Water-Filling Theorem for Channel Capacity Calculation
The water-filling theorem computes channel capacity primarily through the water-filling algorithm, which is highly useful for optimizing power allocation in communication systems
Simulation of Various Channel Capacity Calculations
Standard foundational code for simulating various channel capacity calculations, providing significant assistance for beginners with comprehensive algorithm implementations
Equal Power Allocation vs. Water-Filling Power Allocation Strategies
This function compares channel capacity and outage capacity of MIMO systems under equal power allocation and water-filling power allocation strategies, with implementation analysis of capacity calculation algorithms.
MIMO-OFDM System Simulation and Channel Estimation Algorithms
MIMO (Multiple-Input Multiple-Output) technology was first proposed by Marconi in 1908, utilizing multiple antennas to mitigate channel fading. Based on the number of antennas at both transmitter and receiver ends, MIMO systems can be categorized into SIMO (Single-Input Multiple-Output) and MISO (Multiple-Input Single-Output) systems, in contrast to conventional SISO (Single-Input Single-Output) systems. The implementation typically involves spatial multiplexing algorithms and channel state information processing, where channel capacity increases linearly with the number of antennas. This linear relationship can be demonstrated through capacity calculation algorithms using singular value decomposition (SVD) of channel matrices.
Power Allocation Strategies in Multi-User MIMO Systems with Implementation Examples
This code demonstrates power allocation strategies in multi-user MIMO systems, including water-filling algorithm and equal power allocation method, with corresponding channel capacity calculations and MATLAB implementation details.
Iterative Algorithm for Channel Capacity C
This function implements an iterative algorithm to calculate channel capacity using Blahut-Arimoto approach. The main function [CC, Paa] = ChannelCap(P, k) computes optimal channel capacity where P represents the forward transition probability matrix and k specifies the iteration precision. Key variables include: CC (optimal channel capacity), Paa (optimal input probability matrix), Pa (initial input probability), Pba (forward transition matrix), Pb (output probability matrix), C (initial capacity), r (number of input symbols), and s (number of output symbols).
Channel Capacity and Channel Estimation MATLAB Implementation
A comprehensive MATLAB source code package covering various aspects of channel capacity analysis and channel estimation techniques
Open-Loop and Closed-Loop MIMO Channel Capacity Simulation with Water-Filling Algorithm
MATLAB simulation for calculating and comparing ergodic channel capacity of open-loop vs closed-loop MIMO systems using water-filling power allocation algorithm
Analysis of Channel Capacity in Massive MIMO Systems
This analysis investigates the impact of increasing numbers of Base Station (BS) and Mobile Station (MS) antennas on channel capacity in massive MIMO configurations.
MIMO Communication Channel Capacity Simulation
MIMO communication channel capacity simulation program implementing Monte Carlo algorithms for multiple iterations to compute ergodic channel capacity, with detailed code-level explanations of signal processing and statistical averaging methods.