# Multi Input Multi Output System Computer Science Essay

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MIMO (Multi Input Multi Output) system is one of the emerging technologies in wireless communication. Multiple transmitters and receivers provides high link capacity in future wireless systems. Analysis of indoor environment multiple-input-multiple-output (MIMO) has clear view of measurements fields like industrial, scientific, and medical which are increase in capacity, utilizing multiple transmitters and receivers can be compared. MIMO system used increases the channel capacity and data is transferred.

## DEFINITION OF MIMO

MIMO Multi Input Multiple output itself indicates that it is multiple input and multiple output. Here multiple antenna are used for transmitting the information and the multiple receiver been installed to receive the data ,here the the capacity is improved without effect the cost or performance of the system.

Shannon channel capacity available from deploying multiple antennas at both the transmitter (TX) and the receiver (Rx) of a wireless system has come up with interest in recent years [1], Large capacity is obtained via the potential decorrelation between the channel coefficients of the multiple-input/multiple-output (MIMO) radio channel, which can be exploited to create several parallel sub channels. However, the potential capacity gain is highly dependent on the multipath richness, since a fully correlated MIMO radio channel only offers one sub channel, while a completely decor related radio channel potentially offers multiple sub channels depending on the antenna configuration. The measured results, obtained scattering is sufficiently rich to provide substantial link capacity increases. Envelope of the channel coefficients for this obstructed-line-of-sight (OLOS) indoor scenario is approximately Rayleigh distributed and the MIMO channel covariance matrix can be well approximated by a Kronecker product of the covariance matrices describing the correlation at the transmitter side and the receiver side, respectively. A statistical narrowband model for the OLOS indoor MIMO channel based on this covariance structure can be obtained.

## AIM AND OBJECTIVES

To study the behaviour of channel capacity on measured indoor radio signals. Different characteristics of the signal can be done and aimed. Related work on the specific field work is to be studied. Channel capacity will be used while communicating and when signals when multiple antennas used. Results in communication interference the wavelength and hence increases or decreases wavelength and the channel capacity. And so the aim of the project is to research on channel capacity in Multiple Inputs Multiple Output (MIMO) system in radio channel (mobile communication).

The following project deliverables were set. The effects of channel correlation are to be investigated and a way for getting least possible correlation is to be found. Also the propagation models are to be investigated and a model suitable for the MIMO system being used is to be found out.

CHAPTER 2 LITERATURE REVIEW

## HISTORICAL OVERVIEW

Over the years wireless communication has become one of the important parts in communicating. Wireless communication is the fastest growing segment of the communication Industry. Cellular systems have experienced exponential growth over the last decade. The gap between current and emerging systems and the vision for future wireless applications indicates that much works remains to be done to make this vision a reality. Many new applications including wireless sensor networks automated highways and factories smart homes and appliances and remote telemedicine are emerging from research ideas to concrete systems.

HISTORY

"First Wireless network were developed in the pre-industrial age. These later extended to telescopes. Then the later on the information was transferred using smoke signals, torch signalling flashing mirror etc.

These were replaced by telegraph network invented by Samuel Morse in 1838 and later by telephone [1].

In 1895 Marconi demonstrated first radio transmission.

Early radio system transmitted analog signals. In today communication radio communication systems transmit digital signals composed of binary bits where bits are directly obtained by the signal. Then came packet radio. The first network based on packet radio ALOHANET was developed by the University of Hawaii in1971. U.S military used this combination of packet data and broadcast radio.

