hierarchical-nsa.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3de2aa86",
   "metadata": {},
   "source": [
    "# Hierarchical Network Selection Scheme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b5d9267d-024e-46ec-8225-8c0c1a5667d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np;\n",
    "import pandas as pd;\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display, Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8375fd59-7237-4440-b58a-020f2b5a57d4",
   "metadata": {},
   "source": [
    "##### Functions \n",
    "1. Fuzzy Membership Function: User-preferences Weights Calculations\n",
    "2. AHP Function: Service QoS Weights Calculations\n",
    "3. Weights Calculations\n",
    "4. Normalisation Functions  \n",
    "5. MEW Function: RAT Scores Calculations\n",
    "6. SAW Function: AP/Cell Scores Calculations\n",
    "7. RAT Threshold Filter Function\n",
    "##### Simulation Data\n",
    "1. Decision Matrix\n",
    "2. User Preference Weights\n",
    "3. Service Pair-Wise Comparison Matrices\n",
    "\n",
    "##### Evaluations\n",
    "1. Investigating the RAT rankings per service type\n",
    "2. Investigating the distribution of the 27 unique users across the RATs for each service\n",
    "3. Investigate the sensitivity of the user preference weights on RAT scores\n",
    "4. Investigate the sensitivity of the threshold percentage on the number of RATs selected\n",
    "5. Investigate the load distribution among the APs/cells in each RAT\n",
    "6. Investigate the change in load distribution as number of users increase"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bfd22b",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f524c1-d2ca-482f-aef2-045330fed6db",
   "metadata": {},
   "source": [
    "1\\. Fuzzy Membership Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ca28e724-90a6-4fb6-81ac-ed6a91ee1e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeFuzzyValue(criteria_value, preference, min_value, max_value):\n",
    "    \"\"\"\n",
    "    Calculate the fuzzy membership value for a given criteria.\n",
    "    \n",
    "    :param criteria_value: The value of the criteria to evaluate\n",
    "    :param preference: 'low', 'medium', or 'high'\n",
    "    :param min_value: The minimum value for the criteria range\n",
    "    :param max_value: The maximum value for the criteria range\n",
    "    :return: Membership value between 0 and 1\n",
    "    \"\"\"\n",
    "    \n",
    "    def low_mf(x, b, c):\n",
    "        return np.where(x <= b, 1, max(0, (c - x) / (c - b)))\n",
    "    \n",
    "    def medium_mf(x, a, b, c):\n",
    "        return max(0, min((x - a) / (b - a), (c - x) / (c - b)))\n",
    "    \n",
    "    def high_mf(x, a, b):\n",
    "        return np.where(x >= b, 1, max(0, (x - a) / (b - a)))\n",
    "    \n",
    "    # Calculate the range\n",
    "    value_range = max_value - min_value\n",
    "\n",
    "    preference = preference.lower()\n",
    "    # Set ratios for a, b, c based on the range\n",
    "    if preference == 'low':\n",
    "        b = min_value + 0.25 * value_range\n",
    "        c = min_value + 0.5 * value_range\n",
    "        return low_mf(criteria_value, b, c)\n",
    "    \n",
    "    elif preference == 'medium':\n",
    "        a = min_value + 0.25 * value_range\n",
    "        b = min_value + 0.5 * value_range\n",
    "        c = min_value + 0.75 * value_range\n",
    "        return medium_mf(criteria_value, a, b, c)\n",
    "    \n",
    "    elif preference == 'high':\n",
    "        a = min_value + 0.5 * value_range\n",
    "        b = min_value + 0.75 * value_range\n",
    "        return high_mf(criteria_value, a, b)\n",
    "    \n",
    "    else:\n",
    "        raise ValueError(\"Preference must be 'low', 'medium', or 'high'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6a41090a-20d1-47c9-aab7-d6f84692dbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prefToNum(preference: str) -> float:\n",
    "    \"\"\"\n",
    "    Convert preference string to its corresponding numerical value.\n",
    "    \n",
    "    Args:\n",
    "    preference (str): A string representing the QoS preference. \n",
    "                      Must be either \"Low\", \"Medium\", or \"High\" (case-insensitive).\n",
    "    \n",
    "    Returns:\n",
    "    float: The numerical value corresponding to the preference.\n",
    "    \n",
    "    Raises:\n",
    "    ValueError: If an invalid preference is provided.\n",
    "    \"\"\"\n",
    "    preference_map = {\n",
    "        \"low\": 0.01,\n",
    "        \"medium\": 0.5,\n",
    "        \"high\": 0.99\n",
    "    }\n",
    "    \n",
    "    normalized_preference = preference.lower()\n",
    "    \n",
    "    if normalized_preference not in preference_map:\n",
    "        raise ValueError(\"Invalid preference. Must be 'Low', 'Medium', or 'High'.\")\n",
    "    \n",
    "    return preference_map[normalized_preference]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce67d48-aca9-44fe-addb-bc551b1df271",
   "metadata": {},
   "source": [
    "2\\. AHP Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e2fe292e-b70b-435e-8429-4313d15a2078",
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeAHPWeights(matrix):\n",
    "    \"\"\"\n",
    "    Calculate weights and Consistency Index for AHP pairwise comparison matrix.\n",
    "    \n",
    "    Args:\n",
    "    matrix (np.array): Square pairwise comparison matrix\n",
    "    \n",
    "    Returns:\n",
    "    tuple: (weights, consistency_index)\n",
    "    \"\"\"\n",
    "    # Check if the matrix is square\n",
    "    if matrix.shape[0] != matrix.shape[1]:\n",
    "        raise ValueError(\"Matrix must be square\")\n",
    "    \n",
    "    n = matrix.shape[0]\n",
    "    \n",
    "    # Calculate the principal eigenvalue and eigenvector\n",
    "    eigenvalues, eigenvectors = np.linalg.eig(matrix)\n",
    "    max_index = np.argmax(eigenvalues)\n",
    "    max_eigenvalue = eigenvalues[max_index].real\n",
    "    eigenvector = eigenvectors[:, max_index].real\n",
    "    \n",
    "    # Normalize the eigenvector to get the weights\n",
    "    weights = eigenvector / np.sum(eigenvector)\n",
    "    #print(\"Weights:\", weights)\n",
    "    # Calculate the Consistency Index\n",
    "    ci = (max_eigenvalue - n) / (n - 1)\n",
    "    \n",
    "    # Calculate the Random Index\n",
    "    ri_values = {1: 0, 2: 0, 3: 0.58, 4: 0.9, 5: 1.12, 6: 1.24, 7: 1.32, 8: 1.41, 9: 1.45, 10: 1.49}\n",
    "    ri = ri_values.get(n, 1.49)  # Use 1.49 for n > 10\n",
    "    \n",
    "    # Calculate the Consistency Ratio\n",
    "    cr = ci / ri if ri != 0 else 0\n",
    "    if cr > 0.1:\n",
    "        raise ValueError(f\"Not Consistent! cr={cr}\")\n",
    "    \n",
    "    return weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f998588-692a-428a-97fc-16721608a3bb",
   "metadata": {},
   "source": [
    "3\\. Normalisation Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d47eb935-d633-4810-b9f3-fe62e9efb832",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sqrtNormalize(matrix, criteria_types):\n",
    "    \"\"\"\n",
    "    Normalize the matrix using square root normalization,\n",
    "    accounting for upward and downward criteria.\n",
    "    \n",
    "    :param matrix: numpy array of shape (n_rats, n_criteria)\n",
    "    :param criteria_types: list of strings, either 'up' or 'down' for each criterion\n",
    "    :return: normalized matrix\n",
    "    \"\"\"\n",
    "    normalized = np.zeros_like(matrix, dtype=float)\n",
    "    \n",
    "    for j in range(matrix.shape[1]):\n",
    "        if criteria_types[j] == 'up':\n",
    "            normalized[:, j] = matrix[:, j] / np.sqrt(np.sum(matrix[:, j]**2))\n",
    "        elif criteria_types[j] == 'down':\n",
