Multiple Output (MIMO) technology and Orthogonal Frequency Division Multiplexing (OFDM) technology named as OFDM MIMO Intercrossed (OMI) which provides an overview of the basic principles of MIMO-OFDM which is already a standard in IEEE.In this assignment all the work is done on the simulink while using the matlab code. All the graphs are easy to understand and well defined between BER and Eb/No.

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Contents 1

Ber curve 5

omi system 6

simulation 9

Ber curve 10

comparison 11

REFERENCES 11

Introduction:

The assignment is regarding the techniques involved in wireless networking today. This report is an individual report on the topic. I have analyzed the relevant data about the chosen topic, discussing the history, introduction, application and limitation of the selected technique. This approach will enable me to carry out the in depth analysis of the selected technique and will also let me to demonstrate it practical learning through simulating the technique over the Matlab. In this assignment I will also try to explain the methodology behind the subject technique as what are the off screen calculations that are executed in order to extract the results.

Multiple-input and multiple-output MIMO:

Multiple-input and multiple-output (MIMO) is the utilization of several antennas at both the transmitter and receiver to progress the performance of communication. In other words we can say that it is one of numerous kinds of elegant antenna technologies. MIMO technology has fascinated and has drawn attention in of academic and corporate world of wireless communications on account of its unique characteristics. On one hand it offers considerable enhancement in data throughput and on other the link range in addition to no extra baggage of extra transmit power or bandwidth. This unique character of the MIMO technology is attained by higher spectral efficiency (producing higher number of bits per second per hertz of bandwidth). That is the reason why MIMO technology today is the major part of the mainstream research of the [update]international wireless. If we further investigate the subject topic we will examine that the technology can be defined in way that it is further divided in three main categories, pre-coding, spatial multiplexing(SM), and diversity coding. Let's discuss these heads in briefly in the following lines.

Selection of unique coding scheme is required to execute a MIMO communication system. Usually many scheme of space-time coding possess a strong association to renowned single-input single-output (SISO) coding techniques. And interestingly many of these SISO techniques involve uninformed transmitter (UT). Given that, it can be argued that such technique of coding (Space-time) can exploit the MIMO degrees of freedom to enhance redundancy and spectral efficiency.

Simulation:

Let's analyze the technique by understanding through its theory regarding the simulation of the technique. Which is actually represented through a figure explaining the implementation of the MIMO Multiple-input and multiple-output system

In case of such simulation, The OSTBC (orthogonal space-time block code) encoder block program an input series utilizing orthogonal space-time block code (OSTBC). The Encoder block of (OSTBC) has plotted the input characters block-wise. While on the other hand it has concatenated the output symbol matrices in the time domain. So it can be argued that for OSTBC transmission the block of (OSTBC) carries time and spatial domains as a key factor. And there tend to happen no doubt these factors hold the key importance in the OSTBC transmission. Along with this key factor the system does also contain and supports an elective dimension, and that's the factor where the encoding calculation is no dependant. Such dimension can be considered as the frequency domain. In the following given diagram it is illustrated that what are the supported dimensions for the inputs and output of the OSTBC Encoder block.

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'T' is Input symbol for the time domain.

'R' is Symbol rate for the code

'N' is Number of transmit antennas

The block at OSTBC Combiner receives and combines all the input signals from all the relevant source antennas, in order to assist the channel to approximate about the signal to pull out the information encoded in the symbols. Worth stating here that this encoding of the information into the symbols was executed by OSTBS encoder. The estimation by input channel may not be steady during each codeword block transmission and the united algorithm utilizes only the approximation of value for the initial symbol period per codeword block. Later, a symbol decoder will consider the Combiner block in a MIMO communications system. Along with the time and spatial domains for OSTBC transmission, the block supports an optional dimension, over which the combining calculation is independent. This dimension can be thought of as the frequency domain for OFDM-based applications. The following illustration indicates the supported dimensions for inputs and output of the OSTBC Combiner block.

Here, 'M' is Number of receive antennas.

Ber curve

omi system

The use of multiple antennas at both ends of a wireless link (MIMO technology) holds the potential to drastically improve the spectral efficiency and link reliability in future wireless communications systems and OFDM is to increase the robustness against frequency selective fading or narrowband interference. This technique is a combination of Multiple Input and Multiple Output (MIMO) technology and Orthogonal Frequency Division Multiplexing (OFDM) technology named as OFDM MIMO Intercrossed (OMI) which provides an overview of the basic principles of MIMO-OFDM which is already a standard in IEEE.

MIMO-OFDM will allow service providers to deploy a Broadband Wireless Access (BWA) system that has Non-Line-of-Sight (NLOS) functionality. Specifically, MIMO-OFDM takes advantage of the multipath properties of environments using base station antennas that do not have LOS.Orthogonal Frequency Division Multiplexing (OFDM) is one of the most promising physical layer technologies for high data rate wireless communications due to its robustness to frequency selective fading, high spectral efficiency, and low computational complexity. OFDM can be used in conjunction with a Multiple-Input Multiple-Output (MIMO) transceiver to increase the diversity gain and/or the system capacity by exploiting spatial domain. Because the OFDM system effectively provides numerous parallel narrowband channels, MIMO-OFDM is considered a key technology in emerging high-data rate systems such as 4G, IEEE 802.16, and IEEE 802.11n.

MIMO communication uses multiple antennas at both the transmitter and receiver to exploit the spatial domain for spatial multiplexing and/or spatial diversity. Spatial multiplexing has been generally used to increase the capacity of a MIMO link by transmitting independent data streams in the same time slot and frequency band simultaneously from each transmit antenna, and differentiating multiple data streams at the receiver using channel information about each propagation path.

In contrast to spatial multiplexing, the purpose of spatial diversity is to increase the diversity order of a MIMO link to mitigate fading by coding a signal across space and time so that a receiver could receive the replicas of the signal and combine those received signals constructively to achieve a diversity gain.

In MIMO-OFDM systems, channel state information (CSI) is essential at the receiver in order to coherently detect the received signal and to perform diversity combining or spatial interference suppression. The channel is very important to the performance of diversity schemes, and more variable channels give more diversity. Thus, in order to attain accurate CSI at the receiver, pilot-symbol-aided or decision-directed channel estimation must be used to track the variations of the frequency selective fading channel. Among the various resources in MIMO multicarrier systems the power assignment is related to the accuracy of the channel estimation. Pilot symbols facilitate channel estimation, but in addition to consuming bandwidth, they reduce the transmitted energy for data symbols per OFDM symbol under a fixed total transmit power condition. This suggests a tradeoff between the system capacity and the accuracy of the channel estimation in MIMO-OFDM systems according to the power allocation when the total transmits power is fixed.

simulation

In simulation, The Frame Conversion block passes the input through to the output and sets the output sampling mode to the value of the Sampling mode of output signal parameter, which can be either Frame-based or Sample-based. The output sampling mode can also be inherited from the signal at the Ref (reference) input port, which you make visible by selecting the Inherit output sampling mode from <Ref> input port check box.

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The Frame Conversion block does not make any changes to the input signal other than the sampling mode. In particular, the block does not rebuffer or resize 2-D inputs. Because 1-D vectors cannot be frame based, when the input is a length-M 1-D vector and the block is in Frame-based mode, the output is a frame-based M-by-1 matrix - that is, a single channel.

The AWGN Channel block adds white Gaussian noise to a real or complex input signal. When the input signal is real, this block adds real Gaussian noise and produces a real output signal. When the input signal is complex, this block adds complex Gaussian noise and produces a complex output signal. This block inherits its sample time from the input signal.

Ber curve

comparison