In 1970's and early 1980's defence advanced research Project agency DARPA invested significant resources to develop networks using packet radios for tactical communications in the battle field's. Packet Radio network also found commercial application in supporting wide area wireless data service. The introduction of wired Ethernet technology in 1970's steered many commercial companies away from radio based networking. In 1985 federal communication commission (FCC) enabled the commercial development of wireless LANs by authorizing the public use of Industrial, scientific and medical (ISM) frequency bands for wireless LAN products. In 1990's these were replaced by wireless data capabilities of cellular telephones and wireless local area network (WLANs)"

[2]The use of multiple antennas at the TX and Rx in wireless Systems MIMO has rapidly gained in popularity over the past decade due to it powerful enhancing capabilities. Multi path is the arrival of transmitted signal at an intended receiver through differing angles and/or differing time delays and differing frequency shifts due to scattering of electromagnetic waves in the environment [2]. Received signal power fluctuates in space due to angle spread or frequency or time through random superposition of the impinging multi path components which is called fading which can severely affect the quality and reliability of wireless communication.

MIMO technology constitutes a breakthrough in wireless communication system design. The technology offers a number of benefits that help meet the challenges posed by both the impairment in the wireless channel as well as resource constraints.

## BENIFITS OF MIMO

Benefits of MIMO technology

The benefits of MIMO technology that help achieve such significant performance gains are array gain, spatial diversity gain, spatial multiplexing gain and interference reduction

Array gain

Array gain is the increase in receive SNR that results from coherent combining effect of the wireless signals at a receiver. The coherent combining may be realized through spatial processing at the receive antenna array and or spatial pre-processing at transmitted antenna array .array gain improves resistance to noise thereby improving the coverage and the range of a wireless network

Spatial diversity gain

A MIMO channel with Mt and Mr receive antenna offers MtMr independently fading link and hence spatial diversity order of Mt Mr (increase number of independent copies which is diversity order)

Spatial multiplexing gain

MIMO system offers a linear increase in data rate through spatial multiplexing i.e. transmitting multiple, independent data streams within the bandwidth of operations. Under suitable conditions such as scattering in environment the receiver can separate the data streams Spatial Multiplexing gain increases the capacity of a wireless network.

Interference reduction and avoidance

Interference results from multiple users sharing time and frequency resources.

## CAPACITY LIMIT OF MIMO SYSTEM

Channel capacity was pioneered by Claude Shannon in late 1940's using a mathematical theory of communication. The capacity of a channel denoted by C is the maximum rate at which communication can be performed without nay constraints on transmitter and receiver complexity. Shannon showed that any rate R<C there exits R channel codes with arbitrarily small block error probabilities. Thus for any rate R<C and any desired non zero probability of error Pe, there exists a rate R code that achieves Pe. Shannon also showed that codes operating at rates R>C cannot achieve arbitrarily small error rate and thus error probability of a code operating at a rate above capacity is bounded away from zero. Thus channel capacity is truly the fundamental limit of communication [2].

Mathematical definition of Capacity

Shannon pioneering work showed that the capacity of a channel defined to be the maximum rate at which reliable communication is possible can be simply defined in terms of input and output channels

Channel capacity performance is much better and efficient in indoor radio channel as compared with the outdoor channel. Repeated sending of signals received at the receiver will be quick and the indoor environment is less effected by the noise or any other noise. When electromagnetic signals are transmitted from the transmitter the signals scatters and reaches its destination receiver through travelling in propagation spaces. In outdoor communication the possibility of Signal to Noise ratio increases and the medium plays a role in receiving the outside disturbance, sometimes the signal might not reach the destination and gets scattered.

MIMO system provides tremendous capacity gain which spurred significant activity to develop transmitter and receiver techniques which realize these capacity benefits and exploits diversity multiplexing trade-off. As maximum data rate can be transferred using MIMO channel to one or more users with small error probability.

Capacity gain obtained from multiple antennas depends on the available channel information at either receiver or transmitter, the channel Signal to Noise ratio (SNR) and the correlation between the channel gains on each antenna elements. The capacity of a channel denoted by C is the maximum rate at which reliable communication can be performed without any constraints on transmitter and receiver complexity

Shannon showed that for any rate R< C there exits rate R channel code with small block or symbol error probability. Shannon also showed that code operating at rate R>C cannot achieved an arbitrarily small error rate thus error probability of a code operates at a rate above capacity is bounded away from zero. And this shows that channel capacity is truly the fundamental limit to communication.

Fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.

Actual message is one selected from a set of possible messages.

Parameters of engineering importance such as time, bandwidth, number of relays, etc., tend to vary linearly with the logarithm of the number of possibilities

Teletype and telegraphy are two simple examples of a discrete channel for transmitting information

Maximizing the information capacity of Uplink in single cell multiuser communications with frequency flat fading under the assumption that the user's attenuation is measured perfectly. The main characteristics is that only one user transmits over the entire bandwidth at particular time instant and users allocated more power when channels are good and less when bad.

The capacity of Multi Input Multi output Radio channels depend on spatial correlation properties of channel. It is important to have a good channel to obtain realistic results.

High bit rate digital signals in wireless communication systems are received when Multi element array (MEA) technology is used.

Limits of bandwidth efficient delivery's higher bitrates. Advantage of using MEAs in wireless LANs and building wireless communication link where know. Rayleigh faded paths can be calculated. Bit cycle can be obtained for every dB SNR

The narrowband case results where the bandwidth is taken to be narrow enough that the channel can be treated as flat over frequency

Model for Wireless Channels contain are

Number of antennas

Transmitted signal

Noise at receiver

Received signal

Average SNR at each receiver branch

Matrix channel impulse response

A power control scheme can be considered for maximizing the information capacity of Uplink in single cell multi user communications with frequency flat fading.

By examining the bit error-rate with antipodal signal, the inherent diversity can be shown in Multiuser communications over fading channels was covered in this. Uplink channel capacity

TDMA (Time Division), CDMA (Code Division) and FDMA (Frequency division) all the three multiplex techniques are considered in communication while transmitting or receiving a signal.

## Graphical Representation MIMO Channel Model

Scattering environment

Figure 2[3]

## CHAPTER 3 THEORITICAL ANALYSIS

Transmission of signal messages in antenna array are done in many ways and techniques used. It is classified into different types. At first SISO was used as the technology for transmitting signal from transmitter to receiver. Later on as advance technology can in use multiple options were available to be used to transmit and receive the information or data. They are

Single Input Single Output

Multi Input Single Output

Single Input Multi Output

Multi Input Multi Output

## SISO

Radio transmissions earlier used one antenna at the transmitter and one antenna at receiver. This is called single input and Single Output. Both the transmitter and the receiver coder and modulator ,decoder at receivor. SISO is relatively simple and cheap to implement and it has been used age long since the birth of radio technology. It is used in radio and TV broadcast and our personal wireless technologies (e.g. Wi-Fi and Bluetooth).receiver. This system is termed Single Input Single Output (SISO).

## SIMO

To improve performance, a multiple antenna technique has been developed. A system which uses a single antenna at the transmitter and multiple antennas at the receiver is named Single Input Multiple Output (SIMO). The receiver can either choose the best antenna to receive a stronger signal or combine signals from all antennas in such a way that maximizes SNR (Signal to Noise Ratio). The first technique is known as switched diversity or selection diversity. The latter is known as maximal ratio combining (MRC).

## MISO

A system which uses multiple antennas at the transmitter and a single antenna at the receiver is named Multiple Input Single Output (MISO). A technique known as Alamouti STC (Space Time Coding) is employed at the transmitter with two antennas. STC allows the transmitter to transmit signals (information) both in time and space, meaning the information is transmitted by two antennas at two different times consecutively.

Multiple antennas (each with an RF chain) of either SIMO or MISO are usually placed at a base station (BS). This way, the cost of providing either a receive diversity (in SIMO) or transmit diversity (in MISO) can be shared by all subscriber stations (SSs) served by the BS

Figure 1 [1]

## MIMO

To multiply throughput of a radio link, multiple antennas (and multiple RF chains accordingly) are put at both the transmitter and the receiver. This system is referred to as Multiple Input Multiple Output (MIMO). A MIMO system with similar count of antennas at both the transmitter and the receiver in a point-to-point (PTP) link is able to multiply the system throughput linearly with every additional antenna. For example, a 2x2 MIMO will double the throughput.

