# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 1 Introduction  
Orthogonal Frequency Division Multiple Access (OFDMA), an MA scheme for New-Radio (NR), has gained widespread adoption in wireless communication systems owing to its resilience against frequency-selective fading channels. The receiver in wireless communication receives a distorted signal due to delay spread, which is the multipath effect. Therefore, the channel must be estimated to compensate for the distorted signal. In general, channels are estimated using pilot signals known to both the transmitter and receiver. Since the pilot signals used vary depending on the user scenario, channels are estimated by generating signals such as the Demodulation Reference Signal (DMRS) [1] and Sounding RS (SRS) [2; 3; 4; 5]. Existing channel estimation methods usually estimate channels using the Least Square (LS) and Minimum Mean Square Error (MMSE). The LS method estimates the channel by assuming that the channel is deterministic. Specifically, the LS uses only pilot signals except for the channel's statistical information and the computational complexity is quite low. However, the statistical information of the channel is not used, so the performance is relatively low from the perspective of MSE. The MMSE method utilizes the statistical information of the channel to exhibit optimal performance from the MSE perspective. However, there is a disadvantage that optimal performance can be achieved only when the statistical information of the channel is properly known, and the computational complexity is relatively high compared to the LS method. It is necessary to study an improved channel estimation method to compensate for the problems of these existing channel estimation methods. On the other hand, a variety of Machine Learning (ML)-based estimation methods have been studied to compensate for the shortcomings of existing estimation methods. Table 1 depicts representative studies [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] conducted for achieving comparable performance to the MMSE method using ML without the statistical information of the channel in comparison with main contributions from this study. It is noteworthy to design the channel estimation method using the black-box characteristic of ML. However, ML-based Channel Frequency Response (CFR) estimation methods have the disadvantage of high complexity because inputs and outputs are proportional to the length of the pilot signal, and the number of hidden layers is typically large. In the case of ML-based Channel Impulse Response (CIR) estimation, it is complicated to use because the channel is estimated at each time instance of the received signal in the time domain, or the received signal in the frequency domain is estimated through multiple MLs. Furthermore, memory overhead is likely to occur as memory requirements to store ML increase in proportion to the number of hidden layers and nodes. In this paper, we propose a memory-saving ML-based channel estimation for estimating CIR with low complexity. The ML model adopts Deep Neural Networks (DNNs) to design as simple as possible with one hidden layer, and it uses ML's black box characteristics to input received signals in the frequency domain and output CIR in the time domain. The weights of the trained ML can be stored by converting them from floating point 32 to integral point 8 using the weight quantization method [17; 18]. The rest of this paper is organized as follows. The system model for the process before using the channel estimation method is introduced in Section 2. The existing channel estimation and the simplest ML-based CFR estimation are presented in Section 3. The proposed method in a general environment is presented in Section 4. A simulation analysis of the proposed method and simulation analyses based on additional perspectives are presented in Section 5. Our main conclusion is given in Section 6. _Notations_: Vectors and matrices are written in bold letters. \(\mathbb{E}[\mathbf{X}]\) is the expectation of the random vector \(\mathbf{X}\).