    "            normalized[:, j] = 1 - (matrix[:, j] / np.sqrt(np.sum(matrix[:, j]**2)))\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid criterion type: {criteria_types[j]}. Must be 'up' or 'down'.\")\n",
    "    \n",
    "    return normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f0fd443a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxminNormalize(matrix, criteria_types):\n",
    "    \"\"\"\n",
    "    Normalize RAT criteria matrix using Max-Min function.\n",
    "    \n",
    "    :param matrix: 2D numpy array where each row represents a RAT and each column a criterion\n",
    "    :param criteria_types: List of strings ('up' or 'down') indicating whether each criterion is upward or downward\n",
    "    :return: Normalized matrix\n",
    "    \"\"\"\n",
    "    normalized = np.zeros_like(matrix, dtype=float)\n",
    "    \n",
    "    for j in range(matrix.shape[1]):\n",
    "        column = matrix[:, j]\n",
    "        col_min, col_max = np.min(column), np.max(column)\n",
    "        if col_min == col_max:\n",
    "            col_min -= 1\n",
    "        \n",
    "        if criteria_types[j] == 'up':\n",
    "            normalized[:, j] = 1 - abs(column - col_max) / (col_max - col_min)\n",
    "        elif criteria_types[j] == 'down':\n",
    "            normalized[:, j] = 1 - abs(column - col_min) / (col_max - col_min)\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid criterion type for column {j}. Must be 'up' or 'down'.\")\n",
    "    \n",
    "    return normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d923a03c-3bec-45bb-91b0-86c41e654ec9",
   "metadata": {},
   "source": [
    "4\\. Final Weights Calculation Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a49567d3",
   "metadata": {},
   "source": [
    "*4.1 RAT Selection Weights*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d2fce88a-8d3b-4731-ae38-a40d958d87d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def userPrefWeights(userpref_criteria, user_prefs):\n",
    "    \"\"\"\n",
    "    Calculate the weights for a [single] user's preferences.\n",
    "    single user single alternative.\n",
    "    \n",
    "    :param userpref_criteria: [power, cost]\n",
    "        numpy array of shape (n_userpreference_criteria,) containing the [single alternative] criteria values \n",
    "    :param user_prefs: [qos, power, cost] \n",
    "        numpy array of shape (n_userpreference_criteria,) containing the user's preferences \n",
    "    \n",
    "    :return: numpy array of shape (n_userpreference_weights,) containing the weights for the user's preferences\n",
    "    \"\"\"\n",
    "    dm_df = pd.read_csv(\"simulation_data/decision_matrix.csv\",header = None) # RAT+AP/Cell decision matrix dataframe\n",
    "\n",
    "    # extract power and cost min and max values for fuzzy value computation\n",
    "    P_max = np.max(dm_df[1][1:6].values.astype(np.float64)) # power max value\n",
    "    P_min = np.min(dm_df[1][1:6].values.astype(np.float64)) # power min value\n",
    "    C_max = np.max(dm_df[2][1:6].values.astype(np.float64)) # cost max value\n",
    "    C_min = np.min(dm_df[2][1:6].values.astype(np.float64)) # cost min value\n",
    "\n",
    "    userpref_weights = np.zeros(user_prefs.shape[0])\n",
    "    userpref_weights[0] = prefToNum(user_prefs[0])\n",
    "    userpref_weights[1] = computeFuzzyValue(userpref_criteria[0],user_prefs[1],P_min,P_max)\n",
    "    userpref_weights[2] = computeFuzzyValue(userpref_criteria[1],user_prefs[2],C_min,C_max) \n",
    "    \n",
    "    return userpref_weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9eba5fd3-be57-483d-bc3f-19cffedd365b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def usersPrefWeights(criteria, users_prefs):\n",
    "    \"\"\"weights\n",
    "    Calculate the weights for all users' preferences.\n",
    "\n",
    "    :param criteria: [power, cost, delay, jitter, packet loss, throughput] \n",
    "        numpy array of shape (n_criteria,) containing the criteria values\n",
    "    :param users_prefs: numpy array of shape (n_users, n_criteria) containing the users' preferences\n",
    "    :return: numpy array of shape (n_users, n_criteria) containing the weights for the users' preferences\n",
    "    \"\"\"\n",
    "    userspref_weights = np.zeros_like(users_prefs)\n",
    "    \n",
    "    for i in range(users_prefs.shape[0]):\n",
    "        userspref_weights[i] = userPrefWeights(criteria[:2],users_prefs[i])\n",
    "    return userspref_weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b685c01e-8738-41c8-a7a3-1ab52749aabb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combineWeights(user_weights, service_weights):\n",
    "    \"\"\"\n",
    "    Combine user and service weights.\n",
    "\n",
    "    :param user_weights: [qos, power, cost] \n",
    "            numpy array of shape (n_userpreference_criteria) containing the user weights\n",
    "    :param service_weights: [D, J, L, T]\n",
    "            numpy array of shape (n_service_criteria) containing the service weights\n",
    "    :return: numpy array of shape (n_users + n_services, n_criteria) containing\n",
    "                the combined weights for the user and service\n",
    "    \"\"\"\n",
    "    qos = user_weights[0] # qos criteria weight\n",
    "    powerncost = user_weights[1:] # power and cost criteria weights\n",
    "\n",
    "    norm_powerncost = powerncost if (np.sum(powerncost)) <= 0 else powerncost/np.sum(powerncost)\n",
    "    \n",
    "    return np.concatenate((norm_powerncost * (1 - qos) , service_weights * qos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "787edd08-2aed-4c8c-acea-c4af9fd9d198",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combineAllServiceWeights(criteria, users_prefs, services_weights):\n",
    "    \"\"\"\n",
    "    Combined weights for all users per service.\n",
    "\n",
    "    :param criteria: [power, cost, delay, jitter, packet loss, throughput] \n",
    "        numpy array of shape (n_criteria,) containing the criteria values\n",
    "    :param users_prefs: numpy array of shape (n_users, n_userpreference_criteria) containing the users' preferences\n",
    "    :param services_weights: numpy array of shape (n_services, n_service_criteria) containing the services' weights\n",
    "    :return: numpy array of shape (n_users, n_services, n_criteria) containing the combined weights for all users and services\n",
    "    \"\"\"\n",
    "    no_services = services_weights.shape[0] # 4\n",
    "    no_criteria = criteria.shape[0] # 6\n",
    "    no_users = users_prefs.shape[0] # 27\n",
    "\n",
    "    #weights = np.zeros((27,4,6))\n",
    "    weights = np.zeros((no_users, no_services, no_criteria))\n",
    "    userpref_criteria = criteria[:2] # power and cost\n",
    "\n",
    "    usersprefs_weights = usersPrefWeights(userpref_criteria, users_prefs) # 27x3 matrix of weights\n",
    "    for i in range(no_users):\n",
    "        for j in range(no_services):\n",
    "            weights[i][j] = combineWeights(usersprefs_weights[i],services_weights[j])\n",
    "    return weights # comprehensive weights for each user per service "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aff14bef-628e-4fed-b740-40577a0fd0af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def combineAllRATWeights(matrix, users_prefs, services_weights, service):\n",
    "    no_users = users_prefs.shape[0] # 27\n",
    "    no_of_RATs = matrix.shape[0] # 5\n",
    "    no_of_criteria = matrix.shape[1] # 6\n",
    "    \n",
    "    rat_user_weights =  np.zeros((no_of_RATs,no_users,no_of_criteria))\n",
    "\n",
    "    for i in range(5):# for each RAT compute the user weights for each user 5x27    \n",
    "        rat_user_weights[i] = combineAllServiceWeights(matrix[i],users_prefs,services_weights)[:, service, :] # service 1 at index 0\n",
    "       \n",
    "    return rat_user_weights # 5x27x6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f97f85",