## METHODOLOGY

For channel capacity model measuring the indoor radio channel related work on this filed will be referred. Journals papers and related books are referred on the related communication.

Channel capacity features in Indoor Radio channel in MIMO will be done. Literature survey will be done. Simulations of the end result will be obtained. Mat lab will be learned and the simulations and responses will be compared.

## '

Array antenna technologies have been considered and used in Mobile and wireless communication systems to improve the coverage, link quality and the channel capacity. MIMO is one of the emerging technologies used to improve spectral efficiency for communication link.

In traditional systems single antenna are used to transmit and receive signals and simple operation are being used. In Multi antenna systems the array antenna is a part of transceiver so that others characteristics can be controlled. [2]

Advantages of MIMO are

Space time coding improves reliability

Spatial multiplexing increases the link capacity

Beam forming increases the base station coverage and reduces the co channel interference.

Performance of MIMO communication system is heavily dependent on the MIMO channel property determined by array antenna and propagation

Channel models are developed so that link level evaluation can be done but are also used for the system level simulation including higher layers

Different channel models can be classified as follows

Stochastic model: A stochastic channel model is described purely mathematically. This model is reproducible, it is simplified. In 3GPP-3GPP2 spatial channel model ad-hoc group developed a model which shall be used for the system level simulation of the cellular MIMO systems. [1-3]

Site-specific model: This channel model is location dependent and is realistic.

There are two different approaches available for the site-specific modelling,

(I)Simulation based approach and

(ii)Measurement based approach.

Simulation: Numerical analysis of the radio wave propagation is one of the important key elements. Objects under consideration are bigger than the wavelength, it is almost impossible to solve Maxwell's equation rigorously. Therefore, a high frequency approximation such as a ray-tracing technique is usually deployed. [5]

But there are some disadvantages in the ray tracing simulation.

1. The user needs a very detailed map for the buildings and other scattering objects. There

Are commercial databases for GIS (geographical information system), but they are usually

Expensive and they only include building information. Moreover, some significant

Scatterers such as sign-boards and electric light poles are not included.

2. The ray-tracing algorithm itself is just a high frequency approximation of electromagnetic

Scattering. For example, the roughness and the irregularity of the building surfaces may

Cause a non-secular scattering. It may cause relatively a large error in estimating the

Long delayed components [7].

Measurement: MIMO channel sounders are commercially available, and both MIMO and

Double-directional channel measurements are possible. The synthetic channel using measured directional channel parameters and virtual array is used for the system level simulation.

A MIMO channel measurement is easier and more reliable as the real channel response

Including the the antennas is measured [2]. However, the channel parameters are specific to the antennas and the influence of the antenna parameters cannot be evaluated.

A double-directional channel measurement needs special array antennas suitable for the parameter extractions. Parameter estimation techniques such as a maximum likelihood approach are necessary to extract the path parameters. However, the estimated results are influenced by the array geometry and the signal model. Therefore, the improvement of the signal model for the parameter estimation is one of the hottest topics in this field [6].

## Issues of the double-directional channel measurement

Issues the double-directional channel measurement has are.

High frequency approximation and continuous angular distribution can be. In addition, the finite size of the aperture causes the error in resolving the closely arrival multiple paths, which may also result in the error. The problem is more obvious when the cell size is small, e.g., indoor environment, as the size of the scatterers may not be big enough.

Molisch pointed out that the channel capacity neglecting these unresolved components cannot be negligible in the channel capacity evaluation [1]. That is the reason why many researchers focus on new spatial models for better parameter estimation.

