diff --git a/_modules/network_gym_client/adapter.html b/_modules/network_gym_client/adapter.html index 3b5ccea..e6b29aa 100644 --- a/_modules/network_gym_client/adapter.html +++ b/_modules/network_gym_client/adapter.html @@ -319,12 +319,16 @@

Source code for network_gym_client.adapter

         Returns:
             dict : converted data with dictionary format
         """
-        df_cp = df.copy()
-        df_cp['user'] = df_cp['user'].map(lambda u: f'UE{u}_'+description)
-        # Set the index to the 'user' column
-        df_cp = df_cp.set_index('user')
-        # Convert the DataFrame to a dictionary
-        data = df_cp['value'].to_dict()
+        if df is None:
+            return {}
+        get_key = lambda u: f'UE_{u}_'+description
+        dict_key = list(map(get_key, df['user']))
+        dict_value = df['value']
+
+        #print(dict_key)
+        #print(dict_value)
+
+        data = dict(zip(dict_key, dict_value))
         return data
 
     def fill_empty_feature(self, feature, value):
@@ -340,18 +344,34 @@ 
Source code for network_gym_client.adapter
         
         #Fill the missing data with the input value.
         
-        if len(feature) > self.config_json['env_config']['num_users']:
+        if feature is None:
+            print("[WARNING] all users of a feature returns empty measurement.")
+            emptyFeatureArray = np.empty([self.config_json['env_config']['num_users'],], dtype=int)
+            emptyFeatureArray.fill(value)
+            return emptyFeatureArray
+
+        if len(feature['user']) > self.config_json['env_config']['num_users']:
             print ("[WARNING] This feature has more user than input!!")
-            print(feature)
-        elif len(feature) == self.config_json['env_config']['num_users']:
+            print (feature)
+            emptyFeatureArray = np.empty([self.config_json['env_config']['num_users'],], dtype=int)
+            emptyFeatureArray.fill(value)
+            return emptyFeatureArray
+        elif len(feature['user']) == self.config_json['env_config']['num_users']:
             # measurement size match the user number
-            return feature[:]["value"]
-        if len(feature)> 0:
+            return feature["value"]
+        elif len(feature['user'])> 0:
             # some of the user's data are missing, fill with input value.
             print("[WARNING] some users of a feature returns empty measurement.")
-            feature = feature.set_index("user")
-            feature = feature.reindex(list(range(self.config_json['env_config']['num_users'])),fill_value=value)
-            data = feature[:]["value"]
+            #feature = feature.set_index("user")
+            #feature = feature.reindex(list(range(self.config_json['env_config']['num_users'])),fill_value=value)
+            #data = feature[:]["value"]
+
+            data = np.empty([self.config_json['env_config']['num_users'],], dtype=int)
+            data.fill(value)
+
+            for index, user_id in enumerate(feature['user']):
+                data[user_id] = feature['value'][index]
+            #print(data)
             return data
         else:
             # all user's data are missing, fill the entire feature will input value.
@@ -359,7 +379,7 @@ 
Source code for network_gym_client.adapter
             emptyFeatureArray = np.empty([self.config_json['env_config']['num_users'],], dtype=int)
             emptyFeatureArray.fill(value)
             return emptyFeatureArray
-    def get_nested_json_policy (self, action_name, tags, action, user_name='user'):
+    def get_nested_json_policy (self, action_name, tags, action, index_name='user'):
         """Convert the gymnasium action space to nested json format
 
         Args:
@@ -371,24 +391,12 @@ 
Source code for network_gym_client.adapter
             json: a nested json policy for the network
         """
 
-        data = []
-        for user_id in range(len(action)):
-            data.append([user_id, action[user_id]])
-        df = pd.DataFrame(data, columns=[user_name, 'value'])
-        for key, value in reversed(tags.items()):
-            df.insert(0, key, value)
-        df.insert(0,'name', action_name)# the name of your action.
-
-        # the following code tranform the json to a nested structure for lower overhead.
-        group_by_list = list(tags.keys())
-        group_by_list.insert(0, 'name')
-        json_data = (df.groupby(group_by_list)
-                .apply(lambda x: x[[user_name, 'value', ]].to_dict(orient='list'))
-                .reset_index()
-                .rename(columns={0: ''})
-                .to_json(orient='records'))
-
-        policy = json.loads(json_data)
+        policy = tags.copy()
+        policy['name'] = action_name
+        policy['']={}
+        policy[''][index_name] = list(range(len(action)))
+        policy['']['value'] = action.tolist()
+        #print(policy)
         return policy

 

         
diff --git a/_modules/network_gym_client/env.html b/_modules/network_gym_client/env.html
index 217c502..d635978 100644
--- a/_modules/network_gym_client/env.html
+++ b/_modules/network_gym_client/env.html
@@ -333,12 +333,18 @@ 
Source code for network_gym_client.env
 
 
         self.steps_per_episode = int(config_json['env_config']['steps_per_episode'])
+        if self.steps_per_episode < 2:
+            sys.exit('In crease the "steps_per_episode", the min value is 2!')
         self.episodes_per_session = int(config_json['env_config']['episodes_per_session'])
 
-        step_length = config_json['env_config']['measurement_interval_ms'] + config_json['env_config']['measurement_guard_interval_ms']
+        step_length = config_json['env_config']['measurement_interval_ms']
+        if 'measurement_guard_interval_ms' in config_json['env_config']:
+            step_length = step_length + config_json['env_config']['measurement_guard_interval_ms']
+
+
         # compute the simulation time based on setting
-        config_json['env_config']['simulation_time_s'] = int((config_json['env_config']['app_and_measurement_start_time_ms'] + step_length * self.steps_per_episode * self.episodes_per_session)/1000)
-        print("Environment duration: " + str(config_json['env_config']['simulation_time_s']) + "s")
+        config_json['env_config']['env_end_time_ms'] = int(config_json['env_config']['measurement_start_time_ms'] + step_length * (self.steps_per_episode) * self.episodes_per_session)
+        print("Environment duration: " + str(config_json['env_config']['env_end_time_ms']) + " ms")
         #Define config params
         module_path = 'network_gym_client.envs.'+config_json['env_config']['env']+'.adapter'
         module = importlib.import_module(module_path, package=None)
@@ -352,7 +358,6 @@ 
Source code for network_gym_client.env
         self.observation_space = self.adapter.get_observation_space()
 
         self.northbound_interface_client = NorthBoundClient(id, config_json) #initial northbound_interface_client
-        #self.max_counter = int(config_json['env_config']['simulation_time_s'] * 1000/config_json['env_config']['GMA']['measurement_interval_ms'])# Already checked the interval for Wi-Fi and LTE in the main file
 
         #self.link_type = config_json['rl_config']['link_type'] 
         self.current_step = 0
diff --git a/_modules/network_gym_client/envs/network_slicing/adapter.html b/_modules/network_gym_client/envs/network_slicing/adapter.html
index ca2881f..49f73dd 100644
--- a/_modules/network_gym_client/envs/network_slicing/adapter.html
+++ b/_modules/network_gym_client/envs/network_slicing/adapter.html
@@ -251,6 +251,7 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
import sys
 from gymnasium import spaces
 import numpy as np
+from pathlib import Path
 
 
[docs]class Adapter(network_gym_client.adapter.Adapter):
     """network_slicing environment adapter.
@@ -265,7 +266,7 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
            config_json (json): the configuration file
         """
         super().__init__(config_json)
-        self.env = "network_slicing"
+        self.env = Path(__file__).resolve().parent.name
         self.num_slices = len(self.config_json['env_config']['slice_list'])
         self.num_features = 3
         self.end_ts = 0
@@ -279,7 +280,7 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
self.rbg_num = self.config_json['env_config']['LTE']['resource_block_num']/rbg_size
 
         if config_json['env_config']['env'] != self.env:
-            sys.exit("[ERROR] wrong environment helper. config file environment: " + str(config_json['env_config']['env']) + " helper environment: " + str(self.env))
+            sys.exit("[ERROR] wrong environment Adapter. Configured environment: " + str(config_json['env_config']['env']) + " != Launched environment: " + str(self.env))
 
     def get_action_space(self):
         """Get action space for network_slicing env.
@@ -315,26 +316,43 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
        Returns:
             spaces: observation spaces
         """
-        print (df)
+        #print (df)
         if not df.empty:
             self.end_ts = int(df['end_ts'][0])
         #data_recv_flat = df.explode(column=['user', 'value'])
         #print(data_recv_flat)
 
-        df_rate = df[df['name'] == 'rate'].reset_index(drop=True) # get the rate
-        df_rate = df_rate[df_rate['cid'] == 'All'].reset_index(drop=True).explode(column=['user', 'value']) #keep the flow rate.
+        df_rate = None
+        df_phy_wifi_max_rate = None
+        df_phy_lte_max_rate = None
+        df_phy_lte_slice_id = None
+        df_phy_lte_rb_usage = None
+        df_x_loc = None
+        df_y_loc = None
+        for index, row in df.iterrows():
+            if row['name'] == 'rate':
+                if row['cid'] == 'All':
+                    df_rate = row
+            elif row['name'] == 'max_rate':
+                if row['cid'] == 'LTE':
+                    df_phy_lte_max_rate = row
+                elif row['cid'] == 'Wi-Fi':
+                    df_phy_wifi_max_rate = row
+            elif row['name'] == 'slice_id':
+                df_phy_lte_slice_id = row
+            elif row['name'] == 'rb_usage':
+                df_phy_lte_rb_usage = row
+            elif row['name'] == 'x_loc':
+                df_x_loc = row
+            elif row['name'] == 'y_loc':
+                df_y_loc = row
         #print(df_rate)
-
-        df_max_rate = df[df['name'] == 'max_rate'].reset_index(drop=True)
-        df_phy_lte_max_rate = df_max_rate[df_max_rate['cid'] == 'LTE'].reset_index(drop=True).explode(column=['user', 'value']) #get the LTE max_rate
-        df_phy_wifi_max_rate = df_max_rate[df_max_rate['cid'] == 'Wi-Fi'].reset_index(drop=True).explode(column=['user', 'value']) # get the Wi-Fi max rate
-
         #print(df_phy_lte_max_rate)
         #print(df_phy_wifi_max_rate)
-
-        df_phy_lte_slice_id = df[df['name'] == 'slice_id'].reset_index(drop=True).explode(column=['user', 'value'])
-
-        df_phy_lte_rb_usage = df[df['name'] == 'rb_usage'].reset_index(drop=True).explode(column=['user', 'value'])
+        #print(df_phy_lte_slice_id)
+        #Print(df_phy_lte_rb_usage)
+        #print(df_x_loc)
+        #Print(df_y_loc)
 
