# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 4 Neural Networks
### Relevant Papers  
[PERSON] et al. [17] propose a method to predict the next-hour traffic value for cell towers in a large city by feeding pre-processed time series signals into an Elman Neural Network (ENN). An ENN, a deep learning technique, differs slightly from the traditional Neural Network layout of having three main layers: input, hidden, and output. An additional context layer is attached to the hidden layer, which takes the output of the hidden layer and stores it for use in future iterations of training [18]. The city's base stations are first split into 50 groups by K-means clustering. K-means clustering is a set of machine learning algorithms that group similar data points together, generally with each cluster having one mean center. The number of groups is fixed, and the user chooses somewhat arbitrarily based on what they know about the data and what they think is best for the work. This paper uses both unsupervised (K-clustering) and supervised (ENN) machine learning methods to achieve its goal. The base station signals are first split into low- and high-frequency components through wavelength decomposition. Each component is then fed to the ENN and eventually fused to obtain prediction results [17]. Six days of traffic data were used for training, while one day was used for the final prediction testing. The paper finds that with the pre-processing of signal data using wavelength decomposition, the next-hour traffic prediction is more accurate than simply using ENN without prior wavelength decomposition. However, one drawback of this methodology is that the prediction performance decreases as the K-value is set higher. This means that the predictive ability of this method outside of the paper's scenario would depend on the user-selected K-value. Nevertheless, this case shows that a combination of deep learning, data clustering, and signal pre-processing could predict short-term traffic in a Wide Area Network better than traditional means. [PERSON] et al. [19, 20] use time series traffic data sets from a network service provider in a combined Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) training methodology to predict network traffic at 5 min and 1 h time granularities. An RNN network contains a feedback loop in the hidden layer that'remembers' the results of a previous time. The issue with RNN is that its'memory' degrades over time, called vanishing gradients, and thus is not suitable to predict long-term data dependencies [21], including autocorrelation. LSTM is a variant of RNN, which reduces this issue by having each neural cell contain additional memory units, saving cell states for long periods or forgetting information deemed not applicable. In this paper, LSTM is used to predict autocorrelation better. The data are first fed through the LSTM Neural Network, and the output is then fed through a traditional DNN network (Deep Neural Network) to obtain predictive results. One downside to using the LSTM model instead of a simple one is the longer duration required for training convergence. More training iterations are required before the model can accurately predict network traffic. To overcome this issue, the authors propose to use Monte Carlo Tree Search [15]. The key novelty of their approach is the use of various selection strategies applied to the phase of building a search tree under different network scenarios, which solves the problem of long convergence time. [PERSON] et al. [22] use an echo state network (ESN) and autoregressive integrated moving average (ARIMA) model to forecast mobile communication traffic series. ESNs are Recurrent Neural Networks where the input is passed through a randomized but fixed-weighted'reservoir' of neural nodes to produce an output. The target output is then fed back into the reservoir as input. The idea is that the reservoir, with the target input, would 'echo' traces of past input history [23].