# Hardware-Based Architecture for DNN Wireless Communication Models
## 1 Introduction  
Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is more efficient than conventional OFDM system in transmitting data between wireless devices [1, 2]. In the OFDM system, in which the subcarriers are orthogonal to each other, the signal spectrum in the subcarriers allows overlapping while the receiver can still recover the original signals. The overlapped signal spectrum makes the OFDM system much more efficient in spectrum utilization than conventional systems. The OFDM system has also attracted much attention from many researchers because of its simple implementation, robustness against frequency-selective fading channels, and avoidance of inter-symbol interference (ISI) by adding guard intervals (GI). The MIMO OFDM system not only retains the same characteristics of a conventional OFDM system but also provides higher bit error rate (BER) performance and faster data rate. Although MIMO OFDM has many great features, there are also many drawbacks, e.g., the significantly large power consumption of analog-to-digital converters (ADCs), the beam squint effect in the wideband scenario, the sensitivity to the carrier frequency offset (CFO) and the high peak-to-average power ratio (PAPR) issue. Therefore, many works [3, 4, 5, 6] have been motivated to solve these problems. With the high PAPR problem [7], OFDM symbols at the transmitter make the power amplifier (PA) operate in the nonlinear amplification region, which causes clipping distortions to the outputs of the PA. Thennonlinear distortions reduce the performance of the channel estimation and signal detection at the MIMO ODFM receiver devices. The nonlinear distortions generated by the PA need to be minimized to improve the performance of the MIMO OFDM receiver. One method to reduce the high PAPR problem in front of the PA is that OFDM signals need to be cut before passing through PA. It helps PA operate in the linear amplification region. However, the clipped OFDM signals are affected. Through artificial intelligence (AI) developments, many works have applied deep learning to optimize wireless communication systems, such as [8; 9; 10; 11; 12; 13]. Concretely, the authors in [8] proposed a deep learning-based signal detection system to replace conventional MIMO transmitters, which achieves the same accuracy as the conventional MIMO transmitter. However, the deployed deep learning system only applies to the MIMO system and does not include the OFDM technique. In addition, the deep learning (DL) systems in [9; 10; 11] are proposed to support correlated noise cancellation, thus improving signal recovery capacity at the receiver. Although the architectures in [9; 10; 11] show the possibility of improving receiver performance in MIMO systems, the authors do not consider the MIMO OFDM model and the case of the high PAPR problem, so the signal coming from the receiver is ideal with noise from the channel. Authors in [12; 13] resolved the high PAPR issue and achieved the expected results. But in [12], the MIMO OFDM system implemented has 2 and 8 antennas (2 \(\times\) 8) at the transmitter and receiver, respectively. Therefore, the models in [12] are easier to achieve efficiency than the conventional 2 \(\times\) 2 or 4 \(\times\) 4 MIMO OFDM models (a point-to-point MIMO-OFDM system). In addition, the size of its deep learning models is quite large, with tens of millions of parameters leading to significant resource consumption. The architecture in [13] shows that when applied to MIMO OFDM systems, the deep neural network (DNN) model no longer achieves the same efficiency as the single input, single output (SISO) OFDM systems. In general, none of the above architectures is proposed to apply deep learning in the point-to-point MIMO OFDM system concerned with the high PAPR problem. In addition, the above studies also do not implement AI hardware design to prove that it is feasible for practice and only focus on the results performed on software simulations.