         # if not empty and send to wanDB database
         self.wandb_log_buffer_append(self.df_to_dict(df_phy_wifi_max_rate, "max-wifi-rate"))
@@ -342,17 +360,15 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
self.wandb_log_buffer_append(self.df_to_dict(df_phy_lte_max_rate, "max-lte-rate"))
 
         dict_rate = self.df_to_dict(df_rate, 'rate')
-        dict_rate["sum_rate"] = df_rate[:]["value"].sum()
+        dict_rate["sum_rate"] = sum(df_rate["value"])
         self.wandb_log_buffer_append(dict_rate)
 
         self.wandb_log_buffer_append(self.df_to_dict(df_phy_lte_slice_id, "lte-slice-id"))
 
         self.wandb_log_buffer_append(self.df_to_dict(df_phy_lte_rb_usage, "lte-rb-usage"))
 
-        df_x_loc = df[df['name'] == 'x_loc'].reset_index(drop=True).explode(column=['user', 'value'])
         self.wandb_log_buffer_append(self.df_to_dict(df_x_loc, "x_loc"))
 
-        df_y_loc = df[df['name'] == 'y_loc'].reset_index(drop=True).explode(column=['user', 'value'])
         self.wandb_log_buffer_append(self.df_to_dict(df_y_loc, "y_loc"))
         
         # Fill the empy features with -1
@@ -400,7 +416,7 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
tags["rb_type"] = "S"# shared RBG
         policy3 = self.get_nested_json_policy('rb_allocation', tags, np.ones(len(scaled_action))*self.rbg_num, 'slice')
 
-        policy = policy1 + policy2 + policy3
+        policy = [policy1, policy2, policy3]
 
         print('Action --> ' + str(policy))
         return policy
@@ -415,11 +431,21 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
            spaces: reward spaces
         """
 
-        df_tx_rate = df[df['name'] == 'tx_rate'].reset_index(drop=True).explode(column=['user', 'value']) # get the rate
+        df_tx_rate = None
+        df_phy_lte_slice_id = None
+        df_phy_lte_rb_usage = None
 
-        df_phy_lte_rb_usage = df[df['name'] == 'rb_usage'].reset_index(drop=True).explode(column=['user', 'value'])
+        for index, row in df.iterrows():
+            if row['name'] == 'tx_rate':
+                df_tx_rate = row.to_frame().T.explode(column=['user', 'value'])
+            elif row['name'] == 'slice_id':
+                df_phy_lte_slice_id = row.to_frame().T.explode(column=['user', 'value'])
+            elif row['name'] == 'rb_usage':
+                df_phy_lte_rb_usage = row.to_frame().T.explode(column=['user', 'value'])
 
-        df_phy_lte_slice_id = df[df['name'] == 'slice_id'].reset_index(drop=True).explode(column=['user', 'value'])
+        #print(df_tx_rate)
+        #print(df_phy_lte_slice_id)
+        #Print(df_phy_lte_rb_usage)
 
         user_to_slice_id = np.zeros(len(df_phy_lte_slice_id))
         df_phy_lte_slice_id = df_phy_lte_slice_id.reset_index()  # make sure indexes pair with number of rows
@@ -436,7 +462,7 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
df_slice_tx_rate = df_slice_tx_rate.drop(columns=['value'])
 
         #print (df_tx_rate)
-        print (df_slice_tx_rate)
+        #print (df_slice_tx_rate)
 
         df_phy_lte_rb_usage['slice_id']=user_to_slice_id[df_phy_lte_rb_usage['user'].astype(int)]
         df_phy_lte_rb_usage['slice_value']= df_phy_lte_rb_usage.groupby(['slice_id'])['value'].transform('sum')
@@ -446,7 +472,7 @@ 
Source code for network_gym_client.envs.network_slicing.adapter
df_slice_lte_rb_usage = df_slice_lte_rb_usage.drop(columns=['value'])
 
         #print (df_phy_lte_rb_usage)
-        print (df_slice_lte_rb_usage)
+        #print (df_slice_lte_rb_usage)
 
         
         df_slice_tx_rate = self.slice_df_to_dict(df_slice_tx_rate, 'tx_rate')
diff --git a/_modules/network_gym_client/envs/nqos_split/adapter.html b/_modules/network_gym_client/envs/nqos_split/adapter.html
index b6ccd65..b5f0d90 100644
--- a/_modules/network_gym_client/envs/nqos_split/adapter.html
+++ b/_modules/network_gym_client/envs/nqos_split/adapter.html
@@ -252,6 +252,10 @@ 
Source code for network_gym_client.envs.nqos_split.adapter
from gymnasium import spaces
 import numpy as np
 import math
+import time
+import pandas as pd
+import json
+from pathlib import Path
 
 
[docs]class Adapter(network_gym_client.adapter.Adapter):
     """nqos_split env adapter.
@@ -268,14 +272,14 @@ 
Source code for network_gym_client.envs.nqos_split.adapter
super().__init__(config_json)
 
-        self.env = "nqos_split"
+        self.env = Path(__file__).resolve().parent.name
         self.action_max_value = 32
         self.num_features = 3
         self.num_users = int(self.config_json['env_config']['num_users'])
         self.end_ts = 0
 
         if config_json['env_config']['env'] != self.env:
-            sys.exit("[ERROR] wrong environment helper. config file environment: " + str(config_json['env_config']['env']) + " helper environment: " + str(self.env))
+            sys.exit("[ERROR] wrong environment Adapter. Configured environment: " + str(config_json['env_config']['env']) + " != Launched environment: " + str(self.env))
 
     def get_action_space(self):
         """Get action space for the nqos_split env.
@@ -308,26 +312,41 @@ 
Source code for network_gym_client.envs.nqos_split.adapter
        Returns:
             spaces: observation spaces
         """
-        print (df)
+        #print (df)
         if not df.empty:
             self.end_ts = int(df['end_ts'][0])
         #data_recv_flat = df.explode(column=['user', 'value'])
         #print(data_recv_flat)
 
-        df_rate = df[df['name'] == 'rate'].reset_index(drop=True) # get the rate
-        df_rate = df_rate[df_rate['cid'] == 'All'].reset_index(drop=True).explode(column=['user', 'value']) #keep the flow rate.
-        #print(df_rate)
-
-        df_max_rate = df[df['name'] == 'max_rate'].reset_index(drop=True)
-        df_phy_lte_max_rate = df_max_rate[df_max_rate['cid'] == 'LTE'].reset_index(drop=True).explode(column=['user', 'value']) #get the LTE max_rate
-        df_phy_wifi_max_rate = df_max_rate[df_max_rate['cid'] == 'Wi-Fi'].reset_index(drop=True).explode(column=['user', 'value']) # get the Wi-Fi max rate
+        df_rate = None
+        df_phy_wifi_max_rate = None
+        df_phy_lte_max_rate = None
+        df_wifi_split_ratio = None
+        df_x_loc = None
+        df_y_loc = None
+        for index, row in df.iterrows():
+            if row['name'] == 'rate':
+                if row['cid'] == 'All':
+                    df_rate = row
+            elif row['name'] == 'max_rate':
+                if row['cid'] == 'LTE':
+                    df_phy_lte_max_rate = row
+                elif row['cid'] == 'Wi-Fi':
+                    df_phy_wifi_max_rate = row
+            elif row['name'] == 'split_ratio':
+                if row['cid'] == 'Wi-Fi':
+                    df_wifi_split_ratio = row
+            elif row['name'] == 'x_loc':
+                df_x_loc = row
+            elif row['name'] == 'y_loc':
+                df_y_loc = row
 
+        #print(df_rate)
         #print(df_phy_lte_max_rate)
         #print(df_phy_wifi_max_rate)
-
-        df_split_ratio = df[df['name'] == 'split_ratio'].reset_index(drop=True)
-        df_wifi_split_ratio = df_split_ratio[df_split_ratio['cid'] == 'Wi-Fi'].reset_index(drop=True).explode(column=['user', 'value']) # get the Wi-Fi split ratio
         #print(df_wifi_split_ratio)
+        #print(df_x_loc)
+        #Print(df_y_loc)
 
         # if not empty and send to wanDB database
         self.wandb_log_buffer_append(self.df_to_dict(df_phy_wifi_max_rate, "max-wifi-rate"))
@@ -335,15 +354,13 @@ 
Source code for network_gym_client.envs.nqos_split.adapter
self.wandb_log_buffer_append(self.df_to_dict(df_phy_lte_max_rate, "max-lte-rate"))
 
         dict_rate = self.df_to_dict(df_rate, 'rate')
-        dict_rate["sum_rate"] = df_rate[:]["value"].sum()
+        dict_rate["sum_rate"] = sum(df_rate["value"])
         self.wandb_log_buffer_append(dict_rate)
 
         self.wandb_log_buffer_append(self.df_to_dict(df_wifi_split_ratio, "wifi-split-ratio")) 
         
-        df_x_loc = df[df['name'] == 'x_loc'].reset_index(drop=True).explode(column=['user', 'value'])
         self.wandb_log_buffer_append(self.df_to_dict(df_x_loc, "x_loc"))
 
-        df_y_loc = df[df['name'] == 'y_loc'].reset_index(drop=True).explode(column=['user', 'value'])
         self.wandb_log_buffer_append(self.df_to_dict(df_y_loc, "y_loc"))
         
