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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 attracted attention in wireless communications, because 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 current[update] theme of international wireless research .MIMO can be sub-divided into three main categories, precoding, 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 mat lab code. All the graph are easy to understand and well defined between BER and Eb/No.
Multiple-input and multiple-output (MIMO) is the use of multiple antennas at both the transmitter and receiver to improve communication performance. It is one of several forms of smart antenna technology. MIMO technology has attracted attention in wireless communications, because 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 current[update] theme of international wireless research. MIMO can be sub-divided into three main categories, precoding, spatial multiplexing(SM), and diversity coding:
Precoding is multi-layer beamforming, in the narrowest definition. In more general terms, it is considered to be all spatial processing that occurs at the transmitter. In (single-layer) beamforming, the same signal is emitted from each of the transmit antennas with appropriate phase (and sometimes gain) weighting such that the signal power is maximized at the receiver input. The benefits of beamforming are to increase the signal gain from constructive interference and to reduce the multipath fading effect. In the absence of scattering, beamforming results in a well defined directional pattern, but in typical cellular conventional beams are not a good analogy. When the receiver has multiple antennas, the transmit beamforming cannot simultaneously maximize the signal level at all of the receive antennas, and precoding is used. Note that precoding requires knowledge of the channel state information (CSI) at the transmitter. Spatial multiplexing can also be combined with precoding when the channel is known at the transmitter or combined with diversity coding when decoding reliability is in trade-off.
Spatial multiplexing requires MIMO antenna configuration. In spatial multiplexing, a high rate signal is split into multiple lower rate streams and each stream is transmitted from a different transmit antenna in the same frequency channel. If these signals arrive at the receiver antenna array with sufficiently different spatial signatures, the receiver can separate these streams, creating parallel channels free. Spatial multiplexing is a very powerful technique for increasing channel capacity at higher signal-to-noise ratios (SNR). The maximum number of spatial streams is limited by the lesser in the number of antennas at the transmitter or receiver. Spatial multiplexing can be used with or without transmit channel knowledge.
Diversity Coding techniques are used when there is no channel knowledge at the transmitter. In diversity methods a single stream (unlike multiple streams in spatial multiplexing) is transmitted, but the signal is coded using techniques called space-time coding. The signal is emitted from each of the transmit antennas with full or near orthogonal coding. Diversity coding exploits the independent fading in the multiple antenna links to enhance signal diversity. Because there is no channel knowledge, there is no beamforming or array gain from diversity coding.
In order to implement a MIMO communication system, we must first select a particular coding scheme. Most space-time coding schemes have a strong connection to well-known single-input single-output (SISO) coding approaches and assume an uninformed transmitter(UT). Space-time coding can exploit the MIMO degrees of freedom to increase redundancy, spectral efficiency, or some combination of these characteristics.
In this simulation, The OSTBC Encoder block encodes an input symbol sequence using orthogonal space-time block code (OSTBC). The block maps the input symbols block-wise and concatenates the output codeword matrices in the time domain. The block supports time and spatial domains for OSTBC transmission. It also supports an optional dimension, over which the encoding calculation is independent. This dimension can be thought of as the frequency domain. The following illustration indicates the supported dimensions for the inputs and output of the OSTBC Encoder block.
Here, 'T' is Input symbol sequence length for the time domain. 'R' is Symbol rate of the code and 'N' is Number of transmit antennas.
The OSTBC Combiner block combines the input signal (from all of the receive antennas) and the channel estimate signal to extract the soft information of the symbols encoded by an OSTBC. The input channel estimate may not be constant during each codeword block transmission and the combining algorithm uses only the estimate for the first symbol period per codeword block. A symbol demodulator or decoder would follow 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.
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.
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.
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.