In the system level simulation, macroscopic behaviour such as path loss and shadowing of the channel is more important than the instantaneous fading. However, the present channel sounder have difficulty to measure the absolute value of the path loss since it has been designed just to get the channel response. Some calibration is needed to obtain the path gain. For the system level simulation considering the co-channel interference, multiple base stations may be necessary [9-10] However, the license that the authors have obtained strictly limits the transmitter sites and it is impossible to obtain the data for multi cell environment.

Since the maximum TX power is 10 W and there is no vertical beam forming in the base station, only the area within 400-500 m can be measured. Neither outdoor-to-indoor scenario nor bigger macro cell with 1 km radius cannot be measured.

MIMO (multiple-input multiple-output) wireless systems have multiple antennas at transmitter and receiver side. MIMO provides multiple independent transmission channels have multiple antennas at both transmitter and receiver.

In smart antennas the quality of a single data stream is improved where as in MIMO system multiple independent transmission channels which in some conditions lead to channel capacity which increases linearly with number of antenna elements.

The major points are

MIMO radio channel parameterization that allows the determination of the cdf of the capacity. It is based on the determination of the directions-of-arrival (DOAs), direction-of-departure (DODs), and delays of the multipath components, coupled with a synthetic variation of their phases. This procedure allows a drastic reduction of the measurement effort.

To measure at 5.2 GHz both for the frequency-flat and the frequency-selective channel. From this, results can be derived are

a) Results for the capacity of frequency flat channels in microcellular environments, and investigate the effects of the number of antennas, and other parameters;

b) Derive similar results for frequency selective channels, and show how the mean capacity and outage capacity are improved as the bandwidth is increased. From this, we can draw conclusions about how to model the MIMO channel best to capture its essential properties.

The algorithms used for the extraction of the parameters of the multipath components are described. Next, we describe the principle of our capacity evaluation approach,

Both for the frequency-flat and the frequency-selective channel, and discuss its general applicability

In this project the channel capacity performance and the effect of signal to noise ratio at the end result of the capacity value can be research on. Simulation results needed to be carried out with the coding and learning of mat lab. Published books on the related topic can be referred and research work. Mat lab learning will be preferred. The effect of wavelength can be done with using the Mat lab software and results will be done and capacity response on effect of value of SNR. References will be done. Channel capacity is an important issue in the field of communications while communicating. As there was no model which will take into account all the scattering of electromagnetic signals use the free space path loss model in system to predict the path loss, which neglects the losses due to scatterers. So, if a good model is introduced which takes into account all the losses then it will help to study the spatial correlation more effectively.

Focused on information transmission aspects, determining fundamental limits using electromagnetic theory considerations. Basic antenna elements as can be productively used into the transmit and receive spaces.

CHANGES REQUIRED

Channel capacity performance which was in measured Indoor radio channel has been changed to channel capacity performance in indoor radio channel as specific frequencies will require special arrangement and practical equipments.

ISSUES ENCOUNTERED

As signal is propagated over radio link it will be subjected to various forms of noise.

Signal to Noise ratio= (wanted signal power)/ (unwanted noise power)

Signal to Noise ratio has a major impact on Capacity as capacity depends on the value of |S/N

Formulae C =

The minimum required signal to noise ratio determines the minimum power that distant transmitter must transmit.

ISSUES ENCOUNTERED

As signal is propagated over radio link it will be subjected to various forms of noise.

Signal to Noise ratio= (wanted signal power)/ (unwanted noise power)

Signal to Noise ratio has a major impact on Capacity as capacity depends on the value of |S/N

Formulae C =

The minimum required signal to noise ratio determines the minimum power that distant transmitter must transmit.

ISSUES ENCOUNTERED

As signal is propagated over radio link it will be subjected to various forms of noise.

Signal to Noise ratio= (wanted signal power)/ (unwanted noise power)

Signal to Noise ratio has a major impact on Capacity as capacity depends on the value of |S/N

Formulae C =

The minimum required signal to noise ratio determines the minimum power that distant transmitter must transmit.

i

## CHAPTER 5

## RESULTS

1.