   "metadata": {},
   "source": [
    "*4.2 AP/Cell Selection Weights*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7ac2af60",
   "metadata": {},
   "outputs": [],
   "source": [
    "def randomSINR(min = 0, max = 40):\n",
    "    return round(np.random.uniform(min, max), 1)\n",
    "\n",
    "def randomWeights(decision_matrix):\n",
    "    weights = np.random.rand(decision_matrix.shape[1])\n",
    "    return weights\n",
    "\n",
    "def getConstCols(matrix):\n",
    "  \"\"\"\n",
    "  Identifies constant columns in a matrix.\n",
    "\n",
    "  Args:\n",
    "    matrix: A NumPy array.\n",
    "\n",
    "  Returns:\n",
    "    A list of column indices that have constant values.\n",
    "  \"\"\"\n",
    "\n",
    "  const_columns = []\n",
    "  for col_idx in range(matrix.shape[1]):\n",
    "    if np.all(matrix[:, col_idx] == matrix[0, col_idx]):\n",
    "      const_columns.append(col_idx)\n",
    "  return const_columns\n",
    "\n",
    "def criticWeighting(decision_matrix):\n",
    "    \"\"\"\n",
    "    Calculate the CRITIC-based weights for criteria in a decision matrix.\n",
    "\n",
    "    Parameters:\n",
    "    decision_matrix (numpy.ndarray): Matrix where each row is an alternative and each column is a criterion.\n",
    "\n",
    "    Returns:\n",
    "    numpy.ndarray: Array of weights for each criterion.\n",
    "    \"\"\"\n",
    "    # Step 1: Normalize the decision matrix\n",
    "    max_values = decision_matrix.max(axis=0)\n",
    "    #print(max_values)\n",
    "    max_values[max_values == 0] = 1  # Avoid division by zero\n",
    "    #print(max_values)\n",
    "    normalized_matrix = decision_matrix /max_values\n",
    "    #print(normalized_matrix)\n",
    "    const_cols = getConstCols(normalized_matrix)\n",
    "    \n",
    "    # To avoid division by zero, replace constant columns with random values\n",
    "    for col in const_cols:\n",
    "        normalized_matrix[:, col] = np.random.rand(normalized_matrix.shape[0]) \n",
    "        \n",
    "    \n",
    "    # Step 2: Calculate the standard deviation for each criterion\n",
    "    std_devs = np.std(normalized_matrix, axis=0)\n",
    "    \n",
    "    # Correcting the std deviation for constant columns\n",
    "    for col in const_cols:\n",
    "        std_devs[col] = 0 # the std value for constant values is 0\n",
    "    \n",
    "    # Step 3: Calculate the correlation matrix between criteria\n",
    "    correlation_matrix = np.corrcoef(normalized_matrix, rowvar=False)\n",
    "    \n",
    "    # correcting the correlation matrix for constant columns\n",
    "    for col in const_cols:\n",
    "        correlation_matrix[col] = 0\n",
    "        correlation_matrix[:, col] = 0\n",
    "    \n",
    "\n",
    "    #print(correlation_matrix)\n",
    "    # Step 4: Calculate the degree of conflict for each criterion\n",
    "    conflict = 1 - correlation_matrix\n",
    "    conflict_sum = conflict.sum(axis=1)  # Sum across each row (for each criterion)\n",
    "\n",
    "    # Step 5: Calculate the information measure for each criterion (std_devs * conflict_sum)\n",
    "    information_measure = std_devs * conflict_sum\n",
    "\n",
    "    # Step 6: Normalize the information measure to get the final weights\n",
    "    weights = information_measure / information_measure.sum()\n",
    "\n",
    "    return weights\n",
    "\n",
    "def entropyWeighting(decision_matrix):\n",
    "    \"\"\"\n",
    "    Calculate the entropy-based weights for criteria in a decision matrix.\n",
    "\n",
    "    Parameters:\n",
    "    decision_matrix (numpy.ndarray): Matrix where each row is an alternative and each column is a criterion.\n",
    "\n",
    "    Returns:\n",
    "    numpy.ndarray: Array of weights for each criterion.\n",
    "    \"\"\"\n",
    "    # Step 1: Normalize the decision matrix\n",
    "    normalized_matrix = decision_matrix / decision_matrix.sum(axis=0)\n",
    "    \n",
    "    # Step 2: Calculate the entropy for each criterion\n",
    "    # Small epsilon to avoid log(0) error\n",
    "    epsilon = 1e-12\n",
    "    n, m = decision_matrix.shape\n",
    "    k = 1 / np.log(n)\n",
    "    \n",
    "    # Calculate the entropy for each criterion\n",
    "    entropy = -k * (normalized_matrix * np.log(normalized_matrix + epsilon)).sum(axis=0)\n",
    "    \n",
    "    # Step 3: Calculate the degree of divergence (1 - entropy)\n",
    "    divergence = 1 - entropy\n",
    "    \n",
    "    # Step 4: Calculate the weights for each criterion\n",
    "    weights = divergence / divergence.sum()\n",
    "    \n",
    "    return weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ff1273-f181-4678-bfe6-1a51d9f44a55",
   "metadata": {},
   "source": [
    "5\\. MEW Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "286feed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeMEW(decision_matrix, weights):\n",
    "    \"\"\"\n",
    "    Calculate scores using the Multiple Exponen Weighted (MEW) method.\n",
    "    \n",
    "    Parameters:\n",
    "    decision_matrix (numpy.ndarray): A normalized decision matrix where rows represent alternatives and columns represent criteria.\n",
    "    weights (numpy.ndarray): An array of weights for each criterion.\n",
    "    \n",
    "    Returns:\n",
    "    numpy.ndarray: An array of MEW scores for each alternative.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Ensure inputs are numpy arrays\n",
    "    decision_matrix = np.array(decision_matrix)\n",
    "    weights = np.array(weights)\n",
    "    \n",
    "    # Check if the number of weights matches the number of criteria\n",
    "    if decision_matrix.shape[1] != weights.shape[0]:\n",
    "        raise ValueError(\"The number of weights must match the number of criteria (columns) in the decision matrix.\")\n",
    "    \n",
    "    # Calculate MEW scores\n",
    "    weighted_matrix = np.power(decision_matrix, weights)\n",
    "    mew_scores = np.prod(weighted_matrix, axis=1)\n",
    "    \n",
    "    return mew_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ae5fa8b6-4113-421a-9112-10d1e270a8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def modifiedMEW(decision_matrix, weights_matrix):\n",
    "    \"\"\"\n",
    "    Calculate scores using the Multiple Exponential Weighted (MEW) method with individual weights for each alternative.\n",
    "    \n",
    "    Parameters:\n",
    "    decision_matrix (numpy.ndarray): A normalized decision matrix where rows represent alternatives and columns represent criteria.\n",
    "    weights_matrix (numpy.ndarray): A matrix of weights where each row corresponds to weights for the respective alternative.\n",
    "    \n",
    "    Returns:\n",
    "    numpy.ndarray: An array of MEW scores for each alternative.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Ensure inputs are numpy arrays\n",
    "    decision_matrix = np.array(decision_matrix)\n",
    "    weights_matrix = np.array(weights_matrix)\n",
    "    \n",
    "    # Check if the dimensions of the decision matrix and weights matrix match\n",
    "    if decision_matrix.shape != weights_matrix.shape:\n",
    "        raise ValueError(\"The dimensions of the decision matrix and weights matrix must match.\")\n",
    "    \n",
    "    # Calculate MEW scores\n",
    "    weighted_matrix = np.power(decision_matrix, weights_matrix)\n",
    "    mew_scores = np.prod(weighted_matrix, axis=1)\n",
    "    \n",
    "    return mew_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26d6dae-8cc8-42ba-8bcd-d8d4ef82c00f",
   "metadata": {},
   "source": [
    "6\\. SAW Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2e4f5e3f-31ee-48a6-aeee-b87de52da99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def computerSAW(decision_matrix, weights):\n",
    "    \"\"\"\n",
    "    Calculate scores using the Simple Additive Weighting (SAW) method.\n",
