# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 1 Introduction  
In the last decade, wireless communication has been revolutionized by the unprecedented growth of consumer demand. First-generation (1G) wireless communication was implemented for frequency-division multiple access (FDMA), and second-generation (2G) wireless communication was developed for time division multiple access (TDMA) or code division multiple access (CDMA) [1]. In addition, third-generation (3G) wireless communication is used for wideband code division multiple access (WCDMA), and orthogonal frequency division multiple access (OFDMA) is used for fourth-generation (4G) and fifth-generation (5G) communication [1]. The reduction in symbol-error rate (SER) and signal-to-noise ratio (SNR) in the wireless communication system is important in order to improve the system performance. 5G mobile communication has implementation constraints such as high data rates, ultra-low latency, and high reliability [2]. 5G connectivity has a vital role in implementing quality service, big data processing chain, and ubiquitous connectivity for Internet of Things (IoT) devices. Non-orthogonal multiple access (NOMA) is considered a spectral efficient multiple access technique used to enable 5G technology [3]. NOMA has the following capabilities: high spectrum efficiency, low latency, and high connection density for transmitting signals to different users (UEs) on the same frequency andtime slot. NOMA-based communication can be broadly classified as power domain NOMA and code domain NOMA [4]. Power domain NOMA achieves multiplexing by allocating different power to the UEs that are in the coverage area. In the case of code domain NOMA, multiplexing occurs in the UEs by employing specific spreading sequences for each user (UE) which has sparse, low-density, and low inter-correlation properties [5]. NOMA-based communication supports multiuser communication in the power domain. By applying superposition coding at the transmitter and successive interference cancelation (SIC) at the receiver, multiple UE data can be extracted on the same communication channel [6; 7; 8]. Machine learning (ML) techniques can provide significant advantages in performance enhancement in wireless communication [9]. ML can provide solutions to complex problems without explicit programming and the obtained results can be implemented with high accuracy [10]. ML algorithms are provided with example data, and the model can give a prediction after analyzing the data [11]. To take advantage of the ML method, many works have been conducted to solve the issues connected with channel state information (CSI), channel estimation (CE), and signal detection. In this paper, a deep neural network (DNN) which has a bi-directional long short-term memory (Bi-LSTM) based multiuser joint CE and signal detection for NOMA-orthogonal frequency division multiplexing (OFDM) system is proposed. The contributions of the proposed study can be summarized as follows:  
* Multiuser uplink CE and signal detection for NOMA-OFDM wireless communication is considered. The CE and signal detection is performed by the proposed Bi-LSTM model over the Rayleigh fading channel. * The proposed Bi-LSTM model can jointly estimate and detect the transmission data from multiple UE signals directly instead of the traditional SIC method. * To observe the effectiveness of the proposed model, a comparative analysis of convolutional neural network (CNN) and the proposed model in terms of SER is performed. * Using the Monte Carlo simulation, the SER performance of Bi-LSTM is evaluated in terms of different SNR. It is observed that the performance of the proposed model is comparable to the outage performance of the conventional NOMA-SIC methods, including least square (LS) and minimum mean square error (MMSE) and CNN model. The rest of the paper is organized as follows. Section 3 describes the system model, including the channel model and problem statement. In Section 4, the proposed deep-learning model is described.