Calculating the channel capacity

Now for 2x2

Program

clc;

clear all;

T= input( 'Please enter number of Transmitted Antennas =');

R= input( ' Please enter number of Receiver Antennas = ');

I = eye(R);

snr = 0:2:30

for k = 1:1:length(snr)

sum = 0;

for iterations = 1:1:1000

H = randn(R,T)+j*randn(R,T);

% H = rand(R,T);

SNR = (10^(snr(k)/10));

NUM = (SNR/T)*H*ctranspose(H);

capacity = log2(det( I + NUM));

sum = sum + capacity;

end

Capacity(k) = sum/iterations;

end

% figure()

plot(snr,Capacity,'-ko')

hold on; grid on;

xlabel('SNR value[dB] ');

ylabel('Capacity value[bits/s/Hz]');

title ('CHANNEL CAPACITY');

legend('Matrix 2x2','Location','NorthWest')

2.comparing with now for 4x4 and 8x8

clc;

clear all;

T= input( 'Please enter number of Transmitted Antennas =');

R= input( ' Please enter number of Receiver Antennas = ');

I = eye(R);

snr = 0:2:30

for k = 1:1:length(snr)

sum = 0;

for iterations = 1:1:1000

H = randn(R,T)+j*randn(R,T);

% H = rand(R,T);

SNR = (10^(snr(k)/10));

NUM = (SNR/T)*H*ctranspose(H);

capacity = log2(det( I + NUM));

sum = sum + capacity;

end

Capacity(k) = sum/iterations;

end

% figure()

plot(snr,Capacity,'-ks')

hold on; grid on;

xlabel('SNR value[dB] ');

ylabel('Capacity value[bits/s/Hz]');

title ('CHANNEL CAPACITY');

legend('Matrix 2x2','Matrix 4x4','Matrix 8x8','Location','NorthWest')

3.

4.Relaying

clc;

clear;

nt = 2;

nr = 2;

rel = 2;

hop = 2;

g = 4;

CapacityS2=0;

SNR = 0:1:20;

for k = 1:length(SNR)

SNRd=10^((SNR(k))/10); % SNR of the direct MIMO

SNRr= (1/hop) * (hop^g) * 10^((SNR(k))/10); % SNR of the relaying MIMO

C1=find_C2(nt,rel,SNRr); % Channel capacity of DF Relaying MIMO of the 1st hop

C2=find_C2(rel,nr,SNRr); % Channel capacity of DF Relaying MIMO of the 2nd hop

CapacityS2(k)=min(C1,C2);

end

%figure ()

plot(SNR, CapacityS2,'-ks');

hold on;

legend('DF Relaying MIMO','Location','NorthWest')

grid on;

xlabel('SNR [dB]')

ylabel('Capacity [bits / s / Hz]')

title ('Capacity of DF Relaying MIMO ')

legend('Realy 2x2','Relay 4x4','realy 8x8','Location','NorthWest')

Now for 4x4 and 8x8

5. Losses with ITU model

clc;

clear all;

%d=input (' Please enter the distance in meters = ');

N= 1;

f=500;

d=[1:10:40];

%pf(n)=15+4*(n-1); %floor penetration factor

pf=23; % when number of floor n=3

L=20*log10(f)+N*log10(d)+pf-28;

plot(d,L,'-kd');

grid on;

xlabel (' distance[meters]');

ylabel(' losses[dB]');

title('Losses Vs distance');

Now losses vs frequency

clc;

clear all;

%d=input (' Please enter the distance in meters = ');

N= 1;

f=[50:10:900];

d=30;

%pf(n)=15+4*(n-1); %floor penetration factor

pf=23; % when number of floor n=3

L=20*log10(f)+N*log10(d)+pf-28;

plot(f,L,'-kd');

grid on;

xlabel (' frequency[MHz]');

ylabel(' losses[dB]');

title('Losses Vs frequency');

legend('loss vs frequecy','Location','NorthWest')