    "    \n",
    "    Parameters:\n",
    "    decision_matrix (numpy.ndarray): A normalized decision matrix where rows represent alternatives and columns represent criteria.\n",
    "    weights (numpy.ndarray): An array of weights for each criterion.\n",
    "    \n",
    "    Returns:\n",
    "    numpy.ndarray: An array of SAW scores for each alternative.\n",
    "    \"\"\"\n",
    "    \n",
    "    # Ensure inputs are numpy arrays\n",
    "    decision_matrix = np.array(decision_matrix)\n",
    "    weights = np.array(weights)\n",
    "    \n",
    "    # Check if the number of weights matches the number of criteria\n",
    "    if decision_matrix.shape[1] != weights.shape[0]:\n",
    "        raise ValueError(\"The number of weights must match the number of criteria (columns) in the decision matrix.\")\n",
    "    \n",
    "    # Calculate SAW scores\n",
    "    saw_scores = np.sum(decision_matrix * weights, axis=1)\n",
    "    \n",
    "    return saw_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f523d2c",
   "metadata": {},
   "source": [
    "7\\. RAT Threshold Filter Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37e4be12",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filterRATs(RAT_scores,percentage,RATs):\n",
    "    \"\"\"\n",
    "    Filter the RATs based on a percentage threshold.\n",
    "\n",
    "    :param RAT_scores: numpy array of shape (n_RATs,) containing the RAT scores\n",
    "    :param percentage: The percentage threshold to filter the RATs\n",
    "    :param RATs: list of RATs names\n",
    "    :return: pandas DataFrame containing the filtered RATs\n",
    "    \"\"\"\n",
    "    threshold = np.max(RAT_scores) * percentage\n",
    "    \n",
    "    df = pd.DataFrame(RAT_scores, RATs)\n",
    "    selected_RATs = df[df[0] > threshold]\n",
    "    return selected_RATs.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fff5dee-6f9b-4e30-9258-c3918e7452e6",
   "metadata": {},
   "source": [
    "##### Simulation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "da5ed195-d7d2-4cb3-a799-a7b119ad7dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path = \"simulation_data\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7ae9a87-dc31-41e2-bb25-e4254583618c",
   "metadata": {},
   "source": [
    "1\\. Decision Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "eb58539e-692c-43a9-8339-85cfcaae7c7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Criteria</td>\n",
       "      <td>P</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>J</td>\n",
       "      <td>L</td>\n",
       "      <td>T</td>\n",
       "      <td>CL</td>\n",
       "      <td>CC</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3G</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4G</td>\n",
       "      <td>300</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5G</td>\n",
       "      <td>500</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6G</td>\n",
       "      <td>600</td>\n",
       "      <td>15</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.01</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LEO</td>\n",
       "      <td>900</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>down</td>\n",
       "      <td>down</td>\n",
       "      <td>down</td>\n",
       "      <td>down</td>\n",
       "      <td>down</td>\n",
       "      <td>up</td>\n",
       "      <td>down</td>\n",
       "      <td>up</td>\n",
       "      <td>up</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0     1     2     3     4     5     6     7     8   9\n",
       "0  Criteria     P     C     D     J     L     T    CL    CC   S\n",
       "1        3G   200     1    20     8   0.9  0.04     0    70  20\n",
       "2        4G   300     4    10     5   0.1     1     0    40  30\n",
       "3        5G   500     9     3     1  0.02    10     0    20  30\n",
       "4        6G   600    15   0.8   0.4  0.01   100     0   0.5  40\n",
       "5       LEO   900     2    30    10     1   0.1     0  1000  15\n",
       "6       NaN  down  down  down  down  down    up  down    up  up"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "900.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm_df = pd.read_csv(file_path+\"/decision_matrix.csv\",header = None) # RAT+AP/Cell decision matrix dataframe\n",
    "display(dm_df)\n",
    "# extract power and cost min and max values for fuzzy value computation\n",
    "P_max = np.max(dm_df[1][1:6].values.astype(np.float64)) # power max value\n",
    "P_min = np.min(dm_df[1][1:6].values.astype(np.float64)) # power min value\n",
    "C_max = np.max(dm_df[2][1:6].values.astype(np.float64)) # cost max value\n",
    "C_min = np.min(dm_df[2][1:6].values.astype(np.float64)) # cost min value\n",
    "\n",
    "# extract RAT decision matrix\n",
    "rat_dm = dm_df.iloc[1:6,1:7].values.astype(np.float64) # RAT decision matrix\n",
    "RATs = dm_df[0][1:6].values # RAT names\n",
    "crit_types = dm_df.iloc[6:,1:].values[0] # criteria types\n",
    "\n",
    "# extract AP/Cell decision matrix\n",
    "ap_dm = dm_df.iloc[1:6,7:11].values.astype(np.float64)\n",
    "\n",
    "np.max(dm_df[1][1:6].values.astype(np.float64))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c247d92d-a901-48cb-901f-d4d15717d608",
   "metadata": {},
   "source": [
    "2\\. User Preferences Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "18c9de10-b8e6-4d71-b2bf-2cb56d56292d",
   "metadata": {},
   "outputs": [],
   "source": [
    "up_df = pd.read_csv(file_path+\"/user_preferences.csv\", index_col = 0) # user preferences dataframe\n",
    "users_prefs = up_df.values # user preferences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00aa003-ae1a-416c-b6d2-e52085745579",
   "metadata": {},
   "source": [
    "3\\. Service Pair-Wise Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "21390f8d-04d4-4658-9dc2-9d8a53bda734",
   "metadata": {},
   "outputs": [],
   "source": [
    "s_df = pd.read_csv(file_path+\"/pairwise_matrices.csv\",header = None) # services pairwise comparison matrices dataframe\n",
    "\n",
    "s = s_df[0].iloc[[0, 7, 14, 21]].values # service names\n",
    "\n",
    "s_df.dropna(inplace = True) # drop NaN values\n",
    "\n",
    "s_weights = np.zeros((4,4)) # 4 services, 4 criteria\n",
    "\n",
    "rows_per_matrix = len(s_df) // 4 \n",
    "\n",
    "for i in range(s_weights.shape[0]):\n",
    "    df_service = s_df.iloc[rows_per_matrix * i : (rows_per_matrix * (i+1))].iloc[1:5,1:5] # if i<3 else 0\n",
    "    s_weights[i] = computeAHPWeights(df_service.values.astype(np.float64))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae9ac18",
   "metadata": {},
   "source": [
    "##### Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe437b5",
   "metadata": {},
   "source": [
