Multivariate Time Series Characterization and Forecasting of VoIP Traffic in Real Mobile Networks

The equipment we used to build the real-world dataset includes:

• 1 cellular device equipped with Linphone (open-source softphone supporting RTCP-XR protocol) representing the User Equipment 1 (UE1);
• 1 standard PC equipped with: i) Linphone softphone representing the User Equipment 2 (UE2), ii) the software probe Wireshark used to capture the network traffic between UE1 and UE2 and to save it in .pcap format.

The Dataset contains network traffic gathered in a real cellular environment around the city of Salerno (Italy) being classified as a medium-density city (around 2000 people/Km^2). Currently (Mar. 2023), such a territory is served by approximately 100 radio towers supporting a mix of LTE/LTE-Advanced (about 97%) and 5G-NSA (about 3%) technologies (data gathered from https://www.nperf.com/en/map/IT/).

We provide both 

- raw data (.pcap) available at: https://drive.google.com/file/d/1-r2Xd1VK6r7O_1KaXVYPus1Rcj6TF9DF/view?u...

- processed data (.txt) available at: https://github.com/mariodim/ml_mobile_dataset/blob/main/ML_TimeSeries_DA...

The whole dataset is split into 16 sub-datasets divided per codec and per network scenario:

• 8 codecs: G.722, G.729, GSM, G.711, Mpeg4-16, OPUS, Speex-8, Speex-16.
• 2 network scenarios:
  Mobile  (UE1 communicates with UE2 from a moving car at an average speed of 60 Km/H); 
  Fixed  (UE1 communicates with UE2 being fixed in a place).

Please note that, for space constraints, in our paper we analyze mobile scenario with a subset of codecs.

Each sub-dataset is the result of a post-processing stage on the raw .pcap files produced by Wireshark.
Each sub-dataset contains 6 temporal features organized in columns (the first column is the time reference):

• MOS (Mean Opinion Score) --> it measures the call quality (expressed in a pure value between 1 and 5);
• BW (Bandwidth) --> it measures the bandwidth consumed by a voice call ( expressed in kb/s);
• RTT (Round Trip Time) --> it measures the interval between a sent and a received packet (expressed in ms);
• JTR (Jitter) --> it measures the inter-packet jitter (expressed in ms);
• DJB (De-jittering Buffer) --> it measures the buffer length used to reduce jitter (expressed in ms);
• SNR (Signal-to-Noise ratio) --> it measures the objective quality of the communication channel (expressed in dB).

We have developed a Python routine that performs the multivariate time series prediction of features by using different techniques

Such a routine is available at the following link: https://colab.research.google.com/drive/1pe-p8yEP8QaVgWcOpVZ2ZwweJEqAjHh...

Please note that you have to upload a given sub-dataset in the same google Colab Notebook directory containing the routine.

After uploading a sub-dataset (e.g. mob_g722.txt, meaning that the traffic is collected within the mobile scenario and the codec used is G.722), set the parameters in the first "cell" of the Python code:

• filename --> insert the name of the uploaded file (e.g. "mob_g722.txt")
• methods --> you can choose one of the implemented techniques for time series prediction by setting True or False
• param --> size of your ML network (number of dense neurons, number of units, epochs, etc.)
• perc_train --> percentage of training size (the test size is set accordingly)
• n_past --> number of past values used in the training set
• n_fut --> number of future samples to be predicted (default = 1)

Output files include:

• TXT files containing time series predictions per technique --> e.g. the output file mob_g722_cnn.txt is a 12-column file in this format: column #1 contains original values of MOS, column #2 contains predicted values of MOS, column #3 contains original values of BW, column #4 contains predicted values of BW, and so forth. Once exported, such files can be obviously used to reproduce the plots through different plot tools;
• RMSE, MAE, MAPE values per each technique
• Information about training time per each technique (directly shown in the output code).