         # Fill the empy features with -1
@@ -389,7 +406,7 @@ 
Source code for network_gym_client.envs.nqos_split.adapter
tags["cid"] = 'LTE'
         policy2 = self.get_nested_json_policy('split_ratio', tags, (self.action_max_value-scaled_action))
 
-        policy = policy1 + policy2
+        policy = [policy1, policy2]
         print('Action --> ' + str(policy))
         return policy
 
@@ -402,17 +419,21 @@ 
Source code for network_gym_client.envs.nqos_split.adapter
        Returns:
             spaces: reward spaces
         """
-        
-        df_owd = df[df['name'] == 'owd'].reset_index(drop=True) # get the owd
-        df_owd = df_owd[df_owd['cid'] == 'All'].reset_index(drop=True).explode(column=['user', 'value']) #keep the flow owd.
-
-        df_rate = df[df['name'] == 'rate'].reset_index(drop=True) # get the rate
-        df_rate = df_rate[df_rate['cid'] == 'All'].reset_index(drop=True).explode(column=['user', 'value']) #keep the flow rate.
+        df_owd = None
+        df_rate = None
+
+        for index, row in df.iterrows():
+            if row['name'] == 'rate':
+                if row['cid'] == 'All':
+                    df_rate = row
+            elif row['name'] == 'owd':
+                if row['cid'] == 'All':
+                    df_owd = row
+    
+        ave_rate = np.mean(df_rate["value"])
+        avg_delay = np.mean(df_owd["value"])
+        max_delay = np.max(df_owd["value"])
 
-        ave_rate = df_rate["value"].mean()
-        avg_delay = df_owd["value"].mean()
-        max_delay = df_owd["value"].max()
-       
         reward = 0
         if self.config_json["rl_config"]["reward_type"] == "utility":
             reward = self.netowrk_util(ave_rate, avg_delay)
diff --git a/_modules/network_gym_client/envs/qos_steer/adapter.html b/_modules/network_gym_client/envs/qos_steer/adapter.html
index 7becc61..4b07cf5 100644
--- a/_modules/network_gym_client/envs/qos_steer/adapter.html
+++ b/_modules/network_gym_client/envs/qos_steer/adapter.html
@@ -251,6 +251,7 @@ 
Source code for network_gym_client.envs.qos_steer.adapter
import sys
 from gymnasium import spaces
 import numpy as np
+from pathlib import Path
 
 
[docs]class Adapter(network_gym_client.adapter.Adapter):
     """qos_steer environment adapter.
@@ -266,12 +267,12 @@ 
Source code for network_gym_client.envs.qos_steer.adapter
        """
 
         super().__init__(config_json)
-        self.env = "qos_steer"
+        self.env = Path(__file__).resolve().parent.name
         self.num_features = 3
         self.num_users = int(self.config_json['env_config']['num_users'])
         self.end_ts = 0
         if config_json['env_config']['env'] != self.env:
-            sys.exit("[ERROR] wrong environment helper. config file environment: " + str(config_json['env_config']['env']) + " helper environment: " + str(self.env))
+            sys.exit("[ERROR] wrong environment Adapter. Configured environment: " + str(config_json['env_config']['env']) + " != Launched environment: " + str(self.env))
 
     def get_action_space (self):
         """Get action space for qos_steer env.
@@ -306,26 +307,41 @@ 
Source code for network_gym_client.envs.qos_steer.adapter
        Returns:
             spaces: observation spaces
         """
-        print (df)
+        #print (df)
         if not df.empty:
             self.end_ts = int(df['end_ts'][0])
         #data_recv_flat = df.explode(column=['user', 'value'])
         #print(data_recv_flat)
 
-        df_rate = df[df['name'] == 'rate'].reset_index(drop=True) # get the rate
-        df_rate = df_rate[df_rate['cid'] == 'All'].reset_index(drop=True).explode(column=['user', 'value']) #keep the flow rate.
-        #print(df_rate)
-
-        df_max_rate = df[df['name'] == 'max_rate'].reset_index(drop=True)
-        df_phy_lte_max_rate = df_max_rate[df_max_rate['cid'] == 'LTE'].reset_index(drop=True).explode(column=['user', 'value']) #get the LTE max_rate
-        df_phy_wifi_max_rate = df_max_rate[df_max_rate['cid'] == 'Wi-Fi'].reset_index(drop=True).explode(column=['user', 'value']) # get the Wi-Fi max rate
+        df_rate = None
+        df_phy_wifi_max_rate = None
+        df_phy_lte_max_rate = None
+        df_wifi_split_ratio = None
+        df_x_loc = None
+        df_y_loc = None
+        for index, row in df.iterrows():
+            if row['name'] == 'rate':
+                if row['cid'] == 'All':
+                    df_rate = row
+            elif row['name'] == 'max_rate':
+                if row['cid'] == 'LTE':
+                    df_phy_lte_max_rate = row
+                elif row['cid'] == 'Wi-Fi':
+                    df_phy_wifi_max_rate = row
+            elif row['name'] == 'split_ratio':
+                if row['cid'] == 'Wi-Fi':
+                    df_wifi_split_ratio = row
+            elif row['name'] == 'x_loc':
+                df_x_loc = row
+            elif row['name'] == 'y_loc':
+                df_y_loc = row
 
+        #print(df_rate)
         #print(df_phy_lte_max_rate)
         #print(df_phy_wifi_max_rate)
-
-        df_split_ratio = df[df['name'] == 'split_ratio'].reset_index(drop=True)
-        df_wifi_split_ratio = df_split_ratio[df_split_ratio['cid'] == 'Wi-Fi'].reset_index(drop=True).explode(column=['user', 'value']) # get the Wi-Fi split ratio
         #print(df_wifi_split_ratio)
+        #print(df_x_loc)
+        #Print(df_y_loc)
 
         # if not empty and send to wanDB database
         self.wandb_log_buffer_append(self.df_to_dict(df_phy_wifi_max_rate, "max-wifi-rate"))
@@ -333,15 +349,13 @@ 
Source code for network_gym_client.envs.qos_steer.adapter
self.wandb_log_buffer_append(self.df_to_dict(df_phy_lte_max_rate, "max-lte-rate"))
 
         dict_rate = self.df_to_dict(df_rate, 'rate')
-        dict_rate["sum_rate"] = df_rate[:]["value"].sum()
+        dict_rate["sum_rate"] = sum(df_rate["value"])
         self.wandb_log_buffer_append(dict_rate)
 
         self.wandb_log_buffer_append(self.df_to_dict(df_wifi_split_ratio, "wifi-split-ratio")) 
 
-        df_x_loc = df[df['name'] == 'x_loc'].reset_index(drop=True).explode(column=['user', 'value'])
         self.wandb_log_buffer_append(self.df_to_dict(df_x_loc, "x_loc"))
 
-        df_y_loc = df[df['name'] == 'y_loc'].reset_index(drop=True).explode(column=['user', 'value'])
         self.wandb_log_buffer_append(self.df_to_dict(df_y_loc, "y_loc"))
         
         # Fill the empy features with -1
@@ -383,7 +397,7 @@ 
Source code for network_gym_client.envs.qos_steer.adapter
tags["cid"] = 'LTE'
         policy2 = self.get_nested_json_policy('split_ratio', tags, (1-action))
 
-        policy = policy1 + policy2
+        policy = [policy1, policy2]
         print('Action --> ' + str(policy))
         return policy
 
@@ -396,8 +410,12 @@ 
Source code for network_gym_client.envs.qos_steer.adapter
        Returns:
             spaces: reward space
         """
-        df_qos_rate = df[df['name'] == 'qos_rate'].reset_index(drop=True)
-        df_wifi_qos_rate = df_qos_rate[df_qos_rate['cid'] == 'Wi-Fi'].reset_index(drop=True).explode(column=['user', 'value']) # get the Wi-Fi qos_rate
+
+        df_wifi_qos_rate = None
+        for index, row in df.iterrows():
+            if row['name'] == 'qos_rate':
+                if row['cid'] == 'Wi-Fi':
+                    df_wifi_qos_rate = row
         #print (df_wifi_qos_rate)
 
         reward = 0
@@ -421,7 +439,10 @@ 
Source code for network_gym_client.envs.qos_steer.adapter
            double: reward
         """
         #print(qos_rate)
-        reward = np.sum(qos_rate>0.1) #we assume the min qos rate is 0.1 mbps
+        reward = 0
+        for r in qos_rate:
+            if r > 0.1: #we assume the min qos rate is 0.1 mbps
+                reward = reward + 1
         return reward

 

         
diff --git a/_modules/network_gym_client/northbound_interface.html b/_modules/network_gym_client/northbound_interface.html
index 1ed4d75..1794f77 100644
--- a/_modules/network_gym_client/northbound_interface.html
+++ b/_modules/network_gym_client/northbound_interface.html
@@ -360,7 +360,13 @@ 
Source code for network_gym_client.northbound_interface
        Returns:
             pd.DataFrame: the processed network stats measurement
         """
-        network_stats = pd.json_normalize(reply_json['metric_list']) 
+        network_stats = pd.json_normalize(reply_json['network_stats']) 
+        if "workload_stats" in reply_json:
+            # workload measurement available
+            print('Env Measurement --> ' + str(reply_json['workload_stats']))
+            #if "sim_time_lapse_ms" and "time_lapse_ms" in reply_json['workload_stats']:
+            #    if reply_json['workload_stats']['time_lapse_ms']>0:
+            #        print('Env Measurement --> Percentage of time spend on simulation: ' + str(int(100*reply_json['workload_stats']['sim_time_lapse_ms']/reply_json['workload_stats']['time_lapse_ms'])) + '%')
         return network_stats

 

         
diff --git a/_modules/network_gym_env/configure.html b/_modules/network_gym_env/configure.html
index 0657f18..1ad2e4c 100644
--- a/_modules/network_gym_env/configure.html
+++ b/_modules/network_gym_env/configure.html
@@ -251,6 +251,7 @@ 
Source code for network_gym_env.configure
 import json
 import traceback
 import pathlib
+import socket
 FILE_PATH = pathlib.Path(__file__).parent
 from network_gym_env.southbound_interface import *
 