    "Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "af940183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FGK+vtz4X+C1wdM02b4ox3p2WCiVJUov56ONPKCoqAqpHpi6//HJeffVVLrzwQt5++20+/fRTunXrBsBTTz3FjBkzal972GGHAbBlyxbOPPNMpkyZwsCBA2v7Llu2rLbvBx98sMPDklM1d+5czj//fA4++GAA/uVf/oVnn32WwYMHM378eL7//e8zZMgQiouL2bp1Kzk5OYwePZpzzz2XIUOG7NV7Ul+j03whhCxgCvBloAcwPITQo163McCyGONJwOnAzSGEtmmpUJIktZjPrplauHAht912G23btuU73/kOY8eOZfHixdxxxx18/PHHAMQYCSHstI3s7GxOPvlknnzyydq27du388ILL9Ru+6233qJDhw57XF+MscH27t27M3/+fAoKCvjBD37AT3/6U7Kzs3nppZcYNmwYjzzyCIMHD97j/TUklWum+gIrY4yvxxg/BWYAQ+v1iUCHUP0OtgfeA7ampUJJkpRRqqqq6NKlCwDTp0+vbT/77LOZPHly7fJn03whBO666y5eeeUVrr/++gb7fnaRe4cOHfZohGrAgAE88sgjbN68mU2bNjFr1iyKi4tZs2YNBx10EF//+tcZP348CxYsYOPGjVRVVXHOOecwadKk2n0mlUqY6gK8WWd5dU1bXZOBE4A1wGLguzHG7fU3FEK4IoRQEUKoWLdu3V6WLEmSWlJZWRkXXHABxcXFdOrUqbb9Rz/6Ee+//z75+fmcdNJJPP3007XrsrKymDFjBk8//TS33347t956KxUVFRQWFtKjRw/Ky6sfpvyVr3yFWbNm7fIC9HvuuYeuXbvWfnXu3JlRo0bRt29f+vXrx+jRo+nZsyeLFy+mb9++FBUVce211/KjH/2IDz/8kCFDhlBYWMjAgQO55ZZb0vJ+pHLN1M7jddUjUXWVAAuBQcCxwB9DCM/GGD/Y4UUxTgOmAfTu3bvhcTlJktSgVG5lkG4bX3tup7ahQ4cydGj9Sarq+0nVHamq3cbGjQC0bdt2h6m+zz5hV1f37t1ZtGhRg7WMGjWKUaNG7dQ+bty4nW7XUFJSQklJyU59X3rppQa3nUQqI1OrgS/UWe5K9QhUXZcCD8dqK4E3gOPTU6IkSVLmSiVMzQOOCyF0q7mo/CLg0Xp9VgFnAoQQjgD+CXg9nYVKkiRlokan+WKMW0MIY4Enqb41wl0xxqUhhNKa9eXAz4B7QgiLqZ4W/H6McX0T1i1JkpQRUrrPVIzxceDxem3ldb5fA5yd3tIkSZIyn4+TkSRJSsDHyWjfUZabQp+qpq9DkrRfcWRKkiQ16N33NlB01kUUFRXx+c9/ni5dulBUVMShhx5Kjx71H4bStCorK8nPz0/LtjZs2MDtt9+elm2BI1OSJLUaN1+YnmfJfeZ7M/+w2/UdP3coC/84A47qSVlZWe0DiCsrK1N6rt3WrVvJzs68qPFZmPr2t7+dlu05MiVJkvbYtm3b+OY3v8mJJ57I2WefzUcffQTA6aefzg9/+EMGDhzIL3/5S/70pz/Rs2dPCgoKuOyyy/jkk08AyMvLY/366g/+V1RUcPrppwOwbt06zjrrLHr16sW//du/8cUvfrG23+72eeWVV3LqqaeSn59fe2POsrIybrrpptqa8/PzqaysZMKECfzf//0fRUVFXH311YnfC8OUJEnaY6+99hpjxoxh6dKlHHrooTz00EO16zZs2MBf/vIXxowZw6hRo5g5cyaLFy9m69atTJ06dbfb/clPfsKgQYNYsGAB559/PqtWrUppn5s2beL555/n9ttv57LLLtvtPq6//nqOPfZYFi5cyI033riX78D/Z5iSJEl7rFu3bhQVFQFw8sknU1lZWbvuwgsvBODVV1+lW7dudO/eHYCRI0fyzDPP7Ha7c+fO5aKLLgJg8ODBHHbYYSntc/jw4UD1g48/+OADNmzYkODo9kzmTWRKDcib8FijfSpzmqEQSRIABx54YO33WVlZtVNuAAcffDAAMe76MbzZ2dls374dgI8//ri2fXev2d0+Q9jxUcIhhB32UX8/6eTIlCRJahLHH388lZWVrFy5EoDf/OY3DBw4EKi+Zmr+/PkAO0zX9e/fnwceeACA2bNn8/7776e0r88emjx37lxyc3PJzc0lLy+PBQsWALBgwQLeeOMNADp06MCHH36YhiOsZpiSJElNIicnh7vvvpsLLriAgoICDjjgAEpLSwG45ppr+O53v0txcTFZWVm1r7nmmmuYPXs2vXr14oknnuDII4+kQ4cOje7rsMMO49RTT6W0tJRf//rXAAwbNoz33nuPoqIipk6dWjvd2LFjR0477TTy8/PTcgG603ySJLUSjd3KoCmVlZXVfp+Xl8eSJUtql8ePH1/7/Zw5c3Z43Zlnnsnf/va3nbZXXFzMihUrdmrPzc3lySefJDs7mxdeeIGnn36aAw88cLf7hOrgdN111+3Q1q5dO2bPnt3g8dx///0Ntu8Nw5QkScoYq1at4l//9V/Zvn07bdu25c4772zpkhplmJIkSRnjuOOOa3Aka3fqj4Y1N6+ZkiRJSsAwJUlSBtvdrQKUfnvzfjvNp/1KwfSClPotHrm4iSuRpMbl5OTw7rvv0rFjx53uo6T0izHy7rvvkpOzZzcuNExJkpShunbtyurVq1m3bl3T7WTD2sb7VC1vuv1nmJycHLp27bpHrzFMSZKUodq0aUO3bt2adidlp6TQp6ppa2jlvGZKkiQpAcOUJElSAoYpSZKkBAxTkiRJCRimJEmSEjBMSZIkJWCYkiRJSsAwJUmSlIBhSpIkKQHDlCRJUgKGKUmSpAQMU5IkSQkYpiRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAYUqSJCkBw5QkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKwDAlSZKUgGFKkiQpAcOUJElSAoYpSZKkBLJbugApEy0//oTGO50+pekLkSRlPEemJEmSEjBMSZIkJWCYkiRJSsAwJUmSlIBhSpIkKQHDlCRJUgKGKUmSpARSClMhhMEhhFdDCCtDCBN20ef0EMLCEMLSEMJf0lumJElSZmr0pp0hhCxgCnAWsBqYF0J4NMa4rE6fQ4HbgcExxlUhhM5NVK8kSVJGSWVkqi+wMsb4eozxU2AGMLRen4uBh2OMqwBijGvTW6YkSVJmSiVMdQHerLO8uqatru7AYSGEOSGE+SGEbzS0oRDCFSGEihBCxbp16/auYkmSpAySyrP5QgNtsYHtnAycCbQDXgghvBhjXLHDi2KcBkwD6N27d/1tSNoH5U14LKV+ldef28SVSFLTSCVMrQa+UGe5K7CmgT7rY4ybgE0hhGeAk4AVSJIk7cNSmeabBxwXQugWQmgLXAQ8Wq/P74DiEEJ2COEgoB+wPL2lSpIkZZ5GR6ZijFtDCGOBJ4Es4K4Y49IQQmnN+vIY4/IQwv8Ci4DtwK9ijEuasnBJkqRMkMo0HzHGx4HH67WV11u+EbgxfaVJkiRlPu+ALkmSlIBhSpIkKQHDlCRJUgKGKUmSpAQMU5IkSQkYpiRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAYUqSJCkBw5QkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKwDAlSZKUgGFKkiQpAcOUJElSAoYpSZKkBLJbugBJUubKm/BYo30qrz+3GSqRMpcjU5IkSQkYpiRJkhIwTEmSJCVgmJIkSUrAC9AlZYay3Mb7dDu66euQpD3kyJQkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKwDAlSZKUgGFKkiQpAcOUJElSAoYpSZKkBHyczF4omF7QaJ/FIxc3QyWSJKmlOTIlSZKUgGFKkiQpAcOUJElSAoYpSZKkBAxTkiRJCRimJEmSEjBMSZIkJWCYkiRJSsAwJUmSlIBhSpIkKQHDlCRJUgKGKUmSpAQMU5IkSQkYpiRJkhLIbukC9lXLjz+h0T4nvLK8GSqRJElNyZEpSZKkBFIKUyGEwSGEV0MIK0MIE3bTr08IYVsI4WvpK1GSJClzNRqmQghZwBTgy0APYHgIoccu+t0APJnuIiVJkjJVKiNTfYGVMcbXY4yfAjOAoQ30+w7wELA2jfVJkiRltFTCVBfgzTrLq2vaaoUQugDnA+XpK02SJCnzpRKmQgNtsd7yJOD7McZtu91QCFeEECpCCBXr1q1LsURJkqTMlcqtEVYDX6iz3BVYU69Pb2BGCAGgE3BOCGFrjPGRup1ijNOAaQC9e/euH8gkSZJanVTC1DzguBBCN+At4CLg4rodYozdPvs+hHAP8If6QUqSJGlf1GiYijFuDSGMpfpTelnAXTHGpSGE0pr1XiclSZL2WyndAT3G+DjweL22BkNUjHFU8rIkSZJaB++ALkmSlIBhSpIkKQHDlCRJUgKGKUmSpAQMU5IkSQkYpiRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAYUqSJCkBw5QkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKILulC9ifTSn9c0r9xpQPauJKJEnS3nJkSpIkKQHDlCRJUgKGKUmSpAQMU5IkSQkYpiRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAYUqSJCkBw5QkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKwDAlSZKUQHZLFyBJkppG3oTHGu1TmdMMhezjDFOSJCmx5cefkFK/E15Z3sSVND+n+SRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAF6BLkqRmM6X0z432GVM+qBkqSR9HpiRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAYUqSJCkBw5QkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKwDAlSZKUgGFKkiQpAcOUJElSAimFqRDC4BDCqyGElSGECQ2svySEsKjm6/kQwknpL1WSJCnzNBqmQghZwBTgy0APYHgIoUe9bm8AA2OMhcDPgGnpLlSSJCkTpTIy1RdYGWN8Pcb4KTADGFq3Q4zx+Rjj+zWLLwJd01umJElSZspOoU8X4M06y6uBfrvpfznwREMrQghXAFcAHH300SmWKEmSWlLB9IJG+zzQDHVkqlRGpkIDbbHBjiGcQXWY+n5D62OM02KMvWOMvQ8//PDUq5QkScpQqYxMrQa+UGe5K7CmfqcQQiHwK+DLMcZ301OeJElSZktlZGoecFwIoVsIoS1wEfBo3Q4hhKOBh4ERMcYV6S9TkiQpMzU6MhVj3BpCGAs8CWQBd8UYl4YQSmvWlwM/BjoCt4cQALbGGHs3XdmSJEmZIZVpPmKMjwOP12srr/P9aGB0ekuTJEnKfN4BXZIkKQHDlCRJUgKGKUmSpAQMU5IkSQmkdAG6JEm7VJabWr9uPvlC+yZHpiRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAYUqSJCkBw5QkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKwDAlSZKUgGFKkiQpAcOUJElSAoYpSZKkBAxTkiRJCRimJEmSEjBMSZIkJWCYkiRJSsAwJUmSlIBhSpIkKYHsli5AkjLVzRcOabTP92b+oRkqkZTJHJmSJElKwDAlSZKUgGFKkiQpAcOUJElSAoYpSZKkBAxTkiRJCRimJEmSEjBMSZIkJWCYkiRJSsAwJUmSlIBhSpIkKQHDlCRJUgKGKUmSpAQMU5IkSQkYpiRJkhIwTEmSJCVgmJIkSUrAMCVJkpSAYUqSJCkBw5QkSVIChilJkqQEDFOSJEkJGKYkSZISMExJkiQlYJiSJElKwDAlSZKUQEphKoQwOITwaghhZQhhQgPrQwjh1pr1i0IIvdJfqiRJUubJbqxDCCELmAKcBawG5oUQHo0xLqvT7cvAcTVf/YCpNX9K+7WbLxzSaJ/vzfxDM1QiSWoqqYxM9QVWxhhfjzF+CswAhtbrMxS4N1Z7ETg0hHBkmmuVJEnKOI2OTAFdgDfrLK9m51Gnhvp0Ad5OVJ0kSdrvtLZR/RBj3H2HEC4ASmKMo2uWRwB9Y4zfqdPnMeC6GOPcmuU/Af8eY5xfb1tXAFfULP4T8Gq6DiQDdQLWt3QR2muev9bLc9e6ef5ar3393H0xxnh4QytSGZlaDXyhznJXYM1e9CHGOA2YlsI+W70QQkWMsXdL16G94/lrvTx3rZvnr/Xan89dKtdMzQOOCyF0CyG0BS4CHq3X51HgGzWf6jsFqIoxOsUnSZL2eY2OTMUYt4YQxgJPAlnAXTHGpSGE0pr15cDjwDnASmAzcGnTlSxJkpQ5UpnmI8b4ONWBqW5beZ3vIzAmvaW1evvFdOY+zPPXennuWjfPX+u13567Ri9AlyRJ0q75OBlJkqQEDFMJhRByQggvhRBeDiEsDSH8pM66cSGEV0IIi2vW/yKE0KYl69XOQghZIYS/hRD+UKfNc9cKhBAqa87RwhBCRZ12z1+GCyEcGkJ4sOY8LQ8h/HNNu+cuQ4UQNjbQVhZCeKvm3+BnX4fWrOtf8/PxlZqvK3ba6D4ipWumtFufAINijBtr/sHPDSE8ARQBZwOnxBg31HwSchzQDtjSYtWqId8FlgOHANR8uMJz13qcEWOsvbeN56/V+CXwvzHGr9Wco4M8d63WLTHGm+o2hBA+D9wPnBdjXBBC6AQ8GUJ4K8b4WItU2YS8ZiqNQggHAXOBbwEPAgNijG+0bFXanRBCV2A6cC0wLsY4JITwJp67ViGEUAn0rhemPH8ZLoRwCPAycEys80PIc5fZQggbY4zt67WVARsbCFM/o/rzaT+u03YmUBZjLG6OepuT03xpUDNNtBBYC/wRWAa09z+EVmES8O/AdoAQQgc8d61JBGaHEOaHEK7w/LUaxwDrgLtrpth/FULIxXPXWl1VZ4rv6Zq2E4H59fpV1LTvcwxTaRBj3BZjLKL6zu99gW5U/ycPQAihpOYvWWUI4dQWKlP1hBCGAGvrPfYo4LlrTU6LMfYCvkz17Vl64vlrDbKBXsDUGGNPYBNQhueutbolxlhU83VGTdsO/5fWsU9Ohxmm0ijGuAGYQ/Wc/6YQQrea9idrwtYSoG1L1aednAZ8tWaqaAYwCLgdz12rEWNcU/PnWmAW1b/MeP4y32pgdYzxrzXLDwLd8dztS5YC9R8tczLVMzf7HMNUQiGEw+t8cqEd8CXgFeA6YGqddQHIaaEy1YAY4w9ijF1jjHlUPybpzzHGr+O5axVCCAfXTOsRQjiY6l9iluD5y3gxxn8Ab4YQ/qmm6Uyqf8h67vYdU4BRIYQigBBCR+AG4OctWVRT8dN8yR0JTA8hZFEdTh+IMf6h5j+Bg4C/hhA+ATYCzwF/a7lSlaKpeO5agyOAWdX/1MgG7o8x/q//9lqN7wD31Xxi73WqH0O2Ac9dJjsohLC6zvIvav68KoTw9Trt58UYK2va7qz5pScAk2KMv2+uYpuTn+aTJElKwGk+SZKkBAxTkiRJCRimJEmSEjBMSZIkJWCYkiRJSsAwJUmSlIBhSpIkKQHDlCRJUgL/D3XXFwcIVFI+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_rat_dm = sqrtNormalize(rat_dm, crit_types[:6]) # normalize RAT decision matrix\n",
    "\n",
    "no_select_rats = rat_dm.shape[0] # All 5 RATs\n",
    "no_services = s_weights.shape[0] # 4 (3 services + equal importance weights)\n",
    "no_users = users_prefs.shape[0] # 27 unique users\n",
    "no_criteria = rat_dm.shape[1] # 6 criteria (power, cost, delay, jitter, packet loss, throughput)\n",
    "\n",
    "# Charts\n",
    "\n",
    "label_fs = 12\n",
    "title_fs = 14\n",
    "pd.DataFrame(norm_rat_dm, index=RATs, columns=['Power', 'Cost', 'Delay', 'Jitter', 'Packet Loss', 'Throughput']).plot(kind='bar', figsize=(10, 6), rot=0, )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05aca78f",
   "metadata": {},
   "source": [
    "1\\. Investigating the RAT rankings per service type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3b5c5fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "rat_scores = np.zeros((no_services,no_select_rats)) # RAT scores for each service\n",
    "combined_weights = np.zeros((no_services,no_criteria)) # combined weights for each service\n",
