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  .  c2   and    c3 

are the 2nd and 3rd columns, respectively.


Here, columns are orthogonal for instance, i.e.,  c1Hcj =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 U (unitary matrix), the middle one is Σ (eigenmatrix), and 3rd matrix is V (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- σ2/σ12)= (1/λ- σ2/52)

P2=(1/λ- σ2/σ22)= (1/λ- σ2/13)

P3=(1/λ- σ2/σ32)= (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

LDPC Encoding and Decoding Techniques

Low Density Parity Check (LDPC) Guide Comprehensive analysis of linear error-correcting block codes, Tanner graphs, and 5G-NR implementations. 📘 Overview 🧮 Encoding 🧩 Decoding 📚 Resources Theory Encoding Tech Tanner Graph 5G Encoding Decoding 'LDPC' is the abbreviation for 'low density parity check'. LDPC code H matrix contains very few amount of 1's and mostly zeroes. LDPC codes are error correcting code. Using LDPC codes, channel capacities that are close to the theoretical Shannon limit can be achieved. Low density parity check (LDPC) codes are linear error-correcting block code suitable for error correction in a large block sizes transmi...

Q-function in BER vs SNR Calculation (with Simulation)

Q-function in BER vs. SNR Calculation In digital communications and signal processing, the Q-function plays a significant role in predicting system reliability. It allows engineers to quantify the probability that Gaussian noise will exceed a specific threshold, causing a bit error. What is the Q-function? The Q-function is a mathematical function representing the tail probability of the standard normal (Gaussian) distribution. It is the complementary cumulative distribution function (CCDF) of a standard Gaussian distribution. Q(x) = (1 / √(2π)) ∫ₓ∞ e^(-t² / 2) dt The Role of the Q-function in BER vs. SNR The Q-function is the standard tool for calculating BER in systems like BPSK or QPSK over AWGN (Additive White Gaussian Noise) channels. For BPSK: In BPSK, we transmit +√E b (bit 1) and -√E b (bit 0). The decision boundary is set at 0 . If -√E b was sent, an error occurs if noise r > √...

Design of CMOS Flip-Flops (SR, D, JK)

Design of CMOS Flip-Flops (SR, D, JK) A flip-flop or latch is a circuit with two stable states, used to store state information. It is the basic storage element in sequential logic and a fundamental building block in digital electronics systems, including computers and communication devices. Flip-flops and latches act as data storage elements for states, pulse counting, and synchronization of variably-timed input signals to a reference clock. Flip-flops can be transparent/opaque (latches) or clocked (synchronous, edge-triggered). Latches are level-sensitive, while flip-flops are edge-sensitive. In sequential logic, the output depends on current inputs and previous states. Fig.1 shows a sequential circuit combining a combinational block and a memory element. ...

Flat vs Frequency Selective Online Simulator

Flat vs Frequency Selective Online Simulator Channel Type Without Fading Flat Fading Multipaths Nakagami m SNR(dB) Run Simulation Input Signal Signal After Fading Constellation Diagram BER vs SNR Explore Advanced Flat vs Frequency-Selective Fading Simulator Want to see these equations in action? Visualize it. Launch Simulator Tool Interactive Rayleigh Fading Simulator Want to see Rayleigh fading in action? Visualize it. Launch Simulator Tool Return to DSP Simulations Main Page →

Online Simulator for ASK, FSK, and PSK Signal Generation

Interactive Digital Signal Processing (DSP) Tutorial and Simulator for ASK, FSK, and BPSK modulation techniques. Try our new Digital Signal Processing Simulator!   •   Interactive ASK, FSK, and BPSK tools updated for 2025. Start Now Digital Modulation Visualizer: ASK, FSK, & BPSK Simulator Learn and visualize binary modulation techniques (ASK, FSK, BPSK) in real-time with adjustable carrier and sampling parameters. Perfect for DSP students and engineers. 📡 ASK Simulator 📶 FSK Simulator 🎚️ BPSK Simulator 📚 More Topics ASK Modulator FSK Modulator BPSK Modulator Demodulation More Topics 1. ASK (Ampli...

Gaussian minimum shift keying (GMSK)

📘 Overview & Theory 🧮 Simulator for GMSK 🧮 MSK and GMSK: Understanding the Relationship 🧮 MATLAB Code for GMSK 📚 Simulation Results for GMSK 📚 Q & A and Summary 📚 Further Reading Dive into the fascinating world of GMSK modulation, where continuous phase modulation and spectral efficiency come together for robust communication systems! Core Process of GMSK Modulation Phase Accumulation (Integration of Filtered Signal) After applying Gaussian filtering to the Non-Return-to-Zero (NRZ) signal, we integrate the smoothed signal to produce a continuous phase signal. For GMSK, the modulation index is $h=0.5$, meaning a bit '1' results in a phase shift of $\pi/2$: θ(t) = 2πh ∫ 0 t m filtered (τ) dτ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Mo...

Online Simulator for Frequency Modulatiuon and Demodulation

FM Modulation Simulator Frequency Modulation (FM) In Frequency Modulation, the frequency of the carrier signal varies in accordance with the message signal's amplitude. s FM (t) = A c cos(ω c t + k f ∫m(t)dt) where ω = 2πf & k f = Frequency Sensitivity Modulation index, β = (k f * A m ) / f m Change the parameter values to see the effect. Message Freq (Hz) 1 Carrier Freq (Hz) Message Amplitude (Am) Kf (sensitivity): 50 Perform FM Demodulation 🧪 Experiment for Students: ...