Skip to main content

Alamouti's Scheme for MIMO Communication

 

 The Alamouti scheme is a simple and effective space-time block coding (STBC) technique used in wireless communications to achieve diversity gain. It's designed for systems with two transmit antennas and one or more receive antennas, providing transmit diversity.

Alamouti's Space-Time Block Coding (STBC) is a technique used in MIMO wireless communication systems to achieve diversity gain without requiring channel knowledge at the transmitter.

Alamouti 2 X 1 Matrix Equation Representation

y
=
h11
h21
X
s1 -s2*
s2 s1*
+
n
It involves transmitting multiple copies of the same symbols over multiple antennas with specific phase relationships. This allows the receiver to combine the signals effectively and recover the transmitted symbols even in the presence of fading.

The Alamouti precoding matrix is constructed based on the Alamouti code, which defines the phase relationships between the symbols transmitted from different antennas over two consecutive time slots. For a 2x1 MIMO system (two transmit antennas and one receive antenna), the Alamouti precoding matrix is as follows:

Precoding Matrix=[s1  −s2∗;  s2   s1∗]

Where:

    s1 and s2 are the symbols to be transmitted from the two antennas in the current time slot.
    s1∗​ and s2∗​ are the complex conjugates of s1​ and s2​ respectively.

This matrix ensures that the symbols transmitted from the two antennas in the current time slot have the necessary phase relationships to achieve diversity gain at the receiver.

Here's how the Alamouti precoding matrix works:

    In the first time slot, symbols s1​ and s2​ are transmitted from the two antennas without any phase manipulation.
    In the second time slot, symbols −s2∗​ and s1∗​ are transmitted from the two antennas. The negative sign and complex conjugate ensure the correct phase relationship required for diversity gain at the receiver.
    At the receiver, combining the signals from the two time slots using Alamouti decoding allows for effective recovery of the transmitted symbols, even in the presence of fading.

By using Alamouti's STBC and the corresponding precoding matrix, the MIMO system can achieve diversity gain and improve performance without requiring explicit channel knowledge at the transmitter. 

 

Orthogonality Property 

Alamouti's Space-Time Block Coding (STBC) scheme ensures that symbols transmitted from different antennas in successive time slots are orthogonal to each other. This orthogonality property is essential for enabling simple decoding at the receiver and achieving diversity gain without requiring channel knowledge at the transmitter.



Now, let's calculate the inner product (dot product) between two encoded symbols transmitted from different antennas in successive time slots.

Let x1x1​ and x2x2​ be the encoded symbols transmitted from the two antennas in the first and second time slots respectively.

x1=[s1 ; s2]
x2=[−s2∗​ ; s1∗​​]

The inner product x1' * x2​ is given by:

x1' * x2​ = [s1 ; ​​s2​​] * [−s2∗​ ; s1∗​​]
=−∣s2∣^2 + ∣s1∣^2


Since the symbols s1​ and s2​ are independent and identically distributed (IID) random variables with equal power, their magnitudes are equal, i.e., ∣s1∣=∣s2∣. Therefore, the inner product x1' * x2​ simplifies to:

x1' * x2 = −∣s2∣^2 + ∣s1∣^2 = 0x1T​x2​= −∣s1∣^2 + ∣s1∣^2 = 0

This shows that the inner product between the encoded symbols transmitted from different antennas in successive time slots is zero, indicating orthogonality.

This orthogonality property allows the receiver to effectively decode the transmitted symbols by taking advantage of the diversity provided by the multiple antennas without interference between symbols transmitted from different antennas.

 

 
 
Fig 1:  BER vs SNR for Alamouti's Precoding Matrix for 2 X 2 MIMO in MATLAB

(Get MATLAB Code for Alamouti's Precoding Matrix for 2 X 2 MIMO in MATLAB)

Also Read about

[1] Alamouti's Precoding Matrix for 2 X 2 MIMO in MATLAB

[2] Theoretical BER vs SNR for Alamouti's Scheme  

[3] MATLAB Code for Multi-User STBC (using Alamouti's Scheme) 

People are good at skipping over material they already know!

View Related Topics to







Contact Us

Name

Email *

Message *

Popular Posts

Theoretical BER vs SNR for binary ASK, FSK, and PSK with MATLAB Code + Simulator

📘 Overview & Theory 🧮 MATLAB Codes 📚 Further Reading Bit Error Rate (BER) Equations In ASK, noise directly affects the signal amplitude, making it the most vulnerable since the data is carried in amplitude changes. In FSK, data is represented by frequency variations, and because noise typically impacts amplitude more than frequency, FSK is more robust than ASK. In PSK, data is encoded in the signal phase, and BPSK specifically uses 180-degree phase shifts, creating the greatest separation between signal points and therefore achieving the lowest bit error rate (BER) for the same power level. BER formulas for ASK, FSK, and PSK modulation schemes. ASK BER = 0.5 × erfc(0.5 × √SNR) FSK BER = 0.5 × erfc(√(SNR / 2)) PSK BER = 0.5 × erfc(√SNR) Theoretical BER ...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...(MATLAB Code + Simulator)

Bit Error Rate (BER) & SNR Guide Analyze communication system performance with our interactive simulators and MATLAB tools. 📘 Theory 🧮 Simulators 💻 MATLAB Code 📚 Resources BER Definition SNR Formula BER Calculator MATLAB Comparison 📂 Explore M-ary QAM, PSK, and QPSK Topics ▼ 🧮 Constellation Simulator: M-ary QAM 🧮 Constellation Simulator: M-ary PSK 🧮 BER calculation for ASK, FSK, and PSK 🧮 Approaches to BER vs SNR What is Bit Error Rate (BER)? The BER indicates how many corrupted bits are received compared to the total number of bits sent. It is the primary figure of merit for a...