    "rat_ranks = np.zeros((no_services,no_select_rats), dtype=np.int64) # RAT ranks for each service\n",
    "\n",
    "userprefs_weights = np.array([1,0,0]) # user preference criteria not considered i.e. power and cost weights set to zero\n",
    "\n",
    "for i in range(no_services):\n",
    "    combined_weights[i] = combineWeights(userprefs_weights,s_weights[i])\n",
    "    rat_scores[i] = computeMEW(norm_rat_dm, combined_weights[i])\n",
    "    rat_ranks[i] = np.argsort(np.argsort(-rat_scores[i]))+1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "024f166c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rank = pd.DataFrame(rat_ranks, columns=RATs, index=s)\n",
    "#display(df_rank)\n",
    "df_rank.plot(kind='bar', figsize=(10, 6))\n",
    "#plt.rc('font', family='serif')\n",
    "plt.title('RATs Rankings by Service Type\\n', fontsize=16)\n",
    "plt.ylabel('Rank', fontsize=14)\n",
    "plt.xlabel('\\nService Type', fontsize=14)\n",
    "plt.xticks(rotation=0, fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.legend(fontsize=12, loc='upper left')\n",
    "#plt.show()\n",
    "\n",
    "# Save the figure\n",
    "# filename = 'fig_01_rats_rankings.png'\n",
    "# plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "\n",
    "# # Display the saved image\n",
    "# display(Image(filename=filename))\n",
    "plt.show()\n",
    "# Close the figure to free up memory\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80ca1a74",
   "metadata": {},
   "source": [
    "2\\. Investigating the distribution of the 27 unique users across the RATs for each service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "ab841183-4557-489b-b7c7-63992e03d02a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Q</th>\n",
       "      <th>P</th>\n",
       "      <th>C</th>\n",
       "      <th>VoIP</th>\n",
       "      <th>Video Streaming</th>\n",
       "      <th>Cloud Gaming</th>\n",
       "      <th>Equal Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>LEO</td>\n",
       "      <td>LEO</td>\n",
       "      <td>LEO</td>\n",
       "      <td>LEO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>3G</td>\n",
       "      <td>3G</td>\n",
       "      <td>3G</td>\n",
       "      <td>3G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>5G</td>\n",
       "      <td>5G</td>\n",
       "      <td>5G</td>\n",
       "      <td>5G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "      <td>4G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>5G</td>\n",
       "      <td>6G</td>\n",
       "      <td>5G</td>\n",
       "      <td>5G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>4G</td>\n",
       "      <td>6G</td>\n",
       "      <td>5G</td>\n",
       "      <td>5G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>5G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>5G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>4G</td>\n",
       "      <td>6G</td>\n",
       "      <td>5G</td>\n",
       "      <td>5G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Low</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "      <td>6G</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Q       P       C VoIP Video Streaming Cloud Gaming Equal Importance\n",
       "0      Low     Low     Low   6G              6G           6G               6G\n",
       "1      Low     Low  Medium   6G              6G           6G               6G\n",
       "2      Low     Low    High  LEO             LEO          LEO              LEO\n",
       "3      Low  Medium     Low   3G              3G           3G               3G\n",
       "4      Low  Medium  Medium   4G              4G           4G               4G\n",
       "5      Low  Medium    High   4G              4G           4G               4G\n",
       "6      Low    High     Low   5G              5G           5G               5G\n",
       "7      Low    High  Medium   4G              4G           4G               4G\n",
       "8      Low    High    High   4G              4G           4G               4G\n",
       "9   Medium     Low     Low   6G              6G           6G               6G\n",
       "10  Medium     Low  Medium   6G              6G           6G               6G\n",
       "11  Medium     Low    High   5G              6G           5G               5G\n",
       "12  Medium  Medium     Low   6G              6G           6G               6G\n",
       "13  Medium  Medium  Medium   4G              6G           6G               6G\n",
       "14  Medium  Medium    High   4G              6G           5G               5G\n",
       "15  Medium    High     Low   5G              6G           6G               5G\n",
       "16  Medium    High  Medium   4G              6G           6G               6G\n",
       "17  Medium    High    High   4G              6G           5G               5G\n",
       "18    High     Low     Low   6G              6G           6G               6G\n",
       "19    High     Low  Medium   6G              6G           6G               6G\n",
       "20    High     Low    High   6G              6G           6G               6G\n",
       "21    High  Medium     Low   6G              6G           6G               6G\n",
       "22    High  Medium  Medium   6G              6G           6G               6G\n",
       "23    High  Medium    High   6G              6G           6G               6G\n",
       "24    High    High     Low   6G              6G           6G               6G\n",
       "25    High    High  Medium   6G              6G           6G               6G\n",
       "26    High    High    High   6G              6G           6G               6G"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usersperrat = np.zeros((no_services, no_select_rats), dtype=np.int64) # how many times a RAT is ranked 1 by a user for each service\n",
    "userpref_ratselected = users_prefs #np.empty((no_users,3+no_services), dtype=object) # [[\"Low\", \"Low\", \"Low\", \"3G\", \"4G\", \"6G\", \"LEO\"]] # user preferences and selected RAT\n",
    "\n",
    "empty_columns = np.empty((no_users, no_services), dtype=object)\n",
    "userpref_ratselected = np.concatenate((users_prefs, empty_columns), axis=1)\n",
    "\n",
    "for i in range(s.size):\n",
    "    scores = np.zeros((no_users, no_select_rats))\n",
    "    rat_user_weights = combineAllRATWeights(rat_dm, users_prefs, s_weights, i) \n",
    "\n",
    "    for j in range(no_users):\n",
    "        scores[j] = modifiedMEW(norm_rat_dm, rat_user_weights[:,j,:]) # 27x5 \n",
    "        usersperrat[i] += scores[j]==np.max(scores[j])\n",
    "        userpref_ratselected[j][3+i] = RATs[np.argmax(scores[j])]\n",
    "      \n",
    "df_user_rat = pd.DataFrame(usersperrat, columns=RATs, index=s) # RATs ranked 1 by users for each service\n",
    "df_selected = pd.DataFrame(userpref_ratselected, columns=np.append(np.array(['Q','P','C']),s))\n",
    "df_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f49f44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))\n",
    "\n",
    "#plt.suptitle('User Distribution by RAT for Each Service\\n', fontsize=15)\n",
    "for i, service in enumerate(s):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    # Create the pie chart\n",
    "    ax.pie(usersperrat[i], labels=RATs, autopct='%1.1f%%', textprops={'fontsize': 14})\n",
    "    ax.set_title(f'{service}', fontsize=16)\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure\n",
    "# filename = 'fig_02_user_distribution.png'\n",
    "# plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Display the saved image\n",
    "#display(Image(filename=filename))\n",
    "plt.show()\n",
    "# Close the figure to free up memory\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cc5b4af",
   "metadata": {},
   "source": [
    "3\\. Investigate the sensitivity of the user preference weights on RAT scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "277396b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = 1000 \n",
