kalesan

## waterfilling.m
% Number of channels
N = 10;
N0 = 1; % Normalized noise level
SNR_dB = 10; % Signal-to-noise ratio in dB
P = 10**(SNR_dB / 10);


% The channel specific gains drawn from Gaussian distribution
g = abs(randn(N, 1));

## waterfilling-illustration.html
<html>
    <head>
        <title>Water-filling illustration in Chart.js</title>
        <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.5.0/chart.min.js"> </script>
    </head>
    <body>

    <center>
    <div>Bisection step</div>
    <div style="width:50%">

## waterfilling.py
# Example code for https://scicoding.com/waterfilling/
import numpy as np

# Number of channels
N = 10
N0 = 1 # Normalized noise level
SNR_dB = 10 # The signal to noise ratio in dB
P = 10**(SNR_dB/10) # Sum power budget defined via the SNR

# The channel specific gains drawn from Gaussian distribution

## sumrate-cvxpy.py
import cvxpy as cp
import numpy as np

# Number of channels
N = 10
N0 = 1 # Normalized noise level
SNR_dB = 10 # The signal to noise ratio in dB
P = 10**(SNR_dB/10) # Sum power budget defined via the SNR

# The channel specific gains drawn from Gaussian distribution
	% Number of channels
	N = 10;
	N0 = 1; % Normalized noise level
	SNR_dB = 10; % Signal-to-noise ratio in dB
	P = 10**(SNR_dB / 10);


	% The channel specific gains drawn from Gaussian distribution
	g = abs(randn(N, 1));
	<html>
	<head>
	<title>Water-filling illustration in Chart.js</title>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/3.5.0/chart.min.js"> </script>
	</head>
	<body>

	<center>
	<div>Bisection step</div>
	<div style="width:50%">
	# Example code for https://scicoding.com/waterfilling/
	import numpy as np

	# Number of channels
	N = 10
	N0 = 1 # Normalized noise level
	SNR_dB = 10 # The signal to noise ratio in dB
	P = 10**(SNR_dB/10) # Sum power budget defined via the SNR

	# The channel specific gains drawn from Gaussian distribution
	import cvxpy as cp
	import numpy as np

	# Number of channels
	N = 10
	N0 = 1 # Normalized noise level
	SNR_dB = 10 # The signal to noise ratio in dB
	P = 10**(SNR_dB/10) # Sum power budget defined via the SNR

	# The channel specific gains drawn from Gaussian distribution