Simulation of ASK, FSK, and PSK using MATLAB Simulink (with Online Simulator)

📘 Overview 🧮 How to use MATLAB Simulink 🧮 Simulation of ASK using MATLAB Simulink 🧮 Simulation of FSK using MATLAB Simulink 🧮 Simulation of PSK using MATLAB Simulink 🧮 Simulator for ASK, FSK, and PSK 🧮 Digital Signal Processing Simulator 📚 Further Reading ASK, FSK & PSK HomePage MATLAB Simulation Simulation of Amplitude Shift Keying (ASK) using MATLAB Simulink In Simulink, we pick different components/elements from MATLAB Simulink Library. Then we connect the components and perform a particular operation. Result A sine wave source, a pulse generator, a product block, a mux, and a scope are shown in the diagram above. The pulse generator generates the '1' and '0' bit sequences. Sine wave sources produce a specific amplitude and frequency. The scope displays the modulated signal as well as the original bit sequence created by the pulse generator. Mux i...

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Interactive Modulation Simulators Visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator More Topics Simulator for Binary ASK Modulation Digital Message Bits Carrier Freq (Hz) Sampl...

MATLAB Code for Constellation Diagram of QAM configurations such as 4, 8, 16, 32, 64, 128, and 256-QAM

📘 Overview of QAM 🧮 4-QAM MATLAB 🧮 16-QAM MATLAB 🚀 Online Simulator 📂 Other Topics on Constellation Diagrams... ▼ 🧮 MATLAB Code for 4-QAM 🧮 MATLAB Code for 16-QAM 🧮 MATLAB Code for m-ary QAM 🧮 Simulator for m-ary PSK 🧮 Simulator for m-ary QAM 🧮 Overview of Energy per Bit (Eb / N0) 🧮 Simulator for ASK, FSK, and PSK Overview of QAM One of the best-performing modulation techniques is QAM [↗] . Here, we modulate the symbols by varying the carrier signal's amplitude and phase in response to the variation in the message signal (or voltage variation). So, we may say that QAM is a combination of phase and amplitude modulation. Additionally, it performs better than ASK or PSK [↗] . In fact, any constellation for any type of modulatio...

OFDM Waveform with MATLAB Code

  In OFDM (Orthogonal Frequency Division Multiplexing) , we transmit multiple orthogonal subcarriers simultaneously. Since the subcarriers are orthogonal , they do not interfere with each other, which is one of the main advantages of OFDM. Practically, OFDM converts a wideband signal into multiple narrowband orthogonal subcarriers. For typical wireless communication, if the signal bandwidth (or symbol duration) exceeds the coherence bandwidth of the channel, the signal experiences frequency-selective fading . Fading distorts the signal, making it difficult to recover the original information. By using OFDM, we transmit the same wideband signal across multiple orthogonal narrowband subcarriers, reducing the effect of fading. For example, if we want to transmit a signal of bandwidth 1024 kHz , we can divide it into N = 8 subcarriers . Each subcarrier is then spaced by: Δf = Total Bandwidth N = 1024 8 kHz...

How to use MATLAB Simulink

Introduction to MATLAB Simulink MATLAB Simulink is a popular add-on of MATLAB. Here, you can use different blocks like modulator, demodulator, AWGN channel, etc. And you can do experiments on your own. Steps to Get Started 1. Go to the 'Simulink' tab at the top navbar of MATLAB. If not found, click on the add-on tab, search 'Simulink,' and then click on it to add. 2. Once you installed the simulation, click the 'new' tap at the top left corner. 3. Then, search the required blocks in the 'Simulink library.' Then, drag it to the editor space. 4. You can double-click on the blocks to see the input parameters. 5. Then, connect the blocks by dragging a line from one block's output terminal to another block's input. 6. If the connection is complete, click the 'run' tab in the middle of the top navbar. 7. After clicking on the run ...

FastAPI Static Files – Overview

FastAPI Static Files Often, a web application needs to include resources that do not change, even when dynamic data is rendered. These resources are called static assets . Examples of static files include: Images ( .png , .jpg ) JavaScript files ( .js ) Stylesheets ( .css ) Installing Required Library To handle static files in FastAPI, you need the aiofiles library. pip install aiofiles Mounting Static Files FastAPI uses the StaticFiles class to serve static content. You mount a folder (usually named static ) so that all files inside it can be accessed via a URL. from fastapi import FastAPI from fastapi.staticfiles import StaticFiles app = FastAPI() app.mount("/static", StaticFiles(directory="static"), name="static") Example 1: Using an Image Place an image file (for example, fa-logo.png ) inside the static folder. main.py from fastapi import FastAPI, Request from fastapi.responses import HTMLRespon...