    "\n",
    "weights = np.linspace(0,1,samples)\n",
    "\n",
    "userpref_criteria = np.array(['QoS','Power Consumption','Cost']) # Q, P, C\n",
    "\n",
    "rat_scores = np.zeros((userprefs_weights.size,samples,5)) # 1000 samples, 5 RATs, 3 user weights\n",
    "\n",
    "\n",
    "# for user weight Q, P, C plot a var_weight vs RAT_scores\n",
    "for i in range(userprefs_weights.size):\n",
    "    userprefs_weights = np.array([0.05,0.05,0.05]) # equal weights for each user preference criteria, Q, P, C\n",
    "    for j in range(samples):\n",
    "        userprefs_weights[i] = weights[j]\n",
    "        combined_weights = combineWeights(userprefs_weights,s_weights[3]) # service 4: Equal Importance \n",
    "        rat_scores[i][j] = computeMEW(norm_rat_dm, combined_weights) #\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5953e87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1008 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the plot\n",
    "fig, axs = plt.subplots(2, 2, figsize=(16, 14))\n",
    "#fig.suptitle('Sensitivity of RAT Scores to Criterion Weight\\n', fontsize=16)\n",
    "\n",
    "# Flatten the axs array for easier indexing\n",
    "axs = axs.flatten()\n",
    "\n",
    "\n",
    "# Plot each criterion\n",
    "for i, criterion in enumerate(userpref_criteria):\n",
    "    ax = axs[i]\n",
    "    for j, rat in enumerate(RATs):\n",
    "        ax.plot(weights, rat_scores[i, :, j], label=rat)\n",
    "    \n",
    "    ax.set_title(f'{criterion}', fontsize=16)\n",
    "    ax.set_ylabel('RAT Score', fontsize=14)\n",
    "    ax.set_xlabel('Weight', fontsize=14)\n",
    "\n",
    "   \n",
    "    ax.tick_params(axis='both', labelsize=12)\n",
    "    \n",
    "    ax.legend(fontsize=12)\n",
    "    ax.grid(True)\n",
    "\n",
    "\n",
    "\n",
    "# Remove the unused subplot\n",
    "axs[3].remove()\n",
    "\n",
    "# Adjust layout and show the plot\n",
    "# #plt.tight_layout()\n",
    "\n",
    "# Save the figure\n",
    "# filename = 'fig_03_score_sensitivity.png'\n",
    "# plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Display the saved image\n",
    "#display(Image(filename=filename))\n",
    "plt.show()\n",
    "# Close the figure to free up memory\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62cb60d5",
   "metadata": {},
   "source": [
    "4\\. Investigate the sensitivity of the threshold percentage on the number of RATs selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "49763727",
   "metadata": {},
   "outputs": [],
   "source": [
    "userprefs_weights = np.array([0.5,0.5,0.5]) # equal weights for each user preference criteria\n",
    "percentage = np.linspace(0, 1, 100)\n",
    "\n",
    "weights = combineWeights(userprefs_weights,s_weights[1]) # service 4: Equal Importance\n",
    "rat_scores = computeMEW(norm_rat_dm, weights)\n",
    "\n",
    "no_select_rats = np.zeros(percentage.size)\n",
    "for i in range(percentage.size):\n",
    "    no_select_rats[i] = filterRATs(rat_scores,percentage[i],RATs).shape[1]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1d69469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3G</th>\n",
       "      <th>4G</th>\n",
       "      <th>5G</th>\n",
       "      <th>6G</th>\n",
       "      <th>LEO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>VoIP</th>\n",
       "      <td>0.523194</td>\n",
       "      <td>0.671464</td>\n",
       "      <td>0.675196</td>\n",
       "      <td>0.539436</td>\n",
       "      <td>0.292135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Video Streaming</th>\n",
       "      <td>0.071075</td>\n",
       "      <td>0.197700</td>\n",
       "      <td>0.357200</td>\n",
       "      <td>0.542056</td>\n",
       "      <td>0.062393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cloud Gaming</th>\n",
       "      <td>0.229139</td>\n",
       "      <td>0.403624</td>\n",
       "      <td>0.517238</td>\n",
       "      <td>0.540547</td>\n",
       "      <td>0.151791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Equal Importance</th>\n",
       "      <td>0.252164</td>\n",
       "      <td>0.443821</td>\n",
       "      <td>0.542995</td>\n",
       "      <td>0.540857</td>\n",
       "      <td>0.174608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        3G        4G        5G        6G       LEO\n",
       "VoIP              0.523194  0.671464  0.675196  0.539436  0.292135\n",
       "Video Streaming   0.071075  0.197700  0.357200  0.542056  0.062393\n",
       "Cloud Gaming      0.229139  0.403624  0.517238  0.540547  0.151791\n",
       "Equal Importance  0.252164  0.443821  0.542995  0.540857  0.174608"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create 2x2 subplots\n",
    "fig, axs = plt.subplots(2, 2, figsize=(16, 14))\n",
    "#fig.suptitle('Sensitivity of Number of Selected RATs to Threshold Percentage\\n', fontsize=15, fontweight='bold')\n",
    "RAT_scores2 = np.zeros((4,5))\n",
    "# Flatten the axs array for easier indexing\n",
    "axs = axs.flatten()\n",
    "\n",
    "for idx, service in enumerate(s):\n",
    "    user_weights = np.array([0.5, 0.5, 0.5])  # equal weights for each user preference criteria\n",
    "    weights = combineWeights(user_weights, s_weights[idx])\n",
    "    \n",
    "    # Placeholder for normal_dm_rat, replace with actual data\n",
    "    #normal_dm_rat = np.random.rand(10, 3)  # Assuming 10 RATs and 3 QoS parameters\n",
    "    \n",
    "    rat_scores = computeMEW(norm_rat_dm, weights)\n",
    "    RAT_scores2[idx] = rat_scores\n",
    "    \n",
    "    percentage = np.linspace(0, 1, 100)\n",
    "    no_select_rats = np.zeros(percentage.size)\n",
    "    \n",
    "    for i in range(percentage.size):\n",
    "        no_select_rats[i] = filterRATs(rat_scores, percentage[i], rat_scores).size\n",
    "    \n",
    "   # Create step-like data points\n",
    "    x_steps = np.repeat(percentage, 2)[1:]\n",
    "    y_steps = np.repeat(no_select_rats, 2)[:-1]\n",
    "    \n",
    "    # Add the first and last points to close the shape\n",
    "    x_steps = np.concatenate(([0], x_steps, [1]))\n",
    "    y_steps = np.concatenate(([no_select_rats[0]], y_steps, [0]))  \n",
    "\n",
    "    axs[idx].fill_between(x_steps*100, y_steps, step=\"pre\" ) \n",
    "    axs[idx].plot(x_steps*100, y_steps, color='black', linewidth=0.5, drawstyle='steps-post')\n",
    "    axs[idx].set_title(f'{service}', fontsize=16)\n",
    "    axs[idx].set_ylabel('Number of RATs', fontsize=14)\n",
    "    axs[idx].set_xlabel('Threshold (%)', fontsize=14)\n",
    "    axs[idx].tick_params(axis='both', which='major', labelsize=12)\n",
    "    axs[idx].grid(True)\n",
    "display(pd.DataFrame(RAT_scores2, columns=RATs, index=s))\n",
    "\n",
    "\n",
    "# Save the figure\n",
    "# filename = 'fig_04_filter_sensitivity.png'\n",
    "# plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Display the saved image\n",
    "# display(Image(filename=filename))\n",
    "plt.show()\n",
    "# Close the figure to free up memory\n",
    "plt.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d5a789e",
   "metadata": {},
   "source": [
    "5\\. Investigate the load distribution among the APs/cells in each RAT\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9807ac1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3G</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4G</th>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5G</th>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6G</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEO</th>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Load\n",
       "3G    125\n",
       "4G    143\n",
       "5G    103\n",
       "6G    146\n",
       "LEO   483"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of AP/BSs per RAT\n",
    "L  = 5\n",
