Multiple Input Multiple Output(MIMO) using Matlab
MIMO
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MIMO

Introduction

Multiple Input Multiple Output(MIMO) using Matlab.

Multiple Input Multiple Output (MIMO) is a technology used in wireless communication systems to increase the data rate, reliability, and coverage of wireless communications. In MIMO, multiple antennas are used at both the transmitting and receiving ends to simultaneously transmit and receive multiple signals.

The basic idea behind MIMO is to exploit the multipath environment of wireless communications, where signals from a single transmitter reach the receiver via multiple paths. By transmitting multiple signals from multiple antennas at the same frequency and time, MIMO can increase the data rate of the wireless communication system. This is because the additional signals provide independent channels for data transmission, and the receiver can combine the signals from the multiple antennas to improve the quality of the received signal.

MIMO also improves the reliability of wireless communications by reducing the impact of fading, which is a phenomenon that occurs when signals from different paths arrive at the receiver with different strengths. By using multiple antennas at the receiver, MIMO can reduce the impact of fading, because the fading on one antenna path may not affect the signal received by another antenna.

In addition, MIMO can improve the coverage of wireless communications by increasing the spatial reuse of the available frequency spectrum. This is because MIMO enables multiple signals to be transmitted and received simultaneously, without interfering with each other, even if they are using the same frequency.

MIMO is a key technology used in modern wireless communication systems, such as Wi-Fi, LTE, and 5G, and it is an important enabler for high-speed, high-capacity wireless communication systems.

Software Requirement

Matlab Software or

The components of a MIMO system

  1. Multiple Antennas: Both the transmitter and receiver have multiple antennas that are used to transmit and receive multiple signals. The number of antennas can vary depending on the system requirements, but typical configurations include 2×2, 4×4, and 8×8 MIMO systems.
  2. Channel Coding: Channel coding is used to encode the data transmitted over the MIMO system. The channel coding algorithms can include forward error correction (FEC) codes, such as convolutional codes, turbo codes, and low-density parity-check (LDPC) codes, to improve the reliability of the transmitted data.
  3. Modulation: Modulation is used to map the data bits onto the carrier signals transmitted by the antennas. The modulation techniques used in MIMO systems can include orthogonal frequency division multiplexing (OFDM), single-carrier frequency division multiple access (SC-FDMA), and others.
  4. Multiplexing: Multiplexing is used to transmit multiple data streams over the MIMO system. The multiplexing techniques used in MIMO systems can include spatial multiplexing, where different data streams are transmitted by different antennas, and spatial division multiplexing (SDM), where different data streams are transmitted over different subspaces of the MIMO system.
  5. Signal Processing: Signal processing is used at both the transmitter and receiver to perform tasks such as channel estimation, equalization, and data detection. These algorithms can be implemented using analog or digital circuits, or a combination of both.
  6. Load: The load represents the devices that use the MIMO system to communicate, such as mobile phones, laptops, and wireless routers.
  7. Optional Feedback Circuit: In some MIMO systems, a feedback circuit can be used to send information from the receiver to the transmitter about the channel conditions, such as the channel gain and phase, and the quality of the received signals. This information can be used by the transmitter to adjust the transmission parameters, such as the modulation scheme, coding rate, and power level, in order to improve the quality of the communication link.

Matlab Code

clc;
close all;
clear all;
mT = 2; % No.of transmitters
mR = 2; % No.of receivers
ITER = 1000; % number of iterations.
SNRdB = [0:25]; % SNR maximum scale
SNR = 10.^(SNRdB/10); % SNR in decibels

C_SISO = zeros(1,length(SNR));
C_SIMO = zeros(1,length(SNR));
C_MISO = zeros(1,length(SNR));
C_MIMO = zeros(1,length(SNR));

for ite = 1:ITER
h_SISO = (randn +1i*randn)/sqrt(2);
h_SIMO = (randn(mR,1)+1i*randn(mR,1))/sqrt(2);
h_MISO = (randn(1,mT)+1i*randn(1,mT))/sqrt(2);
h_MIMO = (randn(mR,mT)+1i*randn(mR,mT))/sqrt(2);

for K = 1:length(SNR)
C_SISO(K) = C_SISO(K) + log2(1+ SNR(K)*norm(h_SISO)^2);
C_SIMO(K) = C_SIMO(K) + log2(1+ SNR(K)*norm(h_SIMO)^2);
C_MISO(K) = C_MISO(K) + log2(1+ SNR(K)*norm(h_MISO)^2/mT);
C_MIMO(K) = C_MIMO(K) + log2(abs(det(eye(mR)+SNR(K)*h_MIMO*h_MIMO'/mT)));
end
end

C_SISO = C_SISO/ITER;
C_SIMO = C_SIMO/ITER;
C_MISO = C_MISO/ITER;
C_MIMO = C_MIMO/ITER;

plot(SNRdB,C_SISO,'r',SNRdB,C_SIMO,'b',SNRdB,C_MISO,'m',SNRdB,C_MIMO,'k')
%legend('SISO','SIMO','MISO','MIMO')
xlabel('SNR in dB')
ylabel('Capacity (b/s/Hz)')
title('Capacity Vs. SNR')
grid;

MIMO Code Output

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