## 1 Introduction  
New Radio (NR) wireless networks are anticipated to support very high data rates and new applications that will create opportunities for a radically new radio technology paradigm [1]. In this context, machine learning (ML) has regained attention for its achievements especially in the upper layers, such as in cognitive radio [2, 3], self-organizing networks (SONs) [4] and resource management [5, 6]. However, the authors of [7] claim that the potential use of deep learning (DL) to the physical layer will be increasingly recognized. This appears to be reasonable considering the new features of next-generation communications, such as complex scenarios, with a consequent lack of information about the channel model, high data rates and accurate processing requirements [7]. Studies on ML applications to the physical layer have been carried out, for example, for modulation recognition [8, 9], signal classification [10], multiple input multiple output (MIMO) detection [11], channel state information (CSI) feedback reduction [12, 13], non-orthogonal multiple access (NOMA) [14], hybrid precoding [11, 15], encoding and decoding [16], and channel estimation and equalization [17; 18; 19; 20]. Researchers have presented a range of arguments to support the adoption of DL techniques in contemporary communication environments [7; 11; 16]:  
* deep networks are very good and adaptable function approximators--they learn the weights of the model optimizing the overall performance through a training process, instead of requiring a rigorous mathematical description that could be hard to extract in complex scenarios;
* DL-based systems could replace manual feature extractors and learning features automatically, flexibly adapting the structure and the parameters of the model, with considerable improvement to the end-to-end performance;
* DL-based algorithms work very well with large amounts of data due to their intrinsic parallel and distributed computing, leading to speeding up of computation;
* DL models can overcome the block structure to provide a complete view of the system and optimize it end-to-end. This paper proposes a DL-based solution for CSI feedback improvement in 5G-based communication systems. The availability of information about the colorful characteristics of the communication channels has a fundamental impact on many transmission features in most modern radio-access technologies [21]. This framework of information can vary from a rough estimate of the radio link path loss, which is useful for adjusting the transmitted power, to very precise knowledge of the channel amplitude and phase [21]. Measurements and estimates can be performed by either the transmitter or the receiver side. Focusing on the downlink case, the knowledge of the channel characteristics can be acquired by means of device measurements or by the network itself, depending on the particular scenario:  
* when working in frequency division duplexing (FDD) mode, downlink and uplink could potentially be significantly different. In consequence, to obtain reliable information about the downlink channel, measurements must necessarily be acquired by the user device and then reported to the network; the network will exploit this feedback to properly set the transmission parameters for future downlink transmissions;
* in time-division duplexing (TDD) communications, when downlink and uplink transmissions experience the same channel characteristics, feedback from the device is not necessary, since the network itself can obtain useful information about the downlink features of interest by measuring them in the uplink transmissions. In general, the evaluation of the radio environment is referred to as channel sounding and requires the adoption of specific reference signals (RS) on which the receiver can perform measurements. The adoption of appropriate channel modelling is critical when performing simulations involving signal processing, such as channel sensing.