    "# number of simultaneous connections per UE\n",
    "MC = 10\n",
    "# number of users\n",
    "U = 100\n",
    "\n",
    "selected_rats = filterRATs(rat_scores,0.0,RATs).columns # equal importance RAT scores\n",
    "no_select_rats = selected_rats.size\n",
    "\n",
    "# Create a matrix of AP/BS criteria values for each RAT\n",
    "\n",
    "aps = np.zeros((no_select_rats, L, ap_dm.shape[1]))\n",
    "all_aps = np.zeros((no_select_rats*L, ap_dm.shape[1]))\n",
    "cell_nos = np.ones(no_select_rats*L, dtype=np.int64)\n",
    "modified_AP_matrix = np.zeros((no_select_rats,L))\n",
    "\n",
    "rat_ap = []\n",
    "ap_names = []\n",
    "\n",
    "# initial criteria values for the APs decision matrix \n",
    "\n",
    "for i in range(no_select_rats):\n",
    "    rat = selected_rats[i]\n",
    "    selected_row = dm_df[dm_df.iloc[:, 0] == rat].iloc[0][7:].values.astype(np.float64)\n",
    "    rat_ap += [rat] * L\n",
    "    for j in range(L if rat != 'LEO' else L):\n",
    "        selected_row[2] = randomSINR(min=30) \n",
    "        aps[i, j] = selected_row\n",
    "        all_aps[j+i*L] = selected_row\n",
    "        cell_nos[j+i*L] = j+1\n",
    "        \n",
    "\n",
    "# Create a dictionary to store the number of users per RAT\n",
    "usersperrat = {rat: 0 for rat in selected_rats}\n",
    "\n",
    "for i in range(U):\n",
    "    normal_ap = maxminNormalize(all_aps, crit_types[6:])\n",
    "    weights = criticWeighting(normal_ap)   \n",
    "    ap_scores = computerSAW(normal_ap,weights)\n",
    "    df = pd.DataFrame(list(zip(cell_nos,ap_scores)),index=rat_ap)\n",
    "    ap_ranks = df.sort_values(by=1, ascending=False)\n",
    "\n",
    "    # updating the load criteria on the AP matrix after selection\n",
    "    for j in range(MC): # top C AP are selected\n",
    "        rat = ap_ranks.iloc[j].name\n",
    "        rat_index = np.where(selected_rats == rat)[0][0]\n",
    "        ap_index = ap_ranks.iloc[j].values[0].astype(np.int64) - 1\n",
    "        all_aps[ap_index + rat_index*L][0] += 1 \n",
    "        usersperrat[rat] += 1\n",
    "\n",
    "# create a list of AP names\n",
    "for i in range(L):\n",
    "    ap_names.append(f'AP{i+1}')\n",
    "\n",
    "df_loadperaprat = pd.DataFrame(all_aps[:,0].reshape(L,no_select_rats) , columns = ap_names,index=selected_rats)\n",
    "df_load = pd.DataFrame(usersperrat, index=['Load']).T\n",
    "display(df_load)\n",
    "\n",
    "\n",
    "small_value = 1\n",
    "df_plot = df_loadperaprat.replace(0, small_value)\n",
    "\n",
    "\n",
    "\n",
    "df_plot.plot(kind='bar', figsize=(10, 6))\n",
    "plt.title('AP/Cell Load Distribution Across RATs\\n', fontsize=16)\n",
    "plt.ylabel('Load', fontsize=14)\n",
    "plt.xlabel('RAT', fontsize=14)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(rotation=0, fontsize=12)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "\n",
    "# Save the figure\n",
    "filename = 'fig_05_load_distribution.png'\n",
    "plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Display the saved image\n",
    "# display(Image(filename=filename))\n",
    "plt.show()\n",
    "# Close the figure to free up memory\n",
    "plt.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff12b84",
   "metadata": {},
   "source": [
    "6\\. Investigate the change in load distribution as number of users increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1a2ea45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# number of AP/BSs per RAT\n",
    "L  = 1\n",
    "# multi-connectivity\n",
    "MC = 1\n",
    "# cell capacity\n",
    "CC = 3\n",
    "# number of users\n",
    "U = 20\n",
    "\n",
    "selected_rats = filterRATs(rat_scores,0.0,RATs).columns\n",
    "no_select_rats = selected_rats.size\n",
    "\n",
    "# Create a matrix of AP/BS criteria values for each RAT\n",
    "\n",
    "aps = np.zeros((no_select_rats, L, ap_dm.shape[1]))\n",
    "all_aps = np.zeros((no_select_rats*L, ap_dm.shape[1]))\n",
    "cell_nos = np.ones(no_select_rats*L, dtype=np.int64)\n",
    "modified_AP_matrix = np.zeros((no_select_rats,L))\n",
    "\n",
    "rat_ap = []\n",
    "cell_ids = []\n",
    "\n",
    "# initial criteria values for the APs decision matrix \n",
    "      \n",
    "for i in range(no_select_rats):\n",
    "    rat = selected_rats[i]\n",
    "    selected_row = dm_df[dm_df.iloc[:, 0] == rat].iloc[0][7:].values.astype(np.float64)\n",
    "    rat_ap += [rat] * L\n",
    "    for j in range(L):\n",
    "        selected_row[2] = randomSINR(0,40) \n",
    "        aps[i, j] = selected_row\n",
    "        all_aps[j+i*L] = selected_row\n",
    "        cell_nos[j+i*L] = j+1\n",
    "        cell_ids.append(f'{rat} AP{j+1}')\n",
    "\n",
    "\n",
    "# Create a dictionary to store the number of users per RAT\n",
    "usersperrat = {rat: 0 for rat in selected_rats}\n",
    "\n",
    "# Create a matrix to store the AP scores after each user selection\n",
    "all_ap_scores = np.zeros((U, L*no_select_rats))\n",
    "ap_loads = np.zeros((U, L*no_select_rats),dtype=np.int64)\n",
    "load = 0\n",
    "\n",
    "for i in range(U):\n",
    "    normal_ap = maxminNormalize(all_aps, crit_types[6:])\n",
    "    weights = criticWeighting(normal_ap)\n",
    "    ap_scores = computerSAW(normal_ap,weights)\n",
    "    \n",
    "    all_ap_scores[i] = ap_scores\n",
    "    \n",
    "    df = pd.DataFrame(list(zip(cell_nos,ap_scores)),index=rat_ap)\n",
    "    ap_ranks = df.sort_values(by=1, ascending=False)\n",
    "\n",
    "    ap_loads[i] = all_aps[:,0]\n",
    "\n",
    "    for j in range(MC): # top C AP are selected\n",
    "        rat = ap_ranks.iloc[j].name\n",
    "        rat_index = np.where(selected_rats == rat)[0][0]\n",
    "        ap_index = ap_ranks.iloc[j].values[0].astype(np.int64) - 1\n",
    "\n",
    "        #update the load criteria\n",
    "        if(load < CC * L * no_select_rats):\n",
    "            if (all_aps[ap_index + rat_index*L][0] < CC):\n",
    "                all_aps[ap_index + rat_index*L][0] += 1 \n",
    "                load += 1\n",
    "            else:\n",
    "                # eliminate the AP from the selection\n",
    "                all_aps[ap_index + rat_index*L][1] = 0\n",
    "                all_aps[ap_index + rat_index*L][2] = 0\n",
    "                # add to the next best AP\n",
    "                if(j<MC):\n",
    "                    rat = ap_ranks.iloc[j+1].name\n",
    "                    rat_index = np.where(selected_rats == rat)[0][0]\n",
    "                    ap_index = ap_ranks.iloc[j+1].values[0].astype(np.int64) - 1\n",
    "                    \n",
    "                    if(all_aps[ap_index + rat_index*L][0] < CC):\n",
    "                        all_aps[ap_index + rat_index*L][0] += 1\n",
    "                        load += 1\n",
    "                    else:\n",
    "                        # eliminate the AP from the selection\n",
    "                        all_aps[ap_index + rat_index*L][1] = 0\n",
    "                        all_aps[ap_index + rat_index*L][2] = 0\n",
    "\n",
    "\n",
    "df_load = pd.DataFrame(ap_loads, columns=cell_ids)\n",
    "\n",
    "df_load.plot(kind='bar', stacked=True, figsize=(12, 8))\n",
    "plt.title(\"AP/Cell and RAT Load Distribution\\n\", fontsize=16)\n",
    "plt.ylabel('Load', fontsize=14)\n",
    "plt.xlabel('No. of users', fontsize=14)\n",
    "plt.yticks(range(0, sum(ap_loads[-1]) + 1), fontsize=12);\n",
    "plt.xticks(fontsize=12, rotation=0)\n",
    "plt.legend(fontsize=12)\n",
    "\n",
    "\n",
    "# Save the figure\n",
    "# filename = 'fig_06_load_distribution.png'\n",
    "# plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "\n",
    "# # Display the saved image\n",
    "# display(Image(filename=filename))\n",
    "plt.show()\n",
    "# Close the figure to free up memory\n",
    "plt.close()"
   ]
  }
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