@@ -302,7 +303,7 @@ 
Source code for network_gym_env.configure
         #common_config.json is shared by all environments
         f = open(FILE_PATH / 'common_config.json')
         self.config_json = json.load(f)
-        self.identity = u'%s-%d' % (self.config_json["session_name"], id)
+        self.identity = u'%s-%d-%s' % (self.config_json["session_name"], id, socket.gethostname())
         self.env_list = env_list
         self.NetworkGymSim = NetworkGymSim
 
diff --git a/_modules/network_gym_env/dummy_sim.html b/_modules/network_gym_env/dummy_sim.html
index 627963f..ff0fbd7 100644
--- a/_modules/network_gym_env/dummy_sim.html
+++ b/_modules/network_gym_env/dummy_sim.html
@@ -289,9 +289,9 @@ 
Source code for network_gym_env.dummy_sim
         """
         # use the config_json to config the simulator
         self.interval = msg_json['measurement_interval_ms'] + msg_json['measurement_guard_interval_ms'] # measurement interval
-        self.start_ts = msg_json['app_and_measurement_start_time_ms'] # start timestamp of a measurement
+        self.start_ts = msg_json['measurement_start_time_ms'] # start timestamp of a measurement
         self.end_ts = self.start_ts + self.interval # end timestamp of a measurement
-        self.sim_end_ts = self.start_ts + msg_json['simulation_time_s']*1000
+        self.sim_end_ts = self.start_ts + msg_json['env_end_time_ms']
         self.num_users = msg_json['num_users']
         self.start_simulation(env_identity, config_json, client_identity)
 
@@ -314,7 +314,7 @@ 
Source code for network_gym_env.dummy_sim
         # running simualtor
         while True:
             dummy_report = json.loads('{"type":"env-measurement"}')
-            dummy_report["metric_list"] = self.run_one_interval()
+            dummy_report["network_stats"] = self.run_one_interval()
 
             # the first part of the msg is the client_identity, the second part is the measurement report.
             msg = [client_identity.encode('utf-8'), json.dumps(dummy_report, indent=2).encode('utf-8')]
diff --git a/_sources/environments/mx_network_slicing/cellular_network_slicing.md.txt b/_sources/environments/mx_network_slicing/cellular_network_slicing.md.txt
index 31531cf..2d33246 100644
--- a/_sources/environments/mx_network_slicing/cellular_network_slicing.md.txt
+++ b/_sources/environments/mx_network_slicing/cellular_network_slicing.md.txt
@@ -18,7 +18,7 @@ Within each slice, the agent can designate resources as dedicated, prioritized,
 | ----- | ---- |
 | Observation Space |  `Box(0.0, Inf, (5, N,), float32)` |
 | Action Space | `Box(0.0, 1.0, (N,), float32)`  |
-| Arguments | [config.json](https://github.com/IntelLabs/networkgym/network_gym_client/envs/network_slicing/config.json)  |
+| Arguments | [config.json](https://github.com/IntelLabs/networkgym/blob/main/network_gym_client/envs/network_slicing/config.json)  |
 | Select Environment | `config_json = load_config_file('network_slicing')` 
 `env = NetworkGymEnv(client_id, config_json)`  |
 
 ## Description
@@ -50,7 +50,7 @@ The observation space is represented by a multidimensional array (`ndarray`) wit
 ```{eval-rst}
 The Observation Space can be customized in the :meth:`network_gym_client.envs.network_slicing.Adapter.get_observation` function.
 ```
-See [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for more details.
+Refer to [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for further elaboration, along with a list of network statistics measurements.
 ````
 
 ```{tip}
@@ -91,7 +91,7 @@ In this formulation, we can adjust the emphasis given to the delay violation rat
 ```{eval-rst}
 The Reward can be customized in the :meth:`network_gym_client.envs.network_slicing.Adapter.get_reward` function.
 ```
-See [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for more details.
+Refer to [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for further elaboration, along with a list of network statistics measurements.
 ````
 
 ## Arguments
diff --git a/_sources/environments/mx_traffic_management/mx_qos_traffic_steering.md.txt b/_sources/environments/mx_traffic_management/mx_qos_traffic_steering.md.txt
index d214835..14875ca 100644
--- a/_sources/environments/mx_traffic_management/mx_qos_traffic_steering.md.txt
+++ b/_sources/environments/mx_traffic_management/mx_qos_traffic_steering.md.txt
@@ -17,7 +17,7 @@ In this environment, the agent performs periodic actions to select a link to ste
 | ----- | ---- |
 | Observation Space | `Box(0, Inf, (3, N,), float32)`  |
 | Action Space |  `MultiDiscrete([2, 2, ..., 2]) # N dimension`  |
-| Arguments | [config.json](https://github.com/IntelLabs/networkgym/network_gym_client/envs/qos_steer/config.json)  |
+| Arguments | [config.json](https://github.com/IntelLabs/networkgym/blob/main/network_gym_client/envs/qos_steer/config.json)  |
 | Select Environment | `config_json = load_config_file('qos_steer')` 
 `env = NetworkGymEnv(client_id, config_json)`  |
 
 ## Description
@@ -60,7 +60,7 @@ The observation is an `ndarray` with shape `(3,N,)` representing three features
 ```{eval-rst}
 The Observation Space can be customized in the :meth:`network_gym_client.envs.qos_steer.Adapter.get_observation` function.
 ```
-See [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for more details.
+Refer to [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for further elaboration, along with a list of network statistics measurements.
 ````
 
 ```{tip}
@@ -90,7 +90,7 @@ We computes the number of Quality of Service (QoS) users over Wi-Fi as reward fu
 ```{eval-rst}
 The Reward can be customized in the :meth:`network_gym_client.envs.qos_steer.Adapter.get_reward` function.
 ```
-See [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for more details.
+Refer to [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for further elaboration, along with a list of network statistics measurements.
 ````
 
 ## Arguments
diff --git a/_sources/environments/mx_traffic_management/mx_traffic_splitting.md.txt b/_sources/environments/mx_traffic_management/mx_traffic_splitting.md.txt
index 4b06422..e9b07fa 100644
--- a/_sources/environments/mx_traffic_management/mx_traffic_splitting.md.txt
+++ b/_sources/environments/mx_traffic_management/mx_traffic_splitting.md.txt
@@ -17,12 +17,12 @@ In this environment, the agent performs periodic actions to update the traffic s
 | ----- | ---- |
 | Observation Space | `Box(0, Inf, (3, N,), float32)`  |
 | Action Space |  `Box(0.0, 1.0, (N,), float32)`  |
-| Arguments | [config.json](https://github.com/IntelLabs/networkgym/network_gym_client/envs/nqos_split/config.json)  |
+| Arguments | [config.json](https://github.com/IntelLabs/networkgym/blob/main/network_gym_client/envs/nqos_split/config.json)  |
 | Select Environment | `config_json = load_config_file('nqos_split')` 
 `env = NetworkGymEnv(client_id, config_json)`  |
 
 ## Description
 
-The Multi-Access (MX) Traffic Splitting environment represents a traffic management problem where multiple users are randomly distributed on a 2D plane, with each user connecting to a Cellular base station and the closest Wi-Fi access point. The handover between Wi-Fi access point is disabled.
+The Multi-Access (MX) Traffic Splitting environment represents a traffic management problem where multiple users are randomly distributed on a 2D plane, with each user connecting to a Cellular base station and the closest Wi-Fi access point. The received signal-based handover between Wi-Fi access points can be enabled or disabled.
  The goal of traffic management is to strategically split traffic over both links, aiming to achieve high throughput and low latency.
 
 ## Prerequisite
@@ -45,7 +45,7 @@ The observation is an `ndarray` with shape `(3,N,)` representing three features
 ```{eval-rst}
 The Observation Space can be customized in the :meth:`network_gym_client.envs.nqos_split.Adapter.get_observation` function.
 ```
-See [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for more details.
+Refer to [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for further elaboration, along with a list of network statistics measurements.
 ````
 
 ```{tip}
@@ -83,7 +83,7 @@ The goal of the utility function is to maximize the throughput and minimizing de
 ```{eval-rst}
 The Reward can be customized in the :meth:`network_gym_client.envs.nqos_split.Adapter.get_reward` function.
 ```
-See [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for more details.
+Refer to [Customizing Observation and Reward](../../tutorials/customizing_observation_and_reward.md) for further elaboration, along with a list of network statistics measurements.
 ````
 
 ## Arguments
diff --git a/_sources/tutorials/customizing_observation_and_reward.md.txt b/_sources/tutorials/customizing_observation_and_reward.md.txt
index 7629804..07f4524 100644
--- a/_sources/tutorials/customizing_observation_and_reward.md.txt
+++ b/_sources/tutorials/customizing_observation_and_reward.md.txt
@@ -7,35 +7,37 @@ title: Customizing Observation and Reward
 Personalizing the observation and reward functions to match your experimental goals is achievable within the provided environments. Within the `Adapter` class for each environment, both the `get_observation` and `get_reward` functions take a network statistics measurement as input. An illustration of such a network statistics measurement is provided below:
 
