# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.1 Channel State Information Estimation  
CSI estimation is a common problem in wireless communication systems. It refers to the channel properties of a communication link [7]. In a simplified way, these properties describe how the signal will propagate from the transmitter to the receiver. Based on the CSI, the transmission can be adapted according to the current channel conditions, in order to improve the whole communication. CSI is an important factor in determining radio resource allocation, the type of modulation and coding schemes to use, etc.  
Traditional CSI estimation techniques usually require high computation capability [14]. In addition, these techniques may not be suitable for 5G scenarios due to the complexity of the new scenarios and the presence of different technologies (e.g., massive MIMO, orthogonal frequency division multiplexing (OFDM), and millimeter-Wave), that impact the physical medium conditions [7]. Therefore, several authors have used deep learning models for CSI estimation. In our systematic review, we came across five papers related to CSI estimation with deep learning.  
Three works proposed a deep learning-based solution focused on MIMO systems [15; 16; 17]. In MIMO systems both transmitter and receiver are equipped with an array of antennas. This is a very important technology for 5G, offering multiple orders of spectral and energy efficiency gains in comparison to LTE technologies [18]. Note that LTE uses MIMO but 5G takes this technology a notch further as it adopts massive antenna configurations in what is known as massive MIMO.  
In [15], the authors adopted deep learning for decision-directed for channel estimation (DD-CE) in MIMO systems, to avoid the Doppler rate estimation. Authors considered vehicular channels, where the Doppler rate varies from one packet to another, making the CSI estimation difficult. Therefore, the deep learning model was used to learn and estimate the MIMO fading channels over different Doppler rates.  
Figure 1: The problems related to 5G addressed in the works examined.  
In [16], the authors proposed a combination of deep learning and superimposed code (SC) techniques for channel state CSI feedback. The main goal is to estimate downlink CSI and detect user data in the base stations.  
In [17], [PERSON] et al. presented some evaluations for CSI estimation using deep learning models in three use cases. The first one focused on MIMO with multi users where the angular power spectrum (APS) information is estimated using deep learning models; and the two other scenarios were (a) static CSI estimation framework based on deep learning; and (b) a variant of the first scheme, but considering time variation, i.e., a deep learning model is proposed to estimate the CSI through time.  
In [7], [PERSON] et al. proposed an online CSI prediction taking into account relevant features that affect the CSI of a radio link, such as frequency band, user location, time, temperature, humidity, and weather.  
In [19], a residual network was proposed for CSI estimation in filter bank multicarrier (FBMC) systems. The traditional CSI estimation and equalization and demapping module are replaced by deep learning model.