# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 1 Introduction  
Multiple-input multiple-output (MIMO) systems are widely considered to be a key technologies for wireless communications, as they improve aspects of system performance such as reliability and data rates without requiring additional bandwidth. To take advantage of these facts, MIMO systems have to utilize diversity gain, which can be achieved by various receivers. MIMO receivers can be divided into two categories based on their operation: linear and nonlinear receivers. Linear receivers, such as the zero-forcing (ZF) and minimum mean squared error (MMSE) receivers, have lower computational complexity but suffer from performance degradation. In contrast, nonlinear receivers offer superior error performance but demand significantly higher computational complexity [1, 2]. Specifically, the maximum likelihood (ML) receiver requires exponentially increasing computational complexity with the number of transmit antennas, despite providing optimal error performance through brute-force search. To address this problem, detectors such as sphere decoding have been investigated to obtain nearly optimal performance with reasonable computational complexity [3, 4]. Recently, artificial intelligence has been studied for various applications in wireless communications such as beamforming, channel estimation, and localization, due to its ability to address non-convex optimization problems [5, 6, 7, 8, 9, 10, 11]. In [5], deep learning is utilized to optimize the antenna position and antenna weight jointly, which is challenging to perform analytically. The authors of [6] proposed a deep learning-based fast beamforming method to solve the sum rate maximization problem. Moreover, in [7], the performances of the deep learning-based channel estimation are analyzed and compared with the conventional channel estimation methods. In [8], deep learning is used to estimate the MIMO channel coefficient for quasi-static block fading and time-varying fading. Deep learning is utilizedfor 3-D unmanned aerial vehicle localization in [9] by formulating the localization problem as an optimization problem to minimize the error in received signal strength index measurements. The authors of [10] exploit deep learning for indoor localization by converting the localization problem into a classification problem. A deep neural network (DNN)-driven near-field source localization that jointly optimizes the analog beamformer and the localization function is proposed in [11]. Furthermore, artificial intelligence-based approaches have been increasingly employed in MIMO detection to overcome the limitations of conventional receivers [12; 13; 14; 15]. In [12], a deep learning-based scheme is proposed, offering significantly lower computational complexity than the conventional sphere decoding algorithm while achieving near-optimal performance of sphere decoding. The authors of [13] introduced two deep learning networks for MIMO detection named detection network (DetNet) to enable the handling of multiple channels simultaneously with a single training phase and also obtain accuracy comparable to that of sphere decoding with reduced computational complexity. To improve detection performance with imperfect channel state information (CSI), a model-driven deep learning network called the orthogonal approximate message passing (OAMP)-Net2 detector was proposed to jointly perform channel estimation and signal detection in [14]. In [15], a model-driven deep learning-based massive MIMO detector was proposed for multiuser interference cancellation by unfolding an existing iterative algorithm. Existing studies on MIMO receivers using deep learning have proposed neural network structures for MIMO detection or investigated methods to reduce the computational complexity of conventional nonlinear receivers. Considering that average error performance is mainly affected by large burst errors under poor MIMO channel conditions, it is essential to predict the channel conditions when designing receivers [16].