# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What Are the Main Deep Learning Techniques Used in 5G Scenarios?
#### 3.3.2 Recurrent Neural Networks  
As highlighted in [9], a recurrent neural network (RNN) is able to deal with sequential data, such as time series, speech and language. It is due to its capacity for, given an element in a sequence, storing information of past elements. Therefore, one work used RNN [17] and several others used RNN variations (such as LSTM [87; 88; 89; 90]) to deal with sequential data. In [70], [PERSON] et al. proposed a digital cancellation scheme to eliminate linear and non-linear interference based on deep learning. The deep learning model receives a signal and the custom loss function represents the residual interference between the real and estimated self-interference signal. This model was based on RNN but with a custom memory unit. In [17], authors used data from channel estimations using the ray tracing propagation software. The data was processed using traditional RNN layers to capture the time-varying nature of CSI. Similarly, several works adopted deep learning models with LSTM layers. This can be justified as LSTM is widely used in the literature to process sequential data. The authors in [45; 46] used the same dataset to train their models (Telecom Italia, see the Section 3.4). In [46], a multivariate LSTM model was proposed to learn the temporal and spatial correlation among the base station traffic and make an accurate forecast. In [45], an LSTM model was proposed to extract temporal features of mobile Internet traffic and predict Internet flows for cellular networks. In [52], an LSTM model was suggested to deal with another open dataset in order to predict handover. The dataset is composed of historical location of the users, and the model exploits the long-term dependencies and temporal correlation of data. In [48], the authors proposed an LSTM model for handling historical data of traffic volume. The model was constructed to predict real time traffic of base stations in order to give relevant information to increase the accuracy of the offloading scheme proposed. In [47], [PERSON] et al. also proposed an LSTM model to predict traffic in base stations in order to avoid flow congestion in 5G ultra dense networks. Uplink and downlink flows data were considered as input for the model. With the predicted data, it is possible to allocate more resources for uplink or downlink channels accordingly. In [7], an LSTM model was proposed to make online CSI prediction. The model explored the temporal dependency of historical data of frequency band, location, time, temperature, humidity, and weather. The dataset was measured through experiments within a testbed. In [58], a variation of LSTM called X-LSTM was proposed in order to predict a metric called REVA, which measures the amount of PRBs available in a network slice. X-LSTM is based on X-11, which is an interative process that decomposes the data into seasonal patterns. X-LSTM uses different LSTM models to evaluate different time scales of data. _\"It filters out higher order temporal patterns and uses the residual to make additional predictions on data with a shorter time scale\"_[58]. The input data of the model is the historical data of PRB measured through a testbed, where the REVA metric was calculated. In [71], the authors represented the memory aspect PA using a biLSTM model. The authors established a bridge between the theoretical formalism of PA behavior and the characteristic of biLSTM models to consider both forward an backward temporal aspect of the input data (baseband measurements using a testbed). In [35; 36; 51; 59], the authors used LSTM to deal with sequential data generated through simulation. In [59], the LSTM model was used to predict if a new network slice can be allocated given the sequential data of allocated resources and channel conditions. In [51], the LSTM model was used to evaluate historical signal condition in order to classify event in either handover fail or success in advance.