 ```python
-      cid direction end_ts group             name start_ts          user                                              value
-0     LTE        DL   6000   GMA            ap_id     5900  [0, 1, 2, 3]                       [255.0, 255.0, 255.0, 255.0]
-1   Wi-Fi        DL   6000   GMA            ap_id     5900  [0, 1, 2, 3]                               [0.0, 0.0, 1.0, 1.0]
-2     All        DL   6000   GMA  delay_violation     5900  [0, 1, 2, 3]                       [100.0, 100.0, 100.0, 100.0]
-3     All        DL   6000   GMA          max_owd     5900  [0, 1, 2, 3]                       [335.0, 703.0, 422.0, 299.0]
-4     LTE        DL   6000   GMA          max_owd     5900  [0, 1, 2, 3]                       [335.0, 703.0, 422.0, 299.0]
-5   Wi-Fi        DL   6000   GMA          max_owd     5900  [0, 1, 2, 3]                               [0.0, 1.0, 1.0, 0.0]
-6     LTE        DL   6000   PHY         max_rate     5900  [0, 1, 2, 3]                           [35.0, 35.0, 22.0, 35.0]
-7   Wi-Fi        DL   6000   PHY         max_rate     5900  [0, 1, 2, 3]                           [78.0, 78.0, 65.0, 78.0]
-8     All        DL   6000   GMA   measurement_ok     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
-9     All        DL   6000   GMA    missed_action     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-10    All        DL   6000   GMA              owd     5900  [0, 1, 2, 3]                       [327.0, 680.0, 403.0, 279.0]
-11    LTE        DL   6000   GMA              owd     5900  [0, 1, 2, 3]                       [327.0, 680.0, 404.0, 280.0]
-12  Wi-Fi        DL   6000   GMA              owd     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-13    LTE        DL   6000   GMA      qos_marking     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 0.0]
-14  Wi-Fi        DL   6000   GMA      qos_marking     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 0.0]
-15    All        DL   6000   GMA         qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-16    LTE        DL   6000   GMA         qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-17  Wi-Fi        DL   6000   GMA         qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-18    All        DL   6000   GMA             rate     5900  [0, 1, 2, 3]                       [15.912, 9.056, 7.2, 14.984]
-19    LTE        DL   6000   GMA             rate     5900  [0, 1, 2, 3]                       [8.472, 8.472, 5.336, 8.472]
-20  Wi-Fi        DL   6000   GMA             rate     5900  [0, 1, 2, 3]                        [0.696, 4.64, 3.016, 4.992]
-21    LTE        DL   6000   GMA      split_ratio     5900  [0, 1, 2, 3]                           [31.0, 16.0, 19.0, 22.0]
-22  Wi-Fi        DL   6000   GMA      split_ratio     5900  [0, 1, 2, 3]                            [1.0, 16.0, 13.0, 10.0]
-23    LTE        DL   6000   GMA    traffic_ratio     5900  [0, 1, 2, 3]                           [92.0, 65.0, 64.0, 63.0]
-24  Wi-Fi        DL   6000   GMA    traffic_ratio     5900  [0, 1, 2, 3]                            [8.0, 35.0, 36.0, 37.0]
-25    All        DL   6000   GMA          tx_rate     5900  [0, 1, 2, 3]                     [17.075, 9.408, 7.434, 15.797]
-26    All        DL   6000   GMA            x_loc     5900  [0, 1, 2, 3]  [12.040140997999378, 6.710302486045565, 52.547...
-27    All        DL   6000   GMA            y_loc     5900  [0, 1, 2, 3]  [6.167369006263322, 2.450729590703729, 9.45877...
+      cid direction end_ts group                    name start_ts          user                                              value
+0   Wi-Fi        DL   6000   GMA                 cell_id     5900  [0, 1, 2, 3]                               [0.0, 0.0, 1.0, 1.0]
+1     LTE        DL   6000   PHY                 cell_id     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
+2     All        DL   6000   GMA  delay_test_1_violation     5900  [0, 1, 2, 3]             [100.0, 100.0, 72.99270072992701, 0.0]
+3     All        DL   6000   GMA  delay_test_2_violation     5900  [0, 1, 2, 3]                           [100.0, 100.0, 0.0, 0.0]
+4     All        DL   6000   GMA         delay_violation     5900  [0, 1, 2, 3]            [100.0, 100.0, 100.0, 89.8989898989899]
+5     All        DL   6000   GMA                 max_owd     5900  [0, 1, 2, 3]                       [499.0, 538.0, 239.0, 195.0]
+6     LTE        DL   6000   GMA                 max_owd     5900  [0, 1, 2, 3]                       [499.0, 538.0, 239.0, 195.0]
+7   Wi-Fi        DL   6000   GMA                 max_owd     5900  [0, 1, 2, 3]                               [3.0, 2.0, 3.0, 4.0]
+8     LTE        DL   6000   PHY                max_rate     5900  [0, 1, 2, 3]                           [35.0, 35.0, 22.0, 35.0]
+9   Wi-Fi        DL   6000   PHY                max_rate     5900  [0, 1, 2, 3]                           [78.0, 78.0, 65.0, 78.0]
+10    All        DL   6000   GMA          measurement_ok     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
+11    All        DL   6000   GMA           missed_action     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+12    All        DL   6000   GMA                     owd     5900  [0, 1, 2, 3]                       [484.0, 517.0, 214.0, 102.0]
+13    LTE        DL   6000   GMA                     owd     5900  [0, 1, 2, 3]                       [491.0, 517.0, 217.0, 145.0]
+14  Wi-Fi        DL   6000   GMA                     owd     5900  [0, 1, 2, 3]                               [0.0, 1.0, 0.0, 0.0]
+15    LTE        DL   6000   GMA                priority     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 0.0]
+16  Wi-Fi        DL   6000   GMA                priority     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
+17    All        DL   6000   GMA                qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+18    LTE        DL   6000   GMA                qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+19  Wi-Fi        DL   6000   GMA                qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+20    All        DL   6000   GMA                    rate     5900  [0, 1, 2, 3]                    [13.352, 9.056, 15.912, 45.992]
+21    LTE        DL   6000   GMA                    rate     5900  [0, 1, 2, 3]                        [8.472, 8.472, 5.336, 8.24]
+22  Wi-Fi        DL   6000   GMA                    rate     5900  [0, 1, 2, 3]                     [6.848, 0.928, 10.336, 15.912]
+23    LTE        DL   6000   GMA             split_ratio     5900  [0, 1, 2, 3]                             [17.0, 32.0, 9.0, 3.0]
+24  Wi-Fi        DL   6000   GMA             split_ratio     5900  [0, 1, 2, 3]                            [15.0, 0.0, 23.0, 29.0]
+25    LTE        DL   6000   GMA           traffic_ratio     5900  [0, 1, 2, 3]                           [55.0, 90.0, 34.0, 34.0]
+26  Wi-Fi        DL   6000   GMA           traffic_ratio     5900  [0, 1, 2, 3]                           [45.0, 10.0, 66.0, 66.0]
+27    All        DL   6000   GMA                 tx_rate     5900  [0, 1, 2, 3]                     [14.636, 9.757, 14.52, 38.681]
+28    All        DL   6000   GMA                   x_loc     5900  [0, 1, 2, 3]  [13.871206061072092, 6.702857783599929, 52.198...
+29    All        DL   6000   GMA                   y_loc     5900  [0, 1, 2, 3]  [7.964088643728281, 1.6756194439050196, 9.0212...
 ```
 
 ## Working with the Measurement
@@ -61,48 +63,39 @@ Now, we elaborate on each row:
 
 | Row | Name | Description |
 | ----- | ---- | ---- |
-| 0,1 | ap_id | LTE cell ID and Wi-Fi access point ID as measured by each user. *(NOTE: 255 is the default value, representing no measurement.)*|
-| 2 | delay_violation | One-way delay violation percentage (%) as measured by each user; delay bound (delay_bound_ms) can be configured in the JSON file. *(NOTE: Only for QoS use case)* |
-| 3,4,5 | max_owd | Maximum one-way delay measured by each user, involving LTE link, Wi-Fi link, and ALL. *(ALL: after reordering out-of-order packets from both links)*. |
-| 6,7 | max_rate | LTE/Wi-Fi link capacity as measured by each user. |
-| 8 | measurement_ok |	1: Measurement is valid; 0: An issue exists with the measurement.|
-| 9 | missed_action | Count of missed actions by the environment. *(NOTE: Applicable in testbed environment where RL agent might take too long to compute an action. Simulator waits for action by default in simulations, therefore should always be 0.)* |
-| 10,11,12 | owd | One-way delay measured by each user, involving LTE link, Wi-Fi link, and ALL. *(ALL: after reordering out-of-order packets from both links)*. |
-| 13,14 | qos_marking | 1: QoS user; 0: Non-QoS user.|
-| 15,16,17 | qos_rate | QoS delivery rate (output traffic throughput meeting QoS requirement) as measured by each user, comprising traffic over LTE link, Wi-Fi link, and ALL (combined). |
-| 18,19,20 | rate | Delivery rate (output traffic throughput) measured by each user, including traffic over LTE link, Wi-Fi link, and ALL (combined).|
-| 21,22 | split_ratio | Traffic split ratio (in range of [0, 32]) as measured by each user, covering LTE link, Wi-Fi link. LTE split ratio + Wi-Fi split ratio equals 32. |
-| 23,34 | traffic_ratio | Traffic ratio (%) sending to each link measured by each user, involving LTE link, Wi-Fi link. traffic_ratio might not equal split_ratio, since the system takes sine time to apply the new action. |
-| 25 | tx_rate | Load (input traffic throughput) measured by each user. |
-| 26 | x_loc | x coordinate per user. |
-| 27 | y_loc | y coordinate per user. |
+| 0,1 | cell_id | LTE cell ID and Wi-Fi access point ID as measured by each user. *(NOTE: 255 is the default value, representing no measurement.)*|
+| 2,3 | delay_test_*_violation | One-way delay violation percentage (%) *--for testing purpose--* as measured by each user; delay threshold (delay_test_*_thresh_ms) can be configured in the JSON file.|
+| 4 | delay_violation | One-way delay violation percentage (%) as measured by each user; delay bound (delay_bound_ms) can be configured in the JSON file. *(NOTE: Only for QoS use case)* |
+| 5,6,7 | max_owd | Maximum one-way delay measured by each user, involving LTE link, Wi-Fi link, and ALL. *(ALL: after reordering out-of-order packets from both links)*. |
+| 8,9 | max_rate | LTE/Wi-Fi link capacity as measured by each user. |
+| 10 | measurement_ok |	1: Measurement is valid; 0: An issue exists with the measurement.|
+| 11 | missed_action | Count of missed actions by the environment. *(NOTE: Applicable in testbed environment where RL agent might take too long to compute an action. Simulator waits for action by default in simulations, therefore should always be 0.)* |
+| 12,13,14 | owd | One-way delay measured by each user, involving LTE link, Wi-Fi link, and ALL. *(ALL: after reordering out-of-order packets from both links)*. |
+| 15,16 | priority | 1: high priority; 0: low priority. When Dynamic Flow Prioritization is enabled, for each cell, mark 70%~90% of traffic or users to high priority. |
+| 17,18,19 | qos_rate | QoS delivery rate (output traffic throughput meeting QoS requirement) as measured by each user, comprising traffic over LTE link, Wi-Fi link, and ALL (combined). |
+| 20,21,22 | rate | Delivery rate (output traffic throughput) measured by each user, including traffic over LTE link, Wi-Fi link, and ALL (combined).|
+| 23,24 | split_ratio | Traffic split ratio (in range of [0, 32]) as measured by each user, covering LTE link, Wi-Fi link. LTE split ratio + Wi-Fi split ratio equals 32. |
+| 25,26 | traffic_ratio | Traffic ratio (%) sending to each link measured by each user, involving LTE link, Wi-Fi link. traffic_ratio might not equal split_ratio, since the system takes sine time to apply the new action. |
+| 27 | tx_rate | Load (input traffic throughput) measured by each user. |
+| 28 | x_loc | x coordinate per user. |
+| 29 | y_loc | y coordinate per user. |
 
 
 ### Retrieving a Measurement
 
-To retrieve a specific network statistic, such as `rate`, you can utilize the following code:
+To retrieve a specific network statistic, such as `rate`, over a specifi link, such as `All`, you can utilize the following code:
 ```python
-df_rate = df[df['name'] == 'rate'].reset_index(drop=True) # get the rate
+ for index, row in df.iterrows():
+            if row['name'] == 'rate':
+                if row['cid'] == 'All':
+                    df_rate = row
 ```
 
-For isolating the rate measurement to a specific link, such as `All`, you can execute the subsequent code:
-```python
-df_rate = df_rate[df_rate['cid'] == 'All'].reset_index(drop=True) # get the rate for the all link combined
-```
-
-Lastly, employ the `explode` function to transform the nested data frame into its original structure:
+Optionally, employ the `explode` function to transform the row object into the dataframe structure:
 
