Skip to main content

Optimal Power Allocation in MIMO Transmission using SVD


 SVD-Based MIMO Transmission & Optimal Power Allocation



Optimal power allocation is defined as in a MIMO communication system we need to allocate more power to an independent stronger path and allocation of less power to a weaker path. By following this method we can achieve high throughput. Firstly, we talk about SVD-based MIMO. Then we discussed step by step how to find stronger or weaker communication paths between two MIMO antennas. 


Channel Matrix,



Let's assume, the first column in the above matrix is c1  .  c  and    c

are the 2nd and 3rd columns, respectively.


Here, columns are orthogonal for instance, i.e.,  c1Hc=0

Here, r=3, t=3  (r=number of Rx antenna; t=number of Tx antenna)


Now, c1


c2





Now, c1Hc2

 *



=0


Multiplication is 0 since the columns are orthogonal.



Step 1: We normalize each column

We get, H=


Here singular values are not in decreasing order.


Step 2: Now we arrange the singular values in decreasing order


H=



 



That implies,





Again assume, the first matrix is (unitary matrix), the middle one is Σ (eigenmatrix)and 3rd matrix is (unitary matrix).

Alternatively, UUH=I,     VHV=VVH=I


Σ =





In the above matrix, σ1=√52, σ2=√13, σ3=2, and Singular values are in decreasing order.


At receiver side   

            y ̃UHy =

           





At the transmitter side

  ͞x =V x ̃

Or,




Here, notation "x1~, x2~, x3~" represents original message signal vector


Transmit pre-processing or precoding at the receiver side

ỹ= Σx̃ + w̃

Or,




Here, "y~" represents the received signal vector and "w~" represents the noise vector


Now, 3 decoupled channel spatial multiplexing are as follows

ỹ1 = √52x̃1 + w̃1

ỹ2 = √13x̃2 + w̃2

ỹ3 = 2x̃3 + w̃3


Optimal Power allocation

To maximize sum-rate and to achieve the Shannon capacity,

P1=(1/λ- σ212)= (1/λ- σ2/52)

P2=(1/λ- σ222)= (1/λ- σ2/13)

P3=(1/λ- σ232)= (1/λ- σ2/4)

 

Consider the noise power, σ2= 0dB

                                         So, 10log10 σ2=0

                                              σ2=10^(0/10)=1

let P=total power=3dB

                  So, 10log10 P=3

                                      P=10^(3/10)=2 (approx.)

 

So, we must have

                 P1+P2+P3= 2

                (1/λ-1/52)+  (1/λ-1/13)+  (1/λ-1/4)=2

               Or, 1/λ=.7821

Now,

P1=10log10(1/λ- σ2/52)= 10log10(0.7821- 1/52)=-1.1755 dB

P2=10log10(1/λ- σ2/13)= 10log10(0.7821- 1/13)=-1.517 dB

P1=10log10(1/λ- σ2/4)= 10log10(0.7821- 1/4)=-2.74 dB

Power allocation decreases as gain σ2 decreases. So, we can say poor power to poor channel , more power to strong channel.





Contact Us

Name

Email *

Message *

Popular Posts

Hybrid Beamforming | Page 1

Beamforming Techniques Hybrid Beamforming... Page 1 | Page 2 | Hybrid Beamforming: Hybrid beam formation was developed to address some of the limitations of digital pre-coding approaches. Every antenna element is connected to an RF chain in digital pre-coding (beam forming) method. We also know that each RF chain is in charge of providing a separate data stream between the transmitter and the receiver. We know that a larger number of independent data streams leads to higher data rates. It has a spatial multiplexing feature for MIMO. As a result, we may assume that switching from MIMO to massive MIMO will benefit us more in terms of spatial multiplexing in massive MIMO, where each antenna is coupled to a single RF chain. We'll proceed with a definition of hybrid beam forming. Overview of hybrid beam forming with example: Unlike digital beam forming, more than one antenna element is connected to a single RF chain in hybr...

MATLAB Code for 8-PSK, 16-PSK, ...

