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

Constellation Diagrams of ASK, PSK, and FSK

📘 Overview of Energy per Bit (Eb / N0) 🧮 Online Simulator for constellation diagrams of ASK, FSK, and PSK 🧮 Theory behind Constellation Diagrams of ASK, FSK, and PSK 🧮 MATLAB Codes for Constellation Diagrams of ASK, FSK, and PSK 📚 Further Reading 📂 Other Topics on Constellation Diagrams of ASK, PSK, and FSK ... 🧮 Simulator for constellation diagrams of m-ary PSK 🧮 Simulator for constellation diagrams of m-ary QAM BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.    BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals...

Online Simulator for ASK, FSK, and PSK

Try our new Digital Signal Processing Simulator!   Start Simulator for binary ASK Modulation Message Bits (e.g. 1,0,1,0) Carrier Frequency (Hz) Sampling Frequency (Hz) Run Simulation Simulator for binary FSK Modulation Input Bits (e.g. 1,0,1,0) Freq for '1' (Hz) Freq for '0' (Hz) Sampling Rate (Hz) Visualize FSK Signal Simulator for BPSK Modulation ...

Fading : Slow & Fast and Large & Small Scale Fading

📘 Overview 📘 LARGE SCALE FADING 📘 SMALL SCALE FADING 📘 SLOW FADING 📘 FAST FADING 🧮 MATLAB Codes 📚 Further Reading LARGE SCALE FADING The term 'Large scale fading' is used to describe variations in received signal power over a long distance, usually just considering shadowing.  Assume that a transmitter (say, a cell tower) and a receiver  (say, your smartphone) are in constant communication. Take into account the fact that you are in a moving vehicle. An obstacle, such as a tall building, comes between your cell tower and your vehicle's line of sight (LOS) path. Then you'll notice a decline in the power of your received signal on the spectrogram. Large-scale fading is the term for this type of phenomenon. SMALL SCALE FADING  Small scale fading is a term that describes rapid fluctuations in the received signal power on a small time scale. This includes multipath propagation effects as well as movement-induced Doppler fr...

Theoretical BER vs SNR for m-ary PSK and QAM

Relationship Between Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) The relationship between Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR) is a fundamental concept in digital communication systems. Here’s a detailed explanation: BER (Bit Error Rate): The ratio of the number of bits incorrectly received to the total number of bits transmitted. It measures the quality of the communication link. SNR (Signal-to-Noise Ratio): The ratio of the signal power to the noise power, indicating how much the signal is corrupted by noise. Relationship The BER typically decreases as the SNR increases. This relationship helps evaluate the performance of various modulation schemes. BPSK (Binary Phase Shift Keying) Simple and robust. BER in AWGN channel: BER = 0.5 × erfc(√SNR) Performs well at low SNR. QPSK (Quadrature...

DFTs-OFDM vs OFDM: Why DFT-Spread OFDM Reduces PAPR Effectively

DFT-spread OFDM (DFTs-OFDM) has lower Peak-to-Average Power Ratio (PAPR) because it "spreads" the data in the frequency domain before applying IFFT, making the time-domain signal behave more like a single-carrier signal rather than a multi-carrier one like OFDM. Deeper Explanation: Aspect OFDM DFTs-OFDM Signal Type Multi-carrier Single-carrier-like Process IFFT of QAM directly QAM → DFT → IFFT PAPR Level High (due to many carriers adding up constructively) Low (less fluctuation in amplitude) Why PAPR is High Subcarriers can add in phase, causing spikes DFT "pre-spreads" data, smoothing it Used in Wi-Fi, LTE downlink LTE uplink (as SC-FDMA) In OFDM, all subcarriers can...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

📘 Overview of BER and SNR 🧮 Online Simulator for BER calculation of m-ary QAM and m-ary PSK 🧮 MATLAB Code for BER calculation of M-ary QAM, M-ary PSK, QPSK, BPSK, ... 📚 Further Reading 📂 View Other Topics on M-ary QAM, M-ary PSK, QPSK ... 🧮 Online Simulator for Constellation Diagram of m-ary QAM 🧮 Online Simulator for Constellation Diagram of m-ary PSK 🧮 MATLAB Code for BER calculation of ASK, FSK, and PSK 🧮 MATLAB Code for BER calculation of Alamouti Scheme 🧮 Different approaches to calculate BER vs SNR What is Bit Error Rate (BER)? The abbreviation BER stands for Bit Error Rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. BER = (number of bits received in error) / (total number of tran...

Theoretical BER vs SNR for binary ASK, FSK, and PSK

📘 Overview & Theory 🧮 MATLAB Codes 📚 Further Reading Theoretical BER vs SNR for Amplitude Shift Keying (ASK) The theoretical Bit Error Rate (BER) for binary ASK depends on how binary bits are mapped to signal amplitudes. For typical cases: If bits are mapped to 1 and -1, the BER is: BER = Q(√(2 × SNR)) If bits are mapped to 0 and 1, the BER becomes: BER = Q(√(SNR / 2)) Where: Q(x) is the Q-function: Q(x) = 0.5 × erfc(x / √2) SNR : Signal-to-Noise Ratio N₀ : Noise Power Spectral Density Understanding the Q-Function and BER for ASK Bit '0' transmits noise only Bit '1' transmits signal (1 + noise) Receiver decision threshold is 0.5 BER is given by: P b = Q(0.5 / σ) , where σ = √(N₀ / 2) Using SNR = (0.5)² / N₀, we get: BER = Q(√(SNR / 2)) Theoretical BER vs ...

Kalman Filter: Mathematical Description and Example

Kalman Filter: Mathematical Description 1. System Model (State-Space Representation) State Equation \[ \mathbf{x}_{k} = \mathbf{F}_{k-1}\mathbf{x}_{k-1} + \mathbf{B}_{k-1}\mathbf{u}_{k-1} + \mathbf{w}_{k-1} \] Measurement Equation \[ \mathbf{z}_{k} = \mathbf{H}_{k}\mathbf{x}_{k} + \mathbf{v}_{k} \] where: \(\mathbf{x}_k\): system state vector \(\mathbf{u}_k\): control input \(\mathbf{z}_k\): measurement vector \(\mathbf{F}_k\): state transition matrix \(\mathbf{B}_k\): control input matrix \(\mathbf{H}_k\): observation matrix \(\mathbf{w}_k \sim \mathcal{N}(0,\mathbf{Q}_k)\): process noise \(\mathbf{v}_k \sim \mathcal{N}(0,\mathbf{R}_k)\): measurement noise 2. Kalman Filter Assumptions Linear system dynamics Gaussian, white,...