 ```python
 df_rate = df_rate.explode(column=['user', 'value']) #keep the flow rate.
 ```
-This is the final result of the `rate` for each user, considering the combination of `All` links:
-```python
-   cid direction end_ts group  name start_ts user   value
-0  All        DL   6000   GMA  rate     5900    0   13.24
-0  All        DL   6000   GMA  rate     5900    1  18.464
-0  All        DL   6000   GMA  rate     5900    2   6.968
-0  All        DL   6000   GMA  rate     5900    3   19.28
-```
 
 ### Filling the Missing Data
 ```{eval-rst}
diff --git a/api/network_gym_client/adapter.html b/api/network_gym_client/adapter.html
index 7184b0f..3e004b4 100644
--- a/api/network_gym_client/adapter.html
+++ b/api/network_gym_client/adapter.html
@@ -319,7 +319,7 @@ 
Methods
 

-network_gym_client.Adapter.get_nested_json_policy(self, action_name, tags, action, user_name='user')#

+network_gym_client.Adapter.get_nested_json_policy(self, action_name, tags, action, index_name='user')#
 
Convert the gymnasium action space to nested json format

 

 
Parameters:

diff --git a/environments/mx_network_slicing/cellular_network_slicing.html b/environments/mx_network_slicing/cellular_network_slicing.html
index 66d61e0..e675250 100644
--- a/environments/mx_network_slicing/cellular_network_slicing.html
+++ b/environments/mx_network_slicing/cellular_network_slicing.html
@@ -265,7 +265,7 @@
 
Box(0.0, 1.0, (N,), float32)

 
 
Arguments

-
config.json

+
config.json

 
 
Select Environment

 
config_json = load_config_file('network_slicing') 
 env = NetworkGymEnv(client_id, config_json)

@@ -332,7 +332,7 @@ 
Observation Space
 
Note

 
The Observation Space can be customized in the network_gym_client.envs.network_slicing.Adapter.get_observation() function.

-
See Customizing Observation and Reward for more details.

+
Refer to Customizing Observation and Reward for further elaboration, along with a list of network statistics measurements.

 

 

 
Tip

@@ -389,7 +389,7 @@ 
Reward
 
Note

 
The Reward can be customized in the network_gym_client.envs.network_slicing.Adapter.get_reward() function.

-
See Customizing Observation and Reward for more details.

+
Refer to Customizing Observation and Reward for further elaboration, along with a list of network statistics measurements.

 

 
 

diff --git a/environments/mx_traffic_management/mx_qos_traffic_steering.html b/environments/mx_traffic_management/mx_qos_traffic_steering.html
index 7fb5792..4325748 100644
--- a/environments/mx_traffic_management/mx_qos_traffic_steering.html
+++ b/environments/mx_traffic_management/mx_qos_traffic_steering.html
@@ -265,7 +265,7 @@
 
MultiDiscrete([2, 2, ..., 2]) # N dimension

 
 
Arguments

-
config.json

+
config.json

 
 
Select Environment

 
config_json = load_config_file('qos_steer') 
 env = NetworkGymEnv(client_id, config_json)

@@ -327,7 +327,7 @@ 
Observation Space
 
Note

 
The Observation Space can be customized in the network_gym_client.envs.qos_steer.Adapter.get_observation() function.

-
See Customizing Observation and Reward for more details.

+
Refer to Customizing Observation and Reward for further elaboration, along with a list of network statistics measurements.

 

 

 
Tip

@@ -352,7 +352,7 @@ 
Reward
 
Note

 
The Reward can be customized in the network_gym_client.envs.qos_steer.Adapter.get_reward() function.

-
See Customizing Observation and Reward for more details.

+
Refer to Customizing Observation and Reward for further elaboration, along with a list of network statistics measurements.

 

 
 

diff --git a/environments/mx_traffic_management/mx_traffic_splitting.html b/environments/mx_traffic_management/mx_traffic_splitting.html
index 5a51491..0681cd5 100644
--- a/environments/mx_traffic_management/mx_traffic_splitting.html
+++ b/environments/mx_traffic_management/mx_traffic_splitting.html
@@ -265,7 +265,7 @@
 
Box(0.0, 1.0, (N,), float32)

 
 
Arguments

-
config.json

+
config.json

 
 
Select Environment

 
config_json = load_config_file('nqos_split') 
 env = NetworkGymEnv(client_id, config_json)

@@ -275,7 +275,7 @@
 

 

 
Description#

-
The Multi-Access (MX) Traffic Splitting environment represents a traffic management problem where multiple users are randomly distributed on a 2D plane, with each user connecting to a Cellular base station and the closest Wi-Fi access point. The handover between Wi-Fi access point is disabled.
+
The Multi-Access (MX) Traffic Splitting environment represents a traffic management problem where multiple users are randomly distributed on a 2D plane, with each user connecting to a Cellular base station and the closest Wi-Fi access point. The received signal-based handover between Wi-Fi access points can be enabled or disabled.
 The goal of traffic management is to strategically split traffic over both links, aiming to achieve high throughput and low latency.

 

 

@@ -316,7 +316,7 @@ 
Observation Space
 
Note

 
The Observation Space can be customized in the network_gym_client.envs.nqos_split.Adapter.get_observation() function.

-
See Customizing Observation and Reward for more details.

+
Refer to Customizing Observation and Reward for further elaboration, along with a list of network statistics measurements.

 

 

 
Tip

@@ -379,7 +379,7 @@ 
Reward
 
Note

 
The Reward can be customized in the network_gym_client.envs.nqos_split.Adapter.get_reward() function.

-
See Customizing Observation and Reward for more details.

+
Refer to Customizing Observation and Reward for further elaboration, along with a list of network statistics measurements.

 

 
 