📘 Overview & Theory 🧮 MATLAB Code for BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK 🧮 Simulator for m-ary PSK 📚 Further Reading   MATLAB Code for BPSK, QPSK, 8-PSK, 16-PSK, 32-PSK clc; clear all; close all; rng(10) M = 8; % M = 2, 4, 8, 16, 32, etc. N_Bits = 2520; Phase = 0; data_info_bit = randi([0,1],N_Bits,1); data_temp = bi2de(reshape(data_info_bit,N_Bits/log2(M),log2(M))); modData = pskmod(data_temp,M,Phase); figure(1); scatterplot(modData); channelAWGN = 15; rxData2 = awgn(modData, channelAWGN); figure(2); scatterplot(rxData2); demodData = pskdemod(rxData2,M,Phase);   for BPSK, Constellation Size, M = 2 for QPSK, M = 4 for 8-PSK, M = 8, and so on    Output Figure: 8-PSK Modulation Figure: 8-PSK Demodulation after adding AWGN Noise Using the above MATLAB code you'll able be to modulate and demodulate 2-PSK, 4-PSK, 8-PSK, 16-PSK, 32-PSK and so on.  16-PSK   Fig: 16-PSK In this above code ' M ' is the number of the conste...

UGC NET Electronic Science Previous Year Question Papers with Solutions

Home / Engineering & Other Exams / UGC NET 2026 PYQ ⬇️ Download Papers and Solutions 📋 Exam Pattern 💡 Preparation Tips ❓ FAQs 📊 Exam Highlights: Electronic Science (88) Feature Details Junior Research Fellowship (JRF) ₹37,000 + HRA per month Eligibility M.Sc/M.Tech in Electronics (55%) Validity of Certificate JRF (3 Years) | Lectureship (Lifetime) 📥 Download UGC NET Electronics PDFs Complete collection of previous year question papers, answer keys and explanations for Subject Code 88. Start Downloading 📂 View All Question Papers June 2025 - Question Paper Download PDF June 2025 - Solved Paper + Explanation ...

OFDM Symbols and Subcarriers Explained

This article explains how OFDM (Orthogonal Frequency Division Multiplexing) symbols and subcarriers work. It covers modulation, mapping symbols to subcarriers, subcarrier frequency spacing, IFFT synthesis, cyclic prefix, and transmission. Step 1: Modulation First, modulate the input bitstream. For example, with 16-QAM , each group of 4 bits maps to one QAM symbol. Suppose we generate a sequence of QAM symbols: s0, s1, s2, s3, s4, s5, …, s63 Step 2: Mapping Symbols to Subcarriers Assume N sub = 8 subcarriers. Each OFDM symbol in the frequency domain contains 8 QAM symbols (one per subcarrier): Mapping (example) OFDM symbol 1 → s0, s1, s2, s3, s4, s5, s6, s7 OFDM symbol 2 → s8, s9, s10, s11, s12, s13, s14, s15 … OFDM sym...

Constellation Diagrams of ASK, PSK, and FSK (with MATLAB Code + Simulator)

Constellation Diagrams: ASK, FSK, and PSK Comprehensive guide to signal space representation, including interactive simulators and MATLAB implementations. 📘 Overview 🧮 Simulator ⚖️ Theory 📈 Q-function 📚 Resources BASK Modulation Transmits one of two signals: 0 or $\sqrt{E_b}$, representing binary 0 and 1. Simple but sensitive to noise. BFSK Modulation Transmits one of two signals: $\sqrt{E_b}$ on the Y-axis or $\sqrt{E_b}$ on the X-axis. These are orthogonal signals. BPSK Modulation Transmits $+\sqrt{E_b}$ or $-\sqrt{E_b}$ (antipodal signaling). Most efficient binary scheme. ...

Galois Fields: GF(2) and GF(2m) and Primitive Polynomial

Galois Fields: GF(2) and GF(2 m ) 1. What is a Galois Field (GF)? A Galois Field (GF) is a finite set of elements in which the four basic arithmetic operations—addition, subtraction, multiplication, and division (except by zero)— are all well defined and closed. GF(q) ⇒ a field with exactly q elements 2. The Simplest Field: GF(2) GF(2) is the smallest possible finite field and forms the foundation of all digital systems. GF(2) = {0, 1} Addition in GF(2) Addition is performed modulo 2 (XOR operation): + 0 1 0 0 1 1 1 0 Multiplication in GF(2) × 0 1 0 0 0 1 0 1 GF(2) is used in binary logic, XOR operations, and simple error-control codes. 3. Meaning of GF(2 m ) GF(2 m ) is a finite field containing exactly 2 m elements . Each element represents an m-bit symbol . Field Number of Elements GF(2) 2 GF(2²) 4 GF(2³) 8 GF(2⁸) 256 Important: GF(2 m ) is not integer arithmetic modulo 2 m . It is polynomial-based arithmetic. 4....