diff --git a/searchindex.js b/searchindex.js
index 0ec2082..30ffc4c 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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[[15, "networkgym-components-and-interfaces"]], "Client API": [[15, "client-api"]], "Server API": [[15, "server-api"]], "Environment API": [[15, "environment-api"]], "Quickstart": [[16, "quickstart"]], "Accessing the NetworkGym Service via vLab": [[16, "accessing-the-networkgym-service-via-vlab"]], "Basic Usage": [[16, "basic-usage"]], "\u25b6\ufe0f Upon starting the Client, the following series of steps occur:": [[16, null]], "\ud83d\udd01 During the simulation, the process repeats as follows:": [[16, null]], "\u23f9\ufe0f When the NetworkGym or the simulation concludes:": [[16, null]], "Initializing Environments": [[16, "initializing-environments"]], "Interacting with the Environment": [[16, "interacting-with-the-environment"]], "Explaining the code": [[16, "explaining-the-code"]], "Action and observation spaces": [[16, "action-and-observation-spaces"]], "Modifying the environment": [[16, "modifying-the-environment"]], "Network Slicing": [[17, "network-slicing"]], "Cellular Network Slicing": [[18, "cellular-network-slicing"]], "Description": [[18, "description"], [20, "description"], [21, "description"]], "Prerequisite": [[18, "prerequisite"], [20, "prerequisite"], [21, "prerequisite"]], "Observation Space": [[18, "observation-space"], [20, "observation-space"], [21, "observation-space"]], "Action Space": [[18, "action-space"], [20, "action-space"], [21, "action-space"]], "Reward": [[18, "reward"], [20, "reward"], [21, "reward"]], "Arguments": [[18, "arguments"], [20, "arguments"], [21, "arguments"]], "Starting State": [[18, "starting-state"], [20, "starting-state"], [21, "starting-state"]], "Episode End": [[18, "episode-end"], [20, "episode-end"], [21, "episode-end"]], "Traffic Management": [[19, "traffic-management"]], "Multi-Access QoS Traffic Steering": [[20, "multi-access-qos-traffic-steering"]], "Multi-Access Traffic Splitting": [[21, "multi-access-traffic-splitting"]], "Transition Dynamics": [[21, "transition-dynamics"]], "NetworkGym: Democratizing Network AI": [[22, "networkgym-democratizing-network-ai"]], "Benefits of NetworkGym": [[22, "benefits-of-networkgym"]], "Agent": [[22, "agent"]], "Gymnasium API": [[22, "gymnasium-api"]], "NetworkGym API": [[22, "networkgym-api"]], "NetworkGym Scope and Limitations": [[22, "networkgym-scope-and-limitations"]], "Customizing Observation and Reward": [[23, "customizing-observation-and-reward"]], "Working with the Measurement": [[23, "working-with-the-measurement"]], "Measurement Columns": [[23, "measurement-columns"]], "Measurement Rows": [[23, "measurement-rows"]], "Retrieving a Measurement": [[23, "retrieving-a-measurement"]], "Filling the Missing Data": [[23, "filling-the-missing-data"]], "Handling Time Limits": [[24, "handling-time-limits"]], "Truncation": [[24, "truncation"]], "Termination": [[24, "termination"]], "Sequential Training Example": [[24, "sequential-training-example"]], "Python code:": [[24, "python-code"]], "Implementing Custom Environment": [[25, "implementing-custom-environment"]], "Initiating your Custom Environment": [[25, "initiating-your-custom-environment"]], "Connecting to Your Custom Environment": [[25, "connecting-to-your-custom-environment"]], "Substituting the Dummy Simulator": [[25, "substituting-the-dummy-simulator"]], "Releasing Your Custom Environment": [[25, "releasing-your-custom-environment"]], "Training Agents": [[26, "training-agents"]], "System Default Agent": [[26, "system-default-agent"]], "Custom Algorithm Agent": [[26, "custom-algorithm-agent"]], "Stable-Baselines3 Agent": [[26, "stable-baselines3-agent"]], "CleanRL Agent": [[26, "cleanrl-agent"]]}, "indexentries": {"adapter (class in network_gym_client)": [[2, "network_gym_client.Adapter"]], "df_to_dict() (in module network_gym_client.adapter)": [[2, "network_gym_client.Adapter.df_to_dict"]], "fill_empty_feature() (in module network_gym_client.adapter)": [[2, "network_gym_client.Adapter.fill_empty_feature"]], "get_nested_json_policy() (in module network_gym_client.adapter)": [[2, "network_gym_client.Adapter.get_nested_json_policy"]], "wandb_log() (in module network_gym_client.adapter)": [[2, "network_gym_client.Adapter.wandb_log"]], "wandb_log_buffer_append() (in module network_gym_client.adapter)": [[2, "network_gym_client.Adapter.wandb_log_buffer_append"]], "adapter (class in network_gym_client.envs.network_slicing)": [[3, "network_gym_client.envs.network_slicing.Adapter"]], "get_action_space() (in module network_gym_client.envs.network_slicing.adapter)": [[3, "network_gym_client.envs.network_slicing.Adapter.get_action_space"]], "get_observation() (in module network_gym_client.envs.network_slicing.adapter)": [[3, "network_gym_client.envs.network_slicing.Adapter.get_observation"]], "get_observation_space() (in module network_gym_client.envs.network_slicing.adapter)": [[3, "network_gym_client.envs.network_slicing.Adapter.get_observation_space"]], "get_policy() (in module network_gym_client.envs.network_slicing.adapter)": [[3, "network_gym_client.envs.network_slicing.Adapter.get_policy"]], "get_rbg_size() (in module network_gym_client.envs.network_slicing.adapter)": [[3, "network_gym_client.envs.network_slicing.Adapter.get_rbg_size"]], "get_reward() (in module network_gym_client.envs.network_slicing.adapter)": [[3, "network_gym_client.envs.network_slicing.Adapter.get_reward"]], "adapter (class in network_gym_client.envs.nqos_split)": [[4, "network_gym_client.envs.nqos_split.Adapter"]], "get_action_space() (in module network_gym_client.envs.nqos_split.adapter)": [[4, "network_gym_client.envs.nqos_split.Adapter.get_action_space"]], "get_observation() (in module network_gym_client.envs.nqos_split.adapter)": [[4, "network_gym_client.envs.nqos_split.Adapter.get_observation"]], "get_observation_space() (in module network_gym_client.envs.nqos_split.adapter)": [[4, "network_gym_client.envs.nqos_split.Adapter.get_observation_space"]], "get_policy() (in module network_gym_client.envs.nqos_split.adapter)": [[4, "network_gym_client.envs.nqos_split.Adapter.get_policy"]], "get_reward() (in module network_gym_client.envs.nqos_split.adapter)": [[4, "network_gym_client.envs.nqos_split.Adapter.get_reward"]], "netowrk_util() (in module network_gym_client.envs.nqos_split.adapter)": [[4, "network_gym_client.envs.nqos_split.Adapter.netowrk_util"]], "adapter (class in network_gym_client.envs.qos_steer)": [[5, "network_gym_client.envs.qos_steer.Adapter"]], "calculate_wifi_qos_user_num() (in module network_gym_client.envs.qos_steer.adapter)": [[5, "network_gym_client.envs.qos_steer.Adapter.calculate_wifi_qos_user_num"]], "get_action_space() (in module network_gym_client.envs.qos_steer.adapter)": [[5, "network_gym_client.envs.qos_steer.Adapter.get_action_space"]], "get_observation() (in module network_gym_client.envs.qos_steer.adapter)": [[5, "network_gym_client.envs.qos_steer.Adapter.get_observation"]], "get_observation_space() (in module network_gym_client.envs.qos_steer.adapter)": [[5, "network_gym_client.envs.qos_steer.Adapter.get_observation_space"]], "get_policy() (in module network_gym_client.envs.qos_steer.adapter)": [[5, "network_gym_client.envs.qos_steer.Adapter.get_policy"]], "get_reward() (in module network_gym_client.envs.qos_steer.adapter)": [[5, "network_gym_client.envs.qos_steer.Adapter.get_reward"]], "env (class in network_gym_client)": [[6, "network_gym_client.Env"]], "action_space (network_gym_client.env attribute)": [[6, "network_gym_client.Env.action_space"]], "adapter (network_gym_client.env attribute)": [[6, "network_gym_client.Env.adapter"]], "northbound_interface_client (network_gym_client.env attribute)": [[6, "network_gym_client.Env.northbound_interface_client"]], "observation_space (network_gym_client.env attribute)": [[6, "network_gym_client.Env.observation_space"]], "reset() (in module network_gym_client.env)": [[6, "network_gym_client.Env.reset"]], "step() (in module network_gym_client.env)": [[6, "network_gym_client.Env.step"]], "northboundclient (class in network_gym_client)": [[7, "network_gym_client.NorthBoundClient"]], "connect() (in module network_gym_client.northboundclient)": [[7, "network_gym_client.NorthBoundClient.connect"]], "process_measurement() (in module network_gym_client.northboundclient)": [[7, "network_gym_client.NorthBoundClient.process_measurement"]], "recv() (in module network_gym_client.northboundclient)": [[7, "network_gym_client.NorthBoundClient.recv"]], "send() (in module network_gym_client.northboundclient)": [[7, "network_gym_client.NorthBoundClient.send"]], "configure (class in network_gym_env)": [[9, "network_gym_env.Configure"]], "run() (in module network_gym_env.configure)": [[9, "network_gym_env.Configure.run"]], "dummysim (class in network_gym_env)": [[10, "network_gym_env.DummySim"]], "generate_dummy_measurement() (in module network_gym_env.dummysim)": [[10, "network_gym_env.DummySim.generate_dummy_measurement"]], "run_one_interval() (in module network_gym_env.dummysim)": [[10, "network_gym_env.DummySim.run_one_interval"]], "start_simulation() (in module network_gym_env.dummysim)": [[10, "network_gym_env.DummySim.start_simulation"]], "southbound_connect() (in module network_gym_env.southbound_interface)": [[11, "network_gym_env.southbound_interface.southbound_connect"]]}})
\ No newline at end of file
diff --git a/tutorials/customizing_observation_and_reward.html b/tutorials/customizing_observation_and_reward.html
index 648f5a2..804c7b6 100644
--- a/tutorials/customizing_observation_and_reward.html
+++ b/tutorials/customizing_observation_and_reward.html
@@ -246,35 +246,37 @@
           

 
Customizing Observation and Reward#

 
Personalizing the observation and reward functions to match your experimental goals is achievable within the provided environments. Within the Adapter class for each environment, both the get_observation and get_reward functions take a network statistics measurement as input. An illustration of such a network statistics measurement is provided below:

-
      cid direction end_ts group             name start_ts          user                                              value
-0     LTE        DL   6000   GMA            ap_id     5900  [0, 1, 2, 3]                       [255.0, 255.0, 255.0, 255.0]
-1   Wi-Fi        DL   6000   GMA            ap_id     5900  [0, 1, 2, 3]                               [0.0, 0.0, 1.0, 1.0]
-2     All        DL   6000   GMA  delay_violation     5900  [0, 1, 2, 3]                       [100.0, 100.0, 100.0, 100.0]
-3     All        DL   6000   GMA          max_owd     5900  [0, 1, 2, 3]                       [335.0, 703.0, 422.0, 299.0]
-4     LTE        DL   6000   GMA          max_owd     5900  [0, 1, 2, 3]                       [335.0, 703.0, 422.0, 299.0]
-5   Wi-Fi        DL   6000   GMA          max_owd     5900  [0, 1, 2, 3]                               [0.0, 1.0, 1.0, 0.0]
-6     LTE        DL   6000   PHY         max_rate     5900  [0, 1, 2, 3]                           [35.0, 35.0, 22.0, 35.0]
-7   Wi-Fi        DL   6000   PHY         max_rate     5900  [0, 1, 2, 3]                           [78.0, 78.0, 65.0, 78.0]
-8     All        DL   6000   GMA   measurement_ok     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
-9     All        DL   6000   GMA    missed_action     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-10    All        DL   6000   GMA              owd     5900  [0, 1, 2, 3]                       [327.0, 680.0, 403.0, 279.0]
-11    LTE        DL   6000   GMA              owd     5900  [0, 1, 2, 3]                       [327.0, 680.0, 404.0, 280.0]
-12  Wi-Fi        DL   6000   GMA              owd     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-13    LTE        DL   6000   GMA      qos_marking     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 0.0]
-14  Wi-Fi        DL   6000   GMA      qos_marking     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 0.0]
-15    All        DL   6000   GMA         qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-16    LTE        DL   6000   GMA         qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-17  Wi-Fi        DL   6000   GMA         qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
-18    All        DL   6000   GMA             rate     5900  [0, 1, 2, 3]                       [15.912, 9.056, 7.2, 14.984]
-19    LTE        DL   6000   GMA             rate     5900  [0, 1, 2, 3]                       [8.472, 8.472, 5.336, 8.472]
-20  Wi-Fi        DL   6000   GMA             rate     5900  [0, 1, 2, 3]                        [0.696, 4.64, 3.016, 4.992]
-21    LTE        DL   6000   GMA      split_ratio     5900  [0, 1, 2, 3]                           [31.0, 16.0, 19.0, 22.0]
-22  Wi-Fi        DL   6000   GMA      split_ratio     5900  [0, 1, 2, 3]                            [1.0, 16.0, 13.0, 10.0]
-23    LTE        DL   6000   GMA    traffic_ratio     5900  [0, 1, 2, 3]                           [92.0, 65.0, 64.0, 63.0]
-24  Wi-Fi        DL   6000   GMA    traffic_ratio     5900  [0, 1, 2, 3]                            [8.0, 35.0, 36.0, 37.0]
-25    All        DL   6000   GMA          tx_rate     5900  [0, 1, 2, 3]                     [17.075, 9.408, 7.434, 15.797]
-26    All        DL   6000   GMA            x_loc     5900  [0, 1, 2, 3]  [12.040140997999378, 6.710302486045565, 52.547...
-27    All        DL   6000   GMA            y_loc     5900  [0, 1, 2, 3]  [6.167369006263322, 2.450729590703729, 9.45877...
+
      cid direction end_ts group                    name start_ts          user                                              value
+0   Wi-Fi        DL   6000   GMA                 cell_id     5900  [0, 1, 2, 3]                               [0.0, 0.0, 1.0, 1.0]
+1     LTE        DL   6000   PHY                 cell_id     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
+2     All        DL   6000   GMA  delay_test_1_violation     5900  [0, 1, 2, 3]             [100.0, 100.0, 72.99270072992701, 0.0]
+3     All        DL   6000   GMA  delay_test_2_violation     5900  [0, 1, 2, 3]                           [100.0, 100.0, 0.0, 0.0]
+4     All        DL   6000   GMA         delay_violation     5900  [0, 1, 2, 3]            [100.0, 100.0, 100.0, 89.8989898989899]
+5     All        DL   6000   GMA                 max_owd     5900  [0, 1, 2, 3]                       [499.0, 538.0, 239.0, 195.0]
+6     LTE        DL   6000   GMA                 max_owd     5900  [0, 1, 2, 3]                       [499.0, 538.0, 239.0, 195.0]
+7   Wi-Fi        DL   6000   GMA                 max_owd     5900  [0, 1, 2, 3]                               [3.0, 2.0, 3.0, 4.0]
+8     LTE        DL   6000   PHY                max_rate     5900  [0, 1, 2, 3]                           [35.0, 35.0, 22.0, 35.0]
+9   Wi-Fi        DL   6000   PHY                max_rate     5900  [0, 1, 2, 3]                           [78.0, 78.0, 65.0, 78.0]
+10    All        DL   6000   GMA          measurement_ok     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
+11    All        DL   6000   GMA           missed_action     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+12    All        DL   6000   GMA                     owd     5900  [0, 1, 2, 3]                       [484.0, 517.0, 214.0, 102.0]
+13    LTE        DL   6000   GMA                     owd     5900  [0, 1, 2, 3]                       [491.0, 517.0, 217.0, 145.0]
+14  Wi-Fi        DL   6000   GMA                     owd     5900  [0, 1, 2, 3]                               [0.0, 1.0, 0.0, 0.0]
+15    LTE        DL   6000   GMA                priority     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 0.0]
+16  Wi-Fi        DL   6000   GMA                priority     5900  [0, 1, 2, 3]                               [1.0, 1.0, 1.0, 1.0]
+17    All        DL   6000   GMA                qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+18    LTE        DL   6000   GMA                qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+19  Wi-Fi        DL   6000   GMA                qos_rate     5900  [0, 1, 2, 3]                               [0.0, 0.0, 0.0, 0.0]
+20    All        DL   6000   GMA                    rate     5900  [0, 1, 2, 3]                    [13.352, 9.056, 15.912, 45.992]
+21    LTE        DL   6000   GMA                    rate     5900  [0, 1, 2, 3]                        [8.472, 8.472, 5.336, 8.24]
+22  Wi-Fi        DL   6000   GMA                    rate     5900  [0, 1, 2, 3]                     [6.848, 0.928, 10.336, 15.912]
+23    LTE        DL   6000   GMA             split_ratio     5900  [0, 1, 2, 3]                             [17.0, 32.0, 9.0, 3.0]
+24  Wi-Fi        DL   6000   GMA             split_ratio     5900  [0, 1, 2, 3]                            [15.0, 0.0, 23.0, 29.0]
+25    LTE        DL   6000   GMA           traffic_ratio     5900  [0, 1, 2, 3]                           [55.0, 90.0, 34.0, 34.0]
+26  Wi-Fi        DL   6000   GMA           traffic_ratio     5900  [0, 1, 2, 3]                           [45.0, 10.0, 66.0, 66.0]
+27    All        DL   6000   GMA                 tx_rate     5900  [0, 1, 2, 3]                     [14.636, 9.757, 14.52, 38.681]
+28    All        DL   6000   GMA                   x_loc     5900  [0, 1, 2, 3]  [13.871206061072092, 6.702857783599929, 52.198...
+29    All        DL   6000   GMA                   y_loc     5900  [0, 1, 2, 3]  [7.964088643728281, 1.6756194439050196, 9.0212...
 

 

 

@@ -331,62 +333,66 @@ 
Measurement Rows
0,1

-
ap_id

+
cell_id

 
LTE cell ID and Wi-Fi access point ID as measured by each user. (NOTE: 255 is the default value, representing no measurement.)

 
-
2

+
2,3

+
delay_test_*_violation

+
One-way delay violation percentage (%) –for testing purpose– as measured by each user; delay threshold (delay_test_*_thresh_ms) can be configured in the JSON file.

+
+
4

 
delay_violation

 
One-way delay violation percentage (%) as measured by each user; delay bound (delay_bound_ms) can be configured in the JSON file. (NOTE: Only for QoS use case)

 
-
3,4,5

+
5,6,7

 
max_owd

 
Maximum one-way delay measured by each user, involving LTE link, Wi-Fi link, and ALL. (ALL: after reordering out-of-order packets from both links).

 
-
6,7

+
8,9

 
max_rate

 
LTE/Wi-Fi link capacity as measured by each user.

 
-
8

+
10

 
measurement_ok

 
1: Measurement is valid; 0: An issue exists with the measurement.

 
-
9

+
11

 
missed_action

 
Count of missed actions by the environment. (NOTE: Applicable in testbed environment where RL agent might take too long to compute an action. Simulator waits for action by default in simulations, therefore should always be 0.)

 
-
10,11,12

+
12,13,14

 
owd

 
One-way delay measured by each user, involving LTE link, Wi-Fi link, and ALL. (ALL: after reordering out-of-order packets from both links).

 
-
13,14

-
qos_marking

-
1: QoS user; 0: Non-QoS user.

+
15,16

+
priority

+
1: high priority; 0: low priority. When Dynamic Flow Prioritization is enabled, for each cell, mark 70%~90% of traffic or users to high priority.

 
-
15,16,17

+
17,18,19

 
qos_rate

 
QoS delivery rate (output traffic throughput meeting QoS requirement) as measured by each user, comprising traffic over LTE link, Wi-Fi link, and ALL (combined).

 
-
18,19,20

+
20,21,22

 
rate

 
Delivery rate (output traffic throughput) measured by each user, including traffic over LTE link, Wi-Fi link, and ALL (combined).

 
-
21,22

+
23,24

 
split_ratio

 
Traffic split ratio (in range of [0, 32]) as measured by each user, covering LTE link, Wi-Fi link. LTE split ratio + Wi-Fi split ratio equals 32.

 
-
23,34

+
25,26

 
traffic_ratio

 
Traffic ratio (%) sending to each link measured by each user, involving LTE link, Wi-Fi link. traffic_ratio might not equal split_ratio, since the system takes sine time to apply the new action.

 
-
25

+
27

 
tx_rate

 
Load (input traffic throughput) measured by each user.

 
-
26

+
28

 
x_loc

 
x coordinate per user.

 
-
27

+
29

 
y_loc

 
y coordinate per user.

 
@@ -396,26 +402,17 @@ 
Measurement Rows
 
Retrieving a Measurement#

-
To retrieve a specific network statistic, such as rate, you can utilize the following code:

-
df_rate = df[df['name'] == 'rate'].reset_index(drop=True) # get the rate
-

-

-
For isolating the rate measurement to a specific link, such as All, you can execute the subsequent code:

-
df_rate = df_rate[df_rate['cid'] == 'All'].reset_index(drop=True) # get the rate for the all link combined
+
To retrieve a specific network statistic, such as rate, over a specifi link, such as All, you can utilize the following code:

+
 for index, row in df.iterrows():
+            if row['name'] == 'rate':
+                if row['cid'] == 'All':
+                    df_rate = row
 

 

-
Lastly, employ the explode function to transform the nested data frame into its original structure:

+
Optionally, employ the explode function to transform the row object into the dataframe structure:

 
df_rate = df_rate.explode(column=['user', 'value']) #keep the flow rate.
 

 

-
This is the final result of the rate for each user, considering the combination of All links:

-
   cid direction end_ts group  name start_ts user   value
-0  All        DL   6000   GMA  rate     5900    0   13.24
-0  All        DL   6000   GMA  rate     5900    1  18.464
-0  All        DL   6000   GMA  rate     5900    2   6.968
-0  All        DL   6000   GMA  rate     5900    3   19.28
-

-

 

